arXiv subjects

Michael W. Coughlin

Publications and source records attributed to Michael W. Coughlin.

At least 37 records · Page 2Linked to original sources

arXiv2026

SN~2018erx: A fast-evolving, dust-reddened Type Icn supernova with broad C II emission lines

We present the discovery and characterization of SN~2018erx (ZTF18abkmbpy), a fast-evolving, unusually red, interacting stripped-envelope supernova. Spectroscopically, SN~2018erx shows broad \ion{C}{2} emission with characteristic widths of $\sim\!3800$~km~s$^{-1}$, consistent with interaction with carbon-rich circumstellar material and a Type~Icn core-collapse SN classification. Photometrically, it evolves rapidly, rising from half-maximum to peak in 2.1~d and declining back in 3.1~d. Semi-analytical CSM-interaction modeling favors a compact, shell-like CSM with $M_{\rm CSM}\approx0.3\,M_\odot$, $R_0\approx0.7$~AU, and a low ejecta mass of $M_{\rm ej}\approx0.11\,M_\odot$. The radioactive yield is also small, with $M_{\rm Ni}\lesssim(3$--$5)\times10^{-3}\,M_\odot$, placing SN~2018erx at the low end of the H-poor distribution. At +29~d after peak, we detect a near-infrared excess consistent with pre-existing local circumstellar dust, with $M_{\rm d}\sim10^{-6}$--$10^{-5}\,M_\odot$. Together, the rapid evolution, strong local reddening, carbon-rich emission, and dust point to a multi-component circumstellar environment: a dense inner interaction region from enhanced pre-SN mass loss and an outer dusty layer from an earlier mass-loss episode roughly $10$--$200$~yr before core collapse. These properties favor an ultra-stripped core-collapse explosion of a low-mass He star in a binary system, with fallback-modified Wolf--Rayet collapse or merger-driven mass loss remaining possible alternatives. SN~2018erx provides rare insight into the mass-loss history of stripped-envelope SNe and suggests that dust-enshrouded explosions of this kind may be underrepresented in optical surveys.

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arXiv2026

Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. II: Lightcurve Analysis

The Zwicky Transient Facility Census of the Local Universe survey yielded a sample of 330 Type IIP supernovae (SNe) with well-constrained peak luminosities. In paper I (arXiv:2502.19493), we measured their luminosity function and volumetric rate. Here (paper II), we present the largest systematic study of lightcurve properties for Type IIP SNe from a volume-limited survey, analyzing a selected subset of 129 events, including 16 low-luminosity Type IIP (LLIIP) SNe with M${r,peak} \geq -16$ mag. We find that plateau slope correlates with peak brightness, with many LLIIP SNe showing positive slopes--suggesting smaller progenitor radii and distinct density profiles compared to brighter Type IIP SNe. The plateau duration shows only a weak dependence on peak brightness, likely suggesting binary interaction. One SN exhibits a plateau-to-tail drop of >3.5 mag, consistent with an electron-capture or failed SN with very low or zero nickel mass. We derive explosion and progenitor parameters of the entire Type IIP SN sample using semi-analytical and radiation-hydrodynamical models. Based on radiation-hydrodynamical model fitting, LLIIP SNe are characterized by low nickel masses (0.001-0.025 $\mathrm{M_\odot}$), low explosion energies (0.1-0.28 $\times 10^{51}$ erg), low ejecta masses ($8.1^{+0.8}_{-1.7}$ $\mathrm{M\odot}$), and ZAMS masses below 11 $\mathrm{M_\odot}$. In comparison, the full Type IIP SN sample spans a wider range with nickel masses (0.001-0.222 $\mathrm{M_\odot}$), explosion energies (0.10-4.43 $\times 10^{51}$ erg), ejecta masses (5.4-24.8 $\mathrm{M_\odot}$), and ZAMS masses (9.3-16.7 $\mathrm{M_\odot}$). We find strong correlations between peak brightness, explosion energy, and nickel mass that extend to the low-luminosity end. We conclude that LLIIP SNe represent the faint, low-energy end of the Type IIP population and originate from the lowest-mass core-collapse progenitors.

