arXiv subjects

Michael W. Coughlin

Publications and source records attributed to Michael W. Coughlin.

At least 55 records · Page 3Linked to original sources

arXiv2025

Compact Binary Coalescence Sensitivity Estimates with Injection Campaigns during the LIGO-Virgo-KAGRA Collaborations' Fourth Observing Run

We describe the effort to characterize gravitational-wave searches and detector sensitivity to different types of compact binary coalescences during the LIGO-Virgo-KAGRA Collaborations' fourth observing run. We discuss the design requirements and example use cases for this data product, constructed from $> 4.33\times10^8$ injections during O4a alone. We also identify subtle effects with high confidence, like diurnal duty cycles within detectors. This paper accompanies a public data release of the curated injection set, and the appendixes give detailed examples of how to use the publicly available data.

gr-qc↗

arXiv2025

Identification and photometric classification of extragalactic transients in the Vera C. Rubin Observatory's Data Preview 1

The Vera C. Rubin Observatory will soon survey the southern sky, delivering a depth and sky coverage that is unprecedented in time domain astronomy. As part of commissioning, Data Preview 1 (DP1) has been released. It comprises a LSSTComCam observing campaign between November and December 2024 with multi-band imaging of seven fields, covering roughly 0.4 square degrees each, providing a first glimpse into the data products that will become available once the Legacy Survey of Space and Time begins. In this work, we search three fields for extragalactic transients. We identify eight new likely supernovae, and three known ones from a sample of 369,644 difference image analysis objects. Photometric classification using Superphot+ assigns sub-classes with >95% confidence to only one SN Ia and one SN II in this sample. Our findings are in agreement with supernova detection rate predictions of $15\pm4$ supernovae from simulations using simsurvey. The supernova detection rate in the data is possibly affected by the lack of suitable templates. Nevertheless, this work demonstrates the quality of the data products delivered in DP1 and indicates that the Rubin Observatory's Legacy Survey of Space and Time (LSST) is well placed to fulfill its discovery potential in time domain astronomy.

astro-ph.HE↗

arXiv2025

AT2025ulz and S250818k: Investigating early time observations of a subsolar mass gravitational-wave binary neutron star merger candidate

Over the past LIGO--Virgo--KAGRA (LVK) observing runs, it has become increasingly clear that identifying the next electromagnetic counterparts to gravitational-wave (GW) neutron star mergers will likely be more challenging compared to the case of GW170817. The rarity of these GW events, and their electromagnetic counterparts, motivates rapid searches of any candidate binary neutron star (BNS) merger detected by the LVK. We present our extensive photometric and spectroscopic campaign of the candidate counterpart AT2025ulz to the low-significance GW event S250818k, which had a ${\sim} 29\%$ probability of being a BNS merger. We demonstrate that during the first five days, the luminosity and color evolution of AT2025ulz are consistent with both kilonova and shock cooling models, although a Bayesian model comparison shows preference for the shock cooling model, underscoring the ambiguity inherent to early data obtained over only a few days. Continued monitoring beyond this window reveals a rise and color evolution incompatible with kilonova models and instead consistent with a supernova. This event emphasizes the difficulty in identifying the electromagnetic counterparts to BNS mergers and the significant allotment of observing time necessary to robustly differentiate kilonovae from impostors.

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arXiv2025

Application of Non-Linear Noise Regression in the Virgo Detector

This work presents the first demonstration of non-linear noise regression in the Virgo detector using deep learning techniques. We use DeepClean, a convolutional autoencoder previously shown to be effective in denoising LIGO data, as our tool for modeling and subtracting environmental and technical noise in Virgo. The method uses auxiliary witness channels to learn correlated noise features and remove them from the strain data. For this study, we apply DeepClean to Virgo O3b data, using 225 witness channels selected across 13 targeted frequency bands. Our analysis confirms the presence of non-linear couplings in the subtracted noise, highlighting the importance of DeepClean-like tools in capturing such effects. We observe up to a 1.3 Mpc improvement in the binary neutron star inspiral range (~2.5% gain), and an average increase of 1.7% in the recovered signal-to-noise ratio for injected binary black hole signals. Parameter estimation studies further confirm that DeepClean does not introduce bias in the recovery of source parameters. These results demonstrate the robustness of DeepClean on Virgo data and support its adoption in real-time noise subtraction frameworks for future observing runs.

