# The S-PLUS Transient Extension Program: Imaging Pipeline, Transient
Identification, and Survey Optimization for Multi-Messenger Astronomy

A. Santos1, C. D. Kilpatrick2, C. R. Bom1,3, P. Darc1, F. R. Herpich10, E. A.
D. Lacerda4, M. J. Sartori4, A. Alvarez-Candal5,6, C. Mendes de Oliveira4, A.
Kanaan7, T. Ribeiro8, W. Schoenell9
1Centro Brasileiro de Pesquisas Físicas, Rua Dr. Xavier Sigaud 150, CEP
22290-180, Rio de Janeiro, RJ, Brazil
2Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA)
and Department of Physics and Astronomy,
Northwestern University, Evanston, IL 60208, USA
3Centro Federal de Educação Tecnológica Celso Suckow da Fonseca, Rodovia Mário
Covas, lote J2, quadra J, CEP 23810-000, Itaguaí, RJ, Brazil
4Universidade de São Paulo, IAG, Rua do Mato 1225, Sao Paulo, SP, Brazil
5Instituto de Astrofísica de Andalucía, CSIC, Apt 3004, E18080 Granada, Spain
6Instituto de Física Aplicada a las Ciencias y las Tecnologías, Universidad de
Alicante, San Vicent del Raspeig, E03080, Alicante, Spain
7Departamento de Física, Universidade Federal de Santa Catarina,
Florianópolis, SC, 88040-900, Brazil
8 Departamento de Astronomia, Instituto de Física, Universidade Federal do Rio
Grande do Sul (UFRGS), Av. Bento Goncalves 9500, Porto Alegre, RS, Brazil
9NOAO, P.O. Box 26732, Tucson, AZ 85726, USA
10Cambridge Survey Astronomical Unit (CASU), Institute of Astronomy, Madingley
Road, Cambridge CB3 0HA, UK E-mail<EMAIL_ADDRESS>
(Accepted XXX. Received YYY; in original form ZZZ)

###### Abstract

We present the S-PLUS Transient Extension Program (STEP): a supernova and fast
transient survey conducted in the southern hemisphere using data from the
Southern Photometric Local Universe Survey (S-PLUS) Main Survey and the
T80-South telescope. Transient astrophysical phenomena have a range of
interest that goes through different fields of astrophysics and cosmology.
With the detection of an electromagnetic counterpart to the gravitational wave
(GW) event GW170817 from a binary neutron stars merger, new techniques and
resources to study fast astrophysical transients in the multi-messenger
context have increased. In this paper, we present the STEP overview, the SN
follow-up data obtained, data reduction, analysis of new transients and deep
learning algorithms to optimize transient candidate selection. Additionally,
we present prospects and optimized strategy for the search of Gravitational
Wave counterparts in the current LIGO/Virgo/Kagra observational run (O4) in
the context of T80-South telescope.

###### keywords:

surveys – transients: supernovae – gravitational waves

††pubyear: 2023††pagerange: The S-PLUS Transient Extension Program: Imaging
Pipeline, Transient Identification, and Survey Optimization for Multi-
Messenger Astronomy–4

##  1 Introduction

Transient surveys have become increasingly important in astronomy,
astrophysics and cosmology due to their ability to provide valuable
information on astrophysical time-dependent phenomena such as supernovae,
variable stars, active galactic nuclei (AGN), tidal disruption events, gamma-
ray bursts, and kilonovae (e.g. Andreoni et al., 2022b; Stein et al., 2023;
Aleo et al., 2023; Yang et al., 2023). These events span a wide range of
luminosities, spectral types, and timescales, often requiring survey
strategies uniquely tailored to find specific classes of transients. By
systematically observing and analyzing these transient phenomena,
astrophysicists can unravel the dynamic process of those events and gain a
deeper understanding of their intricacies.

As transient surveys have advanced, the development of technologies such as
charge-coupled devices (CCDs) and robotic telescopes has greatly facilitated
the systematic search for astrophysical transients (Perlmutter, 1989;
Filippenko, 1992; Richmond et al., 1993). Historically, one of the earlier
studies about a systematic approach for searching extragalactic sources was
developed by Zwicky (1938), increasing the number of discovered events over
the next decades. Searches in the southern hemisphere started around 1980 by
(Maza, 1980). These advancements enable more efficient and automated detection
of transient events, significantly increasing the discovery rate, and allowing
detailed follow-up observations and analysis. With the combined power of wide-
field observations, advanced instruments, and data analysis techniques,
transient surveys push the boundaries in astrophysics research, unveiling new
phenomena and providing a comprehensive view of the ever-evolving universe.

Additionally, the detection, classification, and analysis of Supernova type Ia
changed our understanding of the Universe, by serving as "standard candles"
(Branch & Tamman, 1992). With consistent luminosity distances, Type Ia
supernovae allow astronomers to accurately measure cosmic distances, which
ultimately led to the groundbreaking discovery of the universe’s accelerated
expansion and therefore insights about a novel component known as dark energy
(Hubble, 1929). Transient surveys have been instrumental in discovering and
characterizing these important cosmic explosions arising from massive stars,
compact objects, and stellar interactions (e.g., Hamuy et al., 1993;
Filippenko, 2005; Kulkarni et al., 2007; Bellm et al., 2019; Karambelkar et
al., 2023). Light curve data from those celestial objects plays a crucial role
in tailoring fundamental aspects of explosive events, such as their energy
levels and mass ejecta (Woosley, 1988; Gal-Yam et al., 2009), constraints of
the presence of circumstellar matter in their environments (Smith, 2014), and
the formation of compact objects (MacFadyen et al., 2001; Soderberg et al.,
2010). It’s also possible to use explosive transients for cosmology cases
(Riess et al., 1998; Perlmutter et al., 1999).

Beyond the supernovae, transient surveys also contribute to the study of
gravitational waves (GWs). The detection of GW events, such as the
groundbreaking GW170817, which was initially detected in GW emission (Abbott
et al., 2017b) and precisely localized at optical wavelengths (Soares-Santos
et al., 2017) opened new opportunities to study the compact objects that lead
to LIGO/Virgo GW events. GWs provide unique insights into some of the most
energetic and cataclysmic events, including their contribution to Galactic
chemical enrichment (Eichler et al., 1989; Argast et al., 2004; Arnould et
al., 2007; Metzger et al., 2010; Kasen et al., 2017) and formation of heavier
neutron starts (NS) and black holes (BH) (Belczynski et al., 2002; Dominik et
al., 2015). Furthermore, the use of gravitational waves in cosmology allows
for precise measurements of the Hubble Constant (Abbott et al., 2017a; Soares-
Santos et al., 2019; Chen et al., 2018; Bom & Palmese, 2023) and provides
valuable constraints on cosmological models. With the increased focus on
optical transient surveys for studying the physics of explosive transients,
multi-messenger astronomy and cosmology, individual surveys are increasingly
tailored to focus on some subset of depth, cadence, total area covered and
wavelength coverage in order to maximize their own science returns. For
example, the Vera C. Rubin’s Legacy Survey of Space and Time (LSST; LSST
Science Collaboration et al., 2009; Ivezić et al., 2019) will observe the
Southern hemisphere with unprecedented depth for a wide-field optical time-
domain survey, but without the cadence necessary to find and characterize
rapid transients such as kilonovae (Andreoni et al., 2022a) and saturating at
$\approx$17 mag. The synergy between LSST and other wide-field optical survey
and follow-up telescopes, especially when their data streams are combined
using the latest transient alert brokers and target and observation managers
(TOMs; e.g., Volgenau et al., 2022; Coulter et al., 2023), will enable new
science that either survey cannot perform alone.

Here we describe an optical time-domain survey using the T80-South telescope
located at Cerro Tololo, using imaging from the main S-PLUS Survey, which we
call the S-PLUS Transient Extension Program (STEP, PIs: Bom & Kilpatrick). We
describe our pipeline and image processing procedures for retrieving and
processing image data as well as our methods for identifying and reporting
transient candidates. We then describe several science cases that highlight
the utility of STEP, particularly for discovering supernovae, follow up and
characterization based on their light curves, and follow up of GW events
during Ligo-Virgo-KAGRA (LVK) observing runs.

The paper is structured as follows: in Section 2 we describe our scientific
goals with this project and how STEP is designed to achieve those goals. In
Section 3, we describe each step in our imaging and analysis pipeline starting
from obtaining images from the telescope, our reduction process, our
difference imaging analysis procedure and finally candidate selection. In
Section 4 we detail our deep learning algorithm designed to reject false
positives during difference image analysis. In Section 5 we outline
extragalactic transients detected during our project. In section 6 we overview
the light curve cadence of followed SNe. In section 7 we talk about our
strategy and criteria to follow GW events. Finally, in Section 8 we review our
conclusions and describe our future plans for this project in the era of LSST
time-domain discovery and follow up.

##  2 STEP Science Goals

###  2.1 S-PLUS Main Survey Overview and Discovery of Transients in the
Southern Hemisphere

The Southern Photometric Local Universe Survey (S-PLUS; Mendes de Oliveira et
al. 2019) consists of an imaging survey that will cover approximately $9300$
deg2 in 12 optical bands ($ugriz$ and seven narrow filters). The survey
utilizes a dedicated 0.8-m aperture robotic telescope called T80-South (T80S)
located at Cerro Tololo Interamerican Observatory (CTIO), Chile. The sky
coverage is divided into five sub-surveys, two of which are relevant to this
project: The Main Survey (MS), and the Galactic Survey (GS). The MS covers
8300 deg2 and follows an observing strategy tailored for extragalactic
science, providing photometric redshifts down to $i$=21 mag with a limiting
magnitude up to 21.5 mag in $r$-band (Almeida-Fernandes et al., 2022). The
Galactic Survey (GS) spans an area of 1300 deg2 in the Milky Way Plane in all
12 filters, including two galactic regions - namely the bulge and the disk -
and employs the same observational strategy as MS. T80S have an e2v detector
with a 9232 $\times$ 9216 array with 10 $\rm\mu m$ pixel, a plate scale of
0.55″ pixel-1 and a field-of-view (FoV) of $1.4\times 1.4$ $\mbox{deg}^{2}$.
In total, during our first year of operation, STEP analyzed 1272 deg2 in
$griz$ to a median depth of $\approx$20.5 mag. We outline the light curve of
three followed SNe in appendix A, as our first data release (DR1).

