[ { "Title": "Beyond The Rainbow: High Performance Deep Reinforcement Learning On A\n Desktop PC", "Abstract": "Rainbow Deep Q-Network (DQN) demonstrated combining multiple independent\nenhancements could significantly boost a reinforcement learning (RL) agent's\nperformance. In this paper, we present \"Beyond The Rainbow\" (BTR), a novel\nalgorithm that integrates six improvements from across the RL literature to\nRainbow DQN, establishing a new state-of-the-art for RL using a desktop PC,\nwith a human-normalized interquartile mean (IQM) of 7.4 on atari-60. Beyond\nAtari, we demonstrate BTR's capability to handle complex 3D games, successfully\ntraining agents to play Super Mario Galaxy, Mario Kart, and Mortal Kombat with\nminimal algorithmic changes. Designing BTR with computational efficiency in\nmind, agents can be trained using a desktop PC on 200 million Atari frames\nwithin 12 hours. Additionally, we conduct detailed ablation studies of each\ncomponent, analzying the performance and impact using numerous measures.", "Authors": [ "Tyler Clark", "Mark Towers", "Christine Evers", "Jonathon Hare" ], "Author_company": [ "IQM" ], "Date": "2024-11-06T10:42:04Z", "arXiv_id": "2411.03820v1" }, { "Title": "MotionLeaf: Fine-grained Multi-Leaf Damped Vibration Monitoring for\n Plant Water Stress using Low-Cost mmWave Sensors", "Abstract": "In this paper, we introduce MotionLeaf , a novel mmWave base multi-point\nvibration frequency measurement system that can estimate plant stress by\nanalyzing the surface vibrations of multiple leaves. MotionLeaf features a\nnovel signal processing pipeline that accurately estimates fine-grained damped\nvibration frequencies based on noisy micro-displacement measurements from a\nmmWave radar. Specifically we explore the Interquartile Mean (IQM) of coherent\nphase differences from neighboring Frequency-Modulated Continuous Wave (FMCW)\nradar chirps to calculate micro-displacements. Furthermore, we use the\nmeasurements from multiple received antennas in the radar to estimate the\nvibration signals of different leaves via a Blind Source Separation (BSS)\nmethod. Experimental results demonstrate that MotionLeaf can accurately measure\nthe frequency of multiple leaves in a plant with average error of 0.0176 Hz,\nwhich is less than 50% of that (0.0416 Hz) of the state-of-the-art approach\n(mmVib). Additionally, the estimated natural vibration frequencies from\nMotionLeaf are shown to be an excellent feature to detect the water stress in\nthe plant during 7-day drought experiments.", "Authors": [ "Mark Cardamis", "Chun Tung Chou", "Wen Hu" ], "Author_company": [ "IQM" ], "Date": "2024-09-20T06:22:58Z", "arXiv_id": "2410.03679v1" }, { "Title": "Estimating the coherence of noise in mid-scale quantum systems", "Abstract": "While the power of quantum computers is commonly acknowledged to rise\nexponentially, it is often overlooked that the complexity of quantum noise\nmechanisms generally grows much faster. In particular, quantifying whether the\ninstructions on a quantum processor are close to being unitary has important\nconsequences concerning error rates, e.g., for the confidence in their\nestimation, the ability to mitigate them efficiently, or their relation to\nfault-tolerance thresholds in error correction. However, the complexity of\nestimating the coherence, or unitarity, of noise generally scales exponentially\nin system size. Here, we obtain an upper bound on the average unitarity of\nPauli noise and develop a protocol allowing us to estimate the average\nunitarity of operations in a digital quantum device efficiently and feasibly\nfor mid-size quantum systems. We demonstrate our results through both\nexperimental execution on IQM Spark (TM), a 5-qubit superconducting quantum\ncomputer, and in simulation with up to 10 qubits, discussing the prospects for\nextending our technique to arbitrary scales.", "Authors": [ "Pedro Figueroa-Romero", "Miha Papič", "Adrian Auer", "Inés de Vega" ], "Author_company": [ "IQM" ], "Date": "2024-09-03T17:59:52Z", "arXiv_id": "2409.02110v1" }, { "Title": "Technology and Performance Benchmarks of IQM's 20-Qubit Quantum Computer", "Abstract": "Quantum computing has tremendous potential to overcome some of the\nfundamental limitations present in classical information processing. Yet,\ntoday's technological limitations in the quality and scaling prevent exploiting\nits full potential. Quantum computing based on superconducting quantum\nprocessing units (QPUs) is among the most promising approaches towards\npractical quantum advantage.