arXiv subjects

Michael W. Coughlin

Publications and source records attributed to Michael W. Coughlin.

At least 73 records · Page 4Linked to original sources

arXiv2025

Coherent injection of magnetic noise and its impact on gravitational-wave searches

Correlated noise sources, particularly magnetic noise, form a risk to future gravitational-wave searches aimed at detecting the gravitational-wave background. Potential noise contamination is investigated by making noise projections which typically rely on an accurate measurement of the coupling strength of the noise to the detector. To make these projections, we inject, for the first time, broadband, coherent magnetic noise between two gravitational-wave detectors, LIGO Hanford and LIGO Livingston, separated by several thousands of kilometers. We describe the noise injection as well as its impact on the analysis pipelines and investigate the accuracy of noise projection techniques used in the past decade. Finally, we present a proof-of-concept demonstration of noise subtraction using Wiener filtering, while also highlighting potential risks associated with this method. This unique data set with correlated noise caused by magnetic field fluctuations in two gravitational-wave detectors, as well as in an array of witness sensors, provides an excellent testing ground for additional future studies. Ultimately, this study demonstrates that Wiener filtering is effective and can be applied in the eventual detection of the gravitational-wave background by the LIGO-Virgo-KAGRA Collaboration.

gr-qc↗

arXiv2025

A luminous and hot infrared through X-ray transient at a 5 kpc offset from a dwarf galaxy

We are searching for hot, constant-color, offset optical flares in the Zwicky Transient Facility (ZTF) data stream that are ${>}10''$ from any galaxy in public imaging data from the PanSTARRS survey. Here, we present the first discovery from this search: AT 2024puz, a luminous multiwavelength transient offset by $5\,$kpc from a ${\sim}10^8\,M_\odot$ galaxy at $z=0.356$ with a low-moderate star formation rate. It produced luminous $10^{44.79 \pm 0.04}\,{\rm erg\,s}^{-1}$ optical/UV emission that evolved on a ${\sim}20\,$day timescale, as well as $10^{44.12\pm0.03}\,{\rm erg\,s}^{-1}$ X-ray emission with a photon-index $Γ=1.7$. No associated radio or millimeter emission was detected. We show that the early-time optical emission is likely powered by reprocessing of high-energy, accretion-powered radiation, with a possible contribution from a shock in a dense circum-transient medium. If the shock is dominant at early-times, the circum-transient medium has a mass ${\sim}0.1-1\,M_\odot$, radius $10^{15}\,$cm, and a density profile shallower than ${\sim}r^{-1}$. A near-infrared excess appears at late-times and is suggestive of reprocessing within a wind or other circum-transient medium. The X-rays are most consistent with a central engine. We suggest that AT 2024puz may be associated with an accretion event onto a $50-10^5\,M_\odot$ BH, where the lower masses are preferred based on the large projected offset from the host galaxy. AT2024puz exhibits properties similar to both luminous fast blue optical transients (LFBOTs) and tidal disruption events (TDEs), but is intermediate between them in its energetics and evolution timescale. This highlights the need for broader exploration of the landscape of hot optical transients to trace their origins.

astro-ph.HE↗

arXiv2025

Electromagnetic Follow-up to Gravitational Wave Events with the UltraViolet EXplorer (UVEX)

The Ultraviolet Explorer (UVEX) is expected to fly in 2030 and will have the opportunity -- and the rapid near/far ultraviolet (UV) capabilities -- to glean unprecedented insight into the bright UV emission present in kilonovae like that of AT 170817gfo, the electromagnetic counterpart to binary neutron star merger GW170817. To do so, it will need to perform prompt target-of-opportunity observations following detection of binary neutron star mergers by the LIGO-Virgo-KAGRA gravitational observatories. We present initial simulations to develop UVEX target-of-opportunity strategies for such events and provide the community with detailed initial estimates of the prospects for and characteristics of UVEX target-of-opportunity observations following gravitational-wave events, considering fiducial scenarios for the fifth and sixth LIGO-Virgo-KAGRA observing runs. Additionally, in light of the relatively few binary neutron star mergers observed since GW170817, we consider variant target-of-opportunity strategies for UVEX to maximize scientific gain in the case of a lowered binary neutron star merger rate.

