arXiv subjects

Ashish A. Mahabal

Publications and source records attributed to Ashish A. Mahabal.

At least 19 recordsLinked to original sources

arXiv2026

How to Craft the Right Language AI Policy For Your Research Group (Some Assembly Required)

Language AI is rapidly becoming part of the astronomy research ecosystem, prompting research teams to develop policies governing its use. But resources and advice for AI adoption assume that all research groups share the same goals and values. This paper lays out an argument for why there is no single "correct" AI policy for astronomy research groups. Instead, we introduce four research laboratory archetypes with competing research priorities, and we use them to explore how a small laboratory or research group's priorities shape decisions about AI's impact on research productivity, scientist development, scientific integrity, and data governance. Rather than prescribing a universal set of rules, this paper provides a framework for aligning AI policies with a group's scientific values and mission. The objective is to help research leaders decide how Language AI should be used within their particular research environment.

astro-ph.IM↗

arXiv2026

Catching Disguised Transients with ASTRANet: Anomaly-Aware Spectroscopic Classification and Conformal Calibration

Time-domain surveys discover thousands of transients per year, but the spectroscopic identification of rare and physically peculiar objects remains rate-limited by closed-set classifiers that confidently assign every input to a known class -- including spectra that genuinely belong to no known class. We present the \texttt{ASTRANet} framework, a confidence-aware infrastructure for spectroscopic transient classification built around three coupled modules: a hierarchical spectral classifier that operates directly on observer-frame spectra without requiring host-galaxy redshift or spectral phase as inputs; an anomaly detection layer (\texttt{ASTRANet-Sentinel}) that non-linearly combines $16$ embedding-space anomaly scores spanning four physically motivated families; and a conformal uncertainty quantification layer (\texttt{ASTRANet-CP}). We validate the framework on a held-out evaluation set of $289$ rare and out-of-taxonomy transients spanning $11$ classes deliberately excluded from training, chosen to span the full physical diversity of the rare-anomaly population: AGN-related outliers, GRB-related events, gap transients, novae, and peculiar supernovae. Through five astrophysically distinct failure modes of closed-set classifiers, we show that classifier-internal uncertainty and embedding-based anomaly detection are structurally complementary axes of confidence rather than alternative implementations of the same estimator. We further introduce AD-stratified Mondrian conformal prediction (AD-MCP) within \texttt{ASTRANet-CP}, achieving uniform conditional coverage across anomaly-score strata where vanilla Mondrian under-covers in the operational regime. This establishes the methodological infrastructure for confidence-aware spectroscopic discovery in the Vera C.\ Rubin Observatory era.

astro-ph.IM↗

arXiv2026

Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility

Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a central challenge in time-domain astronomy. Robust early classification is crucial for making informed decisions, but is hindered by sparse light curves and degeneracies between classes. In this work, we leverage multimodality to substantially improve real-time classification and demonstrate the practicality of our approach by deploying our model on the ZTF alert stream. Building on the Online Ranked Astrophysical CLass Estimator (ORACLE), we introduce the ORACLE-2 models, which combine light curves, metadata, and images for real-time hierarchical classification. Using both real and simulated datasets, we show that incorporating additional modalities consistently improves classification performance. On observations from ZTF's Bright Transient Survey, our best-performing model, ORACLE-2 Omni, achieves a macro F1 score of 0.73 -- an improvement of up to 11% over models using light curves and metadata alone, and up to 40% over light-curve-only models, with the strongest gains realized at early times. To demonstrate applicability to the Legacy Survey of Space and Time, which will increase alert volume by more than an order of magnitude, we train a light curve + metadata variant on the simulated ELAsTiCC dataset. This model achieves a macro F1 score of 0.88, an improvement of up to 13% over the light-curve-only variant, matching the performance of other state-of-the-art models. Finally, we quantify the trade-offs between performance and throughput, identifying regimes where multimodal approaches offer the greatest benefit. These results show that combining multiple modalities improves early-time classification, enabling more effective triage of high-volume alert streams for current and future time-domain surveys.

astro-ph.IM↗

arXiv2026

Multi-year Ground-Based Survey Photometry of Active Comet 103P/Hartley 2 and Centaur (2060) Chiron: A Tale of Two Comets in the Pre-LSST Era

