Abstract: A search for astrophysical pointlike neutrino sources using the data collected by the ANTARES detector between 2007 January 29 and 2017 December 31 is presented. A likelihood method is used to assess the significance of an excess of muon neutrinos inducing track-like events in correlation with the location of a list of possible sources. Different sets of objects are tested in the analysis: (a) a subsample of the Fermi 3LAC catalog of blazars, (b) a jet-obscured population of active galactic nuclei, (c) a sample of hard X-ray selected radio galaxies, (d) a star-forming galaxy catalog, and (e) a public sample of 56 very-high-energy track events from the IceCube experiment. None of the tested sources shows a significant association with the sample of neutrinos detected by ANTARES. The smallest p-value is obtained for the catalog of radio galaxies with an equal-weights hypothesis, with a pre-trial p-value equivalent to a 2.8 sigma excess, which is equivalent to 1.6 sigma post-trial. In addition, the results of a dedicated analysis for the blazar MG3 J225517+2409 are also reported: this source is found to be the most significant within the Fermi 3LAC sample, with five ANTARES events located less than one degree from the source. This blazar showed evidence of flaring activity in Fermi data, in spacetime coincidence with a high-energy track detected by IceCube. An a posteriori significance of 2.6 sigma for the combination of ANTARES and IceCube data is reported.

Abstract: Microquasars with high-mass companion stars are promising very high energy (VHE; 0.1-100 TeV) gamma-ray emitters, but their behaviors above 10 TeV are poorly known. Using the High Altitude Water Cerenkov (HAWC) observatory, we search for excess gamma-ray emission coincident with the positions of known high-mass microquasars (HMMQs). No significant emission is observed for LS 5039, Cyg X-1, Cyg X-3, and SS 433 with 1523 days of HAWC data. We set the most stringent limit above 10 TeV obtained to date on each individual source. Under the assumption that HMMQs produce gamma rays via a common mechanism, we have performed source-stacking searches, considering two different scenarios: (I) gamma-ray luminosity is a fraction epsilon ( gamma ) of the microquasar jet luminosity, and (II) VHE gamma rays are produced by relativistic electrons upscattering the radiation field of the companion star in a magnetic field B. We obtain epsilon ( gamma ) < 5.4 x 10(-6) for scenario I, which tightly constrains models that suggest observable high-energy neutrino emission by HMMQs. In the case of scenario II, the nondetection of VHE gamma rays yields a strong magnetic field, which challenges synchrotron radiation as the dominant mechanism of the microquasar emission between 10 keV and 10 MeV.

Abstract: The High Altitude Water Cherenkov (HAWC) observatory and the High Energy Stereoscopic System (H.E.S.S.) are two leading instruments in the ground-based very-high-energy gamma-ray domain. HAWC employs the water Cherenkov detection (WCD) technique, while H.E.S.S. is an array of Imaging Atmospheric Cherenkov Telescopes (IACTs). The two facilities therefore differ in multiple aspects, including their observation strategy, the size of their field of view, and their angular resolution, leading to different analysis approaches. Until now, it has been unclear if the results of observations by both types of instruments are consistent: several of the recently discovered HAWC sources have been followed up by IACTs, resulting in a confirmed detection only in a minority of cases. With this paper, we go further and try to resolve the tensions between previous results by performing a new analysis of the H.E.S.S. Galactic plane survey data, applying an analysis technique comparable between H.E.S.S. and HAWC. Events above 1 TeV are selected for both data sets, the point-spread function of H.E.S.S. is broadened to approach that of HAWC, and a similar background estimation method is used. This is the first detailed comparison of the Galactic plane observed by both instruments. H.E.S.S. can confirm the gamma-ray emission of four HAWC sources among seven previously undetected by IACTs, while the three others have measured fluxes below the sensitivity of the H.E.S.S. data set. Remaining differences in the overall gamma-ray flux can be explained by the systematic uncertainties. Therefore, we confirm a consistent view of the gamma-ray sky between WCD and IACT techniques.

Abstract: On 2019 October 1, the IceCube Collaboration detected a muon track neutrino with a high probability of being of astrophysical origin, IC191001A. After a few hours, the tidal disruption event (TDE) AT2019dsg, observed by the Zwicky Transient Facility (ZTF), was indicated as the most likely counterpart of the IceCube track. More recently, the follow-up campaign of the IceCube alerts by ZTF suggested a second TDE, AT2019fdr, as a promising counterpart of another IceCube muon track candidate, IC200530A, detected on 2020 May 30. Here, these intriguing associations are followed-up by searching for neutrinos in the ANTARES detector from the directions of AT2019dsg and AT2019fdr using a time-integrated approach. As no significant evidence for space clustering is found in the ANTARES data, upper limits on the one-flavor neutrino flux and fluence are set.

Abstract: We present the Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) Multifield Data set (CMD), a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from more than 2000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span similar to 100 million light-years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine-learning models, CMD is the largest data set of its kind containing more than 70 TB of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.