\n In this article the basic technological approach of IQM Quantum Computers is\ndescribed covering both the QPU and the rest of the full-stack quantum\ncomputer. In particular, the focus is on a 20-qubit quantum computer featuring\nthe Garnet QPU and its architecture, which we will scale up to 150 qubits. We\nalso present QPU and system-level benchmarks, including a median 2-qubit gate\nfidelity of 99.5% and genuinely entangling all 20 qubits in a\nGreenberger-Horne-Zeilinger (GHZ) state.", "Authors": [ "Leonid Abdurakhimov", "Janos Adam", "Hasnain Ahmad", "Olli Ahonen", "Manuel Algaba", "Guillermo Alonso", "Ville Bergholm", "Rohit Beriwal", "Matthias Beuerle", "Clinton Bockstiegel", "Alessio Calzona", "Chun Fai Chan", "Daniele Cucurachi", "Saga Dahl", "Rakhim Davletkaliyev", "Olexiy Fedorets", "Alejandro Gomez Frieiro", "Zheming Gao", "Johan Guldmyr", "Andrew Guthrie", "Juha Hassel", "Hermanni Heimonen", "Johannes Heinsoo", "Tuukka Hiltunen", "Keiran Holland", "Juho Hotari", "Hao Hsu", "Antti Huhtala", "Eric Hyyppä", "Aleksi Hämäläinen", "Joni Ikonen", "Sinan Inel", "David Janzso", "Teemu Jaakkola", "Mate Jenei", "Shan Jolin", "Kristinn Juliusson", "Jaakko Jussila", "Shabeeb Khalid", "Seung-Goo Kim", "Miikka Koistinen", "Roope Kokkoniemi", "Anton Komlev", "Caspar Ockeloen-Korppi", "Otto Koskinen", "Janne Kotilahti", "Toivo Kuisma", "Vladimir Kukushkin", "Kari Kumpulainen", "Ilari Kuronen", "Joonas Kylmälä", "Niclas Lamponen", "Julia Lamprich", "Alessandro Landra", "Martin Leib", "Tianyi Li", "Per Liebermann", "Aleksi Lintunen", "Wei Liu", "Jürgen Luus", "Fabian Marxer", "Arianne Meijer-van de Griend", "Kunal Mitra", "Jalil Khatibi Moqadam", "Jakub Mrożek", "Henrikki Mäkynen", "Janne Mäntylä", "Tiina Naaranoja", "Francesco Nappi", "Janne Niemi", "Lucas Ortega", "Mario Palma", "Miha Papič", "Matti Partanen", "Jari Penttilä", "Alexander Plyushch", "Wei Qiu", "Aniket Rath", "Kari Repo", "Tomi Riipinen", "Jussi Ritvas", "Pedro Figueroa Romero", "Jarkko Ruoho", "Jukka Räbinä", "Sampo Saarinen", "Indrajeet Sagar", "Hayk Sargsyan", "Matthew Sarsby", "Niko Savola", "Mykhailo Savytskyi", "Ville Selinmaa", "Pavel Smirnov", "Marco Marín Suárez", "Linus Sundström", "Sandra Słupińska", "Eelis Takala", "Ivan Takmakov", "Brian Tarasinski", "Manish Thapa", "Jukka Tiainen", "Francesca Tosto", "Jani Tuorila", "Carlos Valenzuela", "David Vasey", "Edwin Vehmaanperä", "Antti Vepsäläinen", "Aapo Vienamo", "Panu Vesanen", "Alpo Välimaa", "Jaap Wesdorp", "Nicola Wurz", "Elisabeth Wybo", "Lily Yang", "Ali Yurtalan" ], "Author_company": [ "IQM" ], "Date": "2024-08-22T14:26:10Z", "arXiv_id": "2408.12433v1" }, { "Title": "Generalizing soft actor-critic algorithms to discrete action spaces", "Abstract": "ATARI is a suite of video games used by reinforcement learning (RL)\nresearchers to test the effectiveness of the learning algorithm. Receiving only\nthe raw pixels and the game score, the agent learns to develop sophisticated\nstrategies, even to the comparable level of a professional human games tester.\nIdeally, we also want an agent requiring very few interactions with the\nenvironment. Previous competitive model-free algorithms for the task use the\nvalued-based Rainbow algorithm without any policy head. In this paper, we\nchange it by proposing a practical discrete variant of the soft actor-critic\n(SAC) algorithm. The new variant enables off-policy learning using policy heads\nfor discrete domains. By incorporating it into the advanced Rainbow variant,\ni.e., the ``bigger, better, faster'' (BBF), the resulting SAC-BBF improves the\nprevious state-of-the-art interquartile mean (IQM) from 1.045 to 1.088, and it\nachieves these results using only replay ratio (RR) 