The MS employs a single epoch observation of each field per filter, with a
range of seeing from 0.8 to 2 arcsec. In this survey, three exposures are
taken for each filter with an exposure time that goes between $\sim 30$ and
$\sim 60$ seconds for the griz broad bands (see Table 5 of Mendes de Oliveira
et al. 2019 for exact exposure time of all T80cam filters), dithering a few
arcseconds to the east and west of the field center. The primary objective of
this strategy is to enhance the accuracy of photometric redshift estimation
for objects within the field, targeting those with absolute magnitudes down to
$r_{AB}\sim 20$. Furthermore, the redshift information provided by this
approach aims to achieve a precision of $\Delta z/(1+z)=0.02$. The
individually captured images are overscan-subtracted, trimmed, bias-
subtracted, flat-divided, and fringing subtracted ($z$-band only; see Mendes
de Oliveira et al. 2019 for more details on the reduction process and
pipeline). Besides these steps, the images are cosmetic-corrected to identify
and mask satellite tracks and cosmic rays and then astrometrically solved(see
Almeida-Fernandes et al. 2022 for more on the astrometric precision).

Given the previous description of S-PLUS and T80cam, its wide field-of-view
camera, and an excellent calibration of its photometric system (Mendes de
Oliveira et al., 2019), S-PLUS emerges as a suitable survey for investigating
transient phenomena in the Southern sky. Moreover, the survey benefits from
its overlap with other time-domain surveys including DES, ATLAS, and PAN-
STARRS, which collectively cover various portions of the Southern sky. This
survey also fills a critical gap in time-domain coverage of the Southern sky
before the Vera C. Rubin Observatory (Ivezić et al., 2019) and La Silla
Schmidt Southern Survey (LS4) come online to cover this sky area with high
cadence. Given all this information, we argue that S-PLUS is an appropriate
instrument for investigating transient phenomena in the Southern sky.

###  2.2 Supernova Follow Up

Supernova follow up in the era of the Rubin Observatory will rely on careful
selection of transients from the thousands of potentially valuable transients
that will be discovered each night. Even restricting those samples to events
accessible by smaller aperture telescopes such as T80S requires selecting from
potentially hundreds of events and following those necessary for specific
science cases, such as measuring cosmological parameters from SN Ia light
curves, constructing SN luminosity functions, testing light curve classifiers,
or constraining the incidence of strong circumstellar interaction in core-
collapse SNe. All of these science cases can be addressed by synthesizing data
from multiple sources using transient brokers (e.g., Narayan et al., 2018;
Förster et al., 2021; Möller et al., 2021), photometric classification of
light curves (Boone, 2019; Muthukrishna et al., 2019; Villar et al., 2019;
Sravan et al., 2020), and anomaly identification to prioritize transients with
intrinsically peculiar properties (e.g., total ejecta mass or energy, nickel
mass, incidence of circumstellar interaction) or those at critical or
scientifically useful phases.

Obtaining multi-band light curves for the thousands of transients needed for
light curve parameter estimation will require coordination between a global
network of telescopes. Telescopes such as T80S can improve the effective
cadence of Rubin Observatory by observing between visits from the survey.
Moreover, large samples of transient light curves are needed now to train
photometric classifiers and anomaly detection. We present supernova follow up
in Section 6 and further discuss prospects for follow up in the Rubin era in
Section 8.

###  2.3 Gravitational Wave Follow Up During LIGO/Virgo/KAGRA Observing Run 4

The kilonova counterpart to GW170817 was rapidly declining and required rapid,
high-cadence, multi-band follow up to constrain its total ejecta mass and
composition, including observations from the T80S (see, e.g., Abbott et al.,
2017b; Coulter et al., 2017; Díaz et al., 2017; Kilpatrick et al., 2017;
Soares-Santos et al., 2017). Due to the increased sensitivity of LIGO since
that time, NS mergers found during observing run 4 (O4) are systematically
more distant than GW170817 at 40 Mpc (Abbott et al., 2017b; Soares-Santos et
al., 2017) and may in general be more poorly localized. As these events may
occur and be detected anywhere in the sky, a global network of robotic
telescopes with wide fields of view and multi-band follow up are needed to
localize and obtain optical follow up of these events. Within this context, we
consider the feasibility of future GW follow up to identify new kilonovae with
the T80S.

Numerous dedicated efforts have been undertaken since the localization of the
GW170817 kilonova to identify additional EM counterparts associated with
similar NS mergers (e.g., for events discovered during observing run 3;
Andreoni et al., 2020, 2019; Coughlin et al., 2019; Dobie et al., 2019;
Goldstein et al., 2019; Gomez et al., 2019; Hosseinzadeh et al., 2019;
Lundquist et al., 2019; Ackley et al., 2020; Antier et al., 2020; Coughlin et
al., 2020; Morgan et al., 2020; Paterson et al., 2020; Pozanenko et al., 2020;
Thakur et al., 2020; Vieira et al., 2020; Watson et al., 2020; Alexander et
al., 2021; de Wet et al., 2021; Kilpatrick et al., 2021; Tucker et al., 2022).
To improve the observing efficiency of these search efforts and aid in rapidly
finding kilonova to maximize the scientific opportunities that are possible
with their early light curves (e.g., Arcavi, 2018), we consider a new
observing strategy optimized for the sensitivity, field of view, and broadband
filters on the T80S. Considering this strategy in combination with realistic
selection criteria for O4 GW events, we consider the observations necessary to
find the next kilonova counterpart in Section 7.

##  3 STEP Pipeline Overview and Data Analysis

The STEP pipeline uses two types of images: archive images and on-the-fly
observations. Archive images were obtained before the STEP project started and
found in their final reduction form (updated calibration frames, astrometry,
and quality checked). These are the images co-added to compose the S-PLUS Main
Survey. The on-the-fly images are those run through the STEP pipeline as soon
as they are observed to enable rapid identification of transients. They are
usually “last night images” and are not yet the final form of the reduction
process, using older bias and flat frames. They also do not include fringe
corrections. Although not the final version, they enable STEP to access the
most up-to-date picture of the observed sky and identify possible transient
phenomena as they are ongoing, which allows us to follow up on the explosion
to acquire further information about the phenomena.

Our methodology to discover transient candidates involves utilizing the
comprehensive coverage of the main survey fields in the griz filters to
extract information on potential transients through a state-of-the-art
pipeline described below and summarized in Figure 1. Our objective is to
document and characterize transients identified within these images,
leveraging the photometric redshift to derive the luminosity distance and
other intrinsic properties of the object, as well as complementary
spectroscopy and host galaxy data, when available. The S-PLUS Main Survey is
specifically designed to encompass a wide expanse of the southern hemisphere,
reaching high declination angles. This expansive coverage facilitates the
identification of transient phenomena in the region.

The STEP pipeline operates by processing nightly data from S-PLUS, taking
advantage of its ongoing survey to look for transients. S-PLUS does not
revisit the same field, thus to obtain template images, we use templates from
other surveys that overlap with S-PLUS footprint (e.g. DECam, Pan-STARRS, and
SkyMapper). We transfer and digitally subtract our collected images from
S-PLUS with the templates derived from those other surveys. By employing this
technique, we can identify transient sources within approximately 1 hour of
image acquisition. Each identified source is then carefully evaluated using
source properties within each image frame, contextual data extracted from the
star, minor planet, and galaxy catalogs, and machine learning classification.
We describe our use of the Gaia DR3 and minor planet checker in Section 3.3,
while the galaxy catalogs used are those that provide context to the distance
and thus luminosity scale of each transient detection via spectroscopic or
photometric redshifts. Specifically, we make use of the Pan-STARRS Source
Types and Redshifts with Machine Learning (STRM) catalog (Beck et al., 2021),
the Legacy photometric redshift catalog (Zhou et al., 2021), and the S-PLUS
photometric redshift catalog (Mendes de Oliveira et al., 2019). This
comprehensive approach ensures the thorough vetting of each transient source.
The ultimate goal of our pipeline is to optimize follow-up observations for
multi-messenger astronomy, making use of the multi-band photometry available
from the S-PLUS Main Survey. The subsequent sections in this paper provide
detailed explanations of each step within our pipeline, starting from image
acquisition and extending through the transient identification process.

###  3.1 Reduction Processing of T80S and Template Imaging

Before the reduction process and analysis, we begin with the daily collection
of science images from the S-PLUS Main Survey (and galactic survey, when
available), captured using the griz broad-band filters. The retrieved images
have undergone a pre-reduction process, which encompasses several crucial
steps, including bias subtraction, flat field correction, overscan
subtraction, and trimming. These steps are implemented to enhance the overall
quality of the images. Subsequently, the images are transferred to our
servers, where an initial pre-processing stage is conducted before they are
fed into the reduction process. For astrometric calibration of the raw images
collected, we utilize the command-line tools provided by astrometry.net (Lang
et al., 2010).

Following these pre-processing steps, the main STEP pipeline takes charge of
the subsequent stages, encompassing imaging, photometry, and data analysis.
Throughout the forthcoming sections, we provide detailed descriptions of each
stage of our software, elucidating their functionalities, and highlighting
their integration with essential open-source software tools prevalent within
the astronomical community.

