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ABSTRACT Using blazar light curves from the optical All-Sky Automated Survey for Supernovae (ASAS-SN) and the γ-ray Fermi-LAT telescope, we performed the most extensive statistical correlation study between both bands, using a sample of 1180 blazars. This is almost an order of magnitude larger than other recent studies. Blazars represent more than 98 per cent of the AGNs detected by Fermi-LAT and are the brightest γ-ray sources in the extragalactic sky. They are essential for studying the physical properties of astrophysical jets from central black holes. However, their γ-ray flare mechanism is not fully understood. Multiwavelength correlations help constrain the dominant mechanisms of blazar variability. We search for temporal relationships between optical and γ-ray bands. Using a Bayesian Block Decomposition, we detect 1414 optical and 510 γ-ray flares, we find a strong correlation between both bands. Among all the flares, we find 321 correlated flares from 133 blazars, and derive an average rest-frame time delay of only 1.1$$_{-8.5}^{+7.1}$$ d, with no difference between the flat-spectrum radio quasars, BL Lacertae-like objects or low, intermediate, and high-synchrotron peaked blazar classes. Our time-delay limit rules out the hadronic proton-synchrotron model as the driver for non-orphan flares and suggests a leptonic single-zone model. Limiting our search to well-defined light curves and removing 976 potential but unclear ‘orphan’ flares, we find 191 (13 per cent) and 115 (22 per cent) clear ‘orphan’ optical and γ-ray flares. The presence of ‘orphan’ flares in both bands challenges the standard one-zone blazar flare leptonic model and suggests multizone synchrotron sites or a hadronic model for some blazars. 

Context.Superluminous supernovae (SLSNe) are a rare class of transients with peak luminosities 10–100 times greater than those of standard core-collapse supernovae (SNe). The mechanisms powering their extreme brightness remain debated, with circumstellar medium (CSM) interaction, or energy injection from a central engine like a magnetar wind nebula being the most plausible scenarios. While the optical properties of SLSNe are extensively studied, theirγ-ray signatures remain poorly constrained. Aims.To further constrain the underlying mechanism, we carried out a systematic search for giga-electronvoltγ-ray emission using theFermiLarge Area Telescope (LAT) from a sample of nearby hydrogen-poor (Type I) and hydrogen-rich (Type II) SLSNe over the past 16 years. Our objective is to test predictions from CSM and magnetar models, and to assess the prospects for future detections with the Cherenkov Telescope Array Observatory (CTAO). Methods.For the six targets of this sample, we studied the time variability of a putativeγ-ray signal at the optical position of the SLSN on a six-month timescale, and in the case of SN 2017egm, we further investigated variability on 15-day intervals and applied a Bayesian block algorithm to characterize the time variability of the signal. We then compared the temporal evolution and spectral properties to the predictions from a magnetar and CSM interaction model. Results.Among the sample, only SN 2017egm shows significantγ-ray emission, with likelihood test statistic (TS) values of 26–33 (i.e., > 5σ) depending on the adopted time window. The signal arises between 50 and 160 days after explosion and is well described by a power-law spectrum with index Γ = 2.17 ± 0.23. The emission is consistent both in terms of its light curve and its spectrum, with predictions from magnetar models requiring either low nebular magnetization or faster spin-down than dipole losses. The CSM shell interaction scenario can reproduce the observed flux level but not the observed timing of theγ-ray signal. In addition, the observed ratio,L_γ/L_opt ∼ 1, is inconsistent with theoretical expectations and not in line with ratio measurements in other interacting CSM-dominated objects (e.g., novae or SNe) where this ratio is less than 10⁻². Conclusions.Our study strongly suggests that a central engine like a magnetar plays a key role in this SLSN and could explain the bulk of the optical andγ-ray light curves properties. In order to explain the observed late-time bumps in the optical light curve of SN 2017egm, we require either: a hybrid picture combining magnetar and multiple CSM shells for the optical bumps or a pure magnetar model with infalling matter on an accretion disk. Finally, simulations of 50 hours of CTAO observations indicate that a SN 2017egm-like event would be detectable up to ∼140 Mpc in the magnetar model but not in the CSM model due to strongγ − γabsorption. 

