arXiv subjects

Ashish Mahabal

Publications and source records attributed to Ashish Mahabal.

At least 19 recordsLinked to original sources

arXiv2026

Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP

Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves. On the eve of the Vera C. Rubin Observatory Legacy Survey of Space and Time, its dataset offers unprecedented opportunities for transient science. Yet a key challenge remains its cadence, sparse and irregular across six bands, limiting inference. Interpolation helps mitigate this, with Gaussian Processes the standard, but they struggle with cross-band correlations, require a priori kernel specification, and must be fit to each light curve individually, hence scaling poorly. Here, we introduce the neural process family for light curve reconstruction, combining the probabilistic framework of Gaussian Processes with the scalability of deep learning. By meta-learning on diverse simulated transients, Attentive Neural Processes shift the bulk of computation to training, enabling rapid, amortized inference with a class-agnostic model. Evaluated on realistic Rubin cadences across 15 transient classes, we show that even an unoptimized, out-of-the-box Attentive Neural Process consistently outperforms all benchmarks -- a suite of Gaussian Processes and neural networks -- on every tested metric, spanning regression quality, astrophysical feature recovery, and probabilistic calibration. Our model interpolates all bands simultaneously in microseconds, over four orders of magnitude faster than the next-best neural benchmark and five faster than Gaussian Processes, demonstrating the potential of neural processes for the nightly Rubin alert stream. Attentive Neural Processes avoid the overconfidence of standard neural networks and the underconfidence of Gaussian Processes, delivering sharp, well-calibrated uncertainties. This work establishes the neural process family as a scalable, probabilistic foundation for real-time transient science in the Rubin era.

astro-ph.IM↗

arXiv2026

AstroAlertBench: Evaluating the Accuracy, Reasoning, and Honesty of Multimodal LLMs in Astronomical Classification

Modern astronomical observatories generate a massive volume of multimodal data, creating a critical bottleneck for expert human review. While multimodal large language models (LLMs) have shown promise in interpreting complex visual and textual inputs, their ability to perform specialized scientific classification while providing interpretable reasoning remains understudied. We introduce AstroAlertBench, a comprehensive multimodal benchmark designed to evaluate LLM performance in astronomical event review along a three-stage logical chain: metadata grounding, scientific reasoning, and hierarchical classification over five categories. We use a pilot sample of 1,500 real-world alerts from the Zwicky Transient Facility (ZTF), a wide-field survey that scans the northern sky to detect transient astronomical events. On this dataset, we benchmark 13 frontier closed-source and open-weight LLMs that support visual input. Our results reveal that high accuracy does not always align with model ``honesty,'' defined as the ability to self-evaluate its reasoning, which affects its reliability as a real-world assistant. We further initialize a human-in-the-loop evaluation protocol as a precursor to future community-scale participation. Together, AstroAlertBench provides a framework for developing calibrated and interpretable astronomical assistants.

astro-ph.IM↗

arXiv2026

Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap

The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncertain, but could potentially be constrained with new observations. While NSs have been observed with masses up to $\sim2~M_{\odot}$, there is a dearth of electromagnetic observations of compact objects in the $\sim2-5~M_{\odot}$ range, known as the lower mass gap. Recent observations of gravitational-wave (GW) signals from binary mergers detected by the LIGO-Virgo-KAGRA (LVK) collaboration indicate that this gap is likely not empty. Rapidly distinguishing whether a candidate GW event has components in this purported mass gap can indicate the likelihood of a detectable electromagnetic counterpart, and thus inform decisions for follow-up observations. In this work we train a neural network model, GWSkyNet-MassGap, that simultaneously predicts the probability that a candidate merger has a component in the lower mass gap ($P_{\mathrm{MassGap}}$) and the probability that it involves a NS ($P_{\mathrm{NS}}$). We find that the model is able to infer information about the source chirp mass to predict $P_{\mathrm{MassGap}}$ and $P_{\mathrm{NS}}$, leading to correct predictions for high-mass mergers with $\mathcal{M}_c\gtrsim15~M_{\odot}$, but less accurate predictions for lower-mass systems which require knowledge of the binary mass ratio to break the mass degeneracy. For candidate events in the first part of LVK's fourth observing run (O4a), the model has a mean prediction error of 9% for $P_{\mathrm{MassGap}}$ and 6% for $P_{\mathrm{NS}}$. The model could be further developed to rapidly predict the source chirp mass for candidate events in future observing runs.

