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Massive black holes (BHs) at the centres of massive galaxies are ubiquitous. The population of BHs within dwarf galaxies, on the other hand, is evasive. Dwarf galaxies are thought to harbour BHs with proportionally small masses, including intermediate mass BHs, with masses 102 more » « less

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ABSTRACT The structure of the broad-line region (BLR) is an essential ingredient in the determination of active galactic nucleus (AGN) virial black hole masses, which in turn are important to study the role of black holes in galaxy evolution. Constraints on the BLR geometry and dynamics can be obtained from velocity-resolved studies using reverberation mapping data (i.e. monitoring data). However, monitoring data are observationally expensive and only available for a limited sample of AGNs, mostly confined to the local Universe. Here, we explore a new version of a Bayesian inference, physical model of the BLR that uses an individual spectrum and prior information on the BLR size from the radius–luminosity relation, to model the AGN BLR geometry and dynamics. We apply our model to a sample of 11 AGNs, which have been previously modelled using monitoring data. Our single-epoch BLR model is able to constrain some of the BLR parameters with inferred parameter values that agree within the uncertainties with those determined from the modelling of monitoring data. We find that our model is able to derive stronger constraints on the BLR for AGNs with broad emission lines that qualitatively have more substructure and more asymmetry, presumably as they contain more information to constrain the physical model. The performance of this model makes it a practical and cost-effective tool to determine some of the BLR properties of a large sample of low- and high-redshift AGNs, for which monitoring data are not available. 

Abstract We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE’s magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (∼71%) SNe Ia, 339 (∼23%) SNe II, and 96 (∼6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time.