Abstract: Galactic gamma-ray diffuse emission (GDE) is emitted by cosmic rays (CRs), ultra-relativistic protons, and electrons, interacting with gas and electromagnetic radiation fields in the interstellar medium. Here we present the analysis of teraelectronvolt diffuse emission from a region of the Galactic plane over the range in longitude of l is an element of[43 degrees, 73 degrees], using data collected with the High Altitude Water Cherenkov (HAWC) detector. Spectral, longitudinal, and latitudinal distributions of the teraelectronvolt diffuse emission are shown. The radiation spectrum is compatible with the spectrum of the emission arising from a CR population with an index similar to that of the observed CRs. When comparing with the DRAGON base model, the HAWC GDE flux is higher by about a factor of 2. Unresolved sources such as pulsar wind nebulae and teraelectronvolt halos could explain the excess emission. Finally, deviations of the Galactic CR flux from the locally measured CR flux may additionally explain the difference between the predicted and measured diffuse fluxes.

Abstract: The Cosmology and Astrophysics with Machine Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4233 cosmological simulations, 2049 N-body simulations, and 2184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper, we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogs, power spectra, bispectra, Lya spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over 1000 catalogs that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz semianalytic model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies, and summary statistics. We provide further technical details on how to access, download, read, and process the data at .

Abstract: We report TeV gamma-ray observations of the ultra-high-energy source MGRO J1908+06 using data from the High Altitude Water Cherenkov Observatory. This source is one of the highest-energy known gamma-ray sources, with emission extending past 200 TeV. Modeling suggests that the bulk of the TeV gamma-ray emission is leptonic in nature, driven by the energetic radio-faint pulsar PSR J1907+0602. Depending on what assumptions are included in the model, a hadronic component may also be allowed. Using the results of the modeling, we discuss implications for detection prospects by multi-messenger campaigns.

Abstract: The High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory surveys the very high-energy sky in the 300 GeV to >100 TeV energy range. HAWC has detected two blazars above 11 sigma, Markarian 421 (Mrk 421) and Markarian 501 (Mrk 501). The observations are comprised of data taken in the period between 2015 June and 2018 July, resulting in similar to 1038 days of exposure. In this work, we report the time-averaged spectral analyses for both sources, above 0.5 TeV. Taking into account the flux attenuation due to the extragalactic background light, the intrinsic spectrum of Mrk 421 is described by a power law with an exponential energy cutoff with index alpha = 2.26 +/- (0.12)(stat)((+0.17)(-0.2))(sys) and energy cutoff E-c = 5.1 +/- (1.6)(stat)((+1.4)(-2.5))(sys) TeV, while the intrinsic spectrum of Mrk 501 is better described by a simple power law with index alpha = 2.61 +/- (0.11)(stat)((+)(0.01)(-0.07))(sys). The maximum energies at which the Mrk 421 and Mrk 501 signals are detected are 9 and 12 TeV, respectively. This makes these some of the highest energy detections to date for spectra averaged over years-long timescales. Since the observation of gamma radiation from blazars provides information about the physical processes that take place in their relativistic jets, it is important to study the broadband spectral energy distributions (SEDs) of these objects. For this purpose, contemporaneous data in the gamma-ray band to the X-ray range, and literature data in the radio to UV range, were used to build time-averaged SEDs that were modeled within a synchrotron-self Compton leptonic scenario.

Abstract: Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).