astro-ph.IM↗

arXiv2026

Ghosts of eruptions past: Searching for historical Galactic supernovae using variable thermal dust echoes and machine learning

The Galactic core-collapse supernova (SN) rate is estimated at $\approx 1-3$ per century; however, no optically visible SN has been discovered in the past 400 years. Although records of the last optically detected SN (Cassiopeia A) are debated, it is revealed today via its bright, variable mid-infrared (MIR) dust echoes -- offering the possibility of identifying dust-obscured, missed events via their dust echoes. We present the first all-sky, untargeted search for thermal dust echoes of luminous Galactic transients using difference imaging on 12 years of time-resolved NEOWISE co-adds (spanning $2009-2022$) followed by statistical detection of variable extended sources. We use echo features around Cas A, together with archival catalogs to train a convolutional neural network to classify transient candidates as dust echoes, point sources, artifacts, and high proper motion stars. Our model achieves $\approx 94$% accuracy in distinguishing echoes from other variable sources. Applying the classifier to $\approx 11$ million transient candidates, we search for spatial over-densities of echoes across the Galactic plane. We find that Cas A is the only region exhibiting echoes at the WISE sensitivity threshold of $W2$ surface brightness of $\approx 20$ Vega mag arcsec$^{-2}$ -- reflecting its unique combination of young age and luminous shock breakout. We present the largest catalog of time-resolved echo positions of Cas A (20477 within 10$^\circ$) that are being used for studies of the surrounding interstellar medium with the James Webb Space Telescope. Our results lay the groundwork for the imminent Roman space telescope surveys -- which will achieve $\approx 100\times$ higher sensitivity and $\approx 30\times$ better spatial resolution at wavelengths of $\lesssim 2.5\,\mu$m.

astro-ph.HE↗

arXiv2025

Large Language Model-driven Analysis of General Coordinates Network (GCN) Circulars

The General Coordinates Network (GCN) is NASA's time-domain and multimessenger alert system. GCN distributes two data products: automated "Notices" and human-generated "Circulars" that report the observations of high-energy and multimessenger astronomical transients. The flexible and nonstructured format of GCN Circulars, comprising more than 40,500 Circulars accumulated over three decades, makes it challenging to manually extract observational information, such as redshift or observed wave bands. In this work, we employ large language models (LLMs) to facilitate the automated parsing of transient reports. We develop a neural topic modeling pipeline with open-source tools for the automatic clustering and summarization of astrophysical topics in the Circulars archive. Using neural topic modeling and contrastive fine-tuning, we classify Circulars based on their observation wave bands and messengers. Additionally, we separate gravitational-wave event clusters and their electromagnetic counterparts from the Circulars archive. Finally, using the open-source Mistral model, we implement a system to automatically extract gamma-ray burst (GRB) redshift information from the Circulars archive, without the need for any training. Evaluation against the manually curated Neil Gehrels Swift Observatory GRB table shows that our simple system, with the help of prompt-tuning, output parsing, and retrieval augmented generation (RAG), can achieve an accuracy of 97.2% for redshift-containing Circulars. Our neural search-enhanced RAG pipeline accurately retrieved 96.8% of redshift Circulars from the manually curated archive. Our study demonstrates the potential of LLMs to automate and enhance astronomical text mining and provides a foundational work for future advances in transient alert analysis.

astro-ph.HE↗

arXiv2025

An Extremely Luminous Flare Recorded from a Supermassive Black Hole

Since their discovery more than 60 years ago, accreting supermassive black holes in active galactic nuclei (AGN) were recognized as highly variable sources, requiring an extremely compact, dynamic environment. Their variability traces to multiple phenomena, including changing accretion rates, temperature changes, foreground absorbers, and structural changes to the accretion disk. Spurred by a new generation of time-domain surveys, the extremes of black hole variability are now being probed. We report the discovery of an extreme flare by the AGN J224554.84+374326.5, which brightened by more than a factor of 40 in 2018. The source has slowly faded since then. The total emitted UV/optical energy to date is $\sim10^{54}$ erg, i.e., the complete conversion of approximately one solar mass into electromagnetic radiation. This flare is 30 times more powerful than the previous most powerful AGN transient. Very few physical events in the Universe can liberate this much electromagnetic energy. We discuss potential mechanisms, including the tidal disruption of a high mass $(>30\, M_\odot)$ star, gravitational lensing of an AGN flare or supernova, or a supermassive (pair instability) supernova in the accretion disk of an AGN. We favor the tidal disruption of a massive star in a prograde orbit in an AGN disk.

