Special Issue "New Frontiers in Astroparticle Physics: From Nuclear Reactions to Multimessenger Astronomy"

A special issue of Universe (ISSN 2218-1997).

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Prof. Dr. Marcello Abbrescia
E-Mail Website
Guest Editor
Department of Physics (Interuniversity), University of Bari Aldo Moro, 70121 Bari, Italy
Interests: particle detectors; nuclear and subnuclear physics; astroparticle physics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Marina Trimarchi
E-Mail Website
Guest Editor
1. Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche, Scienze della Terra, Università degli Studi di Messina, viale F. Stagno d’Alcontres, 31, 98166 Messina, Italy
2. INFN Sezione of Catania, 95123 Catania, Italy
Interests: nuclear reactions; particle detectors; astroparticle physics

Special Issue Information

Dear Colleagues,

Recent observations have opened completely new perspectives in the way we look at the Universe and the tools we can use to comprehend it. For the first time, the same source has been detected by means of both gravitational and electromagnetic waves, and this implies that now, objects of astrophysical interest can be observed in a much more complete way than before. This is indeed the start of a new era, meaning that the means and techniques of investigation will be qualitatively different from before. In this perspective, bringing together expertise from different fields in astroparticle physics is of paramount importance. In addition to the ones cited, there are many other topics considered to have the most comprehensive view of the field. For instance, in this context, a detailed understanding of the complex nuclear reactions taking place in the cores of massive objects is quite important, and for this, the study of the relative cross-sections performed at heavy ion accelerators can be helpful. Moreover, the constant search for candidates for dark matter and dark energy could provide an additional piece to this mosaic and complete the view we have of the Universe. Furthermore, the development of new detection systems, and the possibility to compare different kinds of data, obtained using various techniques, opens the way to multimessenger astronomy, which actually represents one of the most interesting frontiers in astroparticle physics. We believe that this is the right moment to provide a state-of-the-art summary of all most recent discoveries and developments, making an effort to combine the information coming from different fields and techniques of investigation. Contributions are expected to address, but are not limited to, the following areas:

  • Multimessenger astronomy;
  • Gravitational waves;
  • Nuclear reactions of astrophysical interest;
  • Astroparticle physics;
  • Detection techniques for astroparticle physics;
  • Dark matter and dark energy.

Prof. Dr. Marcello Abbrescia
Prof. Dr. Marina Trimarchi
Guest Editors


Related References:

  1. Wang, Y.; Moritz, B.; Chen, C.-C.; Jia, C. J.; Veenendaal, M. van and Devereaux, T. P. Observation of Gravitational Waves from a Binary Black Hole Merger. Phys. Rev. Lett. 2016, doi:10.1103/PhysRevLett.116.061102.
  2. Abbott, B.P. Prospects for observing and localizing gravitational-wave transients with Advanced LIGO, Advanced Virgo and KAGRA. Liv. Rev. Relativ. 2018, doi:10.1007/s41114-018-0012-9.
  3. Li, B.A.; Krastev, P.G.; Wen, D.H.; Zhang, N.B. Towards understanding astrophysical effects of nuclear symmetry energy. Euro. Phys. J. A: 2019. doi:10.1140/epja/i2019-12780-8.
  4. Negoita, F.; Roth, M.; Thirolf, P.G.; et al. Laser Driven Nuclear Physics At Eli-Np. Romanian Report. Phys. 2016, 68 S37–S144.
  5. Ezquiaga, J.M.; Zumalacárregui, M. Dark Energy After GW170817: Dead Ends and the Road Ahead. Phys. Rev. Lett. 2017, doi:10.1103/PhysRevLett.119.251304.
  6. Kampert, K.H.; Unger, M. Measurements of the cosmic ray composition with air shower experiments. Astrop. Phys. 2012, doi:10.1016/j.astropartphys.2012.02.004.
  7. Nagano, M.; Watson, A.A. Observations and implications of the ultrahigh-energy cosmic rays. Rev. Mod. Phys. 2000, doi:10.1103/RevModPhys.72.689.
  8. Aartsen, M.G.; Abraham, K.; Ackermann, M.; Adams, J.; Aguilar, J.A.; Ahlers, M.; Ahrens, M.; Altmann, D.; Andeen, K.; Anderson, T. All-sky Search for Time-integrated Neutrino Emission from Astrophysical Sources with 7 yr of IceCube Data. Astrophys. J. 2017, doi:10.3847/1538-4357/835/2/151.
  9. EEE Collaboration. Search for long distance correlations between extensive air showers detected by the EEE network, Eur. Phys. Journ. Plus, 2018, doi:10.1140/epjp/i2018-11898-x.

 

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Universe is an international peer-reviewed open access monthly journal published by MDPI.

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Keywords

  • Gravitational Waves
  • Gravitational Wave Detectors and Experiments
  • Multimessenger Astronomy
  • Nuclear, Particle, and Astroparticle Physics
  • Nuclear and Particle Accelerators and Detection Systems of Astrophysical Interest
  • Cosmic Rays Interactions and Experiments
  • Astroparticle Physics and Related Detection Techniques
  • Dark Matter and Dark Energy

Published Papers (1 paper)

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Research

Article
Application of Neural Networks to Classification of Data of the TUS Orbital Telescope
Universe 2021, 7(7), 221; https://doi.org/10.3390/universe7070221 - 01 Jul 2021
Viewed by 634
Abstract
We employ neural networks for classification of data of the TUS fluorescence telescope, the world’s first orbital detector of ultra-high energy cosmic rays. We focus on two particular types of signals in the TUS data: track-like flashes produced by cosmic ray hits of [...] Read more.
We employ neural networks for classification of data of the TUS fluorescence telescope, the world’s first orbital detector of ultra-high energy cosmic rays. We focus on two particular types of signals in the TUS data: track-like flashes produced by cosmic ray hits of the photodetector and flashes that originated from distant lightnings. We demonstrate that even simple neural networks combined with certain conventional methods of data analysis can be highly effective in tasks of classification of data of fluorescence telescopes. Full article
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