arXiv subjects

J. Peloton

Publications and source records attributed to J. Peloton.

At least 19 recordsLinked to original sources

arXiv2026

Shape, Orientation and Colors Combined approach for Asteroids (SOCCA)

Large photometric surveys provide sparse multi-band photometry for millions of Solar System objects, offering an opportunity to jointly constrain their physical and compositional properties. However, current phase function models do not account for rotational variability, limiting their ability to retrieve accurate parameters. Similarly, methods that recover shape and rotational parameters remain both computationally and observationally expensive. We present a model capable of simultaneously retrieving the absolute magnitude, phase parameters, spin state, and shape proportions of SSOs from sparse photometric data, while remaining computationally efficient. We introduce the Shape, Orientation and Colors Combined approach for Asteroids (SOCCA), which extends the HG1G2 formalism by incorporating the projected surface of a rotating triaxial ellipsoid. The model jointly fits multi-band photometry, and includes a dedicated treatment of rotational period determination. We implement the model on a 10-year LSST simulation as well as on real data of asteroid (45) Eugenia for validation purposes. SOCCA significantly improves the fit to photometric data, reducing the mean residuals to half, compared to previous models. It retrieves the absolute magnitude with a scatter about three times smaller than existing approaches, and improves the determination of phase parameters by a similar factor. It also recovers the sidereal rotation period, spin axis orientation and the axes ratios of the best fitting ellipsoid. The inclusion of shape and rotation increases the number of physically meaningful solutions by 10-20% per filter, leading to an overall success rate of 53%. Its performance and scalability make it well suited for current and upcoming large surveys such as the Zwicky Transient Facility and the recently started Legacy Survey of Space and Time.

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arXiv2026

Hostless extragalactic transients in Fink: Results from the ELEPHANT pipeline

The ExtragaLactic alErt Pipeline for Hostless AstroNomical Transients (ELEPHANT), has been developed as a framework for filtering hostless candidates, in real time alert systems, and implemented as a filter in the Fink broker. ELEPHANT works on stamps and requires minimal information, thus allowing for fast identification of extragalactic transient events. In this work we evaluate the performance of the ELEPHANT pipeline by systematically analyzing flagged hostless candidates identified between 1 September 2023 and 31 December 2025. Our goal is to quantifying its accuracy and identify dominant sources of contamination. For each flagged candidate we collected additional information from multiple catalogues and archival repositories. We further examined their light-curve evolution and astrometric consistency (coordinate dispersion over time) to refine source classification. Results. Out of 877 flagged events, 67 are confidently confirmed as genuinely hostless candidates, with no detectable host galaxy in either existing catalogues or archival imaging, representing a high-purity sample of intrinsically faint or absent hosts. Additional 51 events are linked to visually identifiable hosts that are entirely absent from both catalogues and ZTF stamps. For the confirmed hostless subset, the inferred upper limits on host-galaxy absolute magnitudes extend well below the luminosity range of typical dwarf galaxies. The pipeline showed an overall accuracy of 0.84, with the majority of the classified flagged events being Type Ia supernovae, and the second most detected class being Type I superluminous supernova. ELEPHANT has been adapted to deal with the Rubin alert stream and has been processing its alerts since February 2026.

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arXiv2026

Synergy between the Cherenkov Telescope Array Observatory and the Vera C. Rubin Observatory