2. By using lower RR 2, the\ntraining time of SAC-BBF is strictly one-third of the time required for BBF to\nachieve an IQM of 1.045 using RR 8. As a value of IQM greater than one\nindicates super-human performance, SAC-BBF is also the only model-free\nalgorithm with a super-human level using only RR 2. The code is publicly\navailable on GitHub at https://github.com/lezhang-thu/bigger-better-faster-SAC.", "Authors": [ "Le Zhang", "Yong Gu", "Xin Zhao", "Yanshuo Zhang", "Shu Zhao", "Yifei Jin", "Xinxin Wu" ], "Author_company": [ "IQM" ], "Date": "2024-07-08T03:20:45Z", "arXiv_id": "2407.11044v1" }, { "Title": "Adapting Pretrained Networks for Image Quality Assessment on High\n Dynamic Range Displays", "Abstract": "Conventional image quality metrics (IQMs), such as PSNR and SSIM, are\ndesigned for perceptually uniform gamma-encoded pixel values and cannot be\ndirectly applied to perceptually non-uniform linear high-dynamic-range (HDR)\ncolors. Similarly, most of the available datasets consist of\nstandard-dynamic-range (SDR) images collected in standard and possibly\nuncontrolled viewing conditions. Popular pre-trained neural networks are\nlikewise intended for SDR inputs, restricting their direct application to HDR\ncontent. On the other hand, training HDR models from scratch is challenging due\nto limited available HDR data. In this work, we explore more effective\napproaches for training deep learning-based models for image quality assessment\n(IQA) on HDR data. We leverage networks pre-trained on SDR data (source domain)\nand re-target these models to HDR (target domain) with additional fine-tuning\nand domain adaptation. We validate our methods on the available HDR IQA\ndatasets, demonstrating that models trained with our combined recipe outperform\nprevious baselines, converge much quicker, and reliably generalize to HDR\ninputs.", "Authors": [ "Andrei Chubarau", "Hyunjin Yoo", "Tara Akhavan", "James Clark" ], "Author_company": [ "IQM" ], "Date": "2024-05-01T17:57:12Z", "arXiv_id": "2405.00670v1" }, { "Title": "IQMDose3D: a software tool for reconstructing the dose in patient using\n patient planning CT images and the signals measured by IQM detector", "Abstract": "The integral quality monitor (IQM) system compares the signal measured with a\nlarge volume chamber mounted to the linear accelerator's head to the signal\ncalculated using the patient DICOM RT plan for patient-specific quality\nassurance (PSQA). A method was developed to reconstruct the dose in patients\nusing the signal measured by IQM chamber and patient planning CT images. A\nsoftware tool named IQMDose3D was implemented to automate this procedure and\nintegrated into the IQM-based PSQA workflow. IQMDose3D enables the physicists\nto evaluate PSQA by focusing on the clinical perspective by comparing the\ndelivered plan to the approved clinical plan in terms of the clinical goals,\ndose-volume histogram (DVH) in addition to the three-dimensional (3D) gamma map\nand gamma pass rate.", "Authors": [ "Aitang Xing", "Gary Goozee", "Alison Gray", "Vaughan Moutrie", "Sankar Arumugam", "Shrikant Deshpande", "Anthony Espinoza", "Vasilis Kondilis", "Marjorie McDonald", "Philip Vial" ], "Author_company": [ "IQM" ], "Date": "2024-03-26T05:24:19Z", "arXiv_id": "2403.17394v2" }, { "Title": "Adaptive Feature Selection for No-Reference Image Quality Assessment by\n Mitigating Semantic Noise Sensitivity", "Abstract": "The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA)\nmethods typically rely on feature extraction from upstream semantic backbone\nnetworks, assuming that all extracted features are relevant. However, we make a\nkey observation that not all features are beneficial, and some may even be\nharmful, necessitating careful selection. Empirically, we find that many image\npairs with small feature spatial distances can have vastly different quality\nscores, indicating that the extracted features may contain a significant amount\nof quality-irrelevant noise. To address this issue, we propose a Quality-Aware\nFeature Matching IQA Metric (QFM-IQM) that employs an adversarial perspective\nto remove harmful