title:
Asymptotic localization in multicomponent mass conserving coagulation
  equations

abstract:
  In this paper we prove that the time dependent solutions of a large class of
Smoluchowski coagulation equations for multicomponent systems concentrate along
a particular direction of the space of cluster compositions for long times. The
direction of concentration is determined by the initial distribution of
clusters. These results allow to prove the uniqueness and global stability of
the self-similar profile with finite mass in the case of coagulation kernels
which are not identically constant, but are constant along any direction of the
space of cluster compositions.


title:
Spin-polarized imaging of the antiferromagnetic structure and
  field-tunable bound states in kagome magnet FeSn

abstract:
  Kagome metals are as an exciting playground for the explorations of novel
phenomena at the intersection of topology, electron correlations and magnetism.
The family of FeSn-based kagome magnets in particular attracted a lot of
attention for simplicity of the layered crystal structure and tunable
topological electronic band structure. Despite a significant progress in
understanding their bulk properties, surface electronic and magnetic structures
are yet to be fully explored in many of these systems. In this work, we focus
on a prototypical kagome metal FeSn. Using a combination of spin-averaged and
spin-polarized scanning tunneling microscopy, we provide the first atomic-scale
visualization of the layered antiferromagnetic structure at the surface of
FeSn. In contrast to the field-tunable electronic structure of cousin material
Fe3Sn2 that is a ferromagnet, we find that electronic density-of-states of FeSn
is robust to the application of external magnetic field. Interestingly, despite
the field-insensitive electronic band structure, FeSn exhibits bounds states
tied to specific impurities with large effective moments that strongly couple
to the magnetic field. Our experiments provide microscopic insights necessary
for theoretical modeling of FeSn and serve as a spring board for spin-polarized
measurements of topological magnets in general.


title:
Search for gamma-ray line signals around the black hole at the galactic
  center with DAMPE observation

abstract:
  The adiabatic growth of a black hole (BH) may enhance the dark matter (DM)
density surrounding it, causing a spike in the DM density profile. The spike
around the supermassive BH at the center of the Milky Way may lead to a
dramatic enhancement of the gamma-ray flux of DM annihilation from the galactic
center (GC). In this work, we analyze the gamma-ray data of the innermost
region (i.e., the inner 1$^\circ$) of the GC to search for potential line-like
signals from the BH spike. Such line-like signals could be generated in the
process of DM particles annihilating into double photons. We adopt the
gamma-ray data from the Dark Matter Particle Explorer (DAMPE). Although the
DAMPE has a much smaller effective area than the Fermi-LAT, the gamma-ray line
search can benefit from its unprecedented high energy resolution. No
significant line-like signals are found in our analysis. We derive upper limits
on the cross section of the annihilation based on this non-detection. We find
that despite the DAMPE's small effective area for photon detection, we can
still place strong constraints on the cross section ($\left<\sigma
v\right>\lesssim10^{-27}\,{\rm cm^3\,s^{-1}}$) in the spike scenario due to the
very bright model-expected flux from the spike. Our results indicate that
either DM does not annihilate primarily through the $\gamma\gamma$ channel in
the mass range we considered or no sharp density spike is present at the GC.


title: PIP2-BD: GeV Proton Beam Dump at Fermilab's PIP-II Linac

abstract:
  The PIP-II superconducting RF linac is currently under construction at
Fermilab and is expected to be completed by the end of 2028. PIP-II is capable
of operating in a continuous-wave mode and can concurrently supply 800 MeV
protons to a mega-watt, GeV-scale beam dump facility and to LBNF/DUNE. Designs
for proton accumulator rings are being studied to bunch the PIP-II protons into
the short pulses needed for neutrino and low-mass dark matter experiments.
PIP2-BD is a proposed 100-ton LAr scintillation-only experiment, whose detector
design is inspired by CENNS-10 and CCM, that would have world-leading
sensitivities to BSM physics, including low-mass dark matter produced in the
PIP-II proton beam dump.


title: Implicit Feature Decoupling with Depthwise Quantization

abstract:
  Quantization has been applied to multiple domains in Deep Neural Networks
(DNNs). We propose Depthwise Quantization (DQ) where $\textit{quantization}$ is
applied to a decomposed sub-tensor along the $\textit{feature axis}$ of weak
statistical dependence. The feature decomposition leads to an exponential
increase in $\textit{representation capacity}$ with a linear increase in memory
and parameter cost. In addition, DQ can be directly applied to existing
encoder-decoder frameworks without modification of the DNN architecture. We use
DQ in the context of Hierarchical Auto-Encoder and train end-to-end on an image
feature representation. We provide an analysis on cross-correlation between
spatial and channel features and we propose a decomposition of the image
feature representation along the channel axis. The improved performance of the
depthwise operator is due to the increased representation capacity from
implicit feature decoupling. We evaluate DQ on the likelihood estimation task,
where it outperforms the previous state-of-the-art on CIFAR-10, ImageNet-32 and
ImageNet-64. We progressively train with increasing image size a single
hierarchical model that uses 69% less parameters and has a faster convergence
than the previous works.


title: Scaling at quantum phase transitions above the upper critical dimension

abstract:
  The hyperscaling relation and standard finite-size scaling (FSS) are known to
break down above the upper critical dimension due to dangerous irrelevant
variables. We establish a coherent formalism for FSS at quantum phase
transitions above the upper critical dimension following the recently
introduced Q-FSS formalism for thermal phase transitions. Contrary to
long-standing belief, the correlation sector is affected by dangerous
irrelevant variables. The presented formalism recovers a generalized
hyperscaling relation and FSS form. Using this new FSS formalism, we determine
the full set of critical exponents for the long-range transverse-field Ising
chain in all criticality regimes ranging from the nearest-neighbor to the
long-range mean field regime. For the same model, we also explicitly confirm
the effect of dangerous irrelevant variables on the characteristic length
scale.


title: Regenerative Particle Thompson Sampling

abstract:
  This paper proposes regenerative particle Thompson sampling (RPTS), a
flexible variation of Thompson sampling. Thompson sampling itself is a Bayesian
heuristic for solving stochastic bandit problems, but it is hard to implement
in practice due to the intractability of maintaining a continuous posterior
distribution. Particle Thompson sampling (PTS) is an approximation of Thompson
sampling obtained by simply replacing the continuous distribution by a discrete
distribution supported at a set of weighted static particles. We observe that
in PTS, the weights of all but a few fit particles converge to zero. RPTS is
based on the heuristic: delete the decaying unfit particles and regenerate new
particles in the vicinity of fit surviving particles. Empirical evidence shows
uniform improvement from PTS to RPTS and flexibility and efficacy of RPTS
across a set of representative bandit problems, including an application to 5G
network slicing.


title:
Interacting electrons and bosons in the doubly screened $G\widetilde{W}$
  approximation: A time-linear scaling method for first-principles simulations

abstract:
  We augment the time-linear formulation of the Kadanoff-Baym equations for
systems of interacting electrons and quantized phonons or photons with the
$G\widetilde{W}$ approximation, the Coulomb interaction $\widetilde{W}$ being
dynamically screened by both electron-hole pairs {\em and} bosonic particles.
We also show how to combine different approximations to include simultaneously
multiple correlation effects in the dynamics. The final outcome is a versatile
framework comprising $2^{12}$ distinct diagrammatic methods, each scaling
linearly in time and preserving all fundamental conservation laws. The dramatic
improvement over current state-of-the-art approximations brought about by
$G\widetilde{W}$ is demonstrated in a study of the correlation-induced charge
migration of the glycine molecule in an optical cavity.

categories: cond-mat.other cond-mat.mes-hall cond-mat.mtrl-sci cond-mat.str-el nucl-th


title:
Snowmass2021 Cosmic Frontier Dark Matter Direct Detection to the
  Neutrino Fog

abstract:
  We present a summary of future prospects for direct detection of dark matter
within the GeV/c2 to TeV/c2 mass range. This is paired with a new definition of
the neutrino fog in order to better quantify the rate of diminishing returns on
sensitivity due to irreducible neutrino backgrounds. A survey of dark matter
candidates predicted to fall within this mass range demonstrates that fully
testing multiple well-motivated theo-ries will require expanding the
currently-funded generation of experiments down to and past the neutrino fog.
We end with the status and plans for next-generation exper-iments and novel R&D
concepts which will get us there.


title:
Measuring the Impact of (Psycho-)Linguistic and Readability Features and
  Their Spill Over Effects on the Prediction of Eye Movement Patterns

abstract:
  There is a growing interest in the combined use of NLP and machine learning
methods to predict gaze patterns during naturalistic reading. While promising
results have been obtained through the use of transformer-based language
models, little work has been undertaken to relate the performance of such
models to general text characteristics. In this paper we report on experiments
with two eye-tracking corpora of naturalistic reading and two language models
(BERT and GPT-2). In all experiments, we test effects of a broad spectrum of
features for predicting human reading behavior that fall into five categories
(syntactic complexity, lexical richness, register-based multiword combinations,
readability and psycholinguistic word properties). Our experiments show that
both the features included and the architecture of the transformer-based
language models play a role in predicting multiple eye-tracking measures during
naturalistic reading. We also report the results of experiments aimed at
determining the relative importance of features from different groups using
SP-LIME.


title:
Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling
  using Spectral Weighting

abstract:
  Many applications in image processing require resampling of arbitrarily
located samples onto regular grid positions. This is important in frame-rate
up-conversion, super-resolution, and image warping among others. A
state-of-the-art high quality model-based resampling technique is
frequency-selective mesh-to-grid resampling which requires pre-estimation of
key points. In this paper, we propose a new key point agnostic
frequency-selective mesh-to-grid resampling that does not depend on
pre-estimated key points. Hence, the number of data points that are included is
reduced drastically and the run time decreases significantly. To compensate for
the key points, a spectral weighting function is introduced that models the
optical transfer function in order to favor low frequencies more than high
ones. Thereby, resampling artefacts like ringing are supressed reliably and the
resampling quality increases. On average, the new AFSMR is conceptually simpler
and gains up to 1.2 dB in terms of PSNR compared to the original mesh-to-grid
resampling while being approximately 14.5 times faster.


