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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. 

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. 

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. 

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. 

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification. 

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 filtersXandY. 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_XYis 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_XYcan be understood as a result of chemical mixing due to large-scale Rayleigh–Taylor instabilities.