semantic noise features from the upstream task. Specifically,\nQFM-IQM enhances the semantic noise distinguish capabilities by matching image\npairs with similar quality scores but varying semantic features as adversarial\nsemantic noise and adaptively adjusting the upstream task's features by\nreducing sensitivity to adversarial noise perturbation. Furthermore, we utilize\na distillation framework to expand the dataset and improve the model's\ngeneralization ability. Our approach achieves superior performance to the\nstate-of-the-art NR-IQA methods on eight standard IQA datasets.", "Authors": [ "Xudong Li", "Timin Gao", "Runze Hu", "Yan Zhang", "Shengchuan Zhang", "Xiawu Zheng", "Jingyuan Zheng", "Yunhang Shen", "Ke Li", "Yutao Liu", "Pingyang Dai", "Rongrong Ji" ], "Author_company": [ "IQM" ], "Date": "2023-12-11T06:50:27Z", "arXiv_id": "2312.06158v2" }, { "Title": "Augmenting Unsupervised Reinforcement Learning with Self-Reference", "Abstract": "Humans possess the ability to draw on past experiences explicitly when\nlearning new tasks and applying them accordingly. We believe this capacity for\nself-referencing is especially advantageous for reinforcement learning agents\nin the unsupervised pretrain-then-finetune setting. During pretraining, an\nagent's past experiences can be explicitly utilized to mitigate the\nnonstationarity of intrinsic rewards. In the finetuning phase, referencing\nhistorical trajectories prevents the unlearning of valuable exploratory\nbehaviors. Motivated by these benefits, we propose the Self-Reference (SR)\napproach, an add-on module explicitly designed to leverage historical\ninformation and enhance agent performance within the pretrain-finetune\nparadigm. Our approach achieves state-of-the-art results in terms of\nInterquartile Mean (IQM) performance and Optimality Gap reduction on the\nUnsupervised Reinforcement Learning Benchmark for model-free methods, recording\nan 86% IQM and a 16% Optimality Gap. Additionally, it improves current\nalgorithms by up to 17% IQM and reduces the Optimality Gap by 31%. Beyond\nperformance enhancement, the Self-Reference add-on also increases sample\nefficiency, a crucial attribute for real-world applications.", "Authors": [ "Andrew Zhao", "Erle Zhu", "Rui Lu", "Matthieu Lin", "Yong-Jin Liu", "Gao Huang" ], "Author_company": [ "IQM" ], "Date": "2023-11-16T09:07:34Z", "arXiv_id": "2311.09692v1" }, { "Title": "Deep Reinforcement Learning for Autonomous Spacecraft Inspection using\n Illumination", "Abstract": "This paper investigates the problem of on-orbit spacecraft inspection using a\nsingle free-flying deputy spacecraft, equipped with an optical sensor, whose\ncontroller is a neural network control system trained with Reinforcement\nLearning (RL). This work considers the illumination of the inspected spacecraft\n(chief) by the Sun in order to incentivize acquisition of well-illuminated\noptical data. The agent's performance is evaluated through statistically\nefficient metrics. Results demonstrate that the RL agent is able to inspect all\npoints on the chief successfully, while maximizing illumination on inspected\npoints in a simulated environment, using only low-level actions. Due to the\nstochastic nature of RL, 10 policies were trained using 10 random seeds to\nobtain a more holistic measure of agent performance. Over these 10 seeds, the\ninterquartile mean (IQM) percentage of inspected points for the finalized model\nwas 98.82%.", "Authors": [ "David van Wijk", "Kyle Dunlap", "Manoranjan Majji", "Kerianne L. Hobbs" ], "Author_company": [ "IQM" ], "Date": "2023-08-04T23:43:53Z", "arXiv_id": "2308.02743v1" }, { "Title": "Enhancing image quality prediction with self-supervised visual masking", "Abstract": "Full-reference image quality metrics (FR-IQMs) aim to measure the visual\ndifferences between a pair of reference and distorted images, with the goal of\naccurately predicting human judgments. However, existing FR-IQMs, including\ntraditional ones like PSNR and SSIM and even perceptual ones such as HDR-VDP,\nLPIPS, and DISTS, still fall short in capturing the complexities and nuances of\nhuman perception. In