The Cherenkov Telescope Array Observatory (CTAO) and the Vera C. Rubin Observatory are set to transform our understanding of the universe over the next decade. These two observatories have multiple areas of complementarity in their scientific applications, ranging from constraints on cosmological parameters to studies of asteroid occultations. The most opportune area of synergy probably lies in the field of time-domain astronomy. Due to their sensitivity and saturation limits, it will be difficult for the two observatories to conduct joint studies of variable and transient sources in the Milky Way. However, they could offer a fresh and rich perspective on non-thermal extragalactic sources, in particular gamma-ray bursts, active galactic nuclei and jetted tidal disruption events. Among these sources lie the best candidates for multi-messenger research into the origin of TeV-PeV neutrinos and multi-EeV cosmic rays. Thus, combined with multi-wavelength observations by X-ray satellites and wide-field gamma-ray instruments, the synergy between Rubin and the CTAO could provide answers to some of the most important questions in astroparticle physics. This scientific potential comes with a challenge: selecting a few alerts from the ten million issued by Rubin each night to repoint the CTAO telescopes. We use the variability of blazars over timescales ranging from a few days to several years as a case study to demonstrate how to address this challenge using the Fink broker of Rubin.

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arXiv2026

NOMAI : A real-time photometric classifier for superluminous supernovae identification. A science module for the Fink broker

Superluminous supernovae (SLSNe) are one of the most luminous stellar explosions known, yet they remain poorly understood. Because they are intrinsically rare, efficiently identifying them in the large alert streams produced by modern time-domain surveys is essential for enabling spectroscopic follow-up. We present NOMAI, a machine learning classifier designed to identify SLSN candidates directly from photometric alerts in the ZTF stream, using light curves accumulated over at least 30 days. It does not require any spectroscopic redshift and is running in real time within the Fink broker. ZTF light curves are transformed into a set of physically motivated features derived primarily from model-fitting procedures using SALT2 and Rainbow, a blackbody-based multi-band fitting framework. These features are used to train an XGBoost classifier on a curated dataset of labeled ZTF sources constructed using literature samples of SLSNe, along with TNS and internal ZTF labeled sources. The final training dataset contains 5280 unique sources, including 225 spectroscopically classified SLSNe. On the training sample, the classifier reaches 66% completeness and 58% purity. Deployed within the Fink broker, NOMAI has been running continuously since 18/12/2025 on the ZTF alert stream and publicly reports SLSN candidates every night by automatically posting them to dedicated communication channels. Based on this, we also report the first two-month as an evaluation period, where the classifier successfully recovered 22 of the 24 active SLSNe reported on the Transient Name Server. The achieved performances demonstrate that the classifier provides a valuable tool for experts to efficiently scan the alert stream and identify promising candidates. In the near future, NOMAI is intended to be adapted to operate on the Legacy Survey of Space and Time conducted by the Vera C. Rubin Observatory.

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arXiv2026

Anomaly detection in Fink. I. Discovery, follow-up, and classification of unusual sources

Modern wide-field time-domain surveys produce alert streams whose scientific potential is often concentrated in rare and unusual events. Efficient discovery therefore requires automated pipelines to be combined with rapid expert validation and follow-up. We present the first-year performance of the anomaly-detection (AD) pipeline operating within the Fink broker on the Zwicky Transient Facility alert stream, and assess its ability to identify scientifically valid outliers and enable discovery of rare phenomena. The pipeline transforms ZTF light curves into a compact set of features and ranks alerts using an Isolation Forest model trained on archival ZTF data. Each night, the 10 most anomalous candidates are distributed to experts via Slack/Telegram and exposed through an API. We also implement an expert-feedback loop using a public Telegram bot and retrain the model using the Active Anomaly Discovery algorithm. During the first year of operations (starting from 25 January 2023), the AD pipeline identified multiple high-interest sources and triggered dedicated photometric and spectroscopic follow-up. We report the discovery and multi-instrument (11-m SALT telescope, 2.5-m CMO telescope, 0.6-m ASA RC600, 0.25-m FRAM-ORM) follow-up of the rare AM CVn system Fink J062452.88+020818.3 of the WZ Sge type, UX Ori-type star Fink J222324.32+744222.0 and the unusual transient with precursor SN 2023mtp. In addition, the module triggered 33 supernovae, including 30 previously unreported ones, with candidates for superluminous and hostless events. Furthermore, nine new dwarf novae were discovered. These results show that broker-level anomaly detection, coupled with rapid dissemination, expert assessment, and follow-up observations, provide an effective bridge between large-scale survey streams and domain expertise, turning anomaly scores into astrophysical insights and concrete discoveries.