astro-ph.HE↗

arXiv2025

The BTSbot-nearby discovery of SN 2024jlf: rapid, autonomous follow-up probes interaction in an 18.5 Mpc Type IIP supernova

We present observations of the Type IIP supernova (SN) 2024jlf, including spectroscopy beginning just 0.7 days ($\sim$17 hours) after first light. Rapid follow-up was enabled by the new $\texttt{BTSbot-nearby}$ program, which involves autonomously triggering target-of-opportunity requests for new transients in Zwicky Transient Facility data that are coincident with nearby ($D<60$ Mpc) galaxies and identified by the $\texttt{BTSbot}$ machine learning model. Early photometry and non-detections shortly prior to first light show that SN 2024jlf initially brightened by $>$4 mag/day, quicker than $\sim$90% of Type II SNe. Early spectra reveal weak flash ionization features: narrow, short-lived ($1.3 < τ~\mathrm{[d]} < 1.8$) emission lines of H$α$, He II, and C IV. Assuming a wind velocity of $v_w=50$ km s$^{-1}$, these properties indicate that the red supergiant progenitor exhibited enhanced mass-loss in the last year before explosion. We constrain the mass-loss rate to $10^{-4} < \dot{M}~\mathrm{[M_\odot~yr^{-1}]} < 10^{-3}$ by matching observations to model grids from two independent radiative hydrodynamics codes. $\texttt{BTSbot-nearby}$ automation minimizes spectroscopic follow-up latency, enabling the observation of ephemeral early-time phenomena exhibited by transients.

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arXiv2025

From existing and new nuclear and astrophysical constraints to stringent limits on the equation of state of neutron-rich dense matter

Through continuous progress in nuclear theory and experiment and an increasing number of neutron-star observations, a multitude of information about the equation of state (EOS) for matter at extreme densities is available. To constrain the EOS across its entire density range, this information needs to be combined consistently. However, the impact and model-dependency of individual observations vary. We present a broad compendium of different constraints and apply them individually to a large set of EOS candidates within a Bayesian framework. Specifically, we explore different ways how chiral effective field theory and perturbative quantum chromodynamics can be used to place a likelihood on EOS candidates. We also investigate the impact of nuclear experimental constraints, as well as different radio and X-ray observations of neutron star (NS) masses and radii. This is augmented by reanalyses of the existing data from BNS coalescences, in particular of GW170817, with improved models for the tidal waveform and kilonova light curves, which we also utilize to construct a tight upper limit of 2.39$\,$M$_\odot$ on the TOV mass based on GW170817's remnant. Our diverse set of constraints is eventually combined to obtain stringent limits on NS properties. We organize the combination in a way to distinguish between constraints where the systematic uncertainties are deemed small and those that rely on less conservative assumptions. For the former, we find the radius of the canonical 1.4$\,$M$_\odot$ neutron star to be $R_{1.4}= 12.26_{-0.91}^{+0.80}\,$km and the TOV mass at $M_{\rm TOV}= 2.25_{-0.22}^{+0.42}\,$M$_\odot$ (95% credibility). Including all the presented constraints yields $R_{1.4}= 12.20_{-0.48}^{+0.50}\,$km and $M_{\rm TOV}= 2.30_{-0.20}^{+0.07}\,$M$_\odot$.

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arXiv2025

The Nature of Optical Afterglows Without Gamma-ray Bursts: Identification of AT2023lcr and Multiwavelength Modeling

In the past few years, the improved sensitivity and cadence of wide-field optical surveys have enabled the discovery of several afterglows without associated detected gamma-ray bursts (GRBs). We present the identification, observations, and multiwavelength modeling of a recent such afterglow (AT2023lcr), and model three literature events (AT2020blt, AT2021any, and AT2021lfa) in a consistent fashion. For each event, we consider the following possibilities as to why a GRB was not observed: 1) the jet was off-axis; 2) the jet had a low initial Lorentz factor; and 3) the afterglow was the result of an on-axis classical GRB (on-axis jet with physical parameters typical of the GRB population), but the emission was undetected by gamma-ray satellites. We estimate all physical parameters using afterglowpy and Markov Chain Monte Carlo methods from emcee. We find that AT2023lcr, AT2020blt, and AT2021any are consistent with on-axis classical GRBs, and AT2021lfa is consistent with both on-axis low Lorentz factor ($Γ_0 \approx 5 - 13$) and off-axis ($θ_\text{obs}=2θ_\text{jet}$) high Lorentz factor ($Γ_0 \approx 100$) jets.

