|
|
HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2022). HAWC Study of the Ultra-high-energy Spectrum of MGRO J1908+06. Astrophys. J., 928(2), 116–13pp.
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.
|
|
|
|
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.
|
|
|
|
HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2022). Long-term Spectra of the Blazars Mrk 421 and Mrk 501 at TeV Energies Seen by HAWC. Astrophys. J., 929(2), 125–12pp.
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.
|
|
|
|
HAWC Collaboration(Albert, A. et al), & Salesa Greus, F. (2022). Probing the Extragalactic Mid-infrared Background with HAWC. Astrophys. J., 933(2), 223–8pp.
Abstract: The extragalactic background light (EBL) contains all the radiation emitted by nuclear and accretion processes in stars and compact objects since the epoch of recombination. Measuring the EBL density directly is challenging, especially in the near-to-far-infrared wave band, mainly due to the zodiacal light foreground. Instead, gamma-ray astronomy offers the possibility to indirectly set limits on the EBL by studying the effects of gamma-ray absorption in the very high energy (VHE: >100 GeV) spectra of distant blazars. The High Altitude Water Cherenkov Gamma Ray Observatory (HAWC) is one of the few instruments sensitive to gamma rays with energies above 10 TeV. This offers the opportunity to probe the EBL in the near/mid-IR region: lambda = 1-100 μm. In this study, we fit physically motivated emission models to Fermi-LAT gigaelectronvolt data to extrapolate the intrinsic teraelectronvolt spectra of blazars. We then simulate a large number of absorbed spectra for different randomly generated EBL model shapes and calculate Bayesian credible bands in the EBL intensity space by comparing and testing the agreement between the absorbed spectra and HAWC extragalactic observations of two blazars. The resulting bands are in agreement with current EBL lower and upper limits, showing a downward trend toward higher wavelength values lambda > 10 μm also observed in previous measurements.
|
|
|
|
Villanueva-Domingo, P., Villaescusa-Navarro, F., Angles-Alcazar, D., Genel, S., Marinacci, F., Spergel, D. N., et al. (2022). Inferring Halo Masses with Graph Neural Networks. Astrophys. J., 935(1), 30–15pp.
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).
|
|