Comets and Centaurs trace the evolution of trans-Neptunian objects (TNOs) into the inner solar system. Their activity reflects the interplay between volatile sublimation, dust dynamics, and ring scattering. Yet the long-term behavior of individual objects is less constrained. To probe this evolutionary transition, we use wide-field survey photometry from the Asteroid Terrestrial-impact Last Alert System, Zwicky Transient Facility, and Las Cumbres Observatory observations of the Jupiter-family comet (JFC) 103P/Hartley 2 during its 2023/24 apparition, and the Centaur (2060) Chiron across 2020-2025, including its 2021 outburst. For 103P, heliocentric activity slopes are asymmetric about perihelion, with a steep inbound index ($n_{r,\rm pre}=-3.48\pm0.08$) and flatter outbound value ($n_{r,\rm post}=-1.16\pm0.04$), consistent with enhanced relative dust contribution post-perihelion. Reduced brightness versus prior apparitions matches reported secular fading trends. Dust mass-loss rates are $\sim4$-16 kg s$^{-1}$ for assumed grain properties. Colors exhibit a blueward trend near perihelion, consistent with enhanced gas contamination of the $g$-band, with possible phase-dependent scattering. A periodogram recovers a $\sim18.7$ hr activity-linked period near perihelion. For Chiron, subtracting a quiescent baseline reveals exponential decay from the 2021 outburst on a $\sim1.4$ yr timescale. Seasonal phase curves flatten from $β_o=0.150\pm0.034$ mag deg$^{-1}$ in 2021 to $\lesssim0.09$ mag deg$^{-1}$ by 2023-2025, converging with quiescent behavior. Broad-band colors remain unchanged at ATLAS ($c-o$)=0.22$\pm$0.09 mag. This extended activity suggests a new epoch of persistent, low-level activity and/or evolving ring-scattering. These objects bracket the TNO-to-JFC evolutionary sequence, with 103P near the volatile-depleted end, and Chiron still volatile-rich and capable of episodic activity.

astro-ph.EP↗

arXiv2026

Identifying Gems from Roman RAPIDly

The Nancy Grace Roman Space Telescope (Roman), set for launch as early as September 2026, will conduct wide-field infrared imaging surveys with unprecedented spatial resolution and cadence, enabling the discovery of millions of astronomical transients. Hence, it is necessary to have automated pipelines for generating alerts in place so that the telescope can begin discovering reliable transients and variable objects soon after it is launched. However, no real Roman data currently exist, making the development of such pipelines difficult. In this work, we present a machine learning model $RuBR$ and a general methodology for distinguishing genuine transient and variable detections from spurious (bogus) detections within the RAPID pipeline. In particular, we present three models using this methodology: $RuBR_{comb}$ trained and tested on combined locally injected and OpenUniverse2024 transients, $RuBR_{loc}$ trained on locally injected transients and tested on OpenUniverse2024 transients, and $RuBR_{DA}$ that combines locally injected transients with a fraction of OpenUniverse2024 transients in domain-adaptation mode for training. This paves the way for strategies to adapt the $RuBR_{comb}$ model to real observations in the absence of any ground-truth labels during the early phases of the Roman mission. While the image differencing pipeline continues to be improved, our experimental results demonstrate the effectiveness of the proposed approach and its promise for robust real-bogus classification in the Roman era.

cs.LG↗

arXiv2026

AT2024wpp: An Extremely Luminous Fast Ultraviolet Transient Powered by Accretion onto a Black Hole

