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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 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 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. 

Abstract The classification of variable objects provides insight into a wide variety of astrophysics ranging from stellar interiors to galactic nuclei. The Zwicky Transient Facility (ZTF) provides time-series observations that record the variability of more than a billion sources. The scale of these data necessitates automated approaches to make a thorough analysis. Building on previous work, this paper reports the results of the ZTF Source Classification Project (SCoPe), which trains neural network and XGBoost (XGB) machine-learning (ML) algorithms to perform dichotomous classification of variable ZTF sources using a manually constructed training set containing 170,632 light curves. We find that several classifiers achieve high precision and recall scores, suggesting the reliability of their predictions for 209,991,147 light curves across 77 ZTF fields. We also identify the most important features for XGB classification and compare the performance of the two ML algorithms, finding a pattern of higher precision among XGB classifiers. The resulting classification catalog is available to the public, and the software developed forSCoPeis open source and adaptable to future time-domain surveys. 

Abstract During the first half of the fourth observing run (O4a) of the International Gravitational Wave Network, 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 (False Alarm Rate less than 1 yr⁻¹) BNS and NSBH candidates in O4a. Our follow-up campaigns relied on both target-of-opportunity observations and re-weighting of the nominal survey schedule to maximize coverage. We describe the toolkit we have been developing,Fritz, an instance ofSkyPortal, 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 deg²within the skymaps of the high-significance GW events, reaching a median depth ofg≈ 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 events), along with our O4a follow-ups. Following Kasliwal et al., 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⁻¹can peak at a magnitude brighter than −17.5 mag.