astro-ph.HE↗

arXiv2025

Data-driven approach for modeling the temporal and spectral evolution of kilonova systematic uncertainties

Kilonovae, possible electromagnetic counterparts to neutron star mergers, provide important information about high-energy transient phenomena and, in principle, also allow us to obtain information about the source properties responsible for powering the kilonova. Unfortunately, numerous uncertainties exist in kilonova modeling that, at the current stage, hinder accurate predictions. Hence, one has to account for possible systematic modeling uncertainties when interpreting the observed transients. In this work, we provide a data-driven approach to account for time-dependent and filter-dependent uncertainties in kilonova models. Through a suite of tests, we find that the most reliable recovery of the source parameters and description of the observational data can be obtained through a combination of kilonova models with time- and filter-dependent systematic uncertainties. We apply our new method to analyze AT2017gfo. While recovering a total ejecta mass consistent with previous studies, our approach gives insights into the temporal and spectral evolution of the systematic uncertainties of this kilonova. We consistently find a systematic error below $1$ mag between $1$ to $5$ days after the merger. Our work addresses the need for early follow-up of kilonovae at earlier times, and improved modeling of the kilonova at later times, to reduce the uncertainties outside of this time window.

astro-ph.HE↗

arXiv2025

The Effect of Vera C. Rubin Observatory Cadence Selections on Kilonova Detectability

The discovery of the optical/infra-red counterpart (AT2017gfo) to the binary neutron star gravitational-wave detection (GW170817), which was followed by a short gamma-ray burst (GRB170817), marked a groundbreaking moment in multi-messenger astronomy. To date, it remains the only confirmed joint detection of its kind. However, many experiments are actively searching for similar fast-fading electromagnetic counterparts, known as kilonovae. Fortunately, the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) provides excellent prospects for identifying kilonova candidates either from, or independent of, gravitational-wave and gamma-ray burst triggers. Cadence choices for LSST surveys are especially important for maximising the likelihood of kilonovae detections. In this work, we explore the possibility of optimizing Rubin Observatory's ability to detect kilonovae by implementing a fast transient metric shown to be successful with an existing wide field survey, e.g. the Zwicky Transient Facility (ZTF). We study existing LSST cadences, how detection rates are affected by filter selections, the return timescales for visits of the same area in the sky, and other relevant factors. Through our analysis, we have found that employing baseline cadences and utilizing triplet families like presto gap produced the highest likelihood of kilonova detection.

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↗

arXiv2025

Searching for electromagnetic emission in an AGN from the gravitational wave binary black hole merger candidate S230922g

We carried out long-term monitoring of the LIGO/Virgo/KAGRA binary black hole (BBH) merger candidate S230922g in search of electromagnetic emission from the interaction of the merger remnant with an embedding active galactic nuclei (AGN) accretion disk. Using a dataset primarily composed of wide-field imaging from the Dark Energy Camera (DECam) and supplemented by additional photometric and spectroscopic resources, we searched ~ 70% of the sky area probability for transient phenomena, and discovered 6 counterpart candidates. One especially promising candidate - AT 2023aagj - exhibited temporally varying asymmetric components in spectral broad line regions, a feature potentially indicative of an off-center event such as a BBH merger. This represents the first live search and multiwavelength, photometric, and spectroscopic monitoring of a GW BBH optical counterpart candidate in the disk of an AGN.

astro-ph.HE↗

arXiv2025

Multi-Wavelength Analysis of AT 2023sva: a Luminous Orphan Afterglow With Evidence for a Structured Jet