title: From twistor-particle models to massive amplitudes

abstract:
  In his twistor-particle programme of the 1970s, Roger Penrose introduced a
representation of the massive particle phase space in terms of a pair of
twistors subject to an internal symmetry group. Here we use this representation
to introduce a chiral string whose target is a complexification of this space,
extended so as to incorporate supersymmetry. We show that the gauge anomalies
associated to the internal symmetry group vanish only for maximal
supersymmetry, and that correlators in these string models describe amplitudes
involving massive particles with manifest supersymmetry. The models and
amplitude formulae exhibit a double copy structure from gauge theory on the
Coulomb branch to gravity, although the graviton remains massless. The formulae
are closely related to those obtained earlier by the authors expressed in terms
of the polarised scattering equations.


title: Future Collider Options for the US

abstract:
  The United States has a rich history in high energy particle accelerators and
colliders -- both lepton and hadron machines, which have enabled several major
discoveries in elementary particle physics. To ensure continued progress in the
field, U.S. leadership as a key partner in building next generation collider
facilities abroad is essential; also critically important is the exploring of
options to host a future collider in the U.S. The "Snowmass" study and the
subsequent Particle Physics Project Prioritization Panel (P5) process provide
the timely opportunity to develop strategies for both. What we do now will
shape the future of our field and whether the U.S. will remain a world leader
in these areas. In this white paper, we briefly discuss the US engagement in
proposed collider projects abroad and describe future collider options for the
U.S. We also call for initiating an integrated R\&D program for future
colliders.


title: On Suspicious Coincidences and Pointwise Mutual Information

abstract:
  Barlow (1985) hypothesized that the co-occurrence of two events $A$ and $B$
is "suspicious" if $P(A,B) \gg P(A) P(B)$. We first review classical measures
of association for $2 \times 2$ contingency tables, including Yule's $Y$ (Yule,
1912), which depends only on the odds ratio $\lambda$, and is independent of
the marginal probabilities of the table. We then discuss the mutual information
(MI) and pointwise mutual information (PMI), which depend on the ratio
$P(A,B)/P(A)P(B)$, as measures of association. We show that, once the effect of
the marginals is removed, MI and PMI behave similarly to $Y$ as functions of
$\lambda$. The pointwise mutual information is used extensively in some
research communities for flagging suspicious coincidences, but it is important
to bear in mind the sensitivity of the PMI to the marginals, with increased
scores for sparser events.


title:
An image sensor based on single-pulse photoacoustic electromagnetic
  detection (SPEED): a simulation study

abstract:
  Image sensors are the backbone of many imaging technologies of great
importance to modern sciences, being particularly relevant in biomedicine. An
ideal image sensor should be usable through all the electromagnetic spectrum
(large bandwidth), it should be fast (millions of frames per second) to fulfil
the needs of many microscopy applications, and it should be cheap, in order to
ensure the sustainability of the healthcare system. However, current image
sensor technologies have fundamental limitations in terms of bandwidth, imaging
rate or price. In here, we briefly sketch the principles of an alternative
image sensor concept termed Single-pulse Photoacoustic Electromagnetic
Detection (SPEED). SPEED leverages the principles of optoacoustic
(photoacoustic) tomography to overcome several of the hard limitations of
todays image sensors. Specifically, SPEED sensors can operate with a massive
portion of the electromagnetic spectrum at high frame rate (millions of frames
per second) and low cost. Using simulations, we demonstrate the feasibility of
the SPEED methodology and we discuss the step towards its implementation.


title: On Genus 1 Gromov-Witten invariants of Fano complete intersections

abstract:
  We study genus 1 Gromov-Witten invariants of Fano complete intersections in
the projective spaces. Among other things, we show a reconstruction theorem for
genus 1 invariants with only ambient insertions, and compute the genus 1
invariants with 1 marked point. For cubic hypersurfaces of dimension $\neq 4$
and odd dimensional intersections of two quadrics, we obtain a complete
reconstruction theorem for genus 1 Gromov-Witten invariants.


title:
Atomic Layer Epitaxy of Kagome Magnet Fe${_3}$Sn${_2}$ and Sn-modulated
  Heterostructures

abstract:
  Magnetic materials with kagome crystal structure exhibit rich physics such as
frustrated magnetism, skyrmion formation, topological flat bands, and
Dirac/Weyl points. Until recently, most studies on kagome magnets have been
performed on bulk crystals or polycrystalline films. Here we report the atomic
layer molecular beam epitaxy synthesis of high-quality thin films of
topological kagome magnet Fe${_3}$Sn${_2}$. Structural and magnetic
characterization of Fe${_3}$Sn${_2}$ on epitaxial Pt(111) identifies highly
ordered films with c-plane orientation and an in-plane magnetic easy axis.
Studies of the local magnetic structure by anomalous Nernst effect imaging
reveals in-plane oriented micrometer size domains. Superlattice structures
consisting of Fe${_3}$Sn${_2}$ and Fe${_3}$Sn are also synthesized by atomic
layer molecular beam epitaxy, demonstrating the ability to modulate the sample
structure at the atomic level. The realization of high-quality films by atomic
layer molecular beam epitaxy opens the door to explore the rich physics of this
system and investigate novel spintronic phenomena by interfacing
Fe${_3}$Sn${_2}$ with other materials.


title:
New tests of dark sector interactions from the full-shape galaxy power
  spectrum

abstract:
  We explore the role of redshift-space galaxy clustering data in constraining
non-gravitational interactions between dark energy (DE) and dark matter (DM),
for which state-of-the-art limits have so far been obtained from late-time
background measurements. We use the joint likelihood for pre-reconstruction
full-shape (FS) galaxy power spectrum and post-reconstruction Baryon Acoustic
Oscillation (BAO) measurements from the BOSS DR12 sample, alongside Cosmic
Microwave Background (CMB) data from \textit{Planck}: from this dataset
combination we infer $H_0=68.02^{+0.49}_{-0.60}\,{\rm km}/{\rm s}/{\rm Mpc}$
and the 2$\sigma$ lower limit $\xi>-0.12$, among the strongest limits ever
reported on the DM-DE coupling strength $\xi$ for the particular model
considered. Contrary to what has been observed for the $\Lambda$CDM model and
simple extensions thereof, we find that the CMB+FS combination returns tighter
constraints compared to the CMB+BAO one, suggesting that there is valuable
additional information contained in the broadband of the power spectrum. We
test this finding by running additional CMB-free analyses and removing sound
horizon information, and discuss the important role of the equality scale in
setting constraints on DM-DE interactions. Our results reinforce the critical
role played by redshift-space galaxy clustering measurements in the epoch of
precision cosmology, particularly in relation to tests of non-minimal dark
sector extensions of the $\Lambda$CDM model.


title: The Physics Case for a Neutrino Factory

abstract:
  Neutrino factories, neutrino beams produced in the decay of a muon or
antimuon beam inside a storage ring, yield cleaner, richer, and more flexible
neutrino beams relative to super-beams. We explore the physics case for this
type of beam both for standard oscillation as well as new physics searches and
present some machine options. We argue that there is a rich program beyond what
the current neutrino program can cover and a string synergy with the muon
collider program.


title: Wehrl entropy, coherent states and quantum channels

abstract:
  We review Wehrl's definition of a semiclassical entropy in terms of coherent
states and give an introductory overview of Lieb's conjecture, its proof
(including earlier results), generalizations, and the role of covariant quantum
channels in this context. These structures motivate an alternative definition
of coherent states and have interesting physical applications and implications.


title: High-Energy and Ultra-High-Energy Neutrinos

abstract:
  Astrophysical neutrinos are excellent probes of astroparticle physics and
high-energy physics. With energies far beyond solar, supernovae, atmospheric,
and accelerator neutrinos, high-energy and ultra-high-energy neutrinos probe
fundamental physics from the TeV scale to the EeV scale and beyond. They are
sensitive to physics both within and beyond the Standard Model through their
production mechanisms and in their propagation over cosmological distances.
They carry unique information about their extreme non-thermal sources by giving
insight into regions that are opaque to electromagnetic radiation. This white
paper describes the opportunities astrophysical neutrino observations offer for
astrophysics and high-energy physics, today and in coming years.


title:
Understanding chemical reactions via variational autoencoder and atomic
  representations

abstract:
  On the time scales accessible to atomistic numerical modelling, chemical
reactions are considered rare events. Atomistic simulations are typically
biased along a low-dimensional representation of a chemical reaction in an
atomic structure space, i.e., along the collective variable, to accelerate
sampling of these improbable events. However, suitable collective variables are
often complicated to guess due to the complexity of the transitions. Therefore,
we present an automatic method of generating robust collective variables from
atomic representation vectors, using either fixed Behler-Parrinello functions
or representations extracted from pre-trained machine learning potentials.
Variational autoencoder with these representations as inputs is trained while
its latent space with arbitrary dimension gives us the set of collective
variables. The resulting collective variables inherit all necessary invariances
from the atomic representations and can be trained entirely unsupervised. The
method's effectiveness is demonstrated using three different chemical
reactions, one being the complex hydrolysis of a heterogeneous aluminosilicate
catalyst. Lastly, we consider the method in the context of unseen atomic
structure prediction, efficiently creating structures for different values of
collective variables in a generative model fashion.


title: RB2: Robotic Manipulation Benchmarking with a Twist

abstract:
  Benchmarks offer a scientific way to compare algorithms using objective
performance metrics. Good benchmarks have two features: (a) they should be
widely useful for many research groups; (b) and they should produce
reproducible findings. In robotic manipulation research, there is a trade-off
between reproducibility and broad accessibility. If the benchmark is kept
restrictive (fixed hardware, objects), the numbers are reproducible but the
setup becomes less general. On the other hand, a benchmark could be a loose set
of protocols (e.g. object sets) but the underlying variation in setups make the
results non-reproducible. In this paper, we re-imagine benchmarking for robotic
manipulation as state-of-the-art algorithmic implementations, alongside the
usual set of tasks and experimental protocols. The added baseline
implementations will provide a way to easily recreate SOTA numbers in a new
local robotic setup, thus providing credible relative rankings between existing
approaches and new work. However, these local rankings could vary between
different setups. To resolve this issue, we build a mechanism for pooling
experimental data between labs, and thus we establish a single global ranking
for existing (and proposed) SOTA algorithms. Our benchmark, called
Ranking-Based Robotics Benchmark (RB2), is evaluated on tasks that are inspired
from clinically validated Southampton Hand Assessment Procedures. Our benchmark
was run across two different labs and reveals several surprising findings. For
example, extremely simple baselines like open-loop behavior cloning, outperform
more complicated models (e.g. closed loop, RNN, Offline-RL, etc.) that are
preferred by the field. We hope our fellow researchers will use RB2 to improve
their research's quality and rigor.