We present the discovery of AT 2024wpp ("Whippet"), a fast and luminous 18cow-like transient. At a redshift of z=0.0868, revealed by Keck Cosmic Web Imager spectroscopy of its faint star-forming host, it is the fourth-nearest example of its class to date. Rapid identification of the source in the Zwicky Transient Facility data stream permitted ultraviolet-through-optical observations to be obtained prior to peak, allowing the first determination of the peak bolometric luminosity (2x10^45 erg/s), maximum photospheric radius (10^15 cm), and total radiated energy (10^51 erg) of an 18cow-like object. We present results from a comprehensive multiwavelength observing campaign, including a far-UV spectrum from the Cosmic Origins Spectrograph on the Hubble Space Telescope and deep imaging extending >100 days post-explosion from the Very Large Telescope, Hubble Space Telescope, Very Large Array, and Atacama Large Millimetre Array. We interpret the observations under a model in which a rapidly-accreting central engine blows a fast (~0.2c) wind into the surrounding medium and irradiates it with X-rays. The high Doppler velocities and intense ionization within this wind prevent identifiable spectroscopic features from appearing in the ejecta or in the surrounding circumstellar material. Weak H and He signatures do emerge in the spectra after 35 days in the form of double-peaked narrow lines. Each peak is individually narrow (full width ~3000 km/s) but the two components are separated by ~6600 km/s, indicating stable structures of denser material, possibly representing streams of tidal ejecta or an ablated companion star.

astro-ph.HE↗

arXiv2026

Frabjous: Deep Learning Fast Radio Burst Morphologies

The increasing field of view of radio telescopes and improved data processing capabilities have led to a surge in the detection of Fast Radio Bursts (FRBs). The discovery rate of FRBs is already a few per day and is expected to increase rapidly with new surveys coming online. The growing number of events necessitates prioritized follow-up due to limited multi-wavelength resources, requiring rapid and automated classification. In this study, we introduce Frabjous, a deep learning framework for an automated morphology classifier with an aim towards enabling the prompt follow-up of anomalous and intriguing FRBs, and a comprehensive statistical analysis of FRB morphologies. Deep learning models require a large training set of each FRB archetype, however, publicly available data lacks sufficient samples for most FRB types. In this paper, we build a simulation framework for generating realistic examples of FRBs and train a network based on a combination of simulated and real data, starting with the CHIME/FRB catalog. Applying our framework to the first CHIME/FRB catalog, we achieve an overall classification accuracy of approximately 55%, well over a random multiclass classification rate of 20 % with five balanced classes during training. While this falls short of desirable performance, we critically discuss the limitations of our approach and propose potential avenues for improvement. Future work should explore strategies to augment training datasets and broaden the scope of FRB morphological studies, aiming for more accurate and reliable classification results.

astro-ph.IM↗

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.

astro-ph.IM↗

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 $t_{\rm tr}\propto M_{\rm BH}^{-3/4}$.

astro-ph.HE↗

arXiv2025

Deconvolution for Large Astronomical Surveys: A Study of the Scaled Gradient Projection Method on Zwicky Transient Facility Data

Ground-based astronomical observations will continue to produce resolution-limited images due to atmospheric seeing. Deconvolution reverses such effects and thus can benefit extracted science in multifaceted ways. We apply the Scaled Gradient Projection (SGP) algorithm for the single-band deconvolution of several observed images from the Zwicky Transient Facility and mainly discuss the performance on stellar sources. The method shows good photometric flux preservation, which deteriorates for fainter sources but significantly reduces flux uncertainties even for the faintest sources. Deconvolved sources have a well-defined Full-Width-at-Half-Maximum (FWHM) of roughly one pixel (one arcsecond for ZTF) regardless of the observed seeing. Detection after deconvolution results in catalogs with $\gtrsim$99.6% completeness relative to detections in the observed images. A few observed sources that could not be detected in the deconvolved image are found near saturated sources, whereas for others, the deconvolved counterparts are detected when slightly different detection parameters are used. The deconvolution reveals new faint sources previously undetectable, which are confirmed by crossmatching with the deeper DESI Legacy DR10 and with Pan-STARRS1 through forced photometry. The method could identify examples of serendipitous potential deblends that exceeded SExtractor's deblending capabilities, with as extreme as $Δm \approx 3$ and separations as small as one arcsecond between the deblended components. Our survey-agnostic approach is better and eight times faster than Richardson-Lucy deconvolution and could be a reliable method for incorporation into survey pipelines.

astro-ph.IM↗

arXiv2025

Discovery of SN 2025wny: a Strongly Gravitationally Lensed Superluminous Supernova at z = 2.01