Template images are constructed for each unique field and imaging band by
communicating the observed fields to a separate server. Our pipeline utilizes
a daily cron job that first downloads images saved as FITS files (Wells et
al., 1981) from the T80S telescope database for the previous night and
communicates the field center and imaging band continuously to the template
server. The template server then determines if the field has coverage in Pan-
STARRS, DECam, or SkyMapper, and downloads all available imaging covering that
field from the Mikulski Archive for Space Telescopes (MAST) for Pan-STARRS
(Kaiser et al., 2002), the NOIRLab archive for DECam, or the SkyMapper image
cutout service111https://skymapper.anu.edu.au/image-cutout/ (Wolf et al.,
2018). To make the photometric calibration and point-spread function (PSF)
shape as consistent as possible in each template, our pipeline uses only a
single survey to generate each template. From the input images, we perform sky
subtraction, normalization using their reported photometric calibration, and
convolution to a uniform PSF shape using SWarp (Bertin, 2010) to produce an
output template image with the input field center and pixel scale of the T80S
camera. The final template image is then staged in our pipeline, and a final
photometric catalog is generated in the image frame using DoPhot (Schechter et
al., 1993) and a zero point empirically estimated for the image by comparing
to the Pan-STARRS or SkyMapper griz photometric catalogs.

For the T80S science frames, the pipeline uses IRAF to estimate a geometric
distortion solution across the detector using Gaia DR3 astrometric standards
and algorithms implemented in msccmatch (Tody, 1986). The final distortion
solution is saved in TNX format in the image header to regrid each frame to a
common pixel scale and field center as the template images at a later stage.

We then use source extractor (sextractor; Bertin & Arnouts, 1996) to measure
the background levels and generate a catalog of saturated stars. From this
catalog and output segmentation maps in sextractor, we mask out saturated
stars and the unique diffraction spike pattern for T80S based on the number of
saturated pixels within each star. The output file from this stage is a unique
mask image created with
mana222https://www.cfht.hawaii.edu/Instruments/Elixir/Ohana/mana.html. We then
use SWarp (Bertin, 2010) to regrid the science and mask images, assuring that
the science and template images have a common and uniform pixel scale of 0.55″
across the image, a common field center, and a uniform size of
10000$\times$10000 per frame. As with the template images, the final stages of
our science image pipeline perform PSF photometry using DoPhot and flux
calibration by comparing to the Pan-STARRS DR2 and SkyMapper photometric
catalogs. In general, we assume that the bandpasses from each template survey
are identical to those from the S-PLUS survey, that is, that S-PLUS $griz$
bands are identical to PS1, DECam, and SkyMapper $griz$ bands both for
calibration and image subtraction. We are exploring updates to our calibration
methods for each photometric catalog and source of template images to correct
for slight differences, for example, due to mirror coatings, bandpasses, and
detector quantum efficiency. Given the calibration systematics achieved from
the overall 12-filter S-PLUS system (Mendes de Oliveira et al., 2019), we
expect to be able to achieve similar residuals as cross-calibration from other
optical surveys (e.g., Finkbeiner et al., 2016; Scolnic et al., 2015), with
cross-calibration residuals of a few millimag.

###  3.2 Difference Imaging

After the reduction of both science and template images, we match the template
to science images to perform digital image subtraction or difference imaging.
This stage is fundamental to finding transients as it enables the automatic
detection of significant point sources present in the science image but not in
the template (or vice versa). Our difference image procedure utilizes the
calibration science and template images, their noise frames, and mask images
to perform subtraction in hotpants (Becker, 2015), an open-source tool
designed for this purpose. hotpants uses common point sources to define a
Gaussian convolution kernel for PSF matching between the science and template
images then directly subtracts the convolved image from the other image. In
general, the template image is deeper than the science image. It has a sharper
PSF, red in particular for the PS1 and DECam imaging that makeup $>$95% of our
template imaging. For the remaining $\sim$5% of our template imaging that
comes from the 1.35 m SkyMapper telescope (Wolf et al., 2018), the template
image stack is often still deeper than our T80S imaging, but further
optimization of the STEP pipeline is ongoing to explore the effect of image
depth for these data. In general, we set the convolution direction to convolve
the science image. We also use our point source catalog from DoPhot to define
the locations of “substamps” in hotpants, that is the regions of the image
used to estimate the parameters of the convolution kernel. Finally, given the
large field-of-view of the T80S camera, we fit the convolution kernel using a
third-order polynomial to estimate the spatial variation of the kernel across
the image and a second-order polynomial to subtraction background emission.
All other parameters are set to the defaults in hotpants.

After the image subtraction process, we check the quality of the resultant
difference image for the average scatter in the fitted kernel. Using the fixed
PSF parameters of the template image, we then search for significant point
sources and perform photometry using a customized version of DoPhot for this
process. This catalog is used to generate cutout images of possible transients
to be posted on a webpage for visual inspection and to feed a Neural Network
that classifies a given cutoff into real transient or bad subtraction.

###  3.3 Transient Candidate Selection

We identify all candidate transients in our data in every image frame where
DoPhot reports a significant detection, clustering these detections for
sources within 1″ across separate frames of the same field. We then perform
forced photometry across all frames in the same field at the flux-weighted
centroids of each “cluster” using DoPhot. The unforced and forced photometric
data, individual centroid positions, incidence of masking within the PSF
aperture, cutouts of the science, template, and difference images around the
position of the transient are displayed for each candidate in an internal
webpage used for visual inspection (not publicly available). (Figure 7). These
data were initially visually scanned for real/bogus identification of
transients versus subtraction artifacts and other sources of non-astrophysical
emission and cosmic rays (flagged as “BAD” sources). Our visual flagging
criteria for all other transient sources are stored to generate a training
sample for the machine learning process described in Section 4.

From this list of transients, we further vet candidates using methods
implemented in (Kilpatrick, 2023). We use minor planet catalogs to flag known
asteroids within 10″ of the source using the Modified Julian Date and
coordinates of each detection (“ASTEROID” sources) using the Minor Planet
Checker333https://minorplanetcenter.net. We also check for coincidence ($<$1″)
of any source classified as a likely, unblended point source in the Gaia DR3
catalog to flag sources of stellar variability (“VARSTAR” sources; i.e., by
crossmatching to the Gaia point-source catalog in Gaia Collaboration et al.,
2023). Finally, we crossmatch all candidates to known transients in the
Transient Name Server database (Yaron & Gal-Yam, 2012) to flag all “CONFIRMED”
transients. We incorporate all objects and our manually applied flags into the
machine learning process described below. The final reported transients are
the result of this deep learning candidate selection.

Figure 1: A schematic overview of the reduction pipeline. The raw image from
S-PLUS goes through pre-processing like overscan, bias and flat field
correction, and satellite track removals. We retrieve those images and
initiate our reduction pipeline for further analysis. Our pipeline goes
through an astrometric solution of all retrieved images. We then proceed to
masking of saturated stars, diffraction spikes, and sky background (noise).
After the masking stages, We resized all images to a standard of 10.000 x
10.000 points. Finally, our images go through PSF photometry and Flux
calibration. The template images go through similar stages (with the exception
of diffraction spikes masking and astrometric solution because those steps are
not needed in this case. In the end, we perform the difference imaging as a
final image product.

##  4 Deep Learning Candidate selection

In practice, most transient alerts for a given observation night are false
positives as the difference imaging process produces various artifacts. The
most common example of an artifact we classify as a “Bad Subtraction”. When
there is a mismatch between the flux from the search and template images, for
example, due to slight differences in filter transmission between T80S-Cam and
PS1, DECam, or SkyMapper, or if there are variations between the PSFs not
properly accounted for by hotpants, the subtraction process results in under-
and over-subtracted regions in the difference image. The most common bad
subtractions are bright stars that appear in the difference image as positive
or negative point sources. We acknowledge that this phenomenon is difficult to
distinguish from instances of true stellar variability (i.e., “VARSTAR”
sources), but in general we find that $\sim$1% of stars in our images exhibit
variability $>$0.1 mag in optical bands, which is at least two orders of
magnitude more common than stars observed by optical time-domain surveys such
as Kepler (McQuillan et al., 2012). We infer that the vast majority of these
transient alerts are bad subtractions and describe a method for filtering
these alerts below.

We are primarily interested in reducing the number of candidates that require
visual inspection and automating the transient detection process, for which we
adopt a convolutional neural network (CNN) to classify the images as
transients or artifacts (i.e., non-transients). Applying CNNs to difference
images is a common approach in modern time-domain surveys (Duev et al., 2019;
Shandonay et al., 2022) with multiple well-tested procedures in the
literature. We present a new method for systematically classifying sources
that are optimized to our pipeline and data reduction procedures.

To address this classification problem, we made use of a family of CNN models
known as Mobilenet (Howard, 2017) and prepared the data following the
prescription of (Bom et al., 2022). This architecture is based on depth-wise
separable convolutions. This architecture demonstrates superior speed and
accuracy characteristics in our data set when compared to several different
CNN-based models such as VGG (Simonyan & Zisserman, 2014), ResNet (He et al.,
2015) and Inception models (Szegedy et al., 2015).

###  4.1 Training, Validation, and Test Data Set

The dataset used in this section consists of a set of known real transients
observed with T80 and processed by our pipeline, together with different kinds
of artifacts produced by the same pipeline after difference imaging. We
followed 20 different known supernovae (some of them in the same night) to
have enough data to build a set of real transients. The cutout images produced
by our transient detection pipeline are made using four SDSS-like $griz$
filters, having a cutout per filter. We collect three exposures per band,
following the S-PLUS Main Survey observing procedure (Mendes de Oliveira et
al., 2019), resulting in 12 different images for each transient candidate
observation. Figure 3 shows examples of different image cutouts at the end of
our pipeline.

To build the artifact dataset, We randomly selected 4 images (one per filter)
over the set of observations of all followed supernovae. From those, we made
cutouts of all artifacts (excluding the real transient within the image),
having a total sample of 10,000 samples. The Signal-to-noise (SNR)
distribution of the total detection within the selected images is shown in
figure 2. The supernovae dataset was built more straightforwardly. For each
known followed transient, we made a cutout of all exposures per filter, having
a total of 233 samples. We downsampled the artifact dataset to match the real
transients and made use of data augmentation to have a final dataset.