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. 

ABSTRACT We report on the search for electromagnetic counterparts to the nine gravitational-wave events with a >60 per cent probability of containing a neutron star during the third observing run (O3) of the Laser Interferometer Gravitational-Wave Observatory (LIGO)–Virgo Collaboration (LVC) with the All-Sky Automated Survey for SuperNovae (ASAS-SN). No optical counterparts associated with a gravitational-wave event were found. However, thanks to its network of telescopes, the average area visible to at least one ASAS-SN site during the first 10 h after the trigger contained ∼30 per cent of the integrated source location probability. Through a combination of normal operations and target-of-opportunity observations, ASAS-SN observations of the highest probability fields began within 1 h of the trigger for four of the events. After 24 h, ASAS-SN observed >60 per cent of total probability for three events and >40 per cent for all but one of the events. This is the largest area coverage to a depth of g = 18.5 mag from any survey with published coverage statistics for seven of the nine events. With its observing strategy, five sites around the world, and a large field of view, ASAS-SN will be one of the leading surveys to optically search for nearby neutron star mergers during LVC fourth observation run (O4). 

Radio pulses generated by cosmic-ray air showers can be used to reconstruct key properties like the energy and depth of the electromagnetic component of cosmic-ray air showers. Radio detection threshold, influenced by natural and anthropogenic radio background, can be reduced through various techniques. In this work, we demonstrate that convolutional neural networks (CNNs) are an effective way to lower the threshold. We developed two CNNs: a classifier to distinguish radio signal waveforms from background noise and a denoiser to clean contaminated radio signals. Following the training and testing phases, we applied the networks to air-shower data triggered by scintillation detectors of the prototype station for the enhancement of IceTop, IceCube’s surface array at the South Pole. Over a four-month period, we identified 554 cosmic-ray events in coincidence with IceTop, approximately five times more compared to a reference method based on a cut on the signal-to-noise ratio. Comparisons with IceTop measurements of the same air showers confirmed that the CNNs reliably identified cosmic-ray radio pulses and outperformed the reference method. Additionally, we find that CNNs reduce the false-positive rate of air-shower candidates and effectively denoise radio waveforms, thereby improving the accuracy of the power and arrival time reconstruction of radio pulses. 

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. 

The IceCube Upgrade is an extension of the existing IceCube Neutrino Observatory and will be deployed in the 2025–2026 austral summer. It will significantly improve the sensitivity of the detector to atmospheric neutrino oscillations. The existing 86-string IceCube array contains a dense in-fill known as DeepCore which is optimized to measure neutrinos with energies down to a few GeV. The IceCube Upgrade will consist of seven new densely instrumented strings placed within the DeepCore volume to further enhance the performance in the GeV energy range. The additional strings will feature new optical modules, each containing multiple photomultiplier tubes (PMTs), in contrast to the existing modules that each contain a single PMT. This will more than triple the number of PMT channels with respect to the current IceCube configuration, allowing for improved detection efficiency and reconstruction performance at GeV energies. We describe necessary updates to simulation, event selection, and reconstruction to accommodate the higher data rates observed by the upgraded detector and the addition of multi-PMT modules. We determine the expected sensitivity of the IceCube Upgrade to the atmospheric neutrino oscillation parameters ${\sin }^2{\theta }_{23}$ and $\mathrm{\Delta }{m}_{32}^2$ , the appearance of tau neutrinos and the neutrino mass ordering. The IceCube Upgrade will provide neutrino oscillation measurements that are of similar precision to those from accelerator experiments, while providing complementarity by probing higher energies and longer baselines, and with different sources of systematic uncertainties.