We present the discovery of SN 2025wny (ZTF25abnjznp/GOTO25gtq) and spectroscopic classification of this event as the first gravitationally lensed Type I superluminous supernovae (SLSN-I). Deep ground-based follow-up observations resolves four images of the supernova with ~1.7" angular separation from the main lens galaxy, each coincident with the lensed images of a background galaxy seen in archival imaging of the field. Spectroscopy of the brightest point image shows narrow features matching absorption lines at a redshift of z = 2.011 and broad features matching those seen in superluminous SNe with Far-UV coverage. We infer a magnification factor of 20 to 50 for the brightest image in the system, based on photometric and spectroscopic comparisons to other SLSNe-I. SN 2025wny demonstrates that gravitationally-lensed SNe are in reach of ground-based facilities out to redshifts far higher than what has been previously assumed, and provide a unique window into studying distant supernovae, internal properties of dwarf galaxies, as well as for time-delay cosmography.

astro-ph.CO↗

arXiv2025

Low-latency Forecasts of Kilonova Light Curves for Rubin and ZTF

Follow-up of gravitational-wave events by wide-field surveys is a crucial tool for the discovery of electromagnetic counterparts to gravitational wave sources, such as kilonovae. Machine learning tools can play an important role in aiding search efforts. We have developed a public tool to predict kilonova light curves using simulated low-latency alert data from the International Gravitational Wave Network during observing runs 4 (O4) and 5 (O5). It uses a bidirectional long-short-term memory (LSTM) model to forecast kilonova light curves from binary neutron star and neutron star-black hole mergers in the Zwicky Transient Facility (ZTF) and Rubin Observatory's Legacy Survey of Space and Time filters. The model achieves a test mean squared error (MSE) of 0.19 for ZTF filters and 0.22 for Rubin filters, calculated by averaging the squared error over all time steps, filters, and light curves in the test set. We verify the performance of the model against merger events followed-up by the ZTF partnership during O4a and O4b. We also analyze the effect of incorporating skymaps and constraints on physical features such as ejecta mass through a hybrid convolutional neural network and LSTM model. Using ejecta mass, the performance of the model improves to an MSE of 0.1. However, using full skymap information results in slightly lower model performance. Our models are publicly available and can help to add important information to help plan follow-up of candidate events discovered by current and next-generation public surveys.

astro-ph.HE↗

arXiv2025

Optically Overluminous Tidal Disruption Events: Outflow Properties and Implications for Extremely Relativistic Disruptions

Recent studies suggest that tidal disruption events (TDEs) with off-axis jets may manifest as optically overluminous events. To search for jet signatures at late times, we conducted radio observations of eight such optically overluminous ($M_{g, \rm peak} < -20.8$ mag) TDEs with the Very Large Array. We detect radio counterparts in four events. The observed radio luminosities ($L_{\rm 6 GHz} \sim 10^{38}$--$10^{39}$ erg s$^{-1}$) are two orders of magnitude lower than those of on-axis jetted TDEs, and we find no evidence for off-axis jets within rest-frame time of 3 yrs. Two of them (AT2022hvp and AT2021aeou) exhibit evolving radio emission, consistent with synchrotron emission from non-relativistic outflows launched near the time of first optical light. Two events (AT2020ysg and AT2020qhs) show no statistically significant variability, which can be attributed to either non-relativistic outflows or pre-existing active galactic nuclei. Compared to a control sample of fainter TDEs with $M_{g, \rm peak} > -20.5$ mag observed at similar rest-frame timescales ($t_{\rm rest} ~ 1.5$\,yr), our sample shows systematically more luminous radio emission, suggesting that optically overluminous TDEs may launch more powerful prompt non-relativistic outflows. We speculate that strong general relativistic effects near high-mass black holes ($M_{\rm BH} ~ 10^8\,M_\odot$) may play a key role. These findings motivate further investigation into the nature of relativistic disruptions around massive black holes and the physical conditions necessary for jet formation.

astro-ph.HE↗

arXiv2025

CCSNscore: A multi-input deep learning tool for classification of core-collapse supernovae using SED-Machine spectra