Following the reduction procedure described in section 3, each image is
processed with a search (or science, i.e., the original image from T80S),
template (PS1, DECam, or SkyMapper image), and difference images (the
subtraction between the two), all of which are simultaneously fed into the
network through independent channels in the format of (51$\times$51$\times$3),
similar to how the red, green, and blue image arrays that are fed into CNNs in
traditional image analyses. To ensure the integrity of our results, we avoid
data leakage by assigning all 12 images of each observation to the training,
validation, or test dataset. In this way, we prevent any bias in the model’s
test results that might occur if one of these images was used for training and
another for testing.

One of the challenges associated with the training and analysis of this
dataset is that the transient candidates are contaminated by large numbers of
bad subtractions. This resulted in an unbalanced classification problem. To
address this issue, we downsampled the bad subtraction class to have an equal
number of transient images. Finally, the total collection of images was
partitioned approximately into three subsets, with 67% of the data allocated
for training, 13% for validation, and 20% for testing. Finally, an additional
set of 96 images was obtained to ensure our model’s performance metrics.

####  4.1.1 Pre-processing

The deep learning methods are sensitive to visual features by construction, so
we pre-process the data to enhance visualization, easing the training process.
The pre-processing is divided into two main steps: contrast adjustment and
normalization.

First, an image contrast adjustment was performed by clipping the image
histogram, that is, we chose lower and upper limits and set every pixel above
or below those values to be equal to the nearest bound. We choose the upper
limited value to be the 99.2 % percentile level for the search, template, and
difference images. For the lower bound, we choose the 0.01 percentile. In
other terms, every value above the upper limit (percentile 99.2%) would be set
to this threshold value, and every value below the lower bound/limit would be
set to the percentile 1% of the histogram values of each image. this is done
for each channel independently to maintain the unique characteristics of each
channel.

Afterward, the images were normalized such that all pixels lie in the range
[0,1], where for each image and each channel we subtract the minimum pixel
value and divide by the maximum minus the minimum pixel value. This step
improves the neural network training convergence. We made use of data
augmentation techniques to enhance the training by applying rotations of 90,
180, and 270 degrees, horizontal flips, vertical flips, and combinations of
rotations and flips. This increases the dataset by a factor of 8.

####  4.1.2 Training

A slight modification to the MobilNet architecture was made by adding a
flattening layer instead of the two last layers, which were originally an
average pooling layer and a fully connected layer with one neuron (Howard,
2017). The new architecture was designed to improve the generalization
capability of the model.

The previously described model was trained using one of the state-of-art
optimizers: the Adam (Kingma & Ba, 2014), with a binary-cross entropy loss. We
initialize the networks with pre-trained weights from the ImageNet dataset to
improve the computing time needed for convergence, overall performance, and
stability of training, as observed in (Bom et al., 2021).

The model was trained in a Multi-GPU cluster with 8 RTX 3090 with 24 GB of GPU
memory each. The model training was implemented in TensorFlow2 (Abadi et al.,
2015). To determine the optimal set of weights for the model, we train the
network for an unlimited number of epochs until the Validation Loss value
stops improving for 25 epochs. We selected the weights from the epoch with the
lowest validation Loss, this method of weight selection maximizes performance
on the validation set, which serves as a good indicator of how the model will
perform on unseen data.

###  4.2 Results

In a binary classification problem, various metrics can be used to evaluate
model performance, but for our particular problem of filtering artifacts while
preserving transients, three main metrics are of particular interest:
precision, recall, and false positive rate.

The precision (or purity) is defined as the proportion of true positive
predictions made by the model among all positive predictions made. It
represents the ability of the model to correctly identify true transients
among all images classified as transients. The recall (or completeness or true
positive rate), is defined as the proportion of true positive predictions made
by the model among all actual positives. it represents the ability of the
model to correctly identify all true transients. The false positive rate is
the proportion of artifacts that are misclassified as transients.

The output of our CNN model is a score that can be interpreted as the
probability of a given image being transient. Therefore, it is necessary to
define a threshold, $t$, which is used to differentiate between true
transients and artifacts. An image set is classified as a transient if the
model’s probability output exceeds $t$, and as an Artifact if the probability
falls below $t$. The threshold that maximizes the F1 score (the harmonic mean
of precision and recall) is used as the operating threshold for determining
positive and negative results.

With this threshold, the false positive rate was determined to be 3.1%, and
the recall was 92.7%. This means that only 3.1% of the images classified as
transient were actual Artifacts, and 92.7% of the true transient images were
correctly identified as such. A confusion matrix was generated to further
analyze the performance, which is shown in Figure 4.

The Receiver Operating Characteristic (ROC) curve shows the trade-off between
the True Positive Rate (TPR) and False Positive Rate (FPR) of the model as the
threshold is varied in the range [0, 1]. The Area in the image refers to the
area under the curve (AUC), our model achieved AUC$=$0.992 (Figure 5).
Additionally, our model achieved an AUPRC (Area Under Precision-Recall Curve)
of 0.99 (Figure 6), which aligns with the AUC from the ROC curve. This
indicates the model’s capability to accurately identify transients with high
purity and maintain a high completeness. Such results hold significant value
in our analysis, particularly in scenarios where identifying positive examples
is crucial, such as detecting potential multimessengers.

Figure 2: SNR distribution per randomly selected image to build an artifact
dataset for the machine learning algorithm.

Figure 3: Top row: A search image (top left), template image (top middle), and
difference between the search and template image highlighting the position of
a source of transient emission for a real transient source. Second row: Same
as the top, but for a transient embedded in a compact host galaxy. Third row:
A bad subtraction for a poorly aligned star, resulting in anomalous sources of
emission in the difference image. Bottom row: A diffraction spike that was not
fully masked in the science image, resulting in an extended source of emission
in the difference image. Figure 4: Confusion Matrix for the CNN responsible
for filtering possible artifacts on our final stages. Figure 5:  Receiver
operator characteristic curve for the trained network using the test sample.
The area under the curve (AUC) is 0.992. Figure 6: Precision-Recall (PR) Curve
for the trained model using the test sample. The area under the curve (AUPRC)
is 0.99.

##  5 Extragalactic Transients Discovered in STEP Imaging

After classifying transient candidates using the flagging process described in
Sections 3 and 4, we remove all likely image artifacts, asteroids, and
variable stars before visually inspecting sources that we classify as likely
extragalactic transients. In particular, transients close to a source
identified as a host galaxy (i.e., with a photometric or spectroscopic
redshift from S-PLUS, Legacy, PS1, or the NASA/IPAC Extragalactic Database;
Mendes de Oliveira et al., 2019; Zhou et al., 2021; Beck et al., 2021) or at
high Galactic latitudes are inspected.

Here we report the identification of two transients found through our analysis
method described in Sections 3 and 4. Our first supernova candidate detected
within the S-PLUS Main Survey data was in 2022-05-01 at coordinates
$\alpha$=10:31:09.221 and $\delta$=+00:19:36.25 (J2000) and reported to
Transient Name Server (TNS) at 2022-08-21.444https://www.wis-
tns.org/object/2022rri. It was discovered at Magnitude $18.18\pm 0.047$ (AB
system, without MW Extinction correction) on $g$-Sloan band. Figure 7 shows
our difference imaging data in the Sloan griz bands for the candidate.

Figure 7:  From left to right: Template image, Science Raw image, and
difference image from our candidate at2022rri.

The second candidate found on our or list is the SN 2022tiv555https://www.wis-
tns.org/object/2022tiv. It was initially reported by ATLAS (Tonry et al.,
2022) in 2022-09-02 at coordinates $\alpha$=21:23:57.450 and
$\delta$=-22:44:07 (J2000) with magnitude 18.65 (AB System) in orange-ATLAS
filter. It was rediscovered within our project 1 month after the S-PLUS Main
Survey visited the same field. It was classified as a type Ia Supernova with
redshift $z=0.03$ with spectrum data approximately 2 weeks after first
reported (Toy et al., 2022). Figure 8 shows our findings for this candidate.

Figure 8:  From left to right: Template image, science raw image, and
difference image from our candidate. This image corresponds to SN 2022tiv.

##  6 Supernova Follow-up

During the observation run of this project we had followed supernovae that we
flag as scientific interesting, either by its unique physical nature, rarirty
or distance. We collect photometry data with 3 day cadence follow up of those
targets with S-PLUS using the same strategy as the Main Survey, when the time
between the observations were available. We collected data for 4 different
supernova: 2022crv666https://www.wis-tns.org/object/2022crv,
2022ann777https://www.wis-tns.org/object/2022ann, 2022qxu888https://www.wis-
tns.org/object/2022qxu and 2022acko999https://www.wis-tns.org/object/2022acko.
We summarize some information on those supernovae in Table 1 and follow-up
photometry (apart from SN 2022ann, which was originally presented in Davis et
al., 2023) from our pipeline is provided in appendix A.

We show the follow up light curves of SN 2022crv, 2022qxu, and 2022acko in
Fig. 9 with comparison to model light curves for photometric classification.
As a proof of concept, we utilize the Recommender Engine For Intelligent
Transient Tracking (REFITT; Sravan et al., 2020) tool along with PanSNIP
(Boone, 2019) to photometrically classify our follow up light curves using
methods implemented in (Garretson & Milisavljevic, 2023). Each of our targets
has an easily identifiable host galaxy with a spectroscopic redshift, which we
incorporate into our fit to aid in classification. Although each target has a
spectroscopic classification (see Andrews et al., 2022; Izzo et al., 2022; Li
et al., 2022), we do not use any information from the transient other than
follow-up data from STEP, including sky position and the in-band magnitudes
reported in appendix A.

The photometric classification scheme accurately recovers the SN type in each
case despite the limited coverage of the overall light curve in some cases. As
described in (Boone, 2019) and (Garretson & Milisavljevic, 2023), the
combination of multi-band follow up, high cadences such as those from ZTF
(Bellm et al., 2019) and STEP follow up, and host galaxy redshifts (such as
those from the S-PLUS Main Survey; Mendes de Oliveira et al., 2019) provide
significant leverage in photometric classification, especially once
classifiers are trained on full $ugrizy$ light curves. In the era of Rubin,
transient follow up surveys such as STEP can help increase the number of
photometrically classified transients for cosmology, SN rates, and anomaly
detection by providing high cadence ($<$5 day), multi-band photometry for the
large number of transients discovered in the Legacy Survey of Space and Time.
Since we can’t cover the whole list of targets from LSST given our field-of-
view capability, our future plans involve following selected targets, based on
our science cases, and changing our untargeted survey to a targeted facility
in the optical bands. Those targets can be selected by ranking tiles by the
number of targets present on the fields, weighted with enhanced data we have
at our disposal for further analysis.