We present multi-wavelength analysis of ZTF23abelseb (AT 2023sva), an optically discovered fast-fading ($Δm_r = 2.2$ mag in $Δt = 0.74 $ days), luminous ($M_r \sim -30.0$ mag) and red ($g-r = 0.50$ mag) transient at $z = 2.28$ with accompanying luminous radio emission. AT 2023sva does not possess a $γ$-ray burst (GRB) counterpart to an isotropic equivalent energy limit of $E_{\rm{γ, \, iso}} < 1.6 \times 10^{52}$ erg, determined through searching $γ$-ray satellite archives between the last non-detection and first detection, making it the sixth example of an optically-discovered afterglow with a redshift measurement and no detected GRB counterpart. We analyze AT 2023sva's optical, radio, and X-ray observations to characterize the source. From radio analyses, we find the clear presence of strong interstellar scintillation (ISS) 72 days after the initial explosion, allowing us to place constraints on the source's angular size and bulk Lorentz factor. When comparing the source sizes derived from ISS of orphan events to those of the classical GRB population, we find orphan events have statistically smaller source sizes. We also utilize Bayesian techniques to model the multi-wavelength afterglow. Within this framework, we find evidence that AT 2023sva possesses a shallow power-law structured jet viewed slightly off-axis ($θ_{\rm{v}} = 0.07 \pm 0.02$) just outside of the jet's core opening angle ($θ_{\rm{c}} = 0.06 \pm 0.02$). We determine this is likely the reason for the lack of a detected GRB counterpart, but also investigate other scenarios. AT 2023sva's evidence for possessing a structured jet stresses the importance of broadening orphan afterglow search strategies to a diverse range of GRB jet angular energy profiles, to maximize the return of future optical surveys.

astro-ph.HE↗

arXiv2024

A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run

This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGRA. The search targets unmodeled transients with durations of milliseconds to a few seconds in the 30-1500 Hz frequency band, without assumptions about the incoming signal direction, polarization, or morphology. Using the Gravitational Wave Anomalous Knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are successfully detected, along with a range of detector glitches. The algorithm constructs a low-dimensional embedded space to capture the physical features of signals, enabling the detection of CBCs, detector glitches, and unmodeled transients. This study demonstrates GWAK's ability to enhance gravitational-wave searches beyond the limits of existing pipelines, laying the groundwork for future detection strategies.

gr-qc↗

arXiv2024

The Lunar Gravitational-wave Antenna: Mission Studies and Science Case

The Lunar Gravitational-wave Antenna (LGWA) is a proposed array of next-generation inertial sensors to monitor the response of the Moon to gravitational waves (GWs). Given the size of the Moon and the expected noise produced by the lunar seismic background, the LGWA would be able to observe GWs from about 1 mHz to 1 Hz. This would make the LGWA the missing link between space-borne detectors like LISA with peak sensitivities around a few millihertz and proposed future terrestrial detectors like Einstein Telescope or Cosmic Explorer. In this article, we provide a first comprehensive analysis of the LGWA science case including its multi-messenger aspects and lunar science with LGWA data. We also describe the scientific analyses of the Moon required to plan the LGWA mission.

gr-qc↗

arXiv2024

Rubin ToO 2024: Envisioning the Vera C. Rubin Observatory LSST Target of Opportunity program

The Legacy Survey of Space and Time (LSST) at Vera C. Rubin Observatory is planned to begin in the Fall of 2025. The LSST survey cadence has been designed via a community-driven process regulated by the Survey Cadence Optimization Committee (SCOC), which recommended up to 3% of the observing time to carry out Target of Opportunity (ToO) observations. Experts from the scientific community, Rubin Observatory personnel, and members of the SCOC were brought together to deliver a recommendation for the implementation of the ToO program during a workshop held in March 2024. Four main science cases were identified: gravitational wave multi-messenger astronomy, high energy neutrinos, Galactic supernovae, and small potentially hazardous asteroids possible impactors. Additional science cases were identified and briefly addressed in the documents, including lensed or poorly localized gamma-ray bursts and twilight discoveries. Trigger prioritization, automated response, and detailed strategies were discussed for each science case. This document represents the outcome of the Rubin ToO 2024 workshop, with additional contributions from members of the Rubin Science Collaborations. The implementation of the selection criteria and strategies presented in this document has been endorsed in the SCOC Phase 3 Recommendations document (PSTN-056). Although the ToO program is still to be finalized, this document serves as a baseline plan for ToO observations with the Rubin Observatory.

astro-ph.IM↗

arXiv2024

Bayesian real-time classification of multi-messenger electromagnetic and gravitational-wave observations