gr-qc↗

arXiv2025

Likelihood-free inference for gravitational-wave data analysis and public alerts

Rapid and reliable detection and dissemination of source parameter estimation data products from gravitational-wave events, especially sky localization, is critical for maximizing the potential of multi-messenger astronomy. Machine learning based detection and parameter estimation algorithms are emerging as production ready alternatives to traditional approaches. Here, we report validation studies of AMPLFI, a likelihood-free inference solution to low-latency parameter estimation of binary black holes. We use simulated signals added into data from the LIGO-Virgo-KAGRA's (LVK's) third observing run (O3) to compare sky localization performance with BAYESTAR, the algorithm currently in production for rapid sky localization of candidates from matched-filter pipelines. We demonstrate sky localization performance, measured by searched area and volume, to be equivalent with BAYESTAR. We show accurate reconstruction of source parameters with uncertainties for use distributing low-latency coarse-grained chirp mass information. In addition, we analyze several candidate events reported by the LVK in the third gravitational-wave transient catalog (GWTC-3) and show consistency with the LVK's analysis. Altogether, we demonstrate AMPLFI's ability to produce data products for low-latency public alerts.

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arXiv2025

A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes.

gr-qc↗

arXiv2025

Astrophysical or Terrestrial: Machine learning classification of gravitational-wave candidates using multiple-search information

Low-latency gravitational-wave alerts provide the greater multi-messenger community with information about the candidate events detected by the International Gravitational-Wave Network (IGWN). Prompt release of data products such as the sky localization, false alarm rate (FAR), and $p_\mathrm{astro}$ values allow astronomers to make informed decisions on which candidate gravitational-wave events merit target of opportunity (ToO) follow-up. However, false alarms, often referred to as "glitches", where a gravitational-wave candidate, or trigger, is the result of terrestrial noise, are an inherent part of gravitational-wave searches. In addition, with the presence of multiple gravitational-wave searches, different searches may have varying assessments of the significance of a given trigger. As a complement to quantities such as $p_\mathrm{astro}$, we provide a Machine Learning (ML) based approach to determining whether candidate events are astrophysical or terrestrial in nature, specifically a classifier that utilizes information provided by multiple low-latency search pipelines in its feature space. This classifier has a performance an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.96 and accuracy of 0.90 on the Mock Data Challenge training set and an AUC of 0.93 and accuracy of 0.86 on events from the Advanced LIGO (aLIGO)'s and Advanced Virgo (AdVirgo)'s third observing run (O3).

gr-qc↗

arXiv2025

SN 2023uqf: An Interacting Supernova Coincident with a High-Energy Neutrino

Astrophysical high-energy (TeV-PeV) neutrinos were first discovered in 2013, but their origin remains largely unknown. Here we present SN 2023uqf, a supernova found in coincidence with high-energy neutrino IC231004A, as part of a systematic optical follow-up program with the Zwicky Transient Facility. SN 2023uqf had a luminous and rapidly-evolving lightcurve, and spectroscopic observations indicated that the source was a Type Ibn supernova. Spectroscopic signatures confirm ongoing interaction between the supernova ejecta and a dense circumstellar medium, as expected for high-energy neutrino production in a core-collapse supernova. Given the rare nature of Type Ibn supernovae, SN 2023uqf is unlikely to have been discovered by chance over the course of our program (p=0.3%). Our discovery of SN 2023uqf provides the first observational evidence to support long-held theories that interacting supernovae can serve as cosmic hadron accelerators.

astro-ph.HE↗

arXiv2025

Optimal Follow-Up of Gravitational-Wave Events with the UltraViolet EXplorer (UVEX)