Figure 9: STEP light curves of SN 2022crv, 2022qxu, and 2022acko as described in Section 6. We photometrically classify our targets using methods implemented in (Garretson & Milisavljevic, 2023). In each case, the classifier accurately recovers the spectral type determined by optical spectroscopy and reports to the Transient Name Server. All three sources were calibrated using the Pan-STARRS catalog, and so we use the classifications trained on Pan-STARRS photometry from (Garretson & Milisavljevic, 2023), indicated by the “ps1” label for each band. Name | Host Galaxy | Redshift | $D_{L}$
---|---|---|---
|  |  | (Mpc)
2022crv | NGC 3054 | 0.008091 | 40
2022ann | SDSS J101729.72-022535.6 | 0.04938 | 218
2022qxu | MCG -02-05-013 | 0.039964 | 177
2022acko | NGC1300 | 0.005264 | 23
Table 1: List of targeted supernovae during STEP DR1.

##  7 Strategy for Gravitational Wave Follow-up During LVK O4

To perform a systematic search of optical counterparts of gravitational wave
(GW) events, we design a strategy following the prescription presented by (Bom
et al., 2023) using the same formalism. The strategy is defined as a set of
parameters, namely the exposure times, broadband filter combinations g,r,i,z
total probability area coverage, and time at the observation after the burst
to detect the Kilonova with two independent passes over the GW probability
region. For a given set of observational conditions parametrized by the
effective exposure time factor, $t_{eff}$ a set of observational parameters
$\Theta$ consisting of filters and exposure times, a choice of probability
area coverage $\Omega$ in a moment $\tau$ after the GW alert, we calculate the
probability of discovery,
$P(\mathrm{discovery}|\hat{\Omega},\tau,t_{\mathrm{eff}},\Theta,\bar{z})$
defined as:

$\displaystyle P_{d}$
$\displaystyle=\frac{\int_{\hat{\Omega}}d\Omega\,\,d_{L}(\Omega)\,p(\Omega)}{\int_{\hat{\Omega}}d\Omega\,\,d_{L}(\Omega)}\,\times\sum_{j}p_{\alpha_{j}}\,,$
(1)

where $d_{L}$ is the luminosity distance and $p_{\alpha_{j}}$ are weights over
the simulated light curves at redshift $z_{i}$ representing the best fit and
its uncertainties (Kilpatrick et al., 2017) of the GW170817, described in
terms of a Kasen kilonova model as a blue component (Kasen et al., 2017) (see
section 2.3 of Bom et al., 2023, for a description of the kN GW170817-like
bright and blue and light curve model) that are within our limiting magnitude
$m_{lim}(\Theta,t_{eff})$. Similarly, the light curve simulations were
performed by snana (Kessler et al., 2009). We calculate the probability of
confirmation $P_{c}$, i.e. the probability of two independent passes to detect
the transient, this step is relevant to eliminate spurious detections during
the GW counterpart search. We define our strategy by allocating a maximum of
$2$ nights per GW event. In order to define a strategy, we use the same
definitions from (Bom et al., 2023) to select a low telescope time strategy,
i.e., a strategy that takes into account a balance between total telescope
time used per night of observation while maintaining a high discovery
probability. From a grid of possible exposure times
($60,90,120,200,300,600,1200,3600$) and confidence level areas
($0.9,0.8,0.7$). We define and select a low telescope time strategy the one
which uses the minimum telescope time around $10\%$ of the maximum probability
of discovery from our simulations run.

For well-localized events, one can describe the sky probability region of a gw
event as an ellipse101010See subsection Sky Localization Ellipse from
https://emfollow.docs.ligo.org/userguide/content.html. Each confidence
interval is described as a smaller ellipse in comparison with an ellipse of
higher confidence interval. In our strategy design, we left open the
possibility of using deeper exposures on the core confidence levels 0.3 and
0.5 (or "inner" ellipses). We assume the observation can take a maximum of $8$
hours per night, $30s$ readout/slew time per pointing, and dark/grey night. It
is also important to remark that our confirmation probability does not include
day/night rhythm (we set a fixed time for night duration) and time lost for
bad weather.

###  7.1 Gravitational Wave Simulations

For the GW simulations described in this section, we used the
BAYESTAR111111https://lscsoft.docs.ligo.org/ligo.skymap/index.html (Singer &
Price, 2016) ecosystem which uses LALSuite tools (LIGO Scientific
Collaboration, 2018). The method used in this section to create the simulated
events follows the work described in (Petrov et al., 2022).

We begin our simulation with an injection of $10,000$ binary neutron stars
(BNS) merger events with a TaylorF2 waveform limited to a maximum luminosity
distance of 500 Mpc uniformly distributed over a sensitive volume where we
presumed a Planck18 (Planck Collaboration et al., 2020) cosmology. The
sensitivity functions for the Advanced LIGO, VIRGO, and KAGRA detectors were
determined using the noise curve data available in the latest LIGO document
121212https://dcc.ligo.org/LIGO-T2000012/public. All detectors have a duty
cycle of $70\%$, following the discussions in (Abbott et al., 2020). The BNS
injections follow a Gaussian distribution for the masses, with mean $1.5$M⊙
and standard deviation $1.1$M⊙. We truncated the neutron star masses to be in
the interval $1.1<\mbox{M}_{\mbox{NS}}<3.0$, taking in consideration the
limits for Neutron Stars masses. The spins for both stars are uniformly
distributed in the interval $[-0.05,0.05]$ in the $z$ axis. After the
injection, we use a Matched filter search to obtain detected events, using the
same waveform in the injection process for reconstruction, resulting in 1076
detections. Here we define as detection those events which have a SNR $>4$ in
at least two detectors and a net SNR threshold > 12, where we added the
measured SNR with a Gaussian noise. In the end, skymaps are produced using the
BAYESTAR algorithm for the searched-out events. Figure 10 summarizes our
findings, showing the $90\%$ credible area interval over the $90\%$ credible
interval for Luminosity Distance, and the discovery probability of each
simulated event.

Figure 10: The $90\%$ credible sky coverage distribution over luminosity
distance for O4-like BNS detected events used in this paper. The different
marks point to different Discovery probability ranges.

To properly design an observation strategy with the T80-South telescope, we
selected events that have a sq-degree coverage in the $90\%$ credible region
$<300$ sq-deg. $791$ of the simulated events satisfies this condition. Given
the Field of View from the telescope, we can cover up to 100 sq-deg of the sky
per night while saving telescope time using our method. The strategy adopted
to search for an electromagnetic counterpart during LVK Fourth Observing run
(hereafter, O4), follows closely the strategy designed in (Bom et al., 2023),
adapting the telescope specifics to our project. Figure 11 summarizes our
discovery probability given a sample of BNS events, with simulated kilonovae
light curves obtained with snana (Kessler et al., 2009). The Low Telescope
Time aims to increase the discovery probability while trying to minimize the
amount of telescope time used in two passes.

It’s important to remark that the simulations used in this work are based on
assumptions about the planned sensitivity of LVK detectors. The predicted
sensitives are close to those anticipated in mid-2022 for the O4 cycle.
However, the current operation for O4 at the time of this writing diverges
from the expected designed sensitivity. The Virgo detector, for example,
hasn’t joined the LIGO Handford and Livingston detectors from its beginning
and remains uncertain when it will achieve the proposed sensitivity. The KAGRA
detector will participate in O4 for a limited period, with a range for BNS
merger below the estimated in this work (80Mpc). The estimate that comes from
our analysis retains significant value, in terms of methodology and strategy
for observation as an optimistic scenario in O4, although it does not reflect
the reality of O4 so far.

Figure 11: Discovery Probability using a Low Telescope Time strategy with 2
passes. The first red vertical line at 190Mpc shows the current BNS Range
sensitivity for LVK O4. The second line, at 330Mpc, is the limit for neutron
star-black

hole range sensitivity.

In the end, our strategy analysis outputs the best exposure time and filter
that maximizes our chances of finding an electromagnetic counterpart. We
further refine our tiles list using teglon (Kilpatrick et al., 2021; Coulter
et al., 2023), open-source software designed to optimally tile the LVK
localization volume by crossmatching known galaxies in the GLADE galaxy
catalog, under the assumption that the EM counterpart occurred in a galaxy
within in that region.

###  7.2  Kilonovae Sensitivity

We present our probability of discovery in our low-telescope time strategy in
figure 10 which shows the area and distance of each event binned by its
probability of discovery in the first pass. Considering our blue kN model with
absolute magnitude in $i$ band peaks at $\sim-16$ (Bom et al., 2023), most
events have a discovery probability of $\sim 70-80$. Most of the strategies
chosen by each event selected filters $r$ in both passes ($68\%$) or $g$
($20\%$), or a combination of both $gr$ ($12\%$). The typical exposures
selected by our optimization were $60s$ for the outer probability region and
$90s$ in $37\%$($33\%$) of events in the first (second) pass. Another relevant
configuration of exposure times was $200s$ or $300s$ for $22\%$($26\%$) of the
core probability region in the first pass (second). The preferred
configuration in terms of coverage was $90\%$ probability coverage and deeper
exposures in the $30\%$ confidence level core.

###  7.3 Candidate Vetting and Analysis

To help mitigate the presence of false candidates, we employ a candidate
vetting pipeline as outlined in section 3, which involves crossmatching our
candidates with the minor planet center and transient name server, together
with a neural network to help decrease false positives. Furthermore,
conducting a secondary pass on the fields on a second night could aid in the
identification and exclusion of other categories of transients. Whenever
possible, we plan to validate candidates previously reported by the Zwicky
Transient Facility (ZTF) via the FINK Broker API. Ultimately, we verify
whether the targets in our final list have already been reported to the
General Coordinates Network (GCN) and announce those that have not.