astro-ph.GA↗

arXiv2025

The nature of ASASSN-24fw's occultation: modelling the event as dimming by optically thick rings around a sub-stellar companion

ASASSN-24fw is a main-sequence F-type star that experienced a rapid and long-lasting dimming event beginning in late 2024 and continuing until mid 2025. Its pre-dimming spectral energy distribution shows a persistent infrared excess with a fractional luminosity of approximately 0.5 percent. We model this excess using a two-component blackbody fit and find dust components with temperatures of about 1070 K and 390 K. Archival light curves indicate that ASASSN-24fw was photometrically stable prior to the event, suggesting that the dimming is caused by an external occulting body rather than intrinsic stellar variability. The event lasted about 275 days and exhibits a distinctive flat-bottomed profile of nearly 200 days, unlike most long-duration occultation events reported in the last decade. We analyze the light curve and spectra obtained during dimming to study the properties of both the star and the occulting material. A parametric light-curve model reveals multiple ingress phases, consistent with variations in the density and structure of the obscuring material. A second transit model favors an occulting body consistent with a gas giant or brown dwarf with a minimum mass of about 3.4 Jupiter masses and surrounded by an extended circumplanetary disk or rings of radius roughly 0.17 au. Near-infrared spectra taken during dimming show enhanced infrared excess and spectral features consistent with a late-type companion, approximately M8. We also detect variable H-alpha emission, suggesting evolving gas and dust in the occulting structure. Imaging from LCOGT identifies a nearby object within 3 arcsec, likely a bound companion at a projected separation of about 3000 au. Systems like ASASSN-24fw appear rare, and continued follow-up will help constrain the nature of the occulting body and the circumstellar environment.

astro-ph.SR↗

arXiv2025

BOOM and Babamul: a real-time, multi-survey, optical alert broker system operating at scale

With the arrival of ever higher throughput wide-field surveys and a multitude of multi-messenger and multi-wavelength instruments to complement them, software capable of harnessing these associated data streams is urgently required. To meet these needs, a number of community supported alert brokers have been built, currently focused on processing of Zwicky Transient Facility (ZTF; $\sim 10^5$-$10^6$ alerts per night) with an eye towards Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST; $\sim 2 \times 10^7$ alerts per night). Building upon the system that successfully ran in production for ZTF's first seven years of operation, we introduce BOOM (Burst & Outburst Observations Monitor), an analysis framework focused on real-time, joint brokering of these alert streams. BOOM harnesses the performance of a Rust-based software stack relying on a non-relational MongoDB database combined with a Valkey in-memory processing queue and a Kafka cluster for message sharing. With this system, we demonstrate feature parity with the existing ZTF system with a throughput $\sim 7 \times$ higher. We describe the workflow that enables the real-time processing as well as the results with custom filters we have built to demonstrate the system's capabilities. In conclusion, we present the development roadmap for both BOOM and Babamul - the public-facing LSST alert broker built atop BOOM - as we begin the Rubin era.

astro-ph.IM↗

arXiv2025

In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys

Distance-based methods involve the computation of distance values between features and are a well-established paradigm in machine learning. In anomaly detection, anomalies are identified by their large distance from normal data points. However, the performance of these methods often hinges on a single, user-selected distance metric (e.g., Euclidean), which may not be optimal for the complex, high-dimensional feature spaces common in astronomy. Here, we introduce a novel anomaly detection method, Distance Multi-Metric Anomaly Detection (DiMMAD), which uses an ensemble of distance metrics to find novelties. Using multiple distance metrics is effectively equivalent to using different geometries in the feature space. By using a robust ensemble of diverse distance metrics, we overcome the metric-selection problem, creating an anomaly score that is not reliant on any single definition of distance. We demonstrate this multi-metric approach as a tool for simple, interpretable scientific discovery on astronomical time series -- (1) with simulated data for the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time, and (2) real data from the Zwicky Transient Facility. We find that DiMMAD excels at out-of-distribution anomaly detection -- anomalies in the data that might be new classes -- and beats other state-of-the-art methods in the goal of maximizing the diversity of new classes discovered. For rare in-distribution anomaly detection, DiMMAD performs similarly to other methods, but may allow for improved interpretability. All our code is open source: DiMMAD is implemented within DistClassiPy: https://github.com/sidchaini/distclassipy/, while all code to reproduce the results of this paper is available here: https://github.com/sidchaini/dimmad/.

