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We conduct a search for stellar-mass binary black hole mergers in gravitational-wave data collected by the LIGO detectors during the LIGO-Virgo-KAGRA (LVK) third observing run (O3). Our search uses a machine learning (ML)-based method, Aframe, an alternative to traditional matched filtering search techniques. The O3 observing run has been analyzed by the LVK Collaboration, producing GWTC-3, the most recent catalog installment which has been made publicly available in 2021. Various groups outside the LVK have reanalyzed O3 data using both traditional and ML-based approaches. Here, we identify 38 candidates with a probability of astrophysical origin (𝑝astro ) greater than 0.5, which were previously reported in GWTC-3. This is comparable to the number of candidates reported by individual matched-filter searches. In addition, we compare Aframe candidates with catalogs from research groups outside of the LVK, identifying three candidates with 𝑝astro >0.5 . No previously unreported candidates are identified by Aframe. This work demonstrates that Aframe, and ML-based searches more generally, are useful companions to matched filtering pipelines. 

ABSTRACT We identify the progenitor star of SN 2023ixf in Messier 101 using Keck/NIRC2 adaptive optics imaging and pre-explosion Hubble Space Telescope (HST)/Advanced Camera for Surveys (ACS) images. The supernova, localized with diffraction spikes and high-precision astrometry, unambiguously coincides with a progenitor candidate of $$m_\text{F814W}=24.87\pm 0.05$$ (AB). Given its reported infrared excess and semiregular variability, we fit a time-dependent spectral energy distribution (SED) model of a dusty red supergiant (RSG) to a combined data set of HST optical, ground-based near-infrared, and Spitzer Infrared Array Camera (IRAC) [3.6], [4.5] photometry. The progenitor resembles an RSG of $$T_\text{eff}=3488\pm 39$$ K and $$\log (L/\mathrm{L}_\odot)=5.15\pm 0.02$$, with a $$0.13\pm 0.01$$ dex ($$31.1\pm 1.7$$ per cent) luminosity variation at a period of $$P=1144.7\pm 4.8$$ d, obscured by a dusty envelope of $$\tau =2.92\pm 0.02$$ at $$1\, \mu \text{m}$$ in optical depth (or $$A_\text{V}=8.43\pm 0.11$$ mag). The signatures match a post-main-sequence star of $$18.2_{-0.6}^{+1.3}\, \mathrm{M}_\odot$$ in zero-age main-sequence mass, among the most massive SN II progenitor, with a pulsation-enhanced mass-loss rate of $$\dot{M}=(4.32\pm 0.26)\times 10^{-4} \, \mathrm{M}_\odot \, \text{yr}^{-1}$$. The dense and confined circumstellar material is ejected during the last episode of radial pulsation before the explosion. Notably, we find strong evidence for variations of $$\tau$$ or $$T_\text{eff}$$ along with luminosity, a necessary assumption to reproduce the wavelength-dependent variability, which implies periodic dust sublimation and condensation. Given the observed SED, partial dust obscuration remains possible, but any unobstructed binary companion over $$5.6\, \mathrm{ M}_\odot$$ can be ruled out. 

Abstract The Bright Transient Survey (BTS) aims to obtain a classification spectrum for all bright (m_peak≤ 18.5 mag) extragalactic transients found in the Zwicky Transient Facility (ZTF) public survey. BTS critically relies on visual inspection (“scanning”) to select targets for spectroscopic follow-up, which, while effective, has required a significant time investment over the past ∼5 yr of ZTF operations. We presentBTSbot, a multimodal convolutional neural network, which provides a bright transient score to individual ZTF detections using their image data and 25 extracted features.BTSbotis able to eliminate the need for daily human scanning by automatically identifying and requesting spectroscopic follow-up observations of new bright transient candidates.BTSbotrecovers all bright transients in our test split and performs on par with scanners in terms of identification speed (on average, ∼1 hr quicker than scanners). We also find thatBTSbotis not significantly impacted by any data shift by comparing performance across a concealed test split and a sample of very recent BTS candidates.BTSbothas been integrated intoFritzandKowalski, ZTF’s first-party marshal and alert broker, and now sends automatic spectroscopic follow-up requests for the new transients it identifies. Between 2023 December and 2024 May,BTSbotselected 609 sources in real time, 96% of which were real extragalactic transients. WithBTSbotand other automation tools, the BTS workflow has produced the first fully automatic end-to-end discovery and classification of a transient, representing a significant reduction in the human time needed to scan. 

ABSTRACT The identification of extragalactic fast optical transients (eFOTs) as potential multimessenger sources is one of the main challenges in time-domain astronomy. However, recent developments have allowed for probes of rapidly evolving transients. With the increasing number of alert streams from optical time-domain surveys, the next paradigm is building technologies to rapidly identify the most interesting transients for follow-up. One effort to make this possible is the fitting of objects to a variety of eFOT light curve models such as kilonovae and γ-ray burst (GRB) afterglows. In this work, we describe a new framework designed to efficiently fit transients to light curve models and flag them for further follow-up. We describe the pipeline’s workflow and a handful of performance metrics, including the nominal sampling time for each model. We highlight as examples ZTF20abwysqy, the shortest long gamma-ray burst discovered to date, and ZTF21abotose, a core-collapse supernova initially identified as a potential kilonova candidate. 

Abstract Because of the electromagnetic (EM) 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 (GW) signal with EM 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 EM telescopes in deciding whether to follow up on GW events in real time.