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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). 

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. 

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. 

Abstract The IceCube Neutrino Observatory, a cubic-kilometer detector at the South Pole, identifies neutrino flavor through event morphology. Sparse photon detection makes this classification particularly challenging in the 5–100 GeV regime, the energy range relevant for oscillation measurements and searches for physics beyond the Standard Model. We introduceWavePID, a template-based log-likelihood-ratio classifier that exploits nanosecond-scale timing on individual detector modules through three observables: the distance to the reconstructed vertex, the early-charge fraction, and the module-to-module time difference. Evaluated on a cascade-enriched sample selected by a state-of-the-art graph neural network,WavePIDimproves both cascade purity and classification performance over the neural network alone. This demonstrates that per-module pulse timing carries flavor-identification information complementary to morphology-based classifiers, opening a new physics-motivated observable for low-energy neutrino reconstruction.Geant4simulations associate this signal with differences in Cherenkov emission geometry between muon tracks and electromagnetic showers. These results motivate exploiting nanosecond-scale pulse timing in future low-energy classifiers and in detector designs with improved per-module timing in next-generation neutrino telescopes. 

This Letter extends a recent IceCube sterile neutrino search to include unstable sterile neutrinos within the context of a model termed $3+1+\mathrm{Decay}$ , which expands upon the $3+1$ model by introducing sterile neutrino decay to invisible particles with coupling constant $g^2$ . The model is attractive since it reduces tension between oscillation experiments within the global fits and with constraints that come from cosmological observables. The analysis uses 10.7 years of up-going muon neutrino data with energy 500 GeV to 100 TeV and with improved reconstruction and modeling of systematics. The best-fit point is found to be $g^2=0$ , ${\sin }^2\left(2{\theta }_{24}\right)=0.16$ , and $\mathrm{\Delta }{m}_{41}^2=3.5{\mathrm{eV}}^2$ , in agreement with the recent $3+1$ sterile neutrino search. Values of $g^2\ge \pi$ are excluded at 95% confidence level. This result substantially limits decay parameter space indicated by recent global fits, disfavoring the decay scenario. 

The Radio Neutrino Observatory in Greenland (RNO-G) is the first in-ice radio array in the northern hemisphere for the detection of ultra-high energy neutrinos via the coherent radio emission from neutrino-induced particle cascades within the ice. The array is currently in phased construction near Summit Station on the Greenland ice sheet, with 7 stations deployed during the first two boreal summer field seasons of 2021 and 2022. In this paper, we describe the installation and system design of these initial RNO-G stations, and discuss the performance of the array as of summer 2024. 

Abstract Quantum Chromodynamics predicts a phase transition from hadronic matter to quark–gluon plasma (QGP) at high temperatures and energy densities, where quarks and gluons (partons) are no longer confined within hadrons. The QGP forms in ultrarelativistic heavy-ion collisions. Anisotropic flow coefficients, quantifying the azimuthal expansion of produced matter, probe QGP properties. Flow measurements in high-energy heavy-ion collisions show a distinctive grouping of anisotropic flow for baryons and mesons at intermediate transverse momentum – a feature associated with flow imparted at the quark level, confirming QGP existence. The observation of QGP-like features in proton–proton and proton–ion collisions has sparked debate about QGP formation in smaller systems. For the first time, we demonstrate the distinctive grouping of anisotropic flow for baryons and mesons in high-multiplicity proton–lead and proton–proton collisions at the Large Hadron Collider (LHC). These results are described by a model including hydrodynamic flow followed by hadron formation via quark coalescence, consistent with the formation of partonic flowing systems in these collisions. 

This paper describes how intentional and unintentional radio emission from airplanes is recorded with the Radio Neutrino Observatory Greenland (RNO-G). We characterize the received signals and define a procedure to extract a clean set of impulsive signals. These signals are highly suitable for instrument calibration, also for future experiments. A set of signals is used to probe the timing precision of RNO-G in-situ, which is found to match expectations. We also discuss the impact of these signals on the ability to detect neutrinos with RNO-G.