astro-ph.IM↗

arXiv2025

ZTF25abjmnps (AT2025ulz) and S250818k: A Candidate Superkilonova from a Sub-threshold Sub-Solar Gravitational Wave Trigger

On August 18, 2025, the LIGO-Virgo-KAGRA collaboration reported gravitational waves from a sub-threshold binary neutron star merger. If astrophysical, this event would have a surprisingly low chirp mass, suggesting that at least one neutron star was below a solar mass. The Zwicky Transient Facility mapped the coarse localization and discovered a transient, ZTF25abjmnps (AT2025ulz), that was spatially and temporally coincident with the gravitational wave trigger. The first week of follow-up suggested properties reminiscent of a GW170817-like kilonova. Subsequent follow-up suggests properties most similar to a young, stripped-envelope, Type IIb supernova. Although we cannot statistically rule out chance coincidence, we undertake due diligence analysis to explore the possible association between ZTF25abjmnps and S250818k. Theoretical models have been proposed wherein sub-solar neutron star(s) may form (and subsequently merge) via accretion disk fragmentation or core fission inside a core-collapse supernova i.e. a ``superkilonova". Here, we qualitatively discuss our multi-wavelength dataset in the context of the superkilonova picture. Future higher significance gravitational wave detections of sub-solar neutron star mergers with extensive electromagnetic follow-up would conclusively resolve this tantalizing multi-messenger association.

astro-ph.HE↗

arXiv2025

Gaia DR3 Variable White Dwarfs vetted by ZTF

The publications of Gaia DR2 and DR3 have brought major improvements in stellar astrometry and photometry, particularly regarding the description of the white dwarf sequence. Notably, Gaia DR2 enabled the detection of variability in white dwarfs based solely on averaged astrometric and photometric quantities, i.e. the astrometric 5 parameters (positions, proper motion, and parallax) and general photometry properties in the G, BP and RP bands (mean, standard deviation and number of measurements). We identify and classify variable white dwarfs using Gaia DR3 data and Zwicky Transient Facility DR23 observations. The objective is to construct a catalogue of pulsating white dwarf candidates with robust selection criteria. We define a new sample of candidate variable white dwarfs using Gaia DR3 astrometric and photometric data. We cross-match this sample with the ZTF DR23 catalogue and apply a multiband Lomb-Scargle periodogram analysis to detect periodic variability. We then use the OPTICS unsupervised clustering algorithm to to group and classify the confirmed periodic stars. We identify 1423 variable white dwarfs candidates from Gaia DR3, with 864 having ZTF time series. 141 present significant periodicity. We classify these objects into known categories, including ZZ Ceti stars, GW Vir, V777 Her, and white dwarf-main sequence binaries. Our analysis yields several periodic stars, including three ZZ Ceti, 15 GW Vir, one V777 Her, and 24 WD-MS binaries. Furthermore, it reveals a significant population of potentialy variable stars, though without confirmed periodicity. Finally we publish our catalogue of candidate variable white dwarfs including variability status, periodicity, and classification information for the 864 sources with ZTF time series, 519 of them newly identified (including 83 new periodic stars).

astro-ph.SR↗

arXiv2025

A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme

Optical transient surveys continue to generate increasingly large datasets, prompting the introduction of machine-learning algorithms to search for quality transient candidates efficiently. Existing machine-learning infrastructure can be leveraged in novel ways to search these datasets for new classes of transients. We present a machine-learning accelerated search pipeline for the Deeper, Wider, Faster (DWF) programme designed to identify high-quality astrophysical transient candidates that contain a single detection. Given the rapid observing cadence of the DWF programme, these single-detection transient candidates have durations on sub-minute timescales. This work marks the first time optical transients have been systematically explored on these timescales, to a depth of m$\sim$23. We report the discovery of two high-quality sub-minute transient candidates from a pilot study of 671,761 light curves and investigate their potential origins with multiwavelength data. We discuss, in detail, possible non-astrophysical false positives, confidently reject electronic artefacts and asteroids, ruling out glints from satellites below 800 km and strongly disfavouring those at higher altitudes. We calculate a rate on the sky of $4.72^{+6.39}_{-3.28}\times10^5$ per day for these sub-minute transient candidates.

