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Auclair, P., Blanco-Pillado, J. J., Figueroa, D. G., Jenkins, A. C., Lewicki, M., Sakellariadou, M., et al. (2020). Probing the gravitational wave background from cosmic strings with LISA. J. Cosmol. Astropart. Phys., 04(4), 034–50pp.
Abstract: Cosmic string networks offer one of the best prospects for detection of cosmological gravitational waves (GWs). The combined incoherent GW emission of a large number of string loops leads to a stochastic GW background (SGWB), which encodes the properties of the string network. In this paper we analyze the ability of the Laser Interferometer Space Antenna (LISA) to measure this background, considering leading models of the string networks. We find that LISA will be able to probe cosmic strings with tensions G μgreater than or similar to O(10(-17)), improving by about 6 orders of magnitude current pulsar timing arrays (PTA) constraints, and potentially 3 orders of magnitude with respect to expected constraints from next generation PTA observatories. We include in our analysis possible modifications of the SGWB spectrum due to different hypotheses regarding cosmic history and the underlying physics of the string network. These include possible modifications in the SGWB spectrum due to changes in the number of relativistic degrees of freedom in the early Universe, the presence of a non-standard equation of state before the onset of radiation domination, or changes to the network dynamics due to a string inter-commutation probability less than unity. In the event of a detection, LISA's frequency band is well-positioned to probe such cosmic events. Our results constitute a thorough exploration of the cosmic string science that will be accessible to LISA.
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ATLAS Collaboration(Aad, G. et al), Aparisi Pozo, J. A., Bailey, A. J., Barranco Navarro, L., Cabrera Urban, S., Castillo, F. L., et al. (2020). ATLAS data quality operations and performance for 2015-2018 data-taking. J. Instrum., 15(4), P04003–43pp.
Abstract: The ATLAS detector at the Large Hadron Collider reads out particle collision data from over 100 million electronic channels at a rate of approximately 100 kHz, with a recording rate for physics events of approximately 1 kHz. Before being certified for physics analysis at computer centres worldwide, the data must be scrutinised to ensure they are clean from any hardware or software related issues that may compromise their integrity. Prompt identification of these issues permits fast action to investigate, correct and potentially prevent future such problems that could render the data unusable. This is achieved through the monitoring of detector-level quantities and reconstructed collision event characteristics at key stages of the data processing chain. This paper presents the monitoring and assessment procedures in place at ATLAS during 2015-2018 data-taking. Through the continuous improvement of operational procedures, ATLAS achieved a high data quality efficiency, with 95.6% of the recorded proton-proton collision data collected at root s = 13 TeV certified for physics analysis.
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Lopez-Honorez, L., Mena, O., Palomares-Ruiz, S., Villanueva-Domingo, P., & Witte, S. J. (2020). Variations in fundamental constants at the cosmic dawn. J. Cosmol. Astropart. Phys., 06(6), 026–25pp.
Abstract: The observation of space-time variations in fundamental constants would provide strong evidence for the existence of new light degrees of freedom in the theory of Nature. Robustly constraining such scenarios requires exploiting observations that span different scales and probe the state of the Universe at different epochs. In the context of cosmology, both the cosmic microwave background and the Lyman-a forest have proven to be powerful tools capable of constraining variations in electromagnetism, however at the moment there do not exist cosmological probes capable of bridging the gap between recombination and reionization. In the near future, radio telescopes will attempt to measure the 21 cm transition of neutral hydrogen during the epochs of reionization and the cosmic dawn (and potentially the tail end of the dark ages); being inherently sensitive to electromagnetic phenomena, these experiments will offer a unique perspective on space-time variations of the fine-structure constant and the electron mass. We show here that large variations in these fundamental constants would produce features on the 21 cm power spectrum that may be distinguishable from astrophysical uncertainties. Furthermore, we forecast the sensitivity for the Square Kilometer Array, and show that the 21 cm power spectrum may be able to constrain variations at the level of O(10(-3)).
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KM3NeT Collaboration(Aiello, S. et al), Alves Garre, S., Calvo, D., Carretero, V., Colomer, M., Corredoira, I., et al. (2020). Event reconstruction for KM3NeT/ORCA using convolutional neural networks. J. Instrum., 15(10), P10005–39pp.
Abstract: The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.
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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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