title: Kerr-Sen-like Lorentz violating black holes and Superradiance phenomena

abstract:
  A Kerr-Sen-like black hole solution results from Einstein-bumblebee gravity.
It contains a Lorentz violating (LV) parameter that enters when the bumblebee
field receives vacuum expectation value through a spontaneously breaking of the
symmetry of the classical action. The geometrical structure concerning the
singularity of this spacetime is studied with reference to the parameters
involved in the Kerr-Sen-like metric. We introduce this Einstein-bumblebee
modified gravity to probe the role of spontaneous Lorentz violation on the
superradiance scattering phenomena and the instability associated with it. We
observe that for the low-frequency range of the scalar wave the superradiance
scattering gets enhanced when the Lorentz-violating parameter $\ell$ takes the
negative values and it reduces when values of $\ell$ are positive. The study of
the black hole bomb issue reveals that for the negative values of $\ell$, the
parameter space of the scalar field instability increase prominently, however,
for its positive values, it shows a considerable reduction. We also tried to
put constraints on the parameters contained in the Kerr-Sen-like black hole by
comparing the deformation of the shadow produced by the black hole parameters
with the observed deviation from circularity and the angular deviation from the
$M87*$ data.


title:
Local solvability for a quasilinear wave equation with the far field
  degeneracy: 1D case

abstract:
  We study the Cauchy problem for the quasilinear wave equation $ \partial^2 _t
u = u^{2a} \partial^2_x u + F(u) u_x $ with $a \geq 0$ and show a result for
the local in time existence under new conditions. In the previous results, it
is assumed that $u(0,x) \geq c_0>0$ for some constant $c_0$ to prove the
existence and the uniqueness. This assumption ensures that the equation does
not degenerate. In this paper, we allow the equation to degenerate at spacial
infinity. Namely we consider the local well-posedness under the assumption that
$u(0,x)>0$ and $u(0,x) \rightarrow 0$ as $|x| \rightarrow \infty$. Furthermore,
to prove the local well-posedness, we find that the so-called Levi condition
appears. Our proof is based on the method of characteristic and the contraction
mapping principle via weighted $L^\infty$ estimates.


title:
ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and
  Implicit Similarity

abstract:
  An intuitive way to search for images is to use queries composed of an
example image and a complementary text. While the first provides rich and
implicit context for the search, the latter explicitly calls for new traits, or
specifies how some elements of the example image should be changed to retrieve
the desired target image. Current approaches typically combine the features of
each of the two elements of the query into a single representation, which can
then be compared to the ones of the potential target images. Our work aims at
shedding new light on the task by looking at it through the prism of two
familiar and related frameworks: text-to-image and image-to-image retrieval.
Taking inspiration from them, we exploit the specific relation of each query
element with the targeted image and derive light-weight attention mechanisms
which enable to mediate between the two complementary modalities. We validate
our approach on several retrieval benchmarks, querying with images and their
associated free-form text modifiers. Our method obtains state-of-the-art
results without resorting to side information, multi-level features, heavy
pre-training nor large architectures as in previous works.


title:
SBN-BD: $\mathcal{O}$(10 GeV) Proton Beam Dump at Fermilab's PIP-II
  Linac

abstract:
  Proton beam dumps are prolific sources of mesons enabling a powerful
technique to search for vector mediator coupling of dark matter to neutral pion
and higher mass meson decays. By the end of the decade the PIP-II linac will be
delivering up to 1 MW of proton power to the FNAL campus. This includes a
significant increase of power to the Booster Neutrino Beamline (BNB) which
delivers 8 GeV protons to the Short Baseline Neutrino (SBN) detectors. By
building a new dedicated beam dump target station, and using the SBN detectors,
a greater than an order of magnitude increase in search sensitivity for dark
matter relative to the recent MiniBooNE beam dump search can be achieved. This
modest cost upgrade to the BNB would begin testing models of the highly
motivated relic density limit predictions and provide novel ways to test
explanations of the anomalous excess of low energy events seen by MiniBooNE.


title: Electric dipole moments and the search for new physics

abstract:
  Static electric dipole moments of nondegenerate systems probe mass scales for
physics beyond the Standard Model well beyond those reached directly at high
energy colliders. Discrimination between different physics models, however,
requires complementary searches in atomic-molecular-and-optical, nuclear and
particle physics. In this report, we discuss the current status and prospects
in the near future for a compelling suite of such experiments, along with
developments needed in the encompassing theoretical framework.


title:
Heterogeneous gradient flows in the topology of fibered optimal
  transport

abstract:
  We introduce an optimal transport topology on the space of probability
measures over a fiber bundle, which penalizes the transport cost from one fiber
to another. For simplicity, we illustrate our construction in the Euclidean
case $\mathbb{R}^d\times \mathbb{R}^d$, where we penalize the quadratic cost in
the second component. Optimal transport becomes then constrained to happen
along fixed fibers. Despite the degeneracy of the infinitely-valued and
discontinuous cost, we prove that the space of probability measures
$(\mathcal{P}_{2,\nu}(\mathbb{R}^{2d}),W_{2,\nu})$ with fixed marginal $\nu\in
\mathcal{P}(\mathbb{R}^d)$ in the second component becomes a Polish space under
the fibered transport distance, which enjoys a weak Riemannian structure
reminiscent of the one proposed by F. Otto for the classical quadratic
Wasserstein space. Three fundamental issues are addressed: 1) We develop an
abstract theory of gradient flows with respect to the new topology; 2) We show
applications that identify a novel fibered gradient flow structure on a large
class of evolution PDEs with heterogeneities; 3) We exploit our method to
derive long-time behavior and global-in-time mean-field limits in a
multidimensional Cucker-Smale-type alignment model with weakly singular
coupling.


title: Discrete Flavor Symmetries and Lepton Masses and Mixings

abstract:
  We discuss neutrino mass and mixing models based on discrete flavor
symmetries. These models can include a variety of new interactions and
non-standard particles such as sterile neutrinos, scalar Higgs singlets and
multiplets. We point at connections of the models with leptogenesis and dark
matter and the ways to detect the corresponding non-standard particles at
intensity and energy frontier experiments.


title: Broader Impact of Cyclotron-based Neutrino Sources

abstract:
  The cyclotron designed for the IsoDAR neutrino source represents a paradigm
shift in cyclotron performance -- a factor of 10 increase in beam current. This
performance is required to develop a neutrino source of sufficient strength for
meaningful "decay-at-rest" experiments, but it also can have important
ramifications for the isotope-production sector for medical or other
applications, by enabling efficient, high-yield production of long-lived
isotopes, or isotopes where production cross sections are low.


title: Simplified dark matter models with charged mediators

abstract:
  We review simplified models in which a singlet Majorana dark matter candidate
couples to Standard Model (SM) fermions through interactions mediated by scalar
fermion partners. We summarize the two primary production mechanisms in these
scenarios: dark matter annihilation mediated by first or second generation
scalar fermion partners with significant left-right chiral mixing and
co-annihilation with scalar fermion partners nearly degenerate in mass with the
dark matter. We then highlight the most interesting phenomenological aspects of
charged mediator models relevant for current and future searches for new
physics. We describe precision measurements of SM fermion dipole moments,
including models with scalar muon partners that can account for $g_\mu-2$. We
discuss new search strategies for charged mediators at the LHC and the
projected sensitivity of future lepton colliders. We summarize constraints from
direct detection and demonstrate how next generation experiments might probe
QCD-charged mediators at mass scales beyond the sensitivity of the LHC. We also
review the prospects for indirect detection of models with scalar lepton
partners, focusing on the sensitivity of gamma-ray searches to internal
bremsstrahlung emission.


title:
Functional renormalization group for non-Hermitian and
  $\mathcal{PT}$-symmetric systems

abstract:
  We generalize the vertex expansion approach of the functional renormalization
group to non-Hermitian systems. As certain anomalous expectation values might
not vanish, additional terms as compared to the Hermitian case can appear in
the flow equations. We investigate the merits and shortcomings of the vertex
expansion for non-Hermitian systems by considering an exactly solvable
$\mathcal{PT}$-symmetric non-linear toy-model and reveal, that in this model,
the fidelity of the vertex expansion in a perturbatively motivated truncation
schema is comparable with that of the Hermitian case. The vertex expansion
appears to be a viable method for studying correlation effects in non-Hermitian
systems.

categories: cond-mat.str-el cond-mat.other cond-mat.stat-mech quant-ph


title: Non-Archimedean Koksma inequalities, variation, and Fourier analysis

abstract:
  We examine four different notions of variation for real-valued functions
defined on the compact ring of integers of a non-Archimedean local field, with
an emphasis on regularity properties of functions with finite variation, and on
establishing non-Archimedean Koksma inequalities. The first version of
variation is due to Taibleson, the second due to Beer, and the remaining two
are new. Taibleson variation is the simplest of these, but it is a coarse
measure of irregularity and it does not admit a Koksma inequality. Beer
variation can be used to prove a Koksma inequality, but it is order-dependent
and not translation invariant. We define a new version of variation which may
be interpreted as the graph-theoretic variation when a function is naturally
extended to a certain subtree of the Berkovich affine line. This variation is
order-free and translation invariant, and it admits a Koksma inequality which,
for a certain natural family of examples, is always sharper than Beer's.
Finally, we define a Fourier-analytic variation and a corresponding Koksma
inequality which is sometimes sharper than the Berkovich-analytic inequality.


title:
Topology optimization including a model of the layer by layer additive
  manufacturing process

abstract:
  A topology optimization formulation including a model of the layer-by-layer
additive manufacturing (AM) process is considered. Defined as a multi-objective
minimization problem, the formulation accounts for the performance and cost of
both the final and partially manufactured designs and allows for considering
AM-related issues such as overhang and residual stresses in the optimization.
The formulation is exemplified by stiffness optimization in which the overhang
is limited by adding mechanical or thermal compliance as a measure of the cost
of partially manufactured designs. Convergence of the model as the approximate
layer-by-layer model is refined is shown theoretically, and an extensive
numerical study indicates that this convergence can be fast, thus making it a
computationally viable approach useful for including AM-related issues into
topology optimization. The examples also show that drips and sharp corners
associated with some geometry-based formulations for overhang limitation can be
avoided. The codes used in this article are written in Python using only open
sources libraries and are available for reference.