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arXiv2025

Lost and Found - A gallery of overlooked optical nuclear transients from the ZTF archive

Tidal disruption events (TDEs) correspond to the destruction of a star by the tidal forces around a black hole, leading to outbursts which can last from months to years. These transients are rare, and increasing the current sample is paramount to understand them. As part of the Fink alert broker, we have developed an early detection system for TDEs for the Zwicky Transient Facility (ZTF) data. In this paper, we report on the optical transients we found either during the development of this tool, or when applying the classifier to the existing archive. We use this sample to anticipate what improvements to the TDE detection systems will need to be implemented for future surveys. For all the transients, we present optical and infrared archival photometry from ZTF, WISE, and Catalina, and assess the previous nuclear activity of the host. We fit the ZTF lightcurves with both a phenomenological and a physically-motivated model. We report on a total of 19 optical nuclear transients, out of which nine are in passive galaxies, eight in active galaxies, and two for which the activity of the host is uncertain. Two transients are newly discovered repeated TDE candidates, and we compare them to the current sample of repeated optical nuclear transients. One transient is exceptionally long-lived (over 5 years), in an until-now passive galaxy. Three of the TDE-like flares in active galaxies have absolute g-band magnitudes brighter than -24, making them new Extreme Nuclear Transient (ENT) candidates. One seemingly repeated object was revealed to be two independent supernovae in the same galaxy. This sample shows both the potential of our detection system for future discovery, and the relevance of archival searches to reveal overlooked transients. It also raises several points of concern and avenues of improvement for current and future classifiers.

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arXiv2025

AT2021uey: A planetary microlensing event outside the Galactic bulge

We report the analysis of a planetary microlensing event AT2021uey. The event was observed outside the Galactic bulge and was alerted by both space- (Gaia) and ground-based (ZTF and ASAS-SN) surveys. From the observed data, we find that the lens system is located at a distance of 1 kpc and comprises an M-dwarf host star of about half a solar mass, orbited by a Jupiter-like planet beyond the snowline. The source star could be a metal-poor giant located in the halo according to the spectral analyses and modelling. Hence, AT2021uey is a unique example of the binary-lens event outside the bulge that is offered by a disc-halo lens-source combination.

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arXiv2025

Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker

Current and future surveys rely on machine learning classification to obtain large and complete samples of transients. Many of these algorithms are restricted by training samples that contain a limited number of spectroscopically confirmed events. Here, we present the first real-time application of Active Learning to optimise spectroscopic follow-up with the goal of improving training sets of early type Ia supernovae (SNe Ia) classifiers. Using a photometric classifier for early SN Ia, we apply an Active Learning strategy for follow-up optimisation using the real-time FINK broker processing of the ZTF public stream. We perform follow-up observations at the ANU 2.3m telescope in Australia and obtain 92 spectroscopic classified events that are incorporated in our training set. We show that our follow-up strategy yields a training set that, with 25% less spectra, improves classification metrics when compared to publicly reported spectra. Our strategy selects in average fainter events and, not only supernovae types, but also microlensing events and flaring stars which are usually not incorporated on training sets. Our results confirm the effectiveness of active learning strategies to construct optimal training samples for astronomical classifiers. With the Rubin Observatory LSST soon online, we propose improvements to obtain earlier candidates and optimise follow-up. This work paves the way to the deployment of real-time AL follow-up strategies in the era of large surveys.