Supernovae (SNe) come in various flavors and are classified into different types based on emission and absorption lines in their spectra. SN candidates are now abundant with the advent of large systematic sky surveys like the Zwicky Transient Facility (ZTF), however, the identification bottleneck lies in their spectroscopic confirmation and classification. Fully robotic telescopes with dedicated spectrographs optimized for SN follow-up have eased the burden of data acquisition. However, the task of classifying the spectra still largely rests with the astronomers. Automating this classification step reduces human effort and can make the SN type available sooner to the public. For this purpose, we have developed a deep-learning based program for classifying core-collapse supernovae (CCSNe) with ultra-low resolution spectra from the SED-Machine spectrograph on the Palomar 60-inch telescope. The program consists of hierarchical classification task layers, with each layer composed of multiple binary classifiers running in parallel to produce a reliable classification. The binary classifiers utilize RNN and CNN architecture and are designed to take multiple inputs to supplement spectra with $g$- and $r$-band photometry from ZTF. On non-host-contaminated and good quality SEDM spectra ("gold" test set) of CCSNe, CCSNscore is ~94% accurate in distinguishing between hydrogen-rich (Type II) and hydrogen-poor (Type Ibc) CCSNe. With light curve input, CCSNscore classifies ~83% of the gold set with high confidence (score $\geq 0.8$ and score-error $<0.05$), with ~98% accuracy. Based on SNIascore's and CCSNscore's real-time performance on bright transients ($m_{pk}\leq18.5$) and our reporting criteria, we expect ~0.5% (~4) true SNe Ia to be misclassified as SNe Ibc and ~6% (~17) of true CCSNe to be misclassified between Type II and Type Ibc annually on the Transient Name Server.

astro-ph.IM↗

arXiv2024

Expanding the ultracompacts: gravitational wave-driven mass transfer in the shortest-period binaries with accretion disks

We report the discovery of three ultracompact binary white dwarf systems hosting accretion disks, with orbital periods of 7.95, 8.68, and 13.15 minutes. This significantly augments the population of mass-transferring binaries at the shortest periods, and provides the first evidence that accretors in ultracompacts can be dense enough to host accretion disks even below 10 minutes (where previously only direct-impact accretors were known). In the two shortest-period systems, we measured changes in the orbital periods driven by the combined effect of gravitational wave emission and mass transfer; we find $\dot{P}$ is negative in one case, and positive in the other. This is only the second system measured with a positive $\dot{P}$, and it the most compact binary known that has survived a period minimum. Using these systems as examples, we show how the measurement of $\dot{P}$ is a powerful tool in constraining the physical properties of binaries, e.g. the mass and mass-radius relation of the donor stars. We find that the chirp masses of ultracompact binaries at these periods seem to cluster around $\mathcal{M}_c \sim 0.3 M_\odot$, perhaps suggesting a common origin for these systems or a selection bias in electromagnetic discoveries. Our new systems are among the highest-amplitude known gravitational wave sources in the millihertz regime, providing exquisite opportunity for multi-messenger study with future space-based observatories such as \textit{LISA} and TianQin; we discuss how such systems provide fascinating laboratories to study the unique regime where the accretion process is mediated by gravitational waves.

astro-ph.HE↗

arXiv2024

SN 2023zaw: an ultra-stripped, nickel-poor supernova from a low-mass progenitor

We present SN 2023zaw $-$ a sub-luminous ($\mathrm{M_r} = -16.7$ mag) and rapidly-evolving supernova ($\mathrm{t_{1/2,r}} = 4.9$ days), with the lowest nickel mass ($\approx0.002$ $\mathrm{M_\odot}$) measured among all stripped-envelope supernovae discovered to date. The photospheric spectra are dominated by broad He I and Ca NIR emission lines with velocities of $\sim10\ 000 - 12\ 000$ $\mathrm{km\ s^{-1}}$. The late-time spectra show prominent narrow He I emission lines at $\sim$1000$\ \mathrm{km\ s^{-1}}$, indicative of interaction with He-rich circumstellar material. SN 2023zaw is located in the spiral arm of a star-forming galaxy. We perform radiation-hydrodynamical and analytical modeling of the lightcurve by fitting with a combination of shock-cooling emission and nickel decay. The progenitor has a best-fit envelope mass of $\approx0.2$ $\mathrm{M_\odot}$ and an envelope radius of $\approx50$ $\mathrm{R_\odot}$. The extremely low nickel mass and low ejecta mass ($\approx0.5$ $\mathrm{M_\odot}$) suggest an ultra-stripped SN, which originates from a mass-losing low mass He-star (ZAMS mass $<$ 10 $\mathrm{M_\odot}$) in a close binary system. This is a channel to form double neutron star systems, whose merger is detectable with LIGO. SN 2023zaw underscores the existence of a previously undiscovered population of extremely low nickel mass ($< 0.005$ $\mathrm{M_\odot}$) stripped-envelope supernovae, which can be explored with deep and high-cadence transient surveys.