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arXiv2026

Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that increasingly shift the bottleneck in astronomical machine learning (ML) projects from model design to infrastructure. We present Hyrax, an open-source, modular, GPU-enabled Python framework that supports the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive two- and three-dimensional latent-space exploration for unsupervised discovery. We demonstrate Hyrax's versatility through five representative applications on real survey data: (i) unsupervised representation learning on $\sim 4\times10^5$ Rubin Legacy Survey of Space and Time (LSST) Data Preview 1 (DP1) galaxies, surfacing new merger and low-surface-brightness candidates missing from reference Euclid and Dark Energy Survey catalogs, while also isolating imaging artifacts -- all without labeled training data; (ii) hybrid density-based clustering for identifying cluster-scale gravitational lens candidates in DP1 data; (iii) multimodal early-time transient classification in the Zwicky Transient Facility leveraging light curves, spectra, images, and metadata; (iv) supervised false-positive filtering in shift-and-stack searches for distant solar system objects in the Dark Energy Camera Ecliptic Exploration Project survey; and (v) supervised detection of semi-resolved dwarf galaxies in Hyper Suprime-Cam and LSST-like imaging using synthetic source injection. Together, these results demonstrate that Hyrax provides astronomy-specific ML infrastructure that enables systematic discovery and rapid methodological iteration across next-generation astronomical surveys.

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arXiv2026

Low-Luminosity Type IIP Supernovae from the Zwicky Transient Facility Census of the Local Universe. III: Hunting for electron-capture supernovae using nebular spectroscopy

Electron-capture supernovae (ECSNe) may arise from ONeMg-core collapse in super-asymptotic giant branch (sAGB) stars near the low-mass core-collapse limit ($\approx\!8$--$10$\,\Msun). At early times, models predict that ECSNe resemble low-mass red supergiant iron-core-collapse SNe (FeCCSNe), making the two channels difficult to distinguish. Nebular spectroscopy, however, can reveal differences in ejecta composition. We present a systematic sample of nebular spectra of 19 low-luminosity Type IIP (LLIIP) SNe from the ZTF CLU survey, obtained 115$-$450\,d after explosion. Their low velocities expose narrow lines blended in brighter SNe, which we identify and model to constrain progenitor properties. We find a strong correlation between the FWHM of H\,\textsc{i}\,$λ$6563 and peak luminosity, showing that LLIIP SNe occupy the low-energy end of the core-collapse population, but no correlation with plateau duration, suggesting that envelope and core properties are not tightly linked. Only one SN reaches the extremely low H\,\textsc{i}\,$λ$6563 widths predicted for the weakest $\sim$9\,M$_\odot$ explosion models, implying that such low-energy events are intrinsically rare. Combining our sample with 118 literature nebular spectra of Type II SNe, we infer an IMF slope of $2.1\pm1.2$. We also introduce an `ECSN score'' based on the absence of He- and O-shell emission lines, and identify two plausible ECSN candidates, SN~2023bvj and SN~2024btj. However, neither shows the extremely narrow nebular lines predicted by current ECSN models. If ECSNe arise predominantly through the LLIIP channel, we infer an upper limit on the ECSN rate of $\lesssim (5$--$8)\times10^{2}\,\mathrm{Gpc^{-3}\,yr^{-1}}$, corresponding to a narrow sAGB progenitor mass window of $ΔM_{\rm sAGB} \lesssim 0.02$--$0.06\,\mathrm{M_\odot}$.

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arXiv2026

Electromagnetic Follow-up of the Sub-Solar Mass Gravitational Wave Candidate S251112cm: Kilonova Constraints and a Coincident IIb Supernova

On November 12th, 2025 the LIGO--Virgo--KAGRA (LVK) collaboration reported gravitational waves (GWs) from a compact object merger candidate (S251112cm) with at least one sub-solar mass component. Using the Dark Energy Camera (DECam), the Fraunhofer Telescope at Wendelstein Observatory (FTW), and the Zwicky Transient Facility (ZTF), we surveyed $56\%$ of the GW localization region beginning $2.4$~hours after the GW alert. We find no kilonova (KN) counterpart, and use radiative-transfer models to rule out $42\%$ (ZTF), $68\%$ (DECam), and $92\%$ (FTW) of the KN models as possible emission from this GW candidate. Within the recently proposed disk-fragmentation (``superkilonova'') model for generating sub-solar mass neutron star mergers from stellar core-collapse, the delay between the supernova explosion time and the GW merger time is estimated to be less than a few days. Searching this time window prior to the GW event, we identify and spectroscopically classify a IIb supernova (SN~2025adtq), with a spatial association odds ratio of $\log_{10}\mathcal{I} \approx 4.8$, a chance coincidence probability of ${\sim}2$--$9\%$, and an estimated explosion time ${\sim}2$ days prior to S251112cm. SN~2025adtq is the second Type~IIb supernova found in spatial and temporal coincidence with a sub-solar mass GW candidate, following the previously reported S250818k/SN~2025ulz association; jointly, we measure an odds ratio that favors the association hypothesis over the null, however, when conditioned on finding a coincident supernova by chance, the odds ratio disfavors association. Together, these results provide suggestive but inconclusive evidence for the superkilonova formation channel.