this work, rather than devising a novel IQM model, we seek\nto improve upon the perceptual quality of existing FR-IQM methods. We achieve\nthis by considering visual masking, an important characteristic of the human\nvisual system that changes its sensitivity to distortions as a function of\nlocal image content. Specifically, for a given FR-IQM metric, we propose to\npredict a visual masking model that modulates reference and distorted images in\na way that penalizes the visual errors based on their visibility. Since the\nground truth visual masks are difficult to obtain, we demonstrate how they can\nbe derived in a self-supervised manner solely based on mean opinion scores\n(MOS) collected from an FR-IQM dataset. Our approach results in enhanced FR-IQM\nmetrics that are more in line with human prediction both visually and\nquantitatively.", "Authors": [ "Uğur Çoğalan", "Mojtaba Bemana", "Hans-Peter Seidel", "Karol Myszkowski" ], "Author_company": [ "IQM" ], "Date": "2023-05-31T13:48:51Z", "arXiv_id": "2305.19858v2" }, { "Title": "Quantum Speedups for Bayesian Network Structure Learning", "Abstract": "The Bayesian network structure learning (BNSL) problem asks for a directed\nacyclic graph that maximizes a given score function. For networks with $n$\nnodes, the fastest known algorithms run in time $O(2^n n^2)$ in the worst case,\nwith no improvement in the asymptotic bound for two decades. Inspired by recent\nadvances in quantum computing, we ask whether BNSL admits a polynomial quantum\nspeedup, that is, whether the problem can be solved by a quantum algorithm in\ntime $O(c^n)$ for some constant $c$ less than $2$. We answer the question in\nthe affirmative by giving two algorithms achieving $c \\leq 1.817$ and $c \\leq\n1.982$ assuming the number of potential parent sets is, respectively,\nsubexponential and $O(1.453^n)$. Both algorithms assume the availability of a\nquantum random access memory.", "Authors": [ "Juha Harviainen", "Kseniya Rychkova", "Mikko Koivisto" ], "Author_company": [], "Date": "2023-05-31T09:15:28Z", "arXiv_id": "2305.19673v1" }, { "Title": "Efficient Offline Policy Optimization with a Learned Model", "Abstract": "MuZero Unplugged presents a promising approach for offline policy learning\nfrom logged data. It conducts Monte-Carlo Tree Search (MCTS) with a learned\nmodel and leverages Reanalyze algorithm to learn purely from offline data. For\ngood performance, MCTS requires accurate learned models and a large number of\nsimulations, thus costing huge computing time. This paper investigates a few\nhypotheses where MuZero Unplugged may not work well under the offline RL\nsettings, including 1) learning with limited data coverage; 2) learning from\noffline data of stochastic environments; 3) improperly parameterized models\ngiven the offline data; 4) with a low compute budget. We propose to use a\nregularized one-step look-ahead approach to tackle the above issues. Instead of\nplanning with the expensive MCTS, we use the learned model to construct an\nadvantage estimation based on a one-step rollout. Policy improvements are\ntowards the direction that maximizes the estimated advantage with\nregularization of the dataset. We conduct extensive empirical studies with\nBSuite environments to verify the hypotheses and then run our algorithm on the\nRL Unplugged Atari benchmark. Experimental results show that our proposed\napproach achieves stable performance even with an inaccurate learned model. On\nthe large-scale Atari benchmark, the proposed method outperforms MuZero\nUnplugged by 43%. Most significantly, it uses only 5.6% wall-clock time (i.e.,\n1 hour) compared to MuZero Unplugged (i.e., 17.8 hours) to achieve a 150% IQM\nnormalized score with the same hardware and software stacks. Our implementation\nis open-sourced at https://github.com/sail-sg/rosmo.", "Authors": [ "Zichen Liu", "Siyi Li", "Wee Sun Lee", "Shuicheng Yan", "Zhongwen Xu" ], "Author_company": [ "IQM" ], "Date": "2022-10-12T07:41:04Z", "arXiv_id": "2210.05980v2" }, { "Title": "Recent Developments in Quantum-Circuit Refrigeration", "Abstract": "We review the recent progress in