astro-ph.HE↗

arXiv2024

Searching for gravitational wave optical counterparts with the Zwicky Transient Facility: summary of O4a

During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network (IGWN), the Zwicky Transient Facility (ZTF) conducted a systematic search for kilonova (KN) counterparts to binary neutron star (BNS) and neutron star-black hole (NSBH) merger candidates. Here, we present a comprehensive study of the five high-significance (FAR < 1 per year) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations (ToO) and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing, Fritz, an instance of SkyPortal, instrumental in coordinating and managing our telescope scheduling, candidate vetting, and follow-up observations through a user-friendly interface. ZTF covered a total of 2841 deg$^2$ within the skymaps of the high-significance GW events, reaching a median depth of g~20.2 mag. We circulated 15 candidates, but found no viable KN counterpart to any of the GW events. Based on the ZTF non-detections of the high-significance events in O4a, we used a Bayesian approach, nimbus, to quantify the posterior probability of KN model parameters that are consistent with our non-detections. Our analysis favors KNe with initial absolute magnitude fainter than -16 mag. The joint posterior probability of a GW170817-like KN associated with all our O4a follow-ups was 64%. Additionally, we use a survey simulation software, simsurvey, to determine that our combined filtered efficiency to detect a GW170817-like KN is 36%, when considering the 5 confirmed astrophysical events in O3 (1 BNS and 4 NSBH), along with our O4a follow-ups. Following Kasliwal et al. (2020), we derived joint constraints on the underlying KN luminosity function based on our O3 and O4a follow-ups, determining that no more than 76% of KNe fading at 1 mag/day can peak at a magnitude brighter than -17.5 mag.

astro-ph.HE↗

arXiv2024

Sub-relativistic Outflow and Hours-Timescale Large-amplitude X-ray Dips during Super-Eddington Accretion onto a Low-mass Massive Black Hole in the Tidal Disruption Event AT2022lri

We present the tidal disruption event (TDE) AT2022lri, hosted in a nearby ($\approx\!144$ Mpc) quiescent galaxy with a low-mass massive black hole ($10^4\,M_\odot < M_{\rm BH} < 10^6\,M_\odot$). AT2022lri belongs to the TDE-H+He subtype. More than 1 Ms of X-ray data were collected with NICER, Swift, and XMM-Newton from 187 d to 672 d after peak. The X-ray luminosity gradually declined from $1.5\times 10^{44}\,{\rm erg\,s^{-1}}$ to $1.5\times 10^{43}\,{\rm erg\,s^{-1}}$ and remains much above the UV and optical luminosity, consistent with a super-Eddington accretion flow viewed face-on. Sporadic strong X-ray dips atop a long-term decline are observed, with variability timescale of $\approx\!0.5$ hr--1 d and amplitude of $\approx\!2$--8. When fitted with simple continuum models, the X-ray spectrum is dominated by a thermal disk component with inner temperature going from $\sim\! 146$ eV to $\sim\! 86$ eV. However, there are residual features that peak around 1 keV, which, in some cases, cannot be reproduced by a single broad emission line. We analyzed a subset of time-resolved spectra with two physically motivated models describing either a scenario where ionized absorbers contribute extra absorption and emission lines or where disk reflection plays an important role. Both models provide good and statistically comparable fits, show that the X-ray dips are correlated with drops in the inner disk temperature, and require the existence of sub-relativistic (0.1--0.3$c$) ionized outflows. We propose that the disk temperature fluctuation stems from episodic drops of the mass accretion rate triggered by magnetic instabilities or/and wobbling of the inner accretion disk along the black hole's spin axis.

astro-ph.HE↗