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Muñoz, V., Takhistov, V., Witte, S. J., & Fuller, G. M. (2021). Exploring the origin of supermassive black holes with coherent neutrino scattering. J. Cosmol. Astropart. Phys., 11(11), 020–16pp.
Abstract: Collapsing supermassive stars (M greater than or similar to 3 x 10(4) M-circle dot) at high redshifts can naturally provide seeds and explain the origin of the supermassive black holes observed in the centers of nearly all galaxies. During the collapse of supermassive stars, a burst of non-thermal neutrinos is generated with a luminosity that could greatly exceed that of a conventional core collapse supernova explosion. In this work, we investigate the extent to which the neutrinos produced in these explosions can be observed via coherent elastic neutrino-nucleus scattering (CEvNS). Large scale direct dark matter detection experiments provide particularly favorable targets. We find that upcoming O(100) tonne-scale experiments will be sensitive to the collapse of individual supermassive stars at distances as large as O(10) Mpc.
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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NEXT Collaboration(Kekic, M. et al), Benlloch-Rodriguez, J. M., Carcel, S., Carrion, J. V., Diaz, J., Felkai, R., et al. (2021). Demonstration of background rejection using deep convolutional neural networks in the NEXT experiment. J. High Energy Phys., 01(1), 189–22pp.
Abstract: Convolutional neural networks (CNNs) are widely used state-of-the-art computer vision tools that are becoming increasingly popular in high-energy physics. In this paper, we attempt to understand the potential of CNNs for event classification in the NEXT experiment, which will search for neutrinoless double-beta decay in Xe-136. To do so, we demonstrate the usage of CNNs for the identification of electron-positron pair production events, which exhibit a topology similar to that of a neutrinoless double-beta decay event. These events were produced in the NEXT-White high-pressure xenon TPC using 2.6 MeV gamma rays from a Th-228 calibration source. We train a network on Monte Carlo-simulated events and show that, by applying on-the-fly data augmentation, the network can be made robust against differences between simulation and data. The use of CNNs offers significant improvement in signal efficiency and background rejection when compared to previous non-CNN-based analyses.
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HAWC Collaboration(Abeysekara, A. U. et al), & Salesa Greus, F. (2021). HAWC observations of the acceleration of very-high-energy cosmic rays in the Cygnus Cocoon. Nat. Astron., 4, 465–471.
Abstract: Cosmic rays with energies up to a few PeV are known to be accelerated within the Milky Way(1,2). Traditionally, it has been presumed that supernova remnants were the main source of these very-high-energy cosmic rays(3,4), but theoretically it is difficult to accelerate protons to PeV energies(5,6) and observationally there simply is no evidence of the remnants being sources of hadrons with energies above a few tens of TeV7,8. One possible source of protons with those energies is the Galactic Centre region(9). Here, we report observations of 1-100 TeV gamma rays coming from the 'Cygnus Cocoon'(10), which is a superbubble that surrounds a region of massive star formation. These gamma rays are likely produced by 10-1,000 TeV freshly accelerated cosmic rays that originate from the enclosed star-forming region Cyg OB2. Until now it was not known that such regions could accelerate particles to these energies. The measured flux likely originates from hadronic interactions. The spectral shape and the emission profile of the Cocoon changes from GeV to TeV energies, which reveals the transport of cosmic particles and historical activity in the superbubble.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Cabrera Urban, S., Castillo, F. L., Castillo Gimenez, V., et al. (2021). Differential cross-section measurements for the electroweak production of dijets in association with a Z boson in proton-proton collisions at ATLAS. Eur. Phys. J. C, 81(2), 163–42pp.
Abstract: Differential cross-sectionmeasurements are presented for the electroweak production of two jets in association with a Z boson. These measurements are sensitive to the vector-boson fusion production mechanism and provide a fundamental test of the gauge structure of the Standard Model. The analysis is performed using proton-proton collision data collected by ATLAS at root s = 13 TeV and with an integrated luminosity of 139 fb(-1). The differential cross-sections are measured in the Z -> l(+)l(-) decay channel (l = e, mu) as a function of four observables: the dijet invariant mass, the rapidity interval spanned by the two jets, the signed azimuthal angle between the two jets, and the transverse momentum of the dilepton pair. The data are corrected for the effects of detector inefficiency and resolution and are sufficiently precise to distinguish between different state-of-the-art theoretical predictions calculated using Powheg+Pythia8, Herwig7+Vbfnlo and Sherpa 2.2. The differential cross-sections are used to search for anomalous weak-boson self-interactions using a dimension-six effective field theory. Themeasurement of the signed azimuthal angle between the two jets is found to be particularly sensitive to the interference between the Standard-Model and dimension-six scattering amplitudes and provides a direct test of charge-conjugation and parity invariance in the weak-boson self-interactions.
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