HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2021). Evidence of 200 TeV Photons from HAWC J1825-134. Astrophys. J. Lett., 907(2), L30–9pp.
Abstract: The Earth is bombarded by ultrarelativistic particles, known as cosmic rays (CRs). CRs with energies up to a few PeV (=10(15) eV), the knee in the particle spectrum, are believed to have a Galactic origin. One or more factories of PeV CRs, or PeVatrons, must thus be active within our Galaxy. The direct detection of PeV protons from their sources is not possible since they are deflected in the Galactic magnetic fields. Hundred TeV gamma-rays from decaying pi(0), produced when PeV CRs collide with the ambient gas, can provide the decisive evidence of proton acceleration up to the knee. Here we report the discovery by the High Altitude Water Cerenkov (HAWC) observatory of the gamma-ray source, HAWC J1825-134, whose energy spectrum extends well beyond 200 TeV without a break or cutoff. The source is found to be coincident with a giant molecular cloud. The ambient gas density is as high as 700 protons cm(-3). While the nature of this extreme accelerator remains unclear, CRs accelerated to energies of several PeV colliding with the ambient gas likely produce the observed radiation.
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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2021). Evidence that Ultra-high-energy Gamma Rays Are a Universal Feature near Powerful Pulsars. Astrophys. J. Lett., 911(2), L27–8pp.
Abstract: The highest-energy known gamma-ray sources are all located within 0.degrees 5 of extremely powerful pulsars. This raises the question of whether ultra-high-energy (UHE; >56 TeV) gamma-ray emission is a universal feature expected near pulsars with a high spin-down power. Using four years of data from the High Altitude Water Cherenkov Gamma-Ray Observatory, we present a joint-likelihood analysis of 10 extremely powerful pulsars to search for subthreshold UHE gamma-ray emission correlated with these locations. We report a significant detection (>3 sigma), indicating that UHE gamma-ray emission is a generic feature of powerful pulsars. We discuss the emission mechanisms of the gamma rays and the implications of this result. The individual environment, such as the magnetic field and particle density in the surrounding area, appears to play a role in the amount of emission.
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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2021). HAWC Search for High-mass Microquasars. Astrophys. J. Lett., 912(1), L4–12pp.
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.
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Bariego-Quintana, A., Calvo, D., Cecchini, V., Garcia Soto, A., et al. (2025). On the Potential Cosmogenic Origin of the Ultra-high-energy Event KM3-230213A. Astrophys. J. Lett., 984(2), L41–8pp.
Abstract: On 2023 February 13, the KM3NeT/ARCA telescope observed a track-like event compatible with a ultra-high-energy muon with an estimated energy of 120 PeV, produced by a neutrino with an even higher energy, making it the most energetic neutrino event ever detected. A diffuse cosmogenic component is expected to originate from the interactions of ultra-high-energy cosmic rays with ambient photon and matter fields. The flux level required by the KM3NeT/ARCA event is, however, in tension with the standard cosmogenic neutrino predictions based on the observations collected by the Pierre Auger Observatory and Telescope Array over the last decade of the ultra-high-energy cosmic rays above the ankle (hence from the local Universe, z less than or similar to 1). We show here that both observations can be reconciled by extending the integration of the equivalent cosmogenic neutrino flux up to a redshift of zmax=6 and considering either source evolution effects or the presence of a subdominant independent proton component in the ultra-high-energy cosmic-ray flux, thus placing constraints on known cosmic accelerators.
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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2022). The CAMELS Multifield Data Set: Learning the Universe's Fundamental Parameters with Artificial Intelligence. Astrophys. J. Suppl. Ser., 259(2), 61–14pp.
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.
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