In the case of Binary Black Holes (BBHs) mergers, some models predict it’s
possible to find an electromagnetic counterpart as flares in Active Galactic
Nuclei (AGNs) activities (see, e.g. Rodríguez-Ramírez et al., 2023; Bartos et
al., 2017; Tagawa et al., 2023; Graham et al., 2023). The flares from those
events can last from days to weeks. We select a list of possible targets by
crossmatching the 90% credible region from BBHs mergers during O4 with AGN
catalogs together with cuts on redshifts from possible host galaxies,
decreasing the amount of sky area needed to be covered.

##  8 Conclusions

###  8.1 Summary

The STEP Projects works as a transient survey in the Southern Hemisphere,
using the capability of T80South Telescope with its 2 sq-deg field of view and
the Main Survey observation strategy to report supernovae candidates to the
astronomical community. Our project also works as a complementary source of
photometric data of scientific interesting supernovae, helping understand the
nature of those massive explosions. As in the era of multi-messenger
astronomy, STEP will follow up well-localized GW events that are inside T80
coverage, contributing to the discovery of kilonovae candidates and all the
physics that comes within. Using a data reduction pipeline for difference
imaging analysis, we successfully found and reported the transient AT 2022rri
and also recovered the known SN 2022tiv when S-PLUS was visiting the same
field, showing our capability in discovering new transients. We estimate
whether this is comparable to current wide-field surveys such as ZTF (Bellm et
al., 2019) by noting that the average number of ZTF transient discoveries per
area of sky surveyed is $\approx$0.016 transients deg-2. Our survey covered
1272 deg2 with a slightly shallower depth on average than ZTF of 0.5 mag, from
which we estimate an expected $1272\times 0.016\times 2.512^{-0.5}=12.84\pm
3.58$ transient discoveries. Given that our discoveries are not at this rate
yet, we expect that improvements in our template strategy, calibration, and
machine learning methods will yield a much greater efficiency comparable to
modern time-domain surveys.

###  8.2 STEP as a GW Follow-up Program

With the current sensitivity on LVK detectors, new NS mergers are being
discovered at distances far exceeding that of 40 Mpc, consistent with the
projection of a binary NS merger range $>$130 Mpc during O4 (Abbott et al.,
2020). With the vastly increased search volume, many events will require
coordinated follow up between multiple facilities to search the entire two-
dimensional localization region to a depth consistent with kilonovae at the
luminosity distance provided by the LVK. Moreover, optimizing search strategy
in real time requires candidate vetting and follow up and reporting on rapid
timescales such that kilonovae can be rapidly identified and observed with
other facilities. Our GW strategy (Section 7) and participation in community-
wide coordination efforts via NASA GCN (Santos et al., 2023) as well as the GW
Treasure Map (Wyatt et al., 2020) will maximize the impact of STEP’s search
and follow up capabilities.

Throughout O4 and future LVK observing runs, the large localization regions of
new compact object mergers will continue to tax the follow up capabilities of
large footprint surveys such as ZTF (which is also restricted to
$\delta\gtrsim-30^{\circ}$; Coughlin et al., 2019; Graham et al., 2020;
Kasliwal et al., 2020) and DECam (Cowperthwaite et al., 2017; Soares-Santos et
al., 2017; Andreoni et al., 2019; Goldstein et al., 2019; Garcia et al., 2020;
Morgan et al., 2020). By coordinating T80S with these and other GW follow up
facilities and optimizing for the science cases outlined above, we will
maximize the impact of T80S observations for future multi-messenger events.

###  8.3 The Next STEP in the Rubin Era

With first light at the Rubin Observatory planned in 2024, thousands of new
transients will be discovered per night reaching a typical per visit depth of
$r=24.5$ mag (Ivezić et al., 2019). However, the current nominal strategy will
leave $\sim$20 day gaps in the single-filter light curves for Rubin-discovered
transients and saturate on sources at $r\lesssim 17$ mag. This leaves
significant room for small aperture, wide-field surveys to fill in the light
curves of Rubin transients and observe those that peak at brighter magnitudes.

With a complementary filter set to Rubin and located at a similar observing
site, STEP is ideally situated to obtain light curves of Rubin-discovered
transients at $<$21 mag. In particular, transients discovered within the
S-PLUS main survey footprint have the benefit of multi-band host galaxy
information and photometric redshifts to set the basic energy scale of the
transient emission (Mendes de Oliveira et al., 2019). Combining this host
galaxy information for photometric classification of transients has been shown
to significantly improve the accuracy of transient classifiers (Baldeschi et
al., 2020; Gagliano et al., 2021). Although Rubin will eventually produce its
own photometric redshift catalogs, this will take time and Rubin lacks narrow
band UV filters used in S-PLUS that are optimized for photometric redshifts in
the local Universe (i.e., at $z<0.1$). By focusing on transient searches
within the S-PLUS volume, STEP can leverage its ability to obtain high-
cadence, multi-band light curves to maximize the science return for nearby
Rubin-discovered transients.

## Acknowledgments

The S-PLUS project, including the T80-South robotic telescope and the S-PLUS
scientific survey, was founded as a partnership between the Fundação de Amparo
à Pesquisa do Estado de São Paulo (FAPESP), the Observatório Nacional (ON),
the Federal University of Sergipe (UFS), and the Federal University of Santa
Catarina (UFSC), with important financial and practical contributions from
other collaborating institutes in Brazil, Chile (Universidad de La Serena),
and Spain (Centro de Estudios de Física del Cosmos de Aragón, CEFCA). We
further acknowledge financial support from the São Paulo Research Foundation
(FAPESP), the Brazilian National Research Council (CNPq), the Coordination for
the Improvement of Higher Education Personnel (CAPES), the Carlos Chagas Filho
Rio de Janeiro State Research Foundation (FAPERJ), and the Brazilian
Innovation Agency (FINEP).

Andre Santos and Clecio Bom acknowledges the financial support from CNPq
(402577/2022-1) Clecio Bom acknowledges the financial support from CNPq
(316072/2021-4) and from FAPERJ (grants 201.456/2022 and 210.330/2022) and the
FINEP contract 01.22.0505.00 (ref. 1891/22). The authors made use of Sci-Mind
servers machines developed by the CBPF AI LAB team and would like to thank P.
Russano and M. Portes de Albuquerque for all the support in infrastructure
matters.

C.D. Kilpatrick acknowledges support from a CIERA postdoctoral fellowship.

F.R. Herpich acknowledges financial support from FAPESP (grant 2018/21661-9).

A. Alvarez-Candal acknowledges financial support from the Severo Ochoa grant
CEX2021-001131-S funded by MCIN/AEI/ 10.13039/501100011033.

M.J. Sartori acknowledges financial support from FAPESP (grant 2022/00996-8).

E.A.D.L. acknowledges financial support from FAPESP (grant 2023/03688-5).

## Data Availability

The data from S-PLUS Main Survey will be available under data release in
splus-cloud platform. The follow-up photometric data is released under the
tables in appendix A. The data related to gravitational wave simulations will
be shared on reasonable request to the corresponding authors.