title: Does Corpus Quality Really Matter for Low-Resource Languages?

abstract:
  The vast majority of non-English corpora are derived from automatically
filtered versions of CommonCrawl. While prior work has identified major issues
on the quality of these datasets (Kreutzer et al., 2021), it is not clear how
this impacts downstream performance. Taking Basque as a case study, we explore
tailored crawling (manually identifying and scraping websites with high-quality
content) as an alternative to filtering CommonCrawl. Our new corpus, called
EusCrawl, is similar in size to the Basque portion of popular multilingual
corpora like CC100 and mC4, yet it has a much higher quality according to
native annotators. For instance, 66% of documents are rated as high-quality for
EusCrawl, in contrast with <33% for both mC4 and CC100. Nevertheless, we obtain
similar results on downstream tasks regardless of the corpus used for
pre-training. Our work suggests that NLU performance in low-resource languages
is primarily constrained by the quantity rather than the quality of the data,
prompting for methods to exploit more diverse data sources.


title: GLEAM-X J16279.5-523504.3 as a White Dwarf Pulsar

abstract:
  The low frequency radio source GLEAM-X J162759.5$-$523504.3 emits pulsed
coherent polarized emission like that of radio pulsars. Yet its period of 18.18
min (1091 s) is hundreds of times longer than those of confirmed radio pulsars,
and if it is a neutron star its mean radiated power exceeds the upper limit on
its spin-down power by more than an order of magnitude. This may be explained
if it is a white dwarf pulsar, with a moment of inertia several orders of
magnitude greater than those of neutron stars. The Lorentz factor of the
emitting charge bunches may be bounded from below by the widths of the temporal
substructure of their radiation. If the emission is curvature radiation, the
radius of curvature may be estimated from the Lorentz factor and the
frequencies of emission; it is consistent with a white dwarf's inner
magnetosphere but not a neutron star's.


title: Data Preservation for Cosmology

abstract:
  We describe the needs and opportunities for preserving cosmology datasets and
simulations, and facilitating their joint analysis beyond the lifetime of
individual projects. We recommend that DOE fund a new cosmology data archive
center to coordinate this work across the multiple DOE computing facilities.


title:
Algorithmic cooling for resolving state preparation and measurement
  errors in quantum computing

abstract:
  State preparation and measurement errors are commonly regarded as
indistinguishable. The problem of distinguishing state preparation (SPAM)
errors from measurement errors is important to the field of characterizing
quantum processors. In this work, we propose a method to separately
characterize SPAM errors using a novel type of algorithmic cooling protocol
called measurement-based algorithmic cooling (MBAC). MBAC assumes the ability
to perform (potentially imperfect) projective measurements on individual
qubits, which is available on many modern quantum processors. We demonstrate
that MBAC can significantly reduce state preparation error under realistic
assumptions, with a small overhead that can be upper bounded by measurable
quantities. Thus, MBAC can be a valuable tool not only for benchmarking
near-term quantum processors, but also for improving the performance of quantum
processors in an algorithmic manner.


title:
Analysis of the competition among viral strains using a temporal
  interaction-driven contagion model

abstract:
  The temporal dynamics of social interactions were shown to influence the
spread of disease. Here, we model the conditions of progression and competition
for several viral strains, exploring various levels of cross-immunity over
temporal networks. We use our interaction-driven contagion model and
characterize, using it, several viral variants. Our results, obtained on
temporal random networks and on real-world interaction data, demonstrate that
temporal dynamics are crucial to determining the competition results. We
consider two and three competing pathogens and show the conditions under which
a slower pathogen will remain active and create a second wave infecting most of
the population. We then show that when the duration of the encounters is
considered, the spreading dynamics change significantly. Our results indicate
that when considering airborne diseases, it might be crucial to consider the
duration of temporal meetings to model the spread of pathogens in a population.


title:
Indoor Propagation Measurements with Sekisui Transparent Reflectors at
  28/39/120/144 GHz

abstract:
  One of the critical challenges of operating with the terahertz or
millimeter-wave wireless networks is the necessity of at least a strong
non-line-of-sight (NLoS) reflected path to form a stable link. Recent studies
have shown that an economical way of enhancing/improving these NLoS links is by
using passive metallic reflectors that provide strong reflections. However,
despite its inherent radio advantage, metals can dramatically influence the
landscape's appearance - especially the indoor environment. A conceptual view
of escaping this is by using transparent reflectors. In this work, for the very
first time, we evaluate the wireless propagation characteristics of passive
transparent reflectors in an indoor environment at 28 GHz, 39 GHz, 120 GHz, and
144 GHz bands. In particular, we investigate the penetration loss and the
reflection characteristics at different frequencies and compare them against
the other common indoor materials such as ceiling tile, clear glass, drywall,
plywood, and metal. The measurement results suggest that the transparent
reflector, apart from an obvious advantage of transparency, has a higher
penetration loss than the common indoor materials (excluding metal) and
performs similarly to metal in terms of reflection. Our experimental results
directly translate to better reflection performance and preserving the radio
waves within the environment than common indoor materials, with potential
applications in controlled wireless communication.


title: Snowmass White Paper: The Numerical Conformal Bootstrap

abstract: We give a brief overview of the status of the numerical conformal bootstrap.

categories: hep-th cond-mat.stat-mech cond-mat.str-el hep-lat


title: Representation Learning for Resource-Constrained Keyphrase Generation

abstract:
  State-of-the-art keyphrase generation methods generally depend on large
annotated datasets, limiting their performance in domains with constrained
resources. To overcome this challenge, we investigate strategies to learn an
intermediate representation suitable for the keyphrase generation task. We
introduce salient span recovery and salient span prediction as guided denoising
language modeling objectives that condense the domain-specific knowledge
essential for keyphrase generation. Through experiments on multiple scientific
keyphrase generation benchmarks, we show the effectiveness of the proposed
approach for facilitating low-resource and zero-shot keyphrase generation.
Furthermore, we observe that our method especially benefits the generation of
absent keyphrases, approaching the performance of SOTA methods trained with
large training sets.


title: A Constraint Geometry for Inference and Integration

abstract:
  We use the geometric framework describing gauge theories to enrich our
understanding of the principle of maximum entropy, a variational method
appearing in statistical inference and the analysis of stochastic dynamical
systems. Using the connection on a principal $G$-bundle, the gradient flows
found in the calculus of functional optimisation are grounded in a geometric
picture of constraint functions interacting with the dynamics of the
probabilistic degrees of freedom of the process. From this, we can describe the
point of maximum entropy as parallel transport over the state space. A
reinterpretation of splitting results in stochastic dynamical systems is also
suggested. Beyond stochastic analysis, we indicate a collection of geometric
structures surrounding energy-based inference.

categories: math-ph cond-mat.stat-mech math.DS math.MP math.PR


title:
Deep Learning without Shortcuts: Shaping the Kernel with Tailored
  Rectifiers

abstract:
  Training very deep neural networks is still an extremely challenging task.
The common solution is to use shortcut connections and normalization layers,
which are both crucial ingredients in the popular ResNet architecture. However,
there is strong evidence to suggest that ResNets behave more like ensembles of
shallower networks than truly deep ones. Recently, it was shown that deep
vanilla networks (i.e. networks without normalization layers or shortcut
connections) can be trained as fast as ResNets by applying certain
transformations to their activation functions. However, this method (called
Deep Kernel Shaping) isn't fully compatible with ReLUs, and produces networks
that overfit significantly more than ResNets on ImageNet. In this work, we
rectify this situation by developing a new type of transformation that is fully
compatible with a variant of ReLUs -- Leaky ReLUs. We show in experiments that
our method, which introduces negligible extra computational cost, achieves
validation accuracies with deep vanilla networks that are competitive with
ResNets (of the same width/depth), and significantly higher than those obtained
with the Edge of Chaos (EOC) method. And unlike with EOC, the validation
accuracies we obtain do not get worse with depth.


title: Snowmass White Paper: The Cosmological Bootstrap

abstract:
  This white paper summarizes recent progress in the cosmological bootstrap, an
approach to the study of the statistics of primordial fluctuations from
consistency with unitarity, locality and symmetry assumptions. We review the
key ideas of the bootstrap method, with an eye towards future directions and
ambitions of the program. Focusing on recent progress involving de Sitter and
quasi-de Sitter backgrounds, we highlight the role of singularities and
unitarity in constraining the form of the correlators. We also discuss
nonperturbative formulations of the bootstrap, connections to anti-de Sitter
space, and potential implications for holography.


title: From 2D to 3D: Re-thinking Benchmarking of Monocular Depth Prediction

abstract:
  There have been numerous recently proposed methods for monocular depth
prediction (MDP) coupled with the equally rapid evolution of benchmarking
tools. However, we argue that MDP is currently witnessing benchmark
over-fitting and relying on metrics that are only partially helpful to gauge
the usefulness of the predictions for 3D applications. This limits the design
and development of novel methods that are truly aware of - and improving
towards estimating - the 3D structure of the scene rather than optimizing
2D-based distances. In this work, we aim to bring structural awareness to MDP,
an inherently 3D task, by exhibiting the limits of evaluation metrics towards
assessing the quality of the 3D geometry. We propose a set of metrics well
suited to evaluate the 3D geometry of MDP approaches and a novel indoor
benchmark, RIO-D3D, crucial for the proposed evaluation methodology. Our
benchmark is based on a real-world dataset featuring high-quality rendered
depth maps obtained from RGB-D reconstructions. We further demonstrate this to
help benchmark the closely-tied task of 3D scene completion.