The UltraViolet EXplorer (UVEX) is a wide-field ultraviolet space telescope selected as a NASA Medium-Class Explorer (MIDEX) mission for launch in 2030. UVEX will undertake deep, cadenced surveys of the entire sky to probe low mass galaxies and explore the ultraviolet (UV) time-domain sky, and it will carry the first rapidly deployable UV spectroscopic capability for a broad range of science applications. One of UVEX's prime objectives is to follow up gravitational wave (GW) binary neutron star mergers as targets of opportunity (ToOs), rapidly scanning across their localization regions to search for their kilonova (KN) counterparts. Early-time multiband ultraviolet light curves of KNe are key to explaining the interplay between jet and ejecta in binary neutron star mergers. Owing to high Galactic extinction in the ultraviolet and the variation of GW distance estimates over the sky, the sensitivity to kilonovae can vary significantly across the GW localization and even across the footprint of a single image given UVEX's large field of view. Good ToO observing strategies to trade off between area and depth are neither simple nor obvious. We present an optimal strategy for GW follow-up with UVEX in which exposure time is adjusted dynamically for each field individually to maximize the overall probability of detection. We model the scheduling problem using the expressive and powerful mathematical framework of mixed integer linear programming (MILP), and employ a state-of-the-art MILP solver to automatically generate observing plan timelines that achieve high probabilities of kilonova detection. We have implemented this strategy in an open-source astronomical scheduling software package called the Multi-Mission Multi-Messenger Observation Planning Toolkit, on GitHub at https://github.com/m4opt/m4opt.

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arXiv2025

Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure

Modern time-domain surveys like the Zwicky Transient Facility (ZTF) and the Legacy Survey of Space and Time (LSST) generate hundreds of thousands to millions of alerts, demanding automatic, unified classification of transients and variable stars for efficient follow-up. We present AppleCiDEr (Applying Multimodal Learning to Classify Transient Detections Early), a novel framework that integrates four key data modalities (photometry, image cutouts, metadata, and spectra) to overcome limitations of single-modality classification approaches. Our architecture introduces (i) two transformer encoders for photometry, (ii) a multimodal convolutional neural network (CNN) with domain-specialized metadata towers and Mixture-of-Experts fusion for combining metadata and images, and (iii) a CNN for spectra classification. Training on ~ 30,000 real ZTF alerts, AppleCiDEr achieves high accuracy, allowing early identification and suggesting follow-up for rare transient spectra. The system provides the first unified framework for both transient and variable star classification using real observational data, with seamless integration into brokering pipelines, demonstrating readiness for the LSST era.

astro-ph.IM↗

arXiv2025

IIb or not IIb: A Catalog of ZTF Kilonova Imposters

Among the various classes of fast optical transients (FOTs), kilonovae (KNe), which can emerge as a result of neutron star mergers, are extremely challenging to observe because of not only the rapid timescale on which they fade (on the order of days), but also due to the relative scarcity of their occurrence. This scarcity is compounded by the large number of other FOTs that may initially resemble the characteristic rise of a KNe. While these objects can be ruled out as candidate KNe by taking spectroscopy, a method of confidently ruling out transients based on photometric analysis alone would be incredibly valuable. We describe the compilation of various "imposter" transients, including a plurality of IIb SNe, and investigate a number of comparative metrics by which one might be able to remove transients from consideration without the use of spectroscopy. We provide a list of these objects and their classifications as well as a glossary of the transient types included in the sample.