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arXiv2024

Transient Classifiers for Fink: Benchmarks for LSST

The upcoming Legacy Survey of Space and Time (LSST) is expected to detect a few million transients per night, which will generate a live alert stream during the entire ten years of the survey. This stream will be distributed via community brokers whose task is to select subsets of the stream and direct them to scientific communities. Given the volume and complexity of the anticipated data, machine learning algorithms will be paramount for this task. We present the infrastructure tests and classification methods developed within the Fink broker in preparation for LSST. This work aims to provide detailed information regarding the underlying assumptions and methods behind each classifier and enable users to make informed follow-up decisions from Fink photometric classifications. Using simulated data from ELAsTiCC, we showcase the performance of binary and multi-class ML classifiers available in Fink. These include tree-based classifiers coupled with tailored feature extraction strategies as well as deep learning algorithms. Moreover, we introduce CATS, a deep learning architecture specifically designed for this task. Our results show that Fink classifiers are able to handle the extra complexity that is expected from LSST data. CATS achieved $\geq 93\%$ precision for all classes except `long' (for which it achieved $\sim 83\%$), while our best performing binary classifier achieves $\geq 98\%$ precision and $\geq 99\%$ completeness when classifying the periodic class. ELAsTiCC was an important milestone in preparing the Fink infrastructure to deal with LSST-like data. Our results demonstrate that Fink classifiers are well prepared for the arrival of the new stream, but this work also highlights that transitioning from the current infrastructures to Rubin will require significant adaptation of the currently available tools. This work was the first step in the right direction.

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arXiv2024

Combined spin orientation and phase function of asteroids

Large surveys provide numerous non-targeted observations of small bodies (SSOs). The upcoming LSST of the Rubin observatory will be the largest source of SSO photometry in the next decade. With non-coordinated epochs of observation, colors, and therefore taxonomy and composition, can only be computed by comparing absolute magnitudes obtained in each filter by solving the phase function (evolution of brightness of the small body against the solar phase angle). Current models in use in the community (HG, HG12* , HG1G2) however fail to reproduce the long-term photometry of many targets due to the change in aspect angle between apparitions. We aim at deriving a generic yet simple phase function model accounting for the variable geometry of the SSOs over multiple apparitions. We propose the sHG1G2 phase function model in which we introduce a term describing the brightness changes due to spin orientation and polar oblateness. We apply this new model to 13,245,908 observations of 122,675 SSOs. These observations were acquired in the g and r filters with the Zwicky Transient Facility. We retrieve them and implement the new sHG1G2 model in Fink, a broker of alerts designed for the LSST. The sHG1G2 model leads to smaller residuals than other phase function models, providing a better description of the photometry of asteroids. We determine the absolute magnitude H and phase function coefficients (G1, G2) in each filter, the spin orientation (RA_0,DEC_0), and the polar-to-equatorial oblateness R for 95,593 Solar System Objects (SSOs), which constitutes about a tenfold increase in the number of characterised objects compared to current census. The application of the sHG1G2 model on ZTF alert data using the FINK broker shows that the model is appropriate to extract physical properties of asteroids from multi-band and sparse photometry, such as the forthcoming LSST survey.

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arXiv2024

Multi-band analyses of the bright GRB 230812B and the associated SN2023pel

GRB~230812B is a bright and relatively nearby ($z =0.36$) long gamma-ray burst (GRB) that has generated significant interest in the community and has thus been observed over the entire electromagnetic spectrum. We report over 80 observations in X-ray, ultraviolet, optical, infrared, and sub-millimeter bands from the GRANDMA (Global Rapid Advanced Network for Multi-messenger Addicts) network of observatories and from observational partners. Adding complementary data from the literature, we then derive essential physical parameters associated with the ejecta and external properties (i.e. the geometry and environment) of the GRB and compare with other analyses of this event. We spectroscopically confirm the presence of an associated supernova, SN2023pel, and we derive a photospheric expansion velocity of v $\sim$ 17$\times10^3$ km s$^{-1}$. We analyze the photometric data first using empirical fits of the flux and then with full Bayesian Inference. We again strongly establish the presence of a supernova in the data, with a maximum (pseudo-)bolometric luminosity of $5.75 \times 10^{42}$ erg/s, at $15.76^{+0.81}_{-1.21}$ days (in the observer frame) after the trigger, with a half-max time width of 22.0 days. We compare these values with those of SN1998bw, SN2006aj, and SN2013dx. Our best-fit model favours a very low density environment ($\log_{10}({n_{\rm ISM}/{\rm cm}^{-3}}) = -2.38^{+1.45}_{-1.60}$) and small values for the jet's core angle $θ_{\rm core} = 1.54^{+1.02}_{-0.81} \ \rm{deg}$ and viewing angle $θ_{\rm obs} = 0.76^{+1.29}_{-0.76} \ \rm{deg}$. GRB 230812B is thus one of the best observed afterglows with a distinctive supernova bump.