title:
On the spectral gap in the Kac-Luttinger model and Bose-Einstein
  condensation

abstract:
  We consider the Dirichlet eigenvalues of the Laplacian among a Poissonian
cloud of hard spherical obstacles of fixed radius in large boxes of
$\mathbb{R}^d$, $d \ge 2$. In a large box of side-length $2l$ centered at the
origin, the lowest eigenvalue is known to be typically of order $(\log
l)^{-2/d}$. We show here that with probability arbitrarily close to $1$ as $l$
goes to infinity, the spectral gap stays bigger than $\sigma (\log l)^{-(1 +
2/d)}$, where the small positive number $\sigma$ depends on how close to $1$
one wishes the probability. Incidentally, the scale $(\log l)^{-(1+ 2/d)}$ is
expected to capture the correct size of the gap. Our result involves the proof
of new deconcentration estimates. Combining this lower bound on the spectral
gap with the results of Kerner-Pechmann-Spitzer, we infer a type-I generalized
Bose-Einstein condensation in probability for a Kac-Luttinger system of
non-interacting bosons among Poissonian spherical impurities, with the sole
macroscopic occupation of the one-particle ground state when the density
exceeds the critical value.


title:
Can Neural Nets Learn the Same Model Twice? Investigating
  Reproducibility and Double Descent from the Decision Boundary Perspective

abstract:
  We discuss methods for visualizing neural network decision boundaries and
decision regions. We use these visualizations to investigate issues related to
reproducibility and generalization in neural network training. We observe that
changes in model architecture (and its associate inductive bias) cause visible
changes in decision boundaries, while multiple runs with the same architecture
yield results with strong similarities, especially in the case of wide
architectures. We also use decision boundary methods to visualize double
descent phenomena. We see that decision boundary reproducibility depends
strongly on model width. Near the threshold of interpolation, neural network
decision boundaries become fragmented into many small decision regions, and
these regions are non-reproducible. Meanwhile, very narrows and very wide
networks have high levels of reproducibility in their decision boundaries with
relatively few decision regions. We discuss how our observations relate to the
theory of double descent phenomena in convex models. Code is available at
https://github.com/somepago/dbViz


title:
A machine-learning photometric classifier for massive stars in nearby
  galaxies I. The method

abstract:
  (abridged) Mass loss is a key parameter in the evolution of massive stars,
with discrepancies between theory and observations and with unknown importance
of the episodic mass loss. To address this we need increased numbers of
classified sources stars spanning a range of metallicity environments. We aim
to remedy the situation by applying machine learning techniques to recently
available extensive photometric catalogs. We used IR/Spitzer and
optical/Pan-STARRS, with Gaia astrometric information, to compile a large
catalog of known massive stars in M31 and M33, which were grouped in Blue, Red,
Yellow, B[e] supergiants, Luminous Blue Variables, Wolf-Rayet, and background
galaxies. Due to the high imbalance, we implemented synthetic data generation
to populate the underrepresented classes and improve separation by
undersampling the majority class. We built an ensemble classifier using color
indices. The probabilities from Support Vector Classification, Random Forests,
and Multi-layer Perceptron were combined for the final classification. The
overall weighted balanced accuracy is ~83%, recovering Red supergiants at ~94%,
Blue/Yellow/B[e] supergiants and background galaxies at ~50-80%, Wolf-Rayets at
~45%, and Luminous Blue Variables at ~30%, mainly due to their small sample
sizes. The mixing of spectral types (no strict boundaries in their color
indices) complicates the classification. Independent application to IC 1613,
WLM, and Sextans A galaxies resulted in an overall lower accuracy of ~70%,
attributed to metallicity and extinction effects. The missing data imputation
was explored using simple replacement with mean values and an iterative
imputor, which proved more capable. We also found that r-i and y-[3.6] were the
most important features. Our method, although limited by the sampling of the
feature space, is efficient in classifying sources with missing data and at
lower metallicitites.


title: Recent Progress and Next Steps for the MATHUSLA LLP Detector

abstract:
  We report on recent progress and next steps in the design of the proposed
MATHUSLA Long Lived Particle (LLP) detector for the HL-LHC as part of the
Snowmass 2021 process. Our understanding of backgrounds has greatly improved,
aided by detailed simulation studies, and significant R&D has been performed on
designing the scintillator detectors and understanding their performance. The
collaboration is on track to complete a Technical Design Report, and there are
many opportunities for interested new members to contribute towards the goal of
designing and constructing MATHUSLA in time for HL-LHC collisions, which would
increase the sensitivity to a large variety of highly motivated LLP signals by
orders of magnitude.


title:
Directly Probing the CP-structure of the Higgs-Top Yukawa at HL-LHC and
  Future Colliders

abstract:
  Constraining the Higgs boson properties is a cornerstone of the LHC program
and future colliders. In this Snowmass contribution, we study the potential to
directly probe the Higgs-top CP-structure via the $t\bar{t}h$ production at the
HL-LHC, 100 TeV FCC and muon colliders. We find the limits on the CP phase
($\alpha$) at 95% CL are $|\alpha| \lesssim 36^\circ$ with dileptonic $t\bar t
(h\to b\bar b) $ and $|\alpha| \lesssim 25^\circ$ with combined $t\bar t (h\to
\gamma\gamma) $ at the HL-LHC. The 100 TeV FCC brings a significant improvement
in sensitivity with $|\alpha| \lesssim 3^\circ$ for the dileptonic $t\bar t
(h\to b\bar b) $, due to the remarkable gain in the signal cross-section and
the increased luminosity. At future muon colliders, we find that the bounds
with semileptonic $t\bar t (h\to b\bar b) \nu\bar\nu$ are $|\alpha| \lesssim
9^\circ$ for 10 TeV and $|\alpha| \lesssim 3^\circ$ for 30 TeV, respectively.


title: Inflation: Theory and Observations

abstract:
  Cosmic inflation provides a window to the highest energy densities accessible
in nature, far beyond those achievable in any realistic terrestrial experiment.
Theoretical insights into the inflationary era and its observational probes may
therefore shed unique light on the physical laws underlying our universe. This
white paper describes our current theoretical understanding of the inflationary
era, with a focus on the statistical properties of primordial fluctuations. In
particular, we survey observational targets for three important signatures of
inflation: primordial gravitational waves, primordial non-Gaussianity and
primordial features. With the requisite advancements in analysis techniques,
the tremendous increase in the raw sensitivities of upcoming and planned
surveys will translate to leaps in our understanding of the inflationary
paradigm and could open new frontiers for cosmology and particle physics. The
combination of future theoretical and observational developments therefore
offer the potential for a dramatic discovery about the nature of cosmic
acceleration in the very early universe and physics on the smallest scales.


title: White Paper on Forward Physics, BFKL, Saturation Physics and Diffraction

abstract:
  The goal of this whitepaper is to give a comprehensive overview of the rich
field of forward physics. We discuss the occurrences of BFKL resummation
effects in special final states, such as Mueller-Navelet jets, jet gap jets,
and heavy quarkonium production. It further addresses TMD factorization at low
x and the manifestation of a semi-hard saturation scale in (generalized) TMD
PDFs. More theoretical aspects of low x physics, probes of the quark gluon
plasma, as well as the possibility to use photon-hadron collisions at the LHC
to constrain hadronic structure at low x, and the resulting complementarity
between LHC and the EIC are also presented. We also briefly discuss diffraction
at colliders as well as the possibility to explore further the electroweak
theory in central exclusive events using the LHC as a photon-photon collider.


title:
One Network Doesn't Rule Them All: Moving Beyond Handcrafted
  Architectures in Self-Supervised Learning

abstract:
  The current literature on self-supervised learning (SSL) focuses on
developing learning objectives to train neural networks more effectively on
unlabeled data. The typical development process involves taking
well-established architectures, e.g., ResNet demonstrated on ImageNet, and
using them to evaluate newly developed objectives on downstream scenarios.
While convenient, this does not take into account the role of architectures
which has been shown to be crucial in the supervised learning literature. In
this work, we establish extensive empirical evidence showing that a network
architecture plays a significant role in SSL. We conduct a large-scale study
with over 100 variants of ResNet and MobileNet architectures and evaluate them
across 11 downstream scenarios in the SSL setting. We show that there is no one
network that performs consistently well across the scenarios. Based on this, we
propose to learn not only network weights but also architecture topologies in
the SSL regime. We show that "self-supervised architectures" outperform popular
handcrafted architectures (ResNet18 and MobileNetV2) while performing
competitively with the larger and computationally heavy ResNet50 on major image
classification benchmarks (ImageNet-1K, iNat2021, and more). Our results
suggest that it is time to consider moving beyond handcrafted architectures in
SSL and start thinking about incorporating architecture search into
self-supervised learning objectives.


title:
An Efficient Method for Simulating Light Curves of Cosmological
  Microlensing and Caustic Crossing Events

abstract:
  A new window to observing individual stars and other small sources at
cosmological distances was opened recently, with the detection of several
caustic-crossing events in galaxy cluster fields. Many more such events are
expected soon from dedicated campaigns with the \emph{Hubble Space Telescope}
and from the \emph{James Webb Space Telescope}. These events can teach us not
only about the lensed sources themselves, such as individual high-redshift
stars, star clusters, or accretion disks, but through their light-curves they
also hold information about the point-mass function of the lens and thus,
potentially, the composition of dark matter. We present here a simple method
for simulating light curves of such events, i.e., the change in apparent
magnitude of the source as it sweeps over the net of caustics generated by
microlenses embedded around the critical region of the lens. The method is
recursive and so any reasonably sized small source can be accommodated, down to
sub-solar scales, in principle. We compare the method, which we dub
\emph{Adaptive Boundary Method}, with other common methods such as simple
inverse ray shooting, and demonstrate that it is significantly more efficient
and accurate in the small-source and high-magnification regime of interest. A
\textsc{python} version of the code is made publicly available in an
open-source fashion for simulating future events.


title: Hybrid conformal REBCO dipole for a next hadron collider

abstract:
  A strategy is presented by which a tape-stack REBCO cable may be configured
in a conformal winding in such a way that all tape faces are ~parallel to B
thorughout the winding, yielding ~3x more current performance than is possible
in conventional REBCO cables. A method for dynamic current sharing is
discussed, in which current would naturally re-distribute among the tapes of
each cable as the winding current is increased without inducing premature
quench. A dipole geometry is presented in which a conformal REBCO insert
winding is configured within a cable-in-conduit Nb3Sn outsert winding so that
the sub-windings can be separately fabricated, and then assembled in a common
structure and preloaded for effective stress management. The total cost of
superconductor in this dipole is likely a minimum among the design options for
18 T. Plans for development of the coil technologies and a model dipole are
presented.