astro-ph.IM↗

arXiv2025

Estimates of Rotation Periods for Jupiter Trojans with the Zwicky Transient Facility Photometric Lightcurves

We present new rotational period estimates for 216 Jupiter Trojans using photometric data from the Zwicky Transient Facility (ZTF), including 80 Trojans with previously unknown periods. Our analysis reveals rotation periods ranging from 4.6 hours to 447.8 hours. These results support the existence of a spin barrier for Trojans larger than 10 km, with periods clustering between 4 and 4.8 hours. This spin barrier is roughly twice as long as that observed for main-belt asteroids, suggesting that Jupiter Trojans have significantly lower bulk densities, likely due to a higher fraction of ices and volatile materials in their composition. We identify three new Trojans with reliable rotation periods near the spin barrier, doubling the number of known Trojans in this critical period range. Using these results, we estimate a mean density of approximately 0.52 g/cm^3 for rubble-pile Trojans. Our findings support the growing evidence that many Trojans are rubble-pile bodies with distinct physical properties compared to main-belt asteroids. Looking forward, we anticipate that data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will provide rotational period estimates for several hundred thousand Trojans, down to objects as small as 1 km, enabling a more detailed investigation of their rotational properties and internal structure.

astro-ph.EP↗

arXiv2025

Semi-Supervised Learning for Lensed Quasar Detection

Lensed quasars are key to many areas of study in astronomy, offering a unique probe into the intermediate and far universe. However, finding lensed quasars has proved difficult despite significant efforts from large collaborations. These challenges have limited catalogues of confirmed lensed quasars to the hundreds, despite theoretical predictions that they should be many times more numerous. We train machine learning classifiers to discover lensed quasar candidates. By using semi-supervised learning techniques we leverage the large number of potential candidates as unlabelled training data alongside the small number of known objects, greatly improving model performance. We present our two most successful models: (1) a variational autoencoder trained on millions of quasars to reduce the dimensionality of images for input to a dense neural network classifier that can make accurate predictions and (2) a convolutional neural network trained on a mix of labelled and unlabelled data via virtual adversarial training. These models are both capable of producing high-quality candidates, as evidenced by our discovery of GRALJ140833.73+042229.98. The success of our classifier, which uses only multi-band images, is particularly exciting as it can be combined with existing classifiers, which use other data than images, to improve the classifications of both models and discover more lensed quasars.

astro-ph.IM↗

arXiv2025

Inferring CSM Properties of Type II SNe Using a Magnitude-Limited ZTF Sample

Although all Type II supernovae (SNe) originate from massive stars possessing a hydrogen-rich envelope, their light curve morphology is diverse, reflecting poorly characterised heterogeneity in the physical properties of their progenitor systems. Here, we present a detailed light curve analysis of a magnitude-limited sample of 639 Type II SNe from the Zwicky Transient Facility Bright Transient Survey. Using Gaussian processes, we systematically measure empirical light curve features (e.g. rise times, peak colours and luminosities) in a robust sampling-independent manner. We focus on rise times as they are highly sensitive to pre-explosion progenitor properties, especially the presence of a dense circumstellar medium (CSM) shed by the progenitor in the years immediately pre-explosion. By correlating our feature measurements with physical parameters from an extensive grid of STELLA hydrodynamical models with varying progenitor properties (CSM structure, $\dot M$, $R_{CSM}$ and $M_{ZAMS}$), we quantify the proportion of events with sufficient pre-explosion mass-loss to significantly alter the initial light curve (roughly $M_{CSM} \geq 10^{-2.5} M_{\odot}$) in a highly complete sample of 377 spectroscopically classified Type II SNe. We find that 67 $\pm$ 6\% of observed SNe in our magnitude-limited sample show evidence for substantial CSM ($M_{CSM} \geq 10^{-2.5} M_{\odot}$) close to the progenitor ($R_{CSM} <10^{15}$ cm) at the time of explosion. After applying a volumetric-correction, we find 36$^{+5}_{-7}$\% of all Type II SN progenitors possess substantial CSM within $10^{15}$ cm at the time of explosion. This high fraction of progenitors with dense CSM, supported by photometric and spectroscopic evidence of previous SNe, reveals mass-loss rates significantly exceeding those measured in local group red supergiants or predicted by current theoretical models.

