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HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2020). 3HWC: The Third HAWC Catalog of Very-high-energy Gamma-Ray Sources. Astrophys. J., 905(1), 76–14pp.
Abstract: We present a new catalog of TeV gamma-ray sources using 1523 days of data from the High-Altitude Water Cherenkov (HAWC) Observatory. The catalog represents the most sensitive survey of the northern gamma-ray sky at energies above several TeV, with three times the exposure compared to the previous HAWC catalog, 2HWC. We report 65 sources detected at >= 5 sigma significance, along with the positions and spectral fits for each source. The catalog contains eight sources that have no counterpart in the 2HWC catalog, but are within 1 degrees of previously detected TeV emitters, and 20 sources that are more than 1 degrees away from any previously detected TeV source. Of these 20 new sources, 14 have a potential counterpart in the fourth Fermi Large Area Telescope catalog of gamma-ray sources. We also explore potential associations of 3HWC sources with pulsars in the Australia Telescope National Facility (ATNF) pulsar catalog and supernova remnants in the Galactic supernova remnant catalog.
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Seo, H. J. et al, & de Putter, R. (2012). Acoustic scale from the angular power spectra of SDSS-III DR8 photometric luminous galaxies. Astrophys. J., 761(1), 13–16pp.
Abstract: We measure the acoustic scale from the angular power spectra of the Sloan Digital Sky Survey III (SDSS-III) Data Release 8 imaging catalog that includes 872, 921 galaxies over similar to 10,000 deg(2) between 0.45 < z < 0.65. The extensive spectroscopic training set of the Baryon Oscillation Spectroscopic Survey luminous galaxies allows precise estimates of the true redshift distributions of galaxies in our imaging catalog. Utilizing the redshift distribution information, we build templates and fit to the power spectra of the data, which are measured in our companion paper, to derive the location of Baryon acoustic oscillations (BAOs) while marginalizing over many free parameters to exclude nearly all of the non-BAO signal. We derive the ratio of the angular diameter distance to the sound horizon scale D-A(z)/r(s) = 9.212(-0.404)(+0.416) at z = 0.54, and therefore D-A(z) = 1411 +/- 65 Mpc at z = 0.54; the result is fairly independent of assumptions on the underlying cosmology. Our measurement of angular diameter distance D-A(z) is 1.4 sigma higher than what is expected for the concordance Lambda CDM, in accordance to the trend of other spectroscopic BAO measurements for z greater than or similar to 0.35. We report constraints on cosmological parameters from our measurement in combination with the WMAP7 data and the previous spectroscopic BAO measurements of SDSS and WiggleZ. We refer to our companion papers (Ho et al.; de Putter et al.) for investigations on information of the full power spectrum.
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Cermeño, M., Perez-Garcia, M. A., & Lineros, R. A. (2018). Enhanced neutrino emissivities in pseudoscalar-mediated dark matter annihilation in neutron stars. Astrophys. J., 863(2), 157–9pp.
Abstract: We calculate neutrino emissivities from self-annihilating dark matter (DM) (chi) in the dense and hot stellar interior of a (proto)neutron star. Using a model where DM interacts with nucleons in the stellar core through a pseudoscalar boson (a) we find that the neutrino production rates from the dominant reaction channels chi -> nu(nu) over bar or chi chi -> aa, with subsequent decay of the mediator a -> nu(nu) over bar, could locally match and even surpass those of the standard neutrinos from the modified nuclear URCA processes at early ages. We find that the emitting region can be localized in a tiny fraction of the star (less than a few percent of the core volume) and the process can last its entire lifetime for some cases under study. We discuss the possible consequences of our results for stellar cooling in light of existing DM constraints.
Keywords: dark matter; neutrinos; stars: neutron
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Villaescusa-Navarro, F. et al, & Villanueva-Domingo, P. (2023). The CAMELS Project: Public Data Release. Astrophys. J. Suppl. Ser., 265(2), 54–14pp.
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 .
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Villanueva-Domingo, P., & Villaescusa-Navarro, F. (2021). Removing Astrophysics in 21 cm Maps with Neural Networks. Astrophys. J., 907(1), 44–14pp.
Abstract: Measuring temperature fluctuations in the 21 cm signal from the epoch of reionization and the cosmic dawn is one of the most promising ways to study the universe at high redshifts. Unfortunately, the 21 cm signal is affected by both cosmology and astrophysics processes in a nontrivial manner. We run a suite of 1000 numerical simulations with different values of the main astrophysical parameters. From these simulations we produce tens of thousands of 21 cm maps at redshifts 10 <= z <= 20. We train a convolutional neural network to remove the effects of astrophysics from the 21 cm maps and output maps of the underlying matter field. We show that our model is able to generate 2D matter fields not only that resemble the true ones visually but whose statistical properties agree with the true ones within a few percent down to scales 2 Mpc(-1). We demonstrate that our neural network retains astrophysical information that can be used to constrain the value of the astrophysical parameters. Finally, we use saliency maps to try to understand which features of the 21 cm maps the network is using in order to determine the value of the astrophysical parameters.
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