title: Animatable Neural Implicit Surfaces for Creating Avatars from Videos

abstract:
  This paper aims to reconstruct an animatable human model from a video of very
sparse camera views. Some recent works represent human geometry and appearance
with neural radiance fields and utilize parametric human models to produce
deformation fields for animation, which enables them to recover detailed 3D
human models from videos. However, their reconstruction results tend to be
noisy due to the lack of surface constraints on radiance fields. Moreover, as
they generate the human appearance in 3D space, their rendering quality heavily
depends on the accuracy of deformation fields. To solve these problems, we
propose Animatable Neural Implicit Surface (AniSDF), which models the human
geometry with a signed distance field and defers the appearance generation to
the 2D image space with a 2D neural renderer. The signed distance field
naturally regularizes the learned geometry, enabling the high-quality
reconstruction of human bodies, which can be further used to improve the
rendering speed. Moreover, the 2D neural renderer can be learned to compensate
for geometric errors, making the rendering more robust to inaccurate
deformations. Experiments on several datasets show that the proposed approach
outperforms recent human reconstruction and synthesis methods by a large
margin.


title: Privacy-Aware Compression for Federated Data Analysis

abstract:
  Federated data analytics is a framework for distributed data analysis where a
server compiles noisy responses from a group of distributed low-bandwidth user
devices to estimate aggregate statistics. Two major challenges in this
framework are privacy, since user data is often sensitive, and compression,
since the user devices have low network bandwidth. Prior work has addressed
these challenges separately by combining standard compression algorithms with
known privacy mechanisms. In this work, we take a holistic look at the problem
and design a family of privacy-aware compression mechanisms that work for any
given communication budget. We first propose a mechanism for transmitting a
single real number that has optimal variance under certain conditions. We then
show how to extend it to metric differential privacy for location privacy
use-cases, as well as vectors, for application to federated learning. Our
experiments illustrate that our mechanism can lead to better utility vs.
compression trade-offs for the same privacy loss in a number of settings.


title: Anomalous quartic gauge couplings at a muon collider

abstract:
  Prospects for searches of anomalous quartic gauge couplings at a future
high-energy muon collider using the production of $\mathrm{WW}$ boson pairs are
reported. Muon-muon collision events are simulated at $\sqrt{s}=6$ TeV
corresponding to an integrated luminosity of $4$ ab$^{-1}$. The simulated
events are used to study the $\mathrm{W}\mathrm{W}\nu\nu$ and
$\mathrm{W}\mathrm{W}\mu\mu$ final states with the $\mathrm{W}$ bosons decaying
hadronically. The events are analyzed to report expected constraints on the
structure of quartic vector boson interactions in the framework of dimension-8
effective field theory operators.


title: Three coloring via triangle counting

abstract:
  In the first partial result toward the Steinberg's now-disproved three
coloring conjecture, Abbott and Zhou used a counting argument to show that
every planar graph without cycles of lengths 4 through 11 is 3-colorable.
Implicit in their proof is a fact about plane graphs: in any plane graph of
minimum degree 3, if no two triangles share an edge, then triangles make up
strictly less than 2/3 of the faces. We show how this result, combined with
Kostochka and Yancey's resolution of Ore's conjecture for k = 4, implies that
every planar graph without cycles of lengths 4 through 8 is 3-colorable.


title: RKKY interaction at helical edges of topological superconductors

abstract:
  We study spin configurations of magnetic impurities placed close to the edge
of a two-dimensional topological superconductor both analytically and
numerically. First, we demonstrate that the spin of a single magnetic impurity
close to the edge of a topological superconductor tends to align along the
edge. The strong easy-axis spin anisotropy behind this effect originates from
the interaction between the impurity and the gapless helical Majorana edge
states. We then compute the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction
between two magnetic impurities placed close to the edge. We show that, in the
limit of large interimpurity distances, the RKKY interaction between the two
impurities is mainly mediated by the Majorana edge states and leads to a
ferromagnetic alignment of both spins along the edge. This effect can be used
to detect helical Majorana edge states.


title:
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D
  Molecular Volumes from Real Cryo-EM Images

abstract:
  Cryo-electron microscopy (cryo-EM) has become a tool of fundamental
importance in structural biology, helping us understand the basic building
blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the
unknown 3D poses and the 3D electron scattering potential of a biomolecule from
millions of extremely noisy 2D images. Existing reconstruction algorithms,
however, cannot easily keep pace with the rapidly growing size of cryo-EM
datasets due to their high computational and memory cost. We introduce cryoAI,
an ab initio reconstruction algorithm for homogeneous conformations that uses
direct gradient-based optimization of particle poses and the electron
scattering potential from single-particle cryo-EM data. CryoAI combines a
learned encoder that predicts the poses of each particle image with a
physics-based decoder to aggregate each particle image into an implicit
representation of the scattering potential volume. This volume is stored in the
Fourier domain for computational efficiency and leverages a modern coordinate
network architecture for memory efficiency. Combined with a symmetrized loss
function, this framework achieves results of a quality on par with
state-of-the-art cryo-EM solvers for both simulated and experimental data, one
order of magnitude faster for large datasets and with significantly lower
memory requirements than existing methods.


title:
Snowmass2021 Cosmic Frontier White Paper: Numerical relativity for
  next-generation gravitational-wave probes of fundamental physics

abstract:
  The next generation of gravitational-wave detectors, conceived to begin
operations in the 2030s, will probe fundamental physics with exquisite
sensitivity. These observations will measure the equation of state of dense
nuclear matter in the most extreme environments in the universe, reveal with
exquisite fidelity the nonlinear dynamics of warped spacetime, put general
relativity to the strictest test, and perhaps use black holes as cosmic
particle detectors. Achieving each of these goals will require a new generation
of numerical relativity simulations that will run at scale on the
supercomputers of the 2030s to achieve the necessary accuracy, which far
exceeds the capabilities of numerical relativity and high-performance computing
infrastructures available today.


title: Learning Spatio-Temporal Downsampling for Effective Video Upscaling

abstract:
  Downsampling is one of the most basic image processing operations. Improper
spatio-temporal downsampling applied on videos can cause aliasing issues such
as moir\'e patterns in space and the wagon-wheel effect in time. Consequently,
the inverse task of upscaling a low-resolution, low frame-rate video in space
and time becomes a challenging ill-posed problem due to information loss and
aliasing artifacts. In this paper, we aim to solve the space-time aliasing
problem by learning a spatio-temporal downsampler. Towards this goal, we
propose a neural network framework that jointly learns spatio-temporal
downsampling and upsampling. It enables the downsampler to retain the key
patterns of the original video and maximizes the reconstruction performance of
the upsampler. To make the downsamping results compatible with popular image
and video storage formats, the downsampling results are encoded to uint8 with a
differentiable quantization layer. To fully utilize the space-time
correspondences, we propose two novel modules for explicit temporal propagation
and space-time feature rearrangement. Experimental results show that our
proposed method significantly boosts the space-time reconstruction quality by
preserving spatial textures and motion patterns in both downsampling and
upscaling. Moreover, our framework enables a variety of applications, including
arbitrary video resampling, blurry frame reconstruction, and efficient video
storage.


title: Object Manipulation via Visual Target Localization

abstract:
  Object manipulation is a critical skill required for Embodied AI agents
interacting with the world around them. Training agents to manipulate objects,
poses many challenges. These include occlusion of the target object by the
agent's arm, noisy object detection and localization, and the target frequently
going out of view as the agent moves around in the scene. We propose
Manipulation via Visual Object Location Estimation (m-VOLE), an approach that
explores the environment in search for target objects, computes their 3D
coordinates once they are located, and then continues to estimate their 3D
locations even when the objects are not visible, thus robustly aiding the task
of manipulating these objects throughout the episode. Our evaluations show a
massive 3x improvement in success rate over a model that has access to the same
sensory suite but is trained without the object location estimator, and our
analysis shows that our agent is robust to noise in depth perception and agent
localization. Importantly, our proposed approach relaxes several assumptions
about idealized localization and perception that are commonly employed by
recent works in embodied AI -- an important step towards training agents for
object manipulation in the real world.


title: Constraining the Radio Properties of the $z$=6.44 QSO VIK J2318$-$3113

abstract:
  The recent detection of the quasi-stellar object (QSO) VIKING
J231818.3$-$311346 (hereafter VIK J2318$-$3113) at redshift $z=6.44$ in the
Rapid ASKAP Continuum Survey (RACS) uncovered its radio-loud nature, making it
one of the most distant known to date in this class. By using data from several
radio surveys of the Galaxy and Mass Assembly 23$^\mathrm{h}$ field and from
dedicated follow-up, we were able to constrain the radio spectrum of VIK
J2318$-$3113 in the observed range $\sim$0.1--10 GHz. At high frequencies
(0.888--5.5 GHz in the observed frame) the QSO presents a steep spectrum
($\alpha_{\rm r}$=1.24, with $S_\nu\propto \nu^{-\alpha_{\rm r}}$), while at
lower frequencies (0.4--0.888 GHz in the observed frame) it is nearly flat. The
overall spectrum can be modelled by either a curved function with a rest-frame
turnover around 5 GHz, or with a smoothly varying double power law that is flat
below a rest-frame break frequency of about 20 GHz and which significantly
steepens above it. Based on the model adopted, we estimated that the radio jets
of VIK J2318$-$3113 must be a few hundred years old, in the case of a turnover,
or less than few$\times$10$^4$ years, in the case of a break in the spectrum.
Having multiple observations at two frequencies (888 MHz and 5.5 GHz), we
further investigated the radio variability previously reported for this source.
We found that the marginally significant flux density variations are consistent
with the expectations from refractive interstellar scintillation, even though
relativistic effects related to the orientation of the source may still play a
non-negligible role. Further radio and X-ray observations are required to
conclusively discern the nature of this variation.