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arXiv2024

Enabling discovery of solar system objects in large alert data streams

With the advent of large-scale astronomical surveys such as the Zwicky Transient Facility (ZTF), the number of alerts generated by transient, variable and moving astronomical objects is growing rapidly, reaching millions per night. Concerning solar system minor planets, their identification requires linking the alerts of many observations over a potentially large time, leading to a very large combinatorial number. This work aims to identify new candidates for solar system objects from massive alert data streams produced by large-scale surveys, such as the ZTF and the Vera C. Rubin Observatory's Legacy Survey of Space and Time. Our analysis used the Fink alert broker capabilities to reduce the 111,275,131 processed alerts from ZTF between November 2019 and December 2022 to only 389,530 new solar system alert candidates over the same period. We then implemented a linking algorithm, Fink-FAT, to create real-time trajectory candidates from alert data and extract orbital parameters. The analysis was validated on ZTF alert packets linked to confirmed solar system objects from the Minor Planet Center database. Finally, the results were confronted against follow-up observations. Between November 2019 and December 2022, Fink-FAT extracted 327 new orbits from solar system object candidates at the time of the observations, over which 65 were still unreported in the MPC database as of March 2023. After two late follow-up observation campaigns of six orbit candidates, four were associated with known solar system minor planets, and two remain unknown. Fink-FAT is deployed in the Fink broker and successfully analyzes in real time the alert data from the ZTF survey by regularly extracting new candidates for solar system objects. Our scalability tests also show that Fink-FAT can handle the even larger volume of alert data that the Rubin Observatory will send.

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arXiv2023

Ready for O4 II: GRANDMA Observations of Swift GRBs during eight-weeks of Spring 2022

We present a campaign designed to train the GRANDMA network and its infrastructure to follow up on transient alerts and detect their early afterglows. In preparation for O4 II campaign, we focused on GRB alerts as they are expected to be an electromagnetic counterpart of gravitational-wave events. Our goal was to improve our response to the alerts and start prompt observations as soon as possible to better prepare the GRANDMA network for the fourth observational run of LIGO-Virgo-Kagra (which started at the end of May 2023), and future missions such as SM. To receive, manage and send out observational plans to our partner telescopes we set up dedicated infrastructure and a rota of follow-up adcates were organized to guarantee round-the-clock assistance to our telescope teams. To ensure a great number of observations, we focused on Swift GRBs whose localization errors were generally smaller than the GRANDMA telescopes' field of view. This allowed us to bypass the transient identification process and focus on the reaction time and efficiency of the network. During 'Ready for O4 II', 11 Swift/INTEGRAL GRB triggers were selected, nine fields had been observed, and three afterglows were detected (GRB 220403B, GRB 220427A, GRB 220514A), with 17 GRANDMA telescopes and 17 amateur astronomers from the citizen science project Kilonova-Catcher. Here we highlight the GRB 220427A analysis where our long-term follow-up of the host galaxy allowed us to obtain a photometric redshift of $z=0.82\pm0.09$, its lightcurve elution, fit the decay slope of the afterglows, and study the properties of the host galaxy.