## References

  * Abadi et al. (2015) Abadi M., et al., 2015, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, https://www.tensorflow.org/
  * Abbott et al. (2017a) Abbott B. P., et al., 2017a, Nature, 551, 85
  * Abbott et al. (2017b) Abbott B. P., et al., 2017b, ApJ, 848, L12
  * Abbott et al. (2020) Abbott B. P., et al., 2020, Living Reviews in Relativity, 23, 3
  * Ackley et al. (2020) Ackley K., et al., 2020, A&A, 643, A113
  * Aleo et al. (2023) Aleo P. D., et al., 2023, ApJS, 266, 9
  * Alexander et al. (2021) Alexander K. D., et al., 2021, arXiv e-prints, p. arXiv:2102.08957
  * Almeida-Fernandes et al. (2022) Almeida-Fernandes F., et al., 2022, MNRAS, 511, 4590
  * Andreoni et al. (2019) Andreoni I., et al., 2019, ApJ, 881, L16
  * Andreoni et al. (2020) Andreoni I., et al., 2020, ApJ, 890, 131
  * Andreoni et al. (2022a) Andreoni I., et al., 2022a, ApJS, 260, 18
  * Andreoni et al. (2022b) Andreoni I., et al., 2022b, Nature, 612, 430
  * Andrews et al. (2022) Andrews J. E., et al., 2022, Transient Name Server Classification Report, 2022-454, 1
  * Antier et al. (2020) Antier S., et al., 2020, MNRAS, 492, 3904
  * Arcavi (2018) Arcavi I., 2018, ApJ, 855, L23
  * Argast et al. (2004) Argast D., Samland M., Thielemann F. K., Qian Y. Z., 2004, A&A, 416, 997
  * Arnould et al. (2007) Arnould M., Goriely S., Takahashi K., 2007, Phys. Rep., 450, 97
  * Baldeschi et al. (2020) Baldeschi A., Miller A., Stroh M., Margutti R., Coppejans D. L., 2020, ApJ, 902, 60
  * Bartos et al. (2017) Bartos I., Kocsis B., Haiman Z., Márka S., 2017, ApJ, 835, 165
  * Beck et al. (2021) Beck R., Szapudi I., Flewelling H., Holmberg C., Magnier E., Chambers K. C., 2021, MNRAS, 500, 1633
  * Becker (2015) Becker A., 2015, HOTPANTS: High Order Transform of PSF ANd Template Subtraction, Astrophysics Source Code Library, record ascl:1504.004 (ascl:1504.004)
  * Belczynski et al. (2002) Belczynski K., Kalogera V., Bulik T., 2002, ApJ, 572, 407
  * Bellm et al. (2019) Bellm E. C., et al., 2019, PASP, 131, 018002
  * Bertin (2010) Bertin E., 2010, SWarp: Resampling and Co-adding FITS Images Together, Astrophysics Source Code Library, record ascl:1010.068 (ascl:1010.068)
  * Bertin & Arnouts (1996) Bertin E., Arnouts S., 1996, A&AS, 117, 393
  * Bom & Palmese (2023) Bom C. R., Palmese A., 2023, arXiv e-prints, p. arXiv:2307.01330
  * Bom et al. (2021) Bom C. R., et al., 2021, MNRAS, 507, 1937
  * Bom et al. (2022) Bom C., Blanco Valentin M., Teles K., Portes de Albuquerque M., Metcalf R. B., 2022, Monthly Notices of the Royal Astronomical Society, 515, 5121
  * Bom et al. (2023) Bom C. R., et al., 2023, Designing an Optimal Kilonova Search using DECam for Gravitational Wave Events (arXiv:2302.04878)
  * Boone (2019) Boone K., 2019, AJ, 158, 257
  * Branch & Tamman (1992) Branch D., Tamman G., 1992, Annual review of astronomy and astrophysics, 30, 359
  * Chen et al. (2018) Chen H.-Y., Fishbach M., Holz D. E., 2018, Nature, 562, 545
  * Coughlin et al. (2019) Coughlin M. W., et al., 2019, ApJ, 885, L19
  * Coughlin et al. (2020) Coughlin M. W., et al., 2020, MNRAS, 492, 863
  * Coulter et al. (2017) Coulter D. A., et al., 2017, Science, 358, 1556
  * Coulter et al. (2023) Coulter D. A., Kilpatrick C. D., Foley R. J., 2023, in preparation
  * Cowperthwaite et al. (2017) Cowperthwaite P. S., et al., 2017, ApJ, 848, L17
  * Davis et al. (2023) Davis K. W., et al., 2023, MNRAS, 523, 2530
  * Díaz et al. (2017) Díaz M. C., et al., 2017, ApJ, 848, L29
  * Dobie et al. (2019) Dobie D., et al., 2019, ApJ, 887, L13
  * Dominik et al. (2015) Dominik M., et al., 2015, ApJ, 806, 263
  * Duev et al. (2019) Duev D. A., et al., 2019, MNRAS, 489, 3582
  * Eichler et al. (1989) Eichler D., Livio M., Piran T., Schramm D. N., 1989, Nature, 340, 126
  * Filippenko (1992) Filippenko A. V., 1992, in Filippenko A. V., ed., Astronomical Society of the Pacific Conference Series Vol. 103, Robotic Telescopes in the 1990s. pp 55–66
  * Filippenko (2005) Filippenko A. V., 2005, in Humphreys R., Stanek K., eds, Astronomical Society of the Pacific Conference Series Vol. 332, The Fate of the Most Massive Stars. p. 34 (arXiv:astro-ph/0412029), doi:10.48550/arXiv.astro-ph/0412029
  * Finkbeiner et al. (2016) Finkbeiner D. P., et al., 2016, ApJ, 822, 66
  * Förster et al. (2021) Förster F., et al., 2021, AJ, 161, 242
  * Gagliano et al. (2021) Gagliano A., Narayan G., Engel A., Carrasco Kind M., LSST Dark Energy Science Collaboration 2021, ApJ, 908, 170
  * Gaia Collaboration et al. (2023) Gaia Collaboration et al., 2023, A&A, 674, A1
  * Gal-Yam et al. (2009) Gal-Yam A., et al., 2009, Nature, 462, 624
  * Garcia et al. (2020) Garcia A., et al., 2020, ApJ, 903, 75
  * Garretson & Milisavljevic (2023) Garretson B., Milisavljevic D., 2023, in prep.
  * Goldstein et al. (2019) Goldstein D. A., et al., 2019, ApJ, 881, L7
  * Gomez et al. (2019) Gomez S., et al., 2019, ApJ, 884, L55
  * Graham et al. (2020) Graham M. J., et al., 2020, Phys. Rev. Lett., 124, 251102
  * Graham et al. (2023) Graham M. J., et al., 2023, ApJ, 942, 99
  * Hamuy et al. (1993) Hamuy M., et al., 1993, AJ, 106, 2392
  * He et al. (2015) He K., Zhang X., Ren S., Sun J., 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition
  * Hosseinzadeh et al. (2019) Hosseinzadeh G., et al., 2019, ApJ, 880, L4
  * Howard (2017) Howard E. M., 2017, in Lorente N. P. F., Shortridge K., Wayth R., eds, Astronomical Society of the Pacific Conference Series Vol. 512, Astronomical Data Analysis Software and Systems XXV. p. 245
  * Hubble (1929) Hubble E., 1929, Proceedings of the National Academy of Science, 15, 168
  * Ivezić et al. (2019) Ivezić Ž., et al., 2019, ApJ, 873, 111
  * Izzo et al. (2022) Izzo L., Langeroodi D., Davis K., 2022, Transient Name Server Classification Report, 2022-2337, 1
  * Kaiser et al. (2002) Kaiser N., et al., 2002, in Tyson J. A., Wolff S., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 4836, Survey and Other Telescope Technologies and Discoveries. pp 154–164, doi:10.1117/12.457365
  * Karambelkar et al. (2023) Karambelkar V. R., et al., 2023, ApJ, 948, 137
  * Kasen et al. (2017) Kasen D., Metzger B., Barnes J., Quataert E., Ramirez-Ruiz E., 2017, Nature, 551, 80
  * Kasliwal et al. (2020) Kasliwal M. M., et al., 2020, ApJ, 905, 145
  * Kessler et al. (2009) Kessler R., et al., 2009, PASP, 121, 1028
  * Kilpatrick (2023) Kilpatrick C. D., 2023, charliekilpatrick/candidates: candidates v1.0, doi:10.5281/zenodo.8172608, https://doi.org/10.5281/zenodo.8172608
  * Kilpatrick et al. (2017) Kilpatrick C. D., et al., 2017, Science, 358, 1583
  * Kilpatrick et al. (2021) Kilpatrick C. D., et al., 2021, ApJ, 923, 258
  * Kingma & Ba (2014) Kingma D. P., Ba J., 2014, arXiv preprint arXiv:1412.6980
  * Kulkarni et al. (2007) Kulkarni S. R., et al., 2007, Nature, 447, 458
  * LIGO Scientific Collaboration (2018) LIGO Scientific Collaboration 2018, LIGO Algorithm Library - LALSuite, free software (GPL), doi:10.7935/GT1W-FZ16
  * LSST Science Collaboration et al. (2009) LSST Science Collaboration et al., 2009, arXiv e-prints, p. arXiv:0912.0201
  * Lang et al. (2010) Lang D., Hogg D. W., Mierle K., Blanton M., Roweis S., 2010, AJ, 139, 1782
  * Li et al. (2022) Li L., Cai Y., Zhai Q., Zhang J., Wang X., 2022, Transient Name Server Classification Report, 2022-3549, 1
  * Lundquist et al. (2019) Lundquist M. J., et al., 2019, ApJ, 881, L26
  * MacFadyen et al. (2001) MacFadyen A. I., Woosley S. E., Heger A., 2001, ApJ, 550, 410
  * Maza (1980) Maza J., 1980, in Wheeler J. C., ed., Texas Workshop on Type I Supernovae. p. 7
  * McQuillan et al. (2012) McQuillan A., Aigrain S., Roberts S., 2012, A&A, 539, A137
  * Mendes de Oliveira et al. (2019) Mendes de Oliveira C., et al., 2019, MNRAS, 489, 241
  * Metzger et al. (2010) Metzger B. D., et al., 2010, MNRAS, 406, 2650
  * Möller et al. (2021) Möller A., et al., 2021, MNRAS, 501, 3272
  * Morgan et al. (2020) Morgan R., et al., 2020, ApJ, 901, 83
  * Muthukrishna et al. (2019) Muthukrishna D., Narayan G., Mandel K. S., Biswas R., Hložek R., 2019, PASP, 131, 118002
  * Narayan et al. (2018) Narayan G., et al., 2018, ApJS, 236, 9
  * Oke & Gunn (1983) Oke J. B., Gunn J. E., 1983, ApJ, 266, 713
  * Paterson et al. (2020) Paterson K., et al., 2020, arXiv e-prints, p. arXiv:2012.11700
  * Perlmutter (1989) Perlmutter S., 1989, in Particle Astrophysics: Forefront Experimental Issues. p. 196
  * Perlmutter et al. (1999) Perlmutter S., et al., 1999, ApJ, 517, 565
  * Petrov et al. (2022) Petrov P., et al., 2022, The Astrophysical Journal, 924, 54
  * Planck Collaboration et al. (2020) Planck Collaboration et al., 2020, A&A, 641, A6
  * Pozanenko et al. (2020) Pozanenko A. S., Minaev P. Y., Grebenev S. A., Chelovekov I. V., 2020, Astronomy Letters, 45, 710
  * Richmond et al. (1993) Richmond M., Treffers R. R., Filippenko A. V., 1993, PASP, 105, 1164
  * Riess et al. (1998) Riess A. G., et al., 1998, AJ, 116, 1009
  * Rodríguez-Ramírez et al. (2023) Rodríguez-Ramírez J. C., Bom C. R., Fraga B., Nemmen R., 2023, Optical Emission Model for Binary Black Hole Merger Remnants Travelling through Discs of Active Galactic Nucleus (arXiv:2304.10567)
  * Santos et al. (2023) Santos A., Kilpatrick C. D., Bom C. R., Lacerda E., STEP-GW Collaboration 2023, GRB Coordinates Network, 33986, 1
  * Schechter et al. (1993) Schechter P. L., Mateo M., Saha A., 1993, PASP, 105, 1342
  * Scolnic et al. (2015) Scolnic D., et al., 2015, ApJ, 815, 117
  * Shandonay et al. (2022) Shandonay A., et al., 2022, ApJ, 925, 44
  * Simonyan & Zisserman (2014) Simonyan K., Zisserman A., 2014, arXiv e-prints, p. arXiv:1409.1556
  * Singer & Price (2016) Singer L. P., Price L. R., 2016, Phys. Rev. D, 93, 024013
  * Smith (2014) Smith N., 2014, ARA&A, 52, 487
  * Soares-Santos et al. (2017) Soares-Santos M., et al., 2017, The Astrophysical Journal Letters, 848, L16
  * Soares-Santos et al. (2019) Soares-Santos M., et al., 2019, The Astrophysical Journal Letters, 876, L7
  * Soderberg et al. (2010) Soderberg A. M., et al., 2010, Nature, 463, 513
  * Sravan et al. (2020) Sravan N., Milisavljevic D., Reynolds J. M., Lentner G., Linvill M., 2020, ApJ, 893, 127
  * Stein et al. (2023) Stein R., et al., 2023, Monthly Notices of the Royal Astronomical Society, 521, 5046
  * Szegedy et al. (2015) Szegedy C., Vanhoucke V., Ioffe S., Shlens J., Wojna Z., 2015, CoRR, abs/1512.00567
  * Tagawa et al. (2023) Tagawa H., Kimura S. S., Haiman Z., Perna R., Bartos I., 2023, arXiv e-prints, p. arXiv:2303.02172
  * Thakur et al. (2020) Thakur A. L., et al., 2020, MNRAS, 499, 3868
  * Tody (1986) Tody D., 1986, in Crawford D. L., ed., Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 627, Instrumentation in astronomy VI. p. 733, doi:10.1117/12.968154
  * Tonry et al. (2022) Tonry J., et al., 2022, Transient Name Server Discovery Report, 2022-2574, 1
  * Toy et al. (2022) Toy M., Grayling M., Wiseman P., Frohmaier C., Yaron O., 2022, Transient Name Server Classification Report, 2022-2660, 1
  * Tucker et al. (2022) Tucker D. L., et al., 2022, ApJ, 929, 115
  * Vieira et al. (2020) Vieira N., et al., 2020, ApJ, 895, 96
  * Villar et al. (2019) Villar V. A., et al., 2019, ApJ, 884, 83
  * Volgenau et al. (2022) Volgenau N., Harbeck D., Lindstrom W., Collom D., Street R., Johnson M., 2022, in Adler D. S., Seaman R. L., Benn C. R., eds, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series Vol. 12186, Observatory Operations: Strategies, Processes, and Systems IX. p. 121860W, doi:10.1117/12.2628704
  * Watson et al. (2020) Watson A. M., et al., 2020, MNRAS, 492, 5916
  * Wells et al. (1981) Wells D. C., Greisen E. W., Harten R. H., 1981, A&AS, 44, 363
  * Wolf et al. (2018) Wolf C., et al., 2018, Publ. Astron. Soc. Australia, 35, e010
  * Woosley (1988) Woosley S. E., 1988, ApJ, 330, 218
  * Wyatt et al. (2020) Wyatt S. D., Tohuvavohu A., Arcavi I., Lundquist M. J., Howell D. A., Sand D. J., 2020, ApJ, 894, 127
  * Yang et al. (2023) Yang Y.-H., et al., 2023, arXiv e-prints, p. arXiv:2308.00638
  * Yaron & Gal-Yam (2012) Yaron O., Gal-Yam A., 2012, PASP, 124, 668
  * Zhou et al. (2021) Zhou R., et al., 2021, MNRAS, 501, 3309
  * Zwicky (1938) Zwicky F., 1938, Publications of the Astronomical Society of the Pacific, 50, 215
  * de Wet et al. (2021) de Wet S., et al., 2021, arXiv e-prints, p. arXiv:2103.02399