title:
HiSA-SMFM: Historical and Sentiment Analysis based Stock Market
  Forecasting Model

abstract:
  One of the pillars to build a country's economy is the stock market. Over the
years, people are investing in stock markets to earn as much profit as possible
from the amount of money that they possess. Hence, it is vital to have a
prediction model which can accurately predict future stock prices. With the
help of machine learning, it is not an impossible task as the various machine
learning techniques if modeled properly may be able to provide the best
prediction values. This would enable the investors to decide whether to buy,
sell or hold the share. The aim of this paper is to predict the future of the
financial stocks of a company with improved accuracy. In this paper, we have
proposed the use of historical as well as sentiment data to efficiently predict
stock prices by applying LSTM. It has been found by analyzing the existing
research in the area of sentiment analysis that there is a strong correlation
between the movement of stock prices and the publication of news articles.
Therefore, in this paper, we have integrated these factors to predict the stock
prices more accurately.


title:
DeepTrust: A Reliable Financial Knowledge Retrieval Framework For
  Explaining Extreme Pricing Anomalies

abstract:
  Extreme pricing anomalies may occur unexpectedly without a trivial cause, and
equity traders typically experience a meticulous process to source disparate
information and analyze its reliability before integrating it into the trusted
knowledge base. We introduce DeepTrust, a reliable financial knowledge
retrieval framework on Twitter to explain extreme price moves at speed, while
ensuring data veracity using state-of-the-art NLP techniques. Our proposed
framework consists of three modules, specialized for anomaly detection,
information retrieval and reliability assessment. The workflow starts with
identifying anomalous asset price changes using machine learning models trained
with historical pricing data, and retrieving correlated unstructured data from
Twitter using enhanced queries with dynamic search conditions. DeepTrust
extrapolates information reliability from tweet features, traces of generative
language model, argumentation structure, subjectivity and sentiment signals,
and refine a concise collection of credible tweets for market insights. The
framework is evaluated on two self-annotated financial anomalies, i.e., Twitter
and Facebook stock price on 29 and 30 April 2021. The optimal setup outperforms
the baseline classifier by 7.75% and 15.77% on F0.5-scores, and 10.55% and
18.88% on precision, respectively, proving its capability in screening
unreliable information precisely. At the same time, information retrieval and
reliability assessment modules are analyzed individually on their effectiveness
and causes of limitations, with identified subjective and objective factors
that influence the performance. As a collaborative project with Refinitiv, this
framework paves a promising path towards building a scalable commercial
solution that assists traders to reach investment decisions on pricing
anomalies with authenticated knowledge from social media platforms in
real-time.


title:
Learning Transient Partial Differential Equations with Local Neural
  Operators

abstract:
  In decades, enormous computational resources are poured into solving the
transient partial differential equations for multifarious physical fields. The
latest artificial intelligence has shown great potential in accelerating these
computations, but its road to wide applications is hindered by the variety of
computational domains and boundary conditions. Here, we overcome this obstacle
by constructing a learning framework capable of purely representing the
transient PDEs with local neural operators (LNOs). This framework is
demonstrated in learning several transient PDEs, especially the Navier-Stokes
equations, and successfully applied to solve problems with quite different
domains and boundaries, including the internal flow, the external flow, and
remarkably, the flow across the cascade of airfoils. In these applications, our
LNOs are faster than the conventional numerical solver by over 1000 times,
which could be significant for scientific computations and engineering
simulations.


title:
The Design and Implementation of a Broadly Applicable Algorithm for
  Optimizing Intra-Day Surgical Scheduling

abstract:
  Surgical scheduling optimization is an active area of research. However, few
algorithms to optimize surgical scheduling are implemented and see sustained
use. An algorithm is more likely to be implemented, if it allows for surgeon
autonomy, i.e., requires only limited scheduling centralization, and functions
in the limited technical infrastructure of widely used electronic medical
records (EMRs). In order for an algorithm to see sustained use, it must be
compatible with changes to hospital capacity, patient volumes, and scheduling
practices. To meet these objectives, we developed the BEDS (better elective day
of surgery) algorithm, a greedy heuristic for smoothing unit-specific surgical
admissions across days. We implemented BEDS in the EMR of a large pediatric
academic medical center.
  The use of BEDS was associated with a reduction in the variability in the
number of admissions. BEDS is freely available as a dashboard in Tableau, a
commercial software used by numerous hospitals. BEDS is readily implementable
with the limited tools available to most hospitals, does not require reductions
to surgeon autonomy or centralized scheduling, and is compatible with changes
to hospital capacity or patient volumes. We present a general algorithmic
framework from which BEDS is derived based on a particular choice of objectives
and constraints. We argue that algorithms generated by this framework retain
many of the desirable characteristics of BEDS while being compatible with a
wide range of objectives and constraints.


title: Energy-Latency Attacks via Sponge Poisoning

abstract:
  Sponge examples are test-time inputs carefully-optimized to increase energy
consumption and latency of neural networks when deployed on hardware
accelerators. In this work, we demonstrate that sponge attacks can also be
implanted at training time, when model training is outsourced to a third party,
via an attack that we call sponge poisoning. This attack allows one to increase
the energy consumption and latency of machine-learning models indiscriminately
on each test-time input. We present a novel formalization for sponge poisoning,
overcoming the limitations related to the optimization of test-time sponge
examples, and show that this attack is possible even if the attacker only
controls a few poisoning samples and model updates. Our extensive experimental
analysis, involving two deep learning architectures and three datasets, shows
that sponge poisoning can almost completely vanish the effect of such hardware
accelerators. Finally, we analyze activations of the resulting sponge models,
identifying the module components that are more sensitive to this
vulnerability.


title:
RES-HD: Resilient Intelligent Fault Diagnosis Against Adversarial
  Attacks Using Hyper-Dimensional Computing

abstract:
  Industrial Internet of Things (I-IoT) enables fully automated production
systems by continuously monitoring devices and analyzing collected data.
Machine learning methods are commonly utilized for data analytics in such
systems. Cyber-attacks are a grave threat to I-IoT as they can manipulate
legitimate inputs, corrupting ML predictions and causing disruptions in the
production systems. Hyper-dimensional computing (HDC) is a brain-inspired
machine learning method that has been shown to be sufficiently accurate while
being extremely robust, fast, and energy-efficient. In this work, we use HDC
for intelligent fault diagnosis against different adversarial attacks. Our
black-box adversarial attacks first train a substitute model and create
perturbed test instances using this trained model. These examples are then
transferred to the target models. The change in the classification accuracy is
measured as the difference before and after the attacks. This change measures
the resiliency of a learning method. Our experiments show that HDC leads to a
more resilient and lightweight learning solution than the state-of-the-art deep
learning methods. HDC has up to 67.5% higher resiliency compared to the
state-of-the-art methods while being up to 25.1% faster to train.


title: MoReL: Multi-omics Relational Learning

abstract:
  Multi-omics data analysis has the potential to discover hidden molecular
interactions, revealing potential regulatory and/or signal transduction
pathways for cellular processes of interest when studying life and disease
systems. One of critical challenges when dealing with real-world multi-omics
data is that they may manifest heterogeneous structures and data quality as
often existing data may be collected from different subjects under different
conditions for each type of omics data. We propose a novel deep Bayesian
generative model to efficiently infer a multi-partite graph encoding molecular
interactions across such heterogeneous views, using a fused Gromov-Wasserstein
(FGW) regularization between latent representations of corresponding views for
integrative analysis. With such an optimal transport regularization in the deep
Bayesian generative model, it not only allows incorporating view-specific side
information, either with graph-structured or unstructured data in different
views, but also increases the model flexibility with the distribution-based
regularization. This allows efficient alignment of heterogeneous latent
variable distributions to derive reliable interaction predictions compared to
the existing point-based graph embedding methods. Our experiments on several
real-world datasets demonstrate enhanced performance of MoReL in inferring
meaningful interactions compared to existing baselines.


title:
A physics and data co-driven surrogate modeling approach for temperature
  field prediction on irregular geometric domain

abstract:
  In the whole aircraft structural optimization loop, thermal analysis plays a
very important role. But it faces a severe computational burden when directly
applying traditional numerical analysis tools, especially when each
optimization involves repetitive parameter modification and thermal analysis
followed. Recently, with the fast development of deep learning, several
Convolutional Neural Network (CNN) surrogate models have been introduced to
overcome this obstacle. However, for temperature field prediction on irregular
geometric domains (TFP-IGD), CNN can hardly be competent since most of them
stem from processing for regular images. To alleviate this difficulty, we
propose a novel physics and data co-driven surrogate modeling method. First,
after adapting the Bezier curve in geometric parameterization, a body-fitted
coordinate mapping is introduced to generate coordinate transforms between the
irregular physical plane and regular computational plane. Second, a
physics-driven CNN surrogate with partial differential equation (PDE) residuals
as a loss function is utilized for fast meshing (meshing surrogate); then, we
present a data-driven surrogate model based on the multi-level reduced-order
method, aiming to learn solutions of temperature field in the above regular
computational plane (thermal surrogate). Finally, combining the grid position
information provided by the meshing surrogate with the scalar temperature field
information provided by the thermal surrogate (combined model), we reach an
end-to-end surrogate model from geometric parameters to temperature field
prediction on an irregular geometric domain. Numerical results demonstrate that
our method can significantly improve accuracy prediction on a smaller dataset
while reducing the training time when compared with other CNN methods.


title: Magnetic Field Reversal around an Active Fast Radio Burst

abstract:
  The environment of actively repeating fast radio bursts (FRBs) has been shown
to be complex and varying. The recently localized FRB 20190520B is extremely
active, has the largest confirmed host dispersion measure, and is only the
second FRB source associated with a compact, persistent radio source (PRS). The
main tracer of the magneto-ionic environments is the rotation measure (RM), a
path-integral of the line-of-sight component of magnetic field strength (B) and
electron density, which does not allow a direct probe of the B-field
configuration. Here we report direct evidence for a B-field reversal based on
the observed sign change and extreme variation of FRB 20190520B's RM, which
changed from $\sim10000$ rad m$^{-2}$ to $\sim-16000$ rad m$^{-2}$ between June
2021 and January 2022. Such extreme RM reversal has never been observed before
in any FRB nor in any astronomical object. The implied short-term change of the
B-field configuration in or around the FRB could be due to the vicinity of
massive black holes, or a magnetized companion star in binary systems, or a
young supernova remnant along the line of sight.


