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21.
Multi-messenger studies with the Pierre Auger Observatory
Lukas Zehrer, Andrej Filipčič, Gašper Kukec Mezek, Jon Paul Lundquist, Samo Stanič, Marta Trini, Serguei Vorobiov, Marko Zavrtanik, Danilo Zavrtanik, 2021, published scientific conference contribution

Abstract: Over the past decade the multi-messenger astrophysics has emerged as a distinct discipline, providing unique insights into the properties of high-energy phenomena in the Universe. The Pierre Auger Observatory, located in Malargüe, Argentina, is the world’s largest cosmic ray detector sensitive to photons, neutrinos, and hadrons at ultra-high energies. Using its data, stringent limits on photon and neutrino fluxes at EeV energies have been obtained. The collaboration uses the excellent angular resolution and the neutrino identification capabilities of the Observatory for follow-up studies of events detected in gravitational waves or other messengers, through cooperation with global multi-messenger networks. We present a science motivation together with an overview of the multi-messenger capabilities and results of the Pierre Auger Observatory.
Keywords: high-energy cosmic phenomena, multi-messenger astrophysical studies, cosmic rays, gamma-rays, neutrinos, Pierre Auger Observatory
Published in RUNG: 06.05.2022; Views: 1575; Downloads: 0
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Finding (or not) dark matter in gamma-ray images of the Galactic center with computer vision
Gudlaugur Johannesson, Gabrijela Zaharijas, Sascha Caron, Christopher Eckner, Luc Hendriks, Roberto Ruiz de Austri, 2021, published scientific conference contribution abstract

Keywords: machine learning, gamma rays
Published in RUNG: 17.02.2022; Views: 1628; Downloads: 7
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25.
Localisation and classification of gamma ray sources using neural networks
Chris van den Oetelaar, Saptashwa Bhattacharyya, Boris Panes, Sascha Caron, Gabrijela Zaharijas, Roberto Ruiz de Austri, Guõlaugur Jóhannesson, 2021, published scientific conference contribution

Abstract: With limited statistics and spatial resolution of current detectors, accurately localising and separating gamma-ray point sources from the dominating interstellar emission in the GeV energy range is challenging. Motivated by the challenges of the traditional methods used for the gamma-ray source detection, here we demonstrate the application of deep learning based algorithms to automatically detect and classify point sources, which can be applied directly to the binned Fermi-LAT data and potentially be generalised to other wavelengths. For the point source detection task, we use popular deep neural network structure U-NET, together with image segmentation, for precise localisation of sources, various clustering algorithms were tested on the segmented images. The training samples are based on the source properties of AGNs and PSRs from the latest Fermi-LAT source catalog, in addition to the background interstellar emission. Finally, we have created a more complex but robust training data generation exploiting full detector potential, increasing spatial resolution at the highest energies.
Keywords: gamma-rays, deep learning, computer vision
Published in RUNG: 01.10.2021; Views: 1752; Downloads: 42
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26.
On velocity-dependent dark matter annihilations in dwarf satellites
Mihael Petač, Piero Ullio, Mauro Valli, 2018, original scientific article

Abstract: Milky Way dwarf spheroidal satellites are a prime target for Dark Matter (DM) indirect searches. There have been recent reassessments of the expected DM gamma-ray signals in case of long-range interactions, commonly known as Sommerfeld enhancement. Since details of the underlying DM phase-space distribution function become critical, there are potentially large uncertainties in the final result. We provide here a first attempt towards a comprehensive investigation of these systematics, addressing the impact on the expected DM flux from Milky Way dwarfs via Bayesian inference on the available stellar kinematic datasets. After reconsidering the study case of ergodic systems, we investigate for the first time scenarios where DM particle orbits may have a radial or tangential bias. We consider both cuspy and cored parametric DM density profiles, together with the case of a non-parametric halo modelling directly connected to line-of-sight observable quantities. The main findings of our work highlight the relevance of the assumed phase-space distribution: Referring to a generalized J-factor, namely the line-of-sight convolution of the spatial part in case of velocity-dependent annihilation rate, an enhancement (suppression) with respect to the limit of isotropic phase-space distributions is obtained for the case of tangentially (radially) biased DM particle orbits. We provide new estimates for J-factors for the eight brightest Milky Way dwarfs also in the limit of velocity-independent DM annihilation, in good agreement with previous results in literature, and derive data-driven lower-bounds based on the non-parametric modelling of the halo density. The outcome of our broad study stands out as a representative of the state-of-the-art in the field, and falls within the interest of current and future experimental collaborations involved in DM indirect detection programs.
Keywords: dark matter, indirect detection, dwarf satellites, sommerfeld enhancement, gamma-rays
Published in RUNG: 01.10.2021; Views: 1682; Downloads: 43  (1 vote)
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Fermi Large Area Telescope fourth source catalog
S. Abdollahi, Fabio Acero, M. Ajello, W. B. Atwood, M. Axelsson, L. Baldini, J. Ballet, G. Barbiellini, D. Bastieri, Gabrijela Zaharijas, 2020, original scientific article

Keywords: astronomy, gamma rays, sky surveys
Published in RUNG: 22.05.2020; Views: 2946; Downloads: 200
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29.
TeV dark matter search at the Galactic center with the CTA
Gabrijela