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Free, publicly-accessible full text available January 1, 2026
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Abstract Active galactic nuclei (AGNs) can funnel stars and stellar remnants from the vicinity of the galactic center into the inner plane of the AGN disk. Stars reaching this inner region can be tidally disrupted by the stellar-mass black holes in the disk. Such micro tidal disruption events (micro-TDEs) could be a useful probe of stellar interaction with the AGN disk. We find that micro-TDEs in AGNs occur at a rate of ∼170 Gpc −3 yr −1 . Their cleanest observational probe may be the electromagnetic detection of tidal disruption in AGNs by heavy supermassive black holes ( M • ≳ 10 8 M ⊙ ) that cannot tidally disrupt solar-type stars. The reconstructed rate of such events from observations, nonetheless, appears to be much lower than our estimated micro-TDE rate. We discuss two such micro-TDE candidates observed to date (ASASSN-15lh and ZTF19aailpwl).more » « less
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Context. With a rapidly rising number of transients detected in astronomy, classification methods based on machine learning are increasingly being employed. Their goals are typically to obtain a definitive classification of transients, and for good performance they usually require the presence of a large set of observations. However, well-designed, targeted models can reach their classification goals with fewer computing resources. Aims. The aim of this study is to assist in the observational astronomy task of deciding whether a newly detected transient warrants follow-up observations. Methods. This paper presents SNGuess, a model designed to find young extragalactic nearby transients with high purity. SNGuess works with a set of features that can be efficiently calculated from astronomical alert data. Some of these features are static and associated with the alert metadata, while others must be calculated from the photometric observations contained in the alert. Most of the features are simple enough to be obtained or to be calculated already at the early stages in the lifetime of a transient after its detection. We calculate these features for a set of labeled public alert data obtained over a time span of 15 months from the Zwicky Transient Facility (ZTF). The core model of SNGuess consists of an ensemble of decision trees, which are trained via gradient boosting. Results. Approximately 88% of the candidates suggested by SNGuess from a set of alerts from ZTF spanning from April 2020 to August 2021 were found to be true relevant supernovae (SNe). For alerts with bright detections, this number ranges between 92% and 98%. Since April 2020, transients identified by SNGuess as potential young SNe in the ZTF alert stream are being published to the Transient Name Server (TNS) under the AMPEL_ZTF_NEW group identifier. SNGuess scores for any transient observed by ZTF can be accessed via a web service https://ampel.zeuthen.desy.de/api/live/docs . The source code of SNGuess is publicly available https://github.com/nmiranda/SNGuess . Conclusions. SNGuess is a lightweight, portable, and easily re-trainable model that can effectively suggest transients for follow-up. These properties make it a useful tool for optimizing follow-up observation strategies and for assisting humans in the process of selecting candidate transients.more » « less
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Abstract Neutrino events from IceCube have recently been associated with multiple astrophysical sources. Interestingly, these likely detections represent three distinct astrophysical source types: active galactic nuclei (AGNs), blazars, and tidal disruption events (TDEs). Here, we compute the expected contributions of AGNs, blazars, and TDEs to the overall cosmic neutrino flux detected by IceCube based on the associated events, IceCube’s sensitivity, and the source types’ astrophysical properties. We find that, despite being the most commonly identified sources, blazars cannot contribute more than 11% of the total flux (90% credible level), consistent with existing limits from stacked searches. On the other hand, we find that either AGNs or TDEs could contribute more than 50% of the total flux (90% credible level), although stacked searches further limit the TDE contribution to ≲30%. We also find that so-far unknown source types contribute at least 10% of the total cosmic flux with a probability of 80%. We assemble a pie chart that shows the most likely fractional contribution of each source type to IceCube’s total neutrino flux.
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Abstract We construct a physically parameterized probabilistic autoencoder (PAE) to learn the intrinsic diversity of Type Ia supernovae (SNe Ia) from a sparse set of spectral time series. The PAE is a two-stage generative model, composed of an autoencoder that is interpreted probabilistically after training using a normalizing flow. We demonstrate that the PAE learns a low-dimensional latent space that captures the nonlinear range of features that exists within the population and can accurately model the spectral evolution of SNe Ia across the full range of wavelength and observation times directly from the data. By introducing a correlation penalty term and multistage training setup alongside our physically parameterized network, we show that intrinsic and extrinsic modes of variability can be separated during training, removing the need for the additional models to perform magnitude standardization. We then use our PAE in a number of downstream tasks on SNe Ia for increasingly precise cosmological analyses, including the automatic detection of SN outliers, the generation of samples consistent with the data distribution, and solving the inverse problem in the presence of noisy and incomplete data to constrain cosmological distance measurements. We find that the optimal number of intrinsic model parameters appears to be three, in line with previous studies, and show that we can standardize our test sample of SNe Ia with an rms of 0.091 ± 0.010 mag, which corresponds to 0.074 ± 0.010 mag if peculiar velocity contributions are removed. Trained models and codes are released at https://github.com/georgestein/suPAErnova.more » « less
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Abstract We apply the color–magnitude intercept calibration method (CMAGIC) to the Nearby Supernova Factory SNe Ia spectrophotometric data set. The currently existing CMAGIC parameters are the slope and intercept of a straight line fit to the linear region in the color–magnitude diagram, which occurs over a span of approximately 30 days after maximum brightness. We define a new parameter,
ω XY , the size of the “bump” feature near maximum brightness for arbitrary filtersX andY . We find a significant correlation between the slope of the linear region,β XY , in the CMAGIC diagram andω XY . These results may be used to our advantage, as they are less affected by extinction than parameters defined as a function of time. Additionally,ω XY is computed independently of templates. We find that current empirical templates are successful at reproducing the features described in this work, particularly SALT3, which correctly exhibits the negative correlation between slope and “bump” size seen in our data. In 1D simulations, we show that the correlation between the size of the “bump” feature andβ XY can be understood as a result of chemical mixing due to large-scale Rayleigh–Taylor instabilities.