title: CMB lensing power spectrum with next generation surveys

abstract:
  We introduce a new estimator of the CMB lensing power spectrum, together with
its likelihood, based on iterative lensing reconstruction. Despite the
increased complexity of the lensing maps, this estimator shares similarities
with the standard quadratic estimator. Most importantly, it is unbiased towards
the assumptions done on the noise and cosmology for the lensing reconstruction.
This new spectrum estimator can double the constraints on the lensing amplitude
compared to the quadratic estimator, while keeping numerical cost under control
and being robust to errors.


title: NGC 1605 is not a binary cluster

abstract:
  The open star cluster NGC 1605 has recently been reported to in fact consist
of two clusters (one intermediate-aged and one old) that merged via a flyby
capture. Here we show that Gaia data do not support this scenario. We also
report the serendipitous discovery of a new open cluster, Can Batll\'o 1, with
a similar age and distance.


title: Momentum space entanglement of interacting fermions

abstract:
  Momentum space entanglement entropy probes quantum correlations in
interacting fermionic phases. It is very sensitive to interactions, obeying
volume-law scaling in general, while vanishing in the Fermi gas. We show that
the R\'enyi entropy in momentum space has a systematic expansion in terms of
the phase space volume of the partition, which holds at all orders in
perturbation theory. This permits, for example, the controlled computation of
the entropy of thin shells near the Fermi wavevector in isotropic Fermi liquids
and BCS superconductors. In the Fermi liquid, the thin shell entropy is a
universal function of the quasiparticle residue. In the superconductor, it
reflects the formation of Cooper pairs. Momentum space R\'enyi entropies are
accessible in cold atomic and molecular gas experiments through a
time-of-flight generalization of previously implemented measurement protocols.


title:
Weak Mass Loss from the Red Supergiant Progenitor of the Type II SN
  2021yja

abstract:
  We present high-cadence optical, ultraviolet (UV), and near-infrared data of
the nearby ($D\approx23$ Mpc) Type II supernova (SN) 2021yja. Many Type II SNe
show signs of interaction with circumstellar material (CSM) during the first
few days after explosion, implying that their red supergiant (RSG) progenitors
experience episodic or eruptive mass loss. However, because it is difficult to
discover SNe early, the diversity of CSM configurations in RSGs has not been
fully mapped. SN 2021yja, first detected within ${\approx}5.4$ hours of
explosion, shows some signatures of CSM interaction (high UV luminosity, radio
and x-ray emission) but without the narrow emission lines or early light curve
peak that can accompany CSM. Here we analyze the densely sampled early light
curve and spectral series of this nearby SN to infer the properties of its
progenitor and CSM. We find that the most likely progenitor was an RSG with an
extended envelope, encompassed by low-density CSM. We also present archival
Hubble Space Telescope imaging of the host galaxy of SN 2021yja, which allows
us to place a stringent upper limit of ${\lesssim}9\ M_\odot$ on the progenitor
mass. However, this is in tension with some aspects of the SN evolution, which
point to a more massive progenitor. Our analysis highlights the need to
consider progenitor structure when making inferences about CSM properties, and
that a comprehensive view of CSM tracers should be made to give a fuller view
of the last years of RSG evolution.


title: Symmetry-preserving boundary of (2+1)D fractional quantum Hall states

abstract:
  We investigate symmetry-preserving gapped boundary of (2+1)D topological
phases with global symmetry, which can be either bosonic or fermionic. We
develop a general algebraic description for gapped boundary condition for
symmetry-enriched or fermionic topological phases, extending the framework of
Lagrangian algebra anyon for bosonic phases without symmetry. We then focus on
application to the case with U(1) symmetry. We derive new obstructions to
symmetry-preserving gapped boundary for U(1)$^f$-symmetric (2+1)D fermionic
topological phases, which are beyond chiral central charge $c_-$ and electric
Hall conductivity $\sigma_H$. These obstructions are given by a simple
Gauss-Milgram type formula valid for super-modular category, and regarded as a
higher version of $c_-$ and $\sigma_H$.


title:
Galaxy mergers can initiate quenching by unlocking an AGN-driven
  transformation of the baryon cycle

abstract:
  We use zoom simulations to show how merger-driven disruption of the gas disc
in a galaxy provides its central active galactic nucleus (AGN) with fuel to
drive outflows that entrain and expel a significant fraction of the
circumgalactic medium (CGM). This in turn suppresses replenishment of the
interstellar medium, causing the galaxy to quench up to several Gyr after the
merger. We start by performing a zoom simulation of a present-day star-forming
disc galaxy with the EAGLE galaxy formation model. Then, we re-simulate the
galaxy with controlled changes to its initial conditions, using the genetic
modification technique. These modifications either increase or decrease the
stellar mass ratio of the galaxy's last significant merger, which occurs at
$z\approx 0.74$. The halo reaches the same present-day mass in all cases, but
changing the mass ratio of the merger yields markedly different galaxy and CGM
properties. We find that a merger can unlock rapid growth of the central
supermassive black hole if it disrupts the co-rotational motion of gas in the
black hole's vicinity. Conversely, if a less disruptive merger occurs and gas
close to the black hole is not disturbed, the AGN does not strongly affect the
CGM, and consequently the galaxy continues to form stars. Our result
illustrates how a unified view of AGN feedback, the baryon cycle and the
interstellar medium is required to understand how mergers and quenching are
connected over long timescales.


title: Godzilla, a monster lurks in the Sunburst galaxy

abstract:
  We model the strong lensing effect in the galaxy cluster PSZ1 G311.65-18.48
(z=0.443) with an improved version of the hybrid method WSLAP+. We extend the
number of constraints by including the position of critical points, which are
combined with the classic positional constraints of the lensed galaxies. We pay
special attention to a transient candidate source (Tr) previously discovered in
the giant Sunburst arc (z=2.37). Our lens model predicts Tr to be within a
fraction of an arcsecond from the critical curve, having a larger magnification
factor than previously found, but still not large enough to explain the
observed flux and lack of counterimages. Possible candidate counterimages are
discussed that would lower the magnification required to explain Tr, but
extreme magnification factors ($\mu>1000$) are still required, even in that
case. The presence of a small mass perturber with a mass comparable to a dwarf
galaxy ($M\sim 10^8 \,{\rm M}_{\odot}$) near the position of Tr is needed in
order to explain the required magnification and morphology of the lensed
galaxy. We discuss how the existence of this perturber could potentially be
used to constrain models of dark matter. The large apparent brightness and
unresolved nature of the magnified object implies a combination of extreme
magnification and a very luminous and compact source ($r<0.3$ pc). Possible
candidates are discussed, including an hyperluminous star or an accretion disc
around an intermediate-mass black hole (IMBH). Based on spectral information,
we argue that a luminous blue variable (LBV) star caught during an outburst is
the most likely candidate. Owing to the extreme magnification and luminosity of
this source we dub it Godzilla.

categories: astro-ph.GA astro-ph.CO astro-ph.HE astro-ph.SR


title:
Topological magnon band structure of emergent Landau levels in a
  skyrmion lattice

abstract:
  The motion of a spin excitation across topologically non-trivial magnetic
order exhibits a deflection that is analogous to the effect of the Lorentz
force on an electrically charged particle in an orbital magnetic field. We used
polarized inelastic neutron scattering to investigate the propagation of
magnons (i.e., bosonic collective spin excitations) in a lattice of skyrmion
tubes in manganese silicide. For wave vectors perpendicular to the skyrmion
tubes, the magnon spectra are consistent with the formation of finely spaced
emergent Landau levels that are characteristic of the fictitious magnetic field
used to account for the nontrivial topological winding of the skyrmion lattice.
This provides evidence of a topological magnon band structure in reciprocal
space, which is borne out of the nontrivial real-space topology of a magnetic
order.


title: The Pierre Auger Exotic Events and Axion Quark Nuggets

abstract:
  The Pierre Auger Observatory have reported [1-3] observation of several
exotic events (EE) which apparently related to thunderstorms. These events are
much larger in size than conventional cosmic ray (CR) events, and they have
very distinct timing features. A possible nature of the observed phenomenon is
still a matter of active research and debates as many unusual features of these
exotic events are hard to explain. In particular, the frequency of appearance
of these EE is very low (less than 2 events/year), in huge contrast with a
typical rate of a conventional lightning strikes in the area. We propose that
the observed EE can be explained within the so-called axion quark nugget (AQN)
dark matter model. The idea is that the AQNs may trigger and initiate a special
and unique class of lightning strikes during a thunderstorm as a result of
ionization of the atmospheric molecules along its path. The corresponding
AQN-induced lighting flashes may show some specific features not shared by
typical and much more frequent conventional flashes. We support this proposal
by demonstrating that the observations[1-3], including the frequency of
appearance and time duration are consistent with observations. We also comment
on possible relation of AUGER EEs with the Telescope Array bursts and the
terrestrial gamma ray flashes (TGF). We list a number of features of the
AQN-induced EE (such as specific radio pulses synchronized with these events)
which can be directly tested by future experiments. We also suggest to use
distributed acoustic sensing (DAS) instruments to detect the acoustic pulses
which must be synchronized with AUGER EEs.


title:
Sensitivity Estimation for Dark Matter Subhalos in Synthetic Gaia DR2
  using Deep Learning

abstract:
  The abundance of dark matter subhalos orbiting a host galaxy is a generic
prediction of the cosmological framework. It is a promising way to constrain
the nature of dark matter. Here we describe the challenges of detecting stars
whose phase-space distribution may be perturbed by the passage of dark matter
subhalos using a machine learning approach. The training data are three Milky
Way-like galaxies and nine synthetic Gaia DR2 surveys derived from these. We
first quantify the magnitude of the perturbations in the simulated galaxies
using an anomaly detection algorithm. We also estimate the feasibility of this
approach in the Gaia DR2-like catalogues by comparing the anomaly detection
based approach with a supervised classification. We find that a classification
algorithm optimized on about half a billion synthetic star observables exhibits
mild but nonzero sensitivity. This classification-based approach is not
sufficiently sensitive to pinpoint the exact locations of subhalos in the
simulation, as would be expected from the very limited number of subhalos in
the detectable region. The enormous size of the Gaia dataset motivates the
further development of scalable and accurate computational methods that could
be used to select potential regions of interest for dark matter searches to
ultimately constrain the Milky Way's subhalo mass function.

categories: astro-ph.GA astro-ph.IM cs.LG physics.data-an stat.ML