direct active cooling of the\nquantum-electric degrees freedom in engineered circuits, or quantum-circuit\nrefrigeration. In 2017, the invention of a quantum-circuit refrigerator (QCR)\nbased on photon-assisted tunneling of quasiparticles through a\nnormal-metal--insulator--superconductor junction inspired a series of\nexperimental studies demonstrating the following main properties: (i) the\ndirect-current (dc) bias voltage of the junction can change the QCR-induced\ndamping rate of a superconducting microwave resonator by orders of magnitude\nand give rise to non-trivial Lamb shifts, (ii) the damping rate can be\ncontrolled in nanosecond time scales, and (iii) the dc bias can be replaced by\na microwave excitation, the amplitude of which controls the induced damping\nrate. Theoretically, it is predicted that state-of-the-art superconducting\nresonators and qubits can be reset with an infidelity lower than $10^{-4}$ in\ntens of nanoseconds using experimentally feasible parameters. A QCR-equipped\nresonator has also been demonstrated as an incoherent photon source with an\noutput temperature above one kelvin yet operating at millikelvin. This source\nhas been used to calibrate cryogenic amplification chains. In the future, the\nQCR may be experimentally used to quickly reset superconducting qubits, and\nhence assist in the great challenge of building a practical quantum computer.", "Authors": [ "Timm Fabian Mörstedt", "Arto Viitanen", "Vasilii Vadimov", "Vasilii Sevriuk", "Matti Partanen", "Eric Hyyppä", "Gianluigi Catelani", "Matti Silveri", "Kuan Yen Tan", "Mikko Möttönen" ], "Author_company": [], "Date": "2021-11-22T14:27:26Z", "arXiv_id": "2111.11234v1" }, { "Title": "Correlation between image quality metrics of magnetic resonance images\n and the neural network segmentation accuracy", "Abstract": "Deep neural networks with multilevel connections process input data in\ncomplex ways to learn the information.A networks learning efficiency depends\nnot only on the complex neural network architecture but also on the input\ntraining images.Medical image segmentation with deep neural networks for skull\nstripping or tumor segmentation from magnetic resonance images enables learning\nboth global and local features of the images.Though medical images are\ncollected in a controlled environment,there may be artifacts or equipment based\nvariance that cause inherent bias in the input set.In this study, we\ninvestigated the correlation between the image quality metrics of MR images\nwith the neural network segmentation accuracy.For that we have used the 3D\nDenseNet architecture and let the network trained on the same input but\napplying different methodologies to select the training data set based on the\nIQM values.The difference in the segmentation accuracy between models based on\nthe random training inputs with IQM based training inputs shed light on the\nrole of image quality metrics on segmentation accuracy.By running the image\nquality metrics to choose the training inputs,further we may tune the learning\nefficiency of the network and the segmentation accuracy.", "Authors": [ "Rajarajeswari Muthusivarajan", "Adrian Celaya", "Joshua P. Yung", "Satish Viswanath", "Daniel S. Marcus", "Caroline Chung", "David Fuentes" ], "Author_company": [ "IQM" ], "Date": "2021-11-01T17:02:34Z", "arXiv_id": "2111.01093v1" }, { "Title": "Stellar Structure and Stability of Charged Interacting Quark Stars and\n Their Scaling Behaviour", "Abstract": "We explore the stellar structure and radial stability of charged quark stars\ncomposed of interacting quark matter (IQM) in three classes of commonly used\ncharge models. We adopt a general parametrization of IQM equation of state that\nincludes the corrections from perturbative QCD, color superconductivity, and\nthe strange quark mass into one parameter $\\lambda$, or one dimensionless\nparameter $\\bar{\\lambda}=\\lambda^2/(4B_{\\rm eff})$ after being rescaled with\nthe effective bag constant $B_{\\rm eff}$. We find that increasing charge tends\nto increase the mass and radius profiles, and enlarges the separation size in\nmass between the