Because of the electromagnetic radiation produced during the merger, compact binary coalescences with neutron stars may result in multi-messenger observations. In order to follow up on the gravitational-wave signal with electromagnetic telescopes, it is critical to promptly identify the properties of these sources. This identification must rely on the properties of the progenitor source, such as the component masses and spins, as determined by low-latency detection pipelines in real time. The output of these pipelines, however, might be biased, which could decrease the accuracy of parameter recovery. Machine learning algorithms are used to correct this bias. In this work, we revisit this problem and discuss two new implementations of supervised machine learning algorithms, K-Nearest Neighbors and Random Forest, which are able to predict the presence of a neutron star and post-merger matter remnant in low-latency compact binary coalescence searches across different search pipelines and data sets. Additionally, we present a novel approach for calculating the Bayesian probabilities for these two metrics. Instead of metric scores derived from binary machine learning classifiers, our scheme is designed to provide the astronomy community well-defined probabilities. This would deliver a more direct and easily interpretable product to assist electromagnetic telescopes in deciding whether to follow up on gravitational-wave events in real time.

astro-ph.HE↗

arXiv2024

What to expect: kilonova light curve predictions via equation of state marginalization

Efficient multi-messenger observations of gravitational-wave candidates from compact binary coalescence candidate events rely on data products reported in low-latency by the International Gravitational-wave Network (IGWN). While data products such as $\texttt{HasNS}$, the probability of at least one neutron star, and $\texttt{HasRemnant}$, the probability of remnant matter forming after merger, exist, these are not direct observables for a potential kilonova. Here, we present new kilonova light curve and ejecta mass data products derived from merger quantities measured in low latency, by marginalizing over our uncertainty in our understanding of the neutron star equation of state and using measurements of the source properties of the merger, including masses and spins. Two additional types of data products are proposed. The first is the probability of a candidate event having mass ejecta ($m_{\mathrm{ej}}$) greater than $10^{-3} M_\odot$, which we denote as $\texttt{HasEjecta}$. The second are $m_{\mathrm{ej}}$ estimates and accompanying $\texttt{ugrizy}$ and $\texttt{HJK}$ kilonova light curves predictions produced from a surrogate model trained on a grid of kilonova light curves from $\texttt{POSSIS}$, a time-dependent, three-dimensional Monte Carlo radiative transfer code. We are developing these data products in the context of the IGWN low-latency alert infrastructure, and will be advocating for their use and release for future detections.

astro-ph.HE↗

arXiv2024

Machine-directed gravitational-wave counterpart discovery

Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable via siloed observations in each messenger. However, such detections remain challenging due to the rapid and faint nature of counterparts. Protocols for discovery and inference still rely on human experts manually inspecting survey alert streams and intuiting optimal usage of limited follow-up resources. Strategizing an optimal follow-up program requires adaptive sequential decision-making given evolving light curve data that (i) maximizes a global objective despite incomplete information and (ii) is robust to stochasticity introduced by detectors/observing conditions. Reinforcement learning (RL) approaches allow agents to implicitly learn the physics/detector dynamics and the behavior policy that maximize a designated objective through experience. To demonstrate the utility of such an approach for the kilonova follow-up problem, we train a toy RL agent for the goal of maximizing follow-up photometry for the true kilonova among several contaminant transient light curves. In a simulated environment where the agent learns online, it achieves 3x higher accuracy compared to a random strategy. However, it is surpassed by human agents by up to a factor of 2. This is likely because our hypothesis function (Q that is linear in state-action features) is an insufficient representation of the optimal behavior policy. More complex agents could perform at par or surpass human experts. Agents like these could pave the way for machine-directed software infrastructure to efficiently respond to next generation detectors, for conducting science inference and optimally planning expensive follow-up observations, scalably and with demonstrable performance guarantees.

astro-ph.IM↗

arXiv2024

Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning

In this chapter, we will discuss the use of Machine Learning methods for the identification and localization of cometary activity for Solar System objects in ground and in space-based wide-field all-sky surveys. We will begin the chapter by discussing the challenges of identifying known and unknown active, extended Solar System objects in the presence of stellar-type sources and the application of classical pre-ML identification techniques and their limitations. We will then transition to the discussion of implementing ML techniques to address the challenge of extended object identification. We will finish with prospective future methods and the application to future surveys such as the Vera C. Rubin Observatory.

astro-ph.EP↗