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arXiv2023

Enabling the discovery of fast transients: A kilonova science module for the Fink broker

We describe the fast transient classification algorithm in the center of the kilonova (KN) science module currently implemented in the Fink broker and report classification results based on simulated catalogs and real data from the ZTF alert stream. We used noiseless, homogeneously sampled simulations to construct a basis of principal components (PCs). All light curves from a more realistic ZTF simulation were written as a linear combination of this basis. The corresponding coefficients were used as features in training a random forest classifier. The same method was applied to long (>30 days) and medium (<30 days) light curves. The latter aimed to simulate the data situation found within the ZTF alert stream. Classification based on long light curves achieved 73.87% precision and 82.19% recall. Medium baseline analysis resulted in 69.30% precision and 69.74% recall, thus confirming the robustness of precision results when limited to 30 days of observations. In both cases, dwarf flares and point Type Ia supernovae were the most frequent contaminants. The final trained model was integrated into the Fink broker and has been distributing fast transients, tagged as KN_candidates, to the astronomical community, especially through the GRANDMA collaboration. We showed that features specifically designed to grasp different light curve behaviors provide enough information to separate fast (KN-like) from slow (non-KN-like) evolving events. This module represents one crucial link in an intricate chain of infrastructure elements for multi-messenger astronomy which is currently being put in place by the Fink broker team in preparation for the arrival of data from the Vera Rubin Observatory Legacy Survey of Space and Time.

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arXiv2023

GRANDMA and HXMT Observations of GRB 221009A -- the Standard-Luminosity Afterglow of a Hyper-Luminous Gamma-Ray Burst

GRB 221009A is the brightest Gamma-Ray Burst (GRB) detected in more than 50 years of study. In this paper, we present observations in the X-ray and optical domains after the GRB obtained by the GRANDMA Collaboration (which includes observations from more than 30 professional and amateur telescopes) and the Insight-HXMT Collaboration. We study the optical afterglow with empirical fitting from GRANDMA+HXMT data, augmented with data from the literature up to 60 days. We then model numerically, using a Bayesian approach, the GRANDMA and HXMT-LE afterglow observations, that we augment with Swift-XRT and additional optical/NIR observations reported in the literature. We find that the GRB afterglow, extinguished by a large dust column, is most likely behind a combination of a large Milky-Way dust column combined with moderate low-metallicity dust in the host galaxy. Using the GRANDMA+HXMT-LE+XRT dataset, we find that the simplest model, where the observed afterglow is produced by synchrotron radiation at the forward external shock during the deceleration of a top-hat relativistic jet by a uniform medium, fits the multi-wavelength observations only moderately well, with a tension between the observed temporal and spectral evolution. This tension is confirmed when using the extended dataset. We find that the consideration of a jet structure (Gaussian or power-law), the inclusion of synchrotron self-Compton emission, or the presence of an underlying supernova do not improve the predictions, showing that the modelling of GRB22109A will require going beyond the most standard GRB afterglow model. Placed in the global context of GRB optical afterglows, we find the afterglow of GRB 221009A is luminous but not extraordinarily so, highlighting that some aspects of this GRB do not deviate from the global known sample despite its extreme energetics and the peculiar afterglow evolution.