maximum mass point and the point where zero eigenfrequencies\n$\\omega^2_0=0$ of the fundamental radial oscillation mode occur. The sign of\nthe separation in central density depends on the charge model; this separation\nalso has a dependence on $\\lambda$ such that increasing $\\lambda$ (which can\noccur for either large color superconductivity or small strange quark mass)\ntends to decrease this separation size for the first and third classes of\ncharge models monotonically. Moreover, for the second and third classes of\ncharge models, we manage to numerically and analytically identify a new kind of\nstellar structure with a zero central pressure but a finite radius and mass.\nAll the calculations and analysis are performed in a general dimensionless\nrescaling approach so that the results are independent of explicit values of\ndimensional parameters.", "Authors": [ "Chen Zhang", "Michael Gammon", "Robert B. Mann" ], "Author_company": [ "IQM" ], "Date": "2021-08-31T16:54:59Z", "arXiv_id": "2108.13972v2" }, { "Title": "Gravitational wave echoes from interacting quark stars", "Abstract": "We show that interacting quark stars (IQSs) composed of interacting quark\nmatter (IQM), including the strong interaction effects such as perturbative QCD\ncorrections and color superconductivity, can be compact enough to feature a\nphoton sphere that is essential to the signature of gravitational wave echoes.\nWe utilize an IQM equation of state unifying all interacting phases by a simple\nreparametrization and rescaling, through which we manage to maximally reduce\nthe number of degrees of freedom into one dimensionless parameter\n$\\bar{\\lambda}$ that characterizes the relative size of strong interaction\neffects. It turns out that gravitational wave echoes are possible for IQSs with\n$\\bar{\\lambda}\\gtrsim10$ at large center pressure. Rescaling the dimension\nback, we illustrate its implication on the dimensional parameter space of\neffective bag constant $B_{\\rm eff}$ and the superconducting gap $\\Delta$ with\nvariations of the perturbative QCD parameter $a_4$ and the strange quark mass\n$m_s$. We calculate the rescaled GW echo frequencies $\\bar{f}_\\text{echo}$\nassociated with IQSs, from which we obtain a simple scaling relation for the\nminimal echo frequency $f_\\text{echo}^{\\rm min}\\approx 5.76 {\\sqrt{B_{\\rm\neff}/\\text{(100 MeV)}^4}} \\,\\,\\, \\rm kHz$ at the large $\\bar{\\lambda}$ limit.", "Authors": [ "Chen Zhang" ], "Author_company": [ "IQM" ], "Date": "2021-07-20T17:39:28Z", "arXiv_id": "2107.09654v2" }, { "Title": "Unified Interacting Quark Matter and its Astrophysical Implications", "Abstract": "We investigate interacting quark matter (IQM), including the perturbative QCD\ncorrection and color superconductivity, for both up-down quark matter ($ud$QM)\nand strange quark matter (SQM). We first derive an equation of state (EOS)\nunifying all cases by a simple reparametrization and rescaling, through which\nwe manage to maximally reduce the number of degrees of freedom. We find, in\ncontrast to the conventional EOS $p=1/3(\\rho-4B_{\\rm eff})$ for non-interacting\nquark matter, that taking the extreme strongly interacting limit on the unified\nIQM EOS gives $p=\\rho-2B_{\\rm eff}$, where $B_{\\rm eff}$ is the effective bag\nconstant. We employ the unified EOS to explore the properties of pure\ninteracting quark stars (IQSs) composed of IQM. We describe how recent\nastrophysical observations, such as the pulsar-mass measurements, the NICER\nanalysis, and the binary merger gravitational-wave events GW170817, GW190425,\nand GW190814, further constrain the parameter space. An upper bound for the\nmaximum allowed mass of IQSs is found to be $M_{\\rm TOV}\\lesssim 3.23\nM_{\\odot}$. Our analysis indicates a new possibility that the currently\nobserved compact stars, including the recently reported GW190814's secondary\ncomponent ($M=2.59^{+0.08}_{-0.09}\\, M_{\\odot}$), can be quark stars composed\nof interacting quark matter.", "Authors": [ "Chen Zhang", "Robert B. Mann" ], "Author_company": [ "IQM" ], "Date": "2020-09-15T15:45:16Z", "arXiv_id": "2009.07182v3" } ]