astro-ph.HE↗

arXiv2025

Interstellar comet 3I/ATLAS: discovery and physical description

We describe the physical characteristics of interstellar comet 3I/ATLAS, discovered on 2025 July 1 by the Asteroid Terrestrial-impact Last Alert System. The comet has eccentricity, $e$ $\simeq$ 6.08 and velocity at infinity, v$_{\infty}$ $\simeq$ 57 km/s, indicating an interstellar origin. \textbf{We obtained B,V, R, I, g, r, i, and z photometry with the Kottamia Astronomical Observatory 1.88-m telescope, the Palomar 200-inch telescope, and the Astrophysical Research Consortium 3.5-m telescope on 2025 July 2, 3, and 6. We measured colour indices B-V=0.98$\pm$0.23, V-R=0.71$\pm$0.09, R-I=0.14$\pm$0.10, g-r=0.84$\pm$0.05 mag, r-i=0.16$\pm$0.03 mag, i-z=-0.02$\pm$0.07 mag, and g-i=1.00$\pm$0.05 mag and a spectral slope of 16.0$\pm$1.9 $\%$/100 nm.} We calculate the dust cross-section within 10,000 km of the comet to be 184.6$\pm$4.6 km$^2$, assuming an albedo of 0.10. 3I/ATLAS's coma has FWHM$\simeq$2.2 arcsec and A(0$^\circ$)f$ρ$=280.8$\pm$3.2 cm. \textbf{We estimate that 3I/ATLAS's \textmu m-scale to mm-scale dust is ejected at $\sim$0.01-1 m/s, implying a dust production of $\sim$0.1 - 1.0 kg/s.

astro-ph.EP↗

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.

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arXiv2025

Twin peaks: SN 2021uvy and SN 2022hgk in the landscape of double-peaked stripped envelope supernovae

In recent years, a class of stripped-envelope supernovae (SESNe) showing two distinct light-curve peaks has emerged, where the first peak cannot be attributed to shock cooling emission. Such peculiar SNe are often studied individually, explained by a combination of powering mechanisms, but are rarely discussed broadly as a group. In this paper, we attempt to form a picture of the landscape of double-peaked SESNe and their powering mechanisms by adding two more objects -- SN 2021uvy and SN 2022hgk. SN 2021uvy is a broad, luminous SN Ib with an unusually long first peak rise and constant color evolution with rising photospheric temperature during the second peak. Though its first peak resembles SN 2019stc, their second peaks differ, making SN 2021uvy unique. SN 2022hgk shows photometric similarity to SN 2019cad and spectroscopic similarity to SN 2005bf, both proposed to be powered by a double-nickel distribution in their ejecta. We analyze their light curves and colors, compare them with a sample of double-peaked SESNe from the ZTF archive, and analyze the light curve parameters of the sample. We observe a correlation (p-value~0.025) between the peak absolute magnitudes of the first and second peaks. No single definitive powering mechanism applies to the whole sample, as it shows variety in the photometric and spectroscopic properties. However, sub-groups of similarity exist that can be explained by mechanisms like the double-nickel distribution, magnetar central engine, interaction, and fallback accretion. We also map out the duration between the peaks ($Δt^{21}$) vs the difference between peak absolute magnitudes ($ΔM^{21}$) as a phase-space that could potentially delineate the most promising powering mechanisms for the double-peaked SESNe.

astro-ph.HE↗

arXiv2025

LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: Zwicky Transient Facility observations

We present the searches conducted with the Zwicky Transient Facility (ZTF) in response to S250206dm, a bona fide event with a false alarm rate of one in 25 years, detected by the International Gravitational Wave Network (IGWN). Although the event is significant, the nature of the compact objects involved remains unclear, with at least one likely neutron star. ZTF covered 68% of the localization region, though we did not identify any likely optical counterpart. We describe the ZTF strategy, potential candidates, and the observations that helped rule out candidates, including sources circulated by other collaborations. Similar to Ahumada et al. 2024, we perform a frequentist analysis, using simsurvey, as well as Bayesian analysis, using nimbus, to quantify the efficiency of our searches. We find that, given the nominal distance to this event of 373$\pm$104 Mpc, our efficiencies are above 10% for KNe brighter than $-17.5$ absolute magnitude. Assuming the optical counterpart known as kilonova (KN) lies within the ZTF footprint, our limits constrain the brightest end of the KN parameter space. Through dedicated radiative transfer simulations of KNe from binary neutron star (BNS) and black hole-neutron star (BHNS) mergers, we exclude parts of the BNS KN parameter space. Up to 35% of the models with high wind ejecta mass ($M_{\rm wind} \approx 0.13$ M$_{\odot}$) are ruled out when viewed face-on ($\cosθ_{\rm obs} = 1.0$). Finally, we present a joint analysis using the combined coverage from ZTF and the Gravitational Wave Multimessenger Dark Energy Camera Survey (GW-MMADS). The joint observations cover 73% of the localization region, and the combined efficiency has a stronger impact on rising and slowly fading models, allowing us to rule out 55% of the high-mass KN models viewed face-on.