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arXiv2026

How Many Times Should We Matched Filter Gravitational Wave Data? A Comparison of GstLAL's Online and Offline Performance

Searches for gravitational waves from compact binary coalescences employ a process called matched filtering, in which gravitational wave strain data is cross-correlated against a bank of waveform templates. Data from every observing run of the LIGO, Virgo, and KAGRA collaboration is typically analyzed in this way twice, first in a low-latency mode in which gravitational wave candidates are identified in near-real time, and later in a high-latency mode. Such high-latency analyses have traditionally been considered more sensitive, since background data from the full observing run is available for assigning significance to all candidates, as well as more robust, since they do not need to worry about keeping up with live data. In this work, we present a novel technique to use the matched filtering data products from a low-latency analysis and re-process them by assigning significances in a high-latency way, effectively removing the need to perform matched filtering a second time. To demonstrate the efficacy of our method, we analyze 38 days of LIGO and Virgo data from the third observing run (O3) using the GstLAL pipeline, and show that our method is as sensitive and reliable as a traditional high-latency analysis. Since matched filtering represents the vast majority of computing time for a traditional analysis, our method greatly reduces the time and computational burden required to produce the same results as a traditional high-latency analysis. Consequently, it has already been adopted by GstLAL for the fourth observing run (O4) of the LIGO, Virgo, and KAGRA collaboration.

gr-qc↗

arXiv2026

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.

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arXiv2026

The ZTF-ULTRASAT experiment: Characterizing the non-transients in ULTRASAT's high cadence survey

The forthcoming launch of the Ultraviolet Transient Astronomy Satellite (ULTRASAT) will transform our understanding of the transient ultraviolet sky by increasing our ability to identify transients due to its unprecedented 204 deg2 field of view. While rapid (extragalactic) transients are a priority science area for the mission, flaring stars and AGN can often contaminate searches for such objects. To prepare for these challenges, the Zwicky Transient Facility (ZTF)-ULTRASAT experiment observed five fields at high cadence over three nights, in close proximity to ULTRASAT's three northern high-cadence fields. A real-time filter identified seven transient candidates, of which five were persistent variable sources and two were spurious. Periods and amplitudes derived from the ZTF Source Classification Project (SCoPe) showed that three candidates were RR Lyrae stars with short periods and high amplitudes, while the remaining two displayed flaring behavior. We demonstrate that short-timescale, high-amplitude variables can systematically mimic transient alerts in high-cadence UV surveys, and we provide a concrete strategy to this contamination using pre-existing machine learning catalogs.

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arXiv2026

Long GRB 250916A: an Off-axis Powerlaw Jet with Thermal Cocoon

Some gamma-ray bursts (GRBs) exhibit precursor emission episodes preceding the main emission, with a quiescent period in between. The properties of the precursor emission and the duration of the quiescent interval are related to the central engine activity and jet formation processes, thus providing insights into the physics of GRBs. We present a comprehensive analysis of the prompt emission and multi-wavelength afterglow of GRB 250916A. Using detailed afterglow modeling, we find that the broadband data are best described by a powerlaw structured jet with a relatively narrow core ($θ_c \approx 0.8^\circ$), viewed moderately off-axis at a viewing angle $θ_v \approx 2.7^\circ$. The isotropic-equivalent kinetic energy of the jet ($E_{k,iso} \approx 2.4 \times 10^{54}$ erg) is on the higher side for typical GRBs. The precursor emission is well described by a blackbody spectrum with a temperature of kT $\approx$ 13.2 keV and is separated from the main emission by a long quiescent interval of 150 s. Put together, our results indicate that the precursor is likely to be a shock breakout from a cocoon formed by the interaction of the relativistic jet with the progenitor star. The resulting cocoon pressure and shock collimation naturally lead to the launch of a narrowly collimated jet, consistent with the jet geometry inferred from afterglow observations. The long quiescent interval may imply the central engine turn-off in addition to the effect of the off-axis geometry.

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arXiv2026

Pre-training vision models for the classification of alerts from wide-field time-domain surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

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arXiv2026

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.

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arXiv2026

Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for GW data analysis across current and future detectors.