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arXiv2022

The POLARBEAR-2 and Simons Array Focal Plane Fabrication Status

We present on the status of POLARBEAR-2 A (PB2-A) focal plane fabrication. The PB2-A is the first of three telescopes in the Simon Array (SA), which is an array of three cosmic microwave background (CMB) polarization sensitive telescopes located at the POLARBEAR (PB) site in Northern Chile. As the successor to the PB experiment, each telescope and receiver combination is named as PB2-A, PB2-B, and PB2-C. PB2-A and -B will have nearly identical receivers operating at 90 and 150 GHz while PB2-C will house a receiver operating at 220 and 270 GHz. Each receiver contains a focal plane consisting of seven close-hex packed lenslet coupled sinuous antenna transition edge sensor bolometer arrays. Each array contains 271 di-chroic optical pixels each of which have four TES bolometers for a total of 7588 detectors per receiver. We have produced a set of two types of candidate arrays for PB2-A. The first we call Version 11 (V11) and uses a silicon oxide (SiOx) for the transmission lines and cross-over process for orthogonal polarizations. The second we call Version 13 (V13) and uses silicon nitride (SiNx) for the transmission lines and cross-under process for orthogonal polarizations. We have produced enough of each type of array to fully populate the focal plane of the PB2-A receiver. The average wirebond yield for V11 and V13 arrays is 93.2% and 95.6% respectively. The V11 arrays had a superconducting transition temperature (Tc) of 452 +/- 15 mK, a normal resistance (Rn) of 1.25 +/- 0.20 Ohms, and saturations powers of 5.2 +/- 1.0 pW and 13 +/- 1.2 pW for the 90 and 150 GHz bands respectively. The V13 arrays had a superconducting transition temperature (Tc) of 456 +/-6 mK, a normal resistance (Rn) of 1.1 +/- 0.2 Ohms, and saturations powers of 10.8 +/- 1.8 pW and 22.9 +/- 2.6 pW for the 90 and 150 GHz bands respectively.

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arXiv2022

GRANDMA Observations of ZTF/Fink Transients during Summer 2021

We present our follow-up observations with GRANDMA of transient sources revealed by the Zwicky Transient Facility (ZTF). Over a period of six months, all ZTF triggers were examined in real time by a dedicated science module implemented in the Fink broker, which will be used for the data processing of the Vera C. Rubin Observatory. In this article, we present three selection methods to identify kilonova candidates. Out of more than 35 million candidates, a hundred sources have passed our selection criteria. Six were then followed-up by GRANDMA (by both professional and amateur astronomers). The majority were finally classified either as asteroids or as supernovae events. We mobilized 37 telescopes, bringing together a large sample of images, taken under various conditions and quality. To complement the orphan kilonova candidates (those without associated gamma-ray bursts, which were all), we included three additional supernovae alerts to conduct further observations of during summer 2021. We demonstrate the importance of the amateur astronomer community that contributed images for scientific analyzes of new sources discovered in a magnitude range r'=17-19 mag. We based our rapid kilonova classification on the decay rate of the optical source that should exceed 0.3 mag/day. GRANDMA's follow-up determined the fading rate within 1.5+/-1.2 days post-discovery, without waiting for further observations from ZTF. No confirmed kilonovae were discovered during our observing campaign. This work will be continued in the coming months in the view of preparing for kilonova searches in the next gravitational-wave observing run O4.

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arXiv2022

The GRANDMA network in preparation for the fourth gravitational-wave observing run

GRANDMA is a world-wide collaboration with the primary scientific goal of studying gravitational-wave sources, discovering their electromagnetic counterparts and characterizing their emission. GRANDMA involves astronomers, astrophysicists, gravitational-wave physicists, and theorists. GRANDMA is now a truly global network of telescopes, with (so far) 30 telescopes in both hemispheres. It incorporates a citizen science programme (Kilonova-Catcher) which constitutes an opportunity to spread the interest in time-domain astronomy. The telescope network is an heterogeneous set of already-existing observing facilities that operate coordinated as a single observatory. Within the network there are wide-field imagers that can observe large areas of the sky to search for optical counterparts, narrow-field instruments that do targeted searches within a predefined list of host-galaxy candidates, and larger telescopes that are devoted to characterization and follow-up of the identified counterparts. Here we present an overview of GRANDMA after the third observing run of the LIGO/VIRGO gravitational-wave observatories in $2019-2020$ and its ongoing preparation for the forthcoming fourth observational campaign (O4). Additionally, we review the potential of GRANDMA for the discovery and follow-up of other types of astronomical transients.

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