astro-ph.HE↗

arXiv2025

GWSkyNet-Multi II: an updated machine learning model for rapid classification of gravitational-wave events

Multi-messenger observations of gravitational waves and electromagnetic emission from compact object mergers offer unique insights into the structure of neutron stars, the formation of heavy elements, and the expansion rate of the Universe. With the LIGO-Virgo-KAGRA (LVK) gravitational-wave detectors currently in their fourth observing run (O4), it is an exciting time for detecting these mergers. However, assessing whether to follow up a candidate gravitational-wave event given limited telescope time and resources is challenging; the candidate can be a false alert due to detector glitches, or may not have any detectable electromagnetic counterpart even if it is real. GWSkyNet-Multi is a machine learning model developed to facilitate follow-up decisions by providing real-time classification of candidate events, using localization information released in LVK rapid public alerts. Here we introduce GWSkyNet-Multi II, an updated model targeted towards providing more robust and informative predictions during O4 and beyond. Specifically, the model now provides normalized probability scores and associated uncertainties for each of the four corresponding source categories released by the LVK: glitch, binary black hole, neutron star-black hole, and binary neutron star. Informed by explainability studies of the original model, the updated model architecture is also significantly simplified, including replacing input images with intuitive summary values that are more interpretable. For significant event alerts issued during O4a and O4b, GWSkyNet-Multi II produces a prediction that is consistent with the updated LVK classification for 93% of events. The updated model can be used by the community to help make time-critical follow-up decisions.

astro-ph.IM↗

arXiv2024

Selfish Evolution: Making Discoveries in Extreme Label Noise with the Help of Overfitting Dynamics

Motivated by the scarcity of proper labels in an astrophysical application, we have developed a novel technique, called Selfish Evolution, which allows for the detection and correction of corrupted labels in a weakly supervised fashion. Unlike methods based on early stopping, we let the model train on the noisy dataset. Only then do we intervene and allow the model to overfit to individual samples. The ``evolution'' of the model during this process reveals patterns with enough information about the noisiness of the label, as well as its correct version. We train a secondary network on these spatiotemporal ``evolution cubes'' to correct potentially corrupted labels. We incorporate the technique in a closed-loop fashion, allowing for automatic convergence towards a mostly clean dataset, without presumptions about the state of the network in which we intervene. We evaluate on the main task of the Supernova-hunting dataset but also demonstrate efficiency on the more standard MNIST dataset.

cs.CV↗

arXiv2024

GWSkyNet II : a refined machine learning pipeline for real-time classification of public gravitational wave alerts

Electromagnetic follow-up observations of gravitational wave events offer critical insights and provide significant scientific gain from this new class of astrophysical transients. Accurate identification of gravitational wave candidates and rapid release of sky localization information are crucial for the success of these electromagnetic follow-up observations. However, searches for gravitational wave candidates in real time suffer a non-negligible false alarm rate. By leveraging the sky localization information and other metadata associated with gravitational wave candidates, GWSkyNet, a machine learning classifier developed by Cabero et al. (2020), demonstrated promising accuracy for the identification of the origin of event candidates. We improve the performance of the classifier for LIGO-Virgo-KAGRA's fourth observing run by reviewing and updating the architecture and features used as inputs by the algorithm. We also retrain and fine-tune the classifier with data from the third observing run. To improve the prospect of electromagnetic follow-up observations, we incorporate GWSkyNet into LIGO-Virgo-KAGRA's low-latency infrastructure as an automatic pipeline for the evaluation of gravitational wave alerts in real time. We test the readiness of the algorithm on a LIGO-Virgo-KAGRA mock data challenge campaign. The results show that by thresholding on the GWSkyNet score, noise masquerading as astrophysical sources can be rejected efficiently and the majority of true astrophysical signals correctly identified.

astro-ph.IM↗