gr-qc↗

arXiv2026

AT2024lhc and AT2024kmq in the landscape of featureless tidal disruption events

We study AT2024kmq and AT2024lhc, two tidal disruption events (TDEs) with blue featureless spectra associated with high-mass black holes ($M_{\rm BH}\sim 10^8\,M_\odot$). Both events show optical precursors consistent with shock dissipation from stream self-intersection. Their X-ray emission is luminous ($L_{\rm X}\sim 10^{44}\,{\rm erg\,s^{-1}}$), highly variable (with minimum observed variability timescales of 1.3\,hr and 4.8\,hr for factor of $\sim3$ flux changes), long-lasting ($>1\,\rm yr$), emerging no later than the optical peak, and well characterized by power-laws with $1.7<Γ<3$ (where $f_ν\propto ν^{1-Γ}$). The X-ray properties and radio non-detections support a compact corona ($\lesssim 10 r_{\rm g}$) producing Comptonized X-ray emission. Using all published featureless TDEs, we find statistically significant bimodality in the distribution of their peak UV/optical blackbody luminosities and radii. We assemble a comparison TDE sample with early-time X-ray observations with eROSITA, in which we find different $M_{\rm BH}$ distributions in TDEs with different X-ray spectral evolution properties: low-mass black holes ($M_{\rm BH} \sim 10^6 M_\odot$) remain soft ($Γ>4$) within $t\lesssim 2$\,yr, intermediate masses ($\sim 10^7 M_\odot$) transition from soft to hard at $\sim$1 yr, while high masses ($\sim 10^8 M_\odot$) are hard ($1.5<Γ\lesssim 3$) from the outset. We interpret this result as evidence that the soft-to-hard state transition in TDEs occurs at the critical threshold of $\dot{M}_{\rm acc} \sim 0.03 \dot M_{\rm Edd}$ (similar to X-ray binaries), using the fact that the transition timescale predicted by simple disk theory scales with black hole mass as $t_{\rm tr}\propto M_{\rm BH}^{-3/4}$.

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arXiv2026

AT2019cmw: A highly luminous, cooling featureless TDE candidate from the disruption of a high mass star in an early-type galaxy

We present optical/UV photometric and spectroscopic observations, as well as X-ray and radio follow-up, of the extraordinary event AT2019cmw. With a peak bolometric luminosity of ~$\mathrm{10^{45.6}\,erg\,s^{-1}}$, it is one of the most luminous thermal transients ever discovered. Extensive spectroscopic follow-up post-peak showed only a featureless continuum throughout its evolution. This, combined with its nuclear location, blue colour at peak and lack of prior evidence of an AGN in its host lead us to interpret this event as a `featureless' tidal disruption event (TDE). It displays photometric evolution atypical of most TDEs, cooling from ~30 kK to ~10 kK in the first ~300 days post-peak, with potential implications for future photometric selection of candidate TDEs. No X-ray or radio emission is detected, placing constraints on the presence of on-axis jetted emission or a visible inner-accretion disk. Modelling the optical light curve with existing theoretical prescriptions, we find that AT2019cmw may be the result of the disruption of a star in the tens of solar masses by a supermassive black hole (SMBH). Combined with a lack of detectable star formation in its host galaxy, it could imply the existence of a localised region of star formation around the SMBH. This could provide a new window to probe nuclear star formation and the shape of the initial mass function (IMF) in close proximity to SMBHs out to relatively high redshifts.

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arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

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arXiv2026

LUMOS : Linear programming Utility for Multi-messenger Optical Scheduling

The detection of gravitational-wave events by LIGO-Virgo-KAGRA has opened new avenues for multi-messenger astrophysics; however, electromagnetic counterparts remain elusive due to large localization uncertainties. Wide-field optical surveys like the Zwicky Transient Facility (ZTF) play a crucial role in follow-up, but efficient scheduling is essential. In this work, we present LUMOS, a Mixed Integer Linear Programming (MILP) approach that selects fields via a maximum coverage problem and schedules observations to maximize cumulative probability while respecting observability constraints. Using 1199 GW events from O4, we compare the LUMOS scheduler with gwemopt, showing an 84.7 percent higher mean cumulative probability and better performance in nearly all cases. While designed for ZTF, LUMOS's framework parallels the M4OPT toolkit for space missions, highlighting the broader applicability of MILP-based scheduling to both ground- and space-based follow-up.

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arXiv2025

The automation of optical transient discovery and classification in Rubin-era time-domain astronomy

Robotic wide-field time-domain surveys, such as the Zwicky Transient Facility and the Asteroid Terrestrial-impact Last Alert System, capture dozens of transients each night. The workflows for discovering and classifying transients in survey data streams have become increasingly automated over decades of development. The recent integration of machine learning and artificial intelligence tools has produced major milestones, including the fully automated end-to-end discovery and classification of an optical transient, and has enabled automated rapid-response space-based follow-up. The now-operational Vera C. Rubin Observatory and its Legacy Survey of Space and Time are accelerating the rate of transient discovery and producing large volumes of data at incredible rates. Given the expected order-of-magnitude increase in transient discoveries, one promising path forwards for optical time-domain astronomy is heavily investing in accelerating the automation of our workflows. Here we review the current paradigm of real-time transient workflows, project their evolution during the Rubin era and present recommendations for accelerating transient astronomy with automation.

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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.

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