astro-ph.HE↗

arXiv2025

Kilonova constraints for the LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: GW-MMADS observations

Gravitational wave (GW) neutron star mergers with an associated electromagnetic counterpart constitute powerful probes of binary evolution, the production sites of heavy elements, general relativity, and the expansion of the universe. Only a handful of candidate GW binary mergers during the fourth LIGO/Virgo/KAGRA observing run (O4) so far are believed to include a neutron star. We present optical-near infrared follow-up observations of the candidate neutron-star black hole GW merger S250206dm. This is the first high-significance mass gap neutron star-black hole candidate observed by multiple GW detectors (thus having a significantly smaller sky localization than one-detector events), offering the first opportunity to effectively follow up a GW event of this kind. Our GW MultiMessenger Astronomy DECam Survey (GW-MMADS) campaign consisted of a wide-field search using the Dark Energy Camera (DECam) and T80-South (T80S), as well as galaxy-targeted observations using the Southern Astrophysical Research (SOAR) imager and the Wendelstein 2.1m 3-channel camera. No viable kilonova counterpart was found in our observations. We use our observation depths to place competitive constraints on kilonova models similar to or brighter than the GW170817 kilonova AT 2017gfo within our observed fields, ruling out 100\% of such models with SOAR galaxy-targeted observations and $\sim43$\% (48\%) with DECam (DECam and T80S).

astro-ph.HE↗

arXiv2025

$\texttt{BTSbot}$: A Multi-input Convolutional Neural Network to Automate and Expedite Bright Transient Identification for the Zwicky Transient Facility

The Bright Transient Survey (BTS) relies on visual inspection ("scanning") to select sources for accomplishing its mission of spectroscopically classifying all bright extragalactic transients found by the Zwicky Transient Facility (ZTF). We present $\texttt{BTSbot}$, a multi-input convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 14 extracted features. $\texttt{BTSbot}$ eliminates the need for scanning by automatically identifying and requesting follow-up observations of new bright ($m\,<18.5\,\mathrm{mag}$) transient candidates. $\texttt{BTSbot}$ outperforms BTS scanners in terms of completeness (99% vs. 95%) and identification speed (on average, 7.4 hours quicker). See Rehemtulla et al. 2024, ApJ, 972, 7R for the full BTSbot publication

astro-ph.IM↗

arXiv2025

Inferring neutron star merger ejecta morphologies with kilonovae

In this study we incorporate a new grid of kilonova simulations produced by the Monte Carlo radiative transfer code SuperNu in an inference pipeline for astrophysical transients, and evaluate their performance. These simulations contain four different two-component ejecta morphology classes. We analyze follow-up observational strategies by Vera Rubin Observatory in optical, and James Webb Space Telescope (JWST) in mid-infrared (MIR). Our analysis suggests that, within these strategies, it is possible to discriminate between different morphologies only when late-time JWST observations in MIR are available. We conclude that follow-ups by the new Vera Rubin Observatory alone are not sufficient to determine ejecta morphology. Additionally, we make comparisons between surrogate models based on radiative transfer simulation grids by SuperNu and POSSIS, by analyzing the historic kilonova AT2017gfo that accompanied the gravitational wave event GW170817. We show that both SuperNu and POSSIS models provide similar fits to photometric observations. Our results show a slight preference for SuperNu models, since the wind ejecta parameters recovered with these models are in better agreement with expectations from numerical simulations.

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