##  Appendix A Photometry of Followed Targets

In this appendix, we present the photometry of targets, followed by STEP in
its first year of operations. All photometry is obtained in difference imaging
on the AB magnitude system (Oke & Gunn, 1983) and does not consider dust
extinction and other colour correction terms.

2022crv
---
MJD | Band | $m$ | $\sigma_{m}$
|  | (mag) | (mag)
59648.0185 | $g$ | 15.799 | 0.015
59650.0208 | $g$ | 15.892 | 0.016
59653.0853 | $g$ | 16.159 | 0.021
59655.1146 | $g$ | 16.319 | 0.025
59663.0323 | $g$ | 16.992 | 0.018
59665.0080 | $g$ | 17.119 | 0.020
59667.0067 | $g$ | 17.146 | 0.020
59669.0099 | $g$ | 17.233 | 0.019
59673.0019 | $g$ | 17.396 | 0.020
59674.9997 | $g$ | 17.497 | 0.022
59648.0240 | $i$ | 15.384 | 0.009
59650.0270 | $i$ | 15.367 | 0.009
59653.0909 | $i$ | 15.402 | 0.010
59655.1201 | $i$ | 15.472 | 0.010
59657.0362 | $i$ | 15.586 | 0.012
59659.1176 | $i$ | 15.674 | 0.012
59661.0167 | $i$ | 15.759 | 0.010
59663.0379 | $i$ | 15.859 | 0.008
59665.0136 | $i$ | 15.940 | 0.009
59667.0119 | $i$ | 16.042 | 0.009
59669.0160 | $i$ | 16.111 | 0.009
59671.0086 | $i$ | 16.202 | 0.010
59673.0067 | $i$ | 16.239 | 0.010
59675.0053 | $i$ | 16.325 | 0.010
59677.0040 | $i$ | 16.407 | 0.010
59679.0025 | $i$ | 16.456 | 0.012
59681.0010 | $i$ | 16.499 | 0.012
59683.0279 | $i$ | 16.515 | 0.014
59648.0212 | $r$ | 15.336 | 0.008
59650.0243 | $r$ | 15.370 | 0.010
59653.0881 | $r$ | 15.471 | 0.010
59655.1173 | $r$ | 15.585 | 0.010
59657.0334 | $r$ | 15.760 | 0.013
59659.1148 | $r$ | 15.852 | 0.013
59661.0139 | $r$ | 15.982 | 0.011
59663.0350 | $r$ | 16.090 | 0.009
59665.0107 | $r$ | 16.201 | 0.010
59667.0090 | $r$ | 16.302 | 0.009
59669.0131 | $r$ | 16.356 | 0.011
59671.0055 | $r$ | 16.469 | 0.012
59673.0038 | $r$ | 16.531 | 0.010
59675.0024 | $r$ | 16.608 | 0.012
59677.0011 | $r$ | 16.669 | 0.013
59678.9996 | $r$ | 16.697 | 0.014
59680.9981 | $r$ | 16.759 | 0.015
59683.0240 | $r$ | 16.830 | 0.017
59648.0269 | $z$ | 15.351 | 0.011
59650.0300 | $z$ | 15.372 | 0.012
59653.0938 | $z$ | 15.380 | 0.012
59655.1230 | $z$ | 15.427 | 0.011
59657.0392 | $z$ | 15.525 | 0.012
59659.1206 | $z$ | 15.584 | 0.013
59661.0197 | $z$ | 15.681 | 0.013
59663.0412 | $z$ | 15.747 | 0.011
59665.0168 | $z$ | 15.833 | 0.011
59667.0152 | $z$ | 15.895 | 0.009
59669.0193 | $z$ | 15.921 | 0.009
59671.0118 | $z$ | 16.011 | 0.012
59673.0100 | $z$ | 16.037 | 0.011
59675.0085 | $z$ | 16.107 | 0.012
59677.0073 | $z$ | 16.153 | 0.011
59679.0057 | $z$ | 16.177 | 0.014
59681.0037 | $z$ | 16.220 | 0.013
59683.0318 | $z$ | 16.231 | 0.014
Table 2: Light curve data for SN 2022crv. 2022acko
---
MJD | Band | $m$ | $\sigma_{m}$
|  | (mag) | (mag)
59933.0474 | $g$ | 16.215 | 0.042
59933.0487 | $r$ | 16.051 | 0.039
59933.0501 | $i$ | 16.241 | 0.052
59933.0514 | $z$ | 16.394 | 0.075
59939.1292 | $g$ | 16.509 | 0.056
59939.1305 | $r$ | 16.111 | 0.041
59939.1318 | $i$ | 16.210 | 0.059
59939.1332 | $z$ | 16.265 | 0.080
59943.1096 | $r$ | 16.139 | 0.049
59943.1111 | $i$ | 16.261 | 0.061
59943.1123 | $z$ | 16.221 | 0.078
Table 3: Light curve data for SN 2022acko. 2022qxu
---
MJD | Band | $m$ | $\sigma_{m}$
|  | (mag) | (mag)
59815.2268 | $g$ | >16.221 | –
59819.3962 | $g$ | >16.221 | –
59824.3020 | $g$ | >16.221 | –
59828.2908 | $g$ | >16.221 | –
59837.3453 | $g$ | >16.221 | –
59845.3483 | $g$ | >16.221 | –
59851.2632 | $g$ | >16.221 | –
59815.2325 | $i$ | 20.489 | 0.194
59819.4024 | $i$ | 20.562 | 0.254
59824.3080 | $i$ | 20.682 | 0.292
59828.2966 | $i$ | 20.822 | 0.320
59837.3511 | $i$ | >20.822 | –
59841.1792 | $i$ | >20.822 | –
59845.3541 | $i$ | >20.822 | –
59851.2690 | $i$ | >20.822 | –
59815.2293 | $r$ | 20.698 | 0.198
59819.3995 | $r$ | 20.789 | 0.146
59824.3046 | $r$ | 20.945 | 0.211
59828.2936 | $r$ | 21.241 | 0.330
59837.3481 | $r$ | >21.241 | –
59845.3511 | $r$ | >21.241 | –
59851.2660 | $r$ | >21.241 | –
59815.2361 | $z$ | 20.498 | 0.193
59819.4062 | $z$ | 20.512 | 0.210
59824.3119 | $z$ | 20.652 | 0.221
59828.3000 | $z$ | 20.710 | 0.330
59837.3544 | $z$ | >20.710 | –
59841.1825 | $z$ | >20.710 | –
59845.3574 | $z$ | >20.710 | –
59851.2723 | $z$ | >20.710 | –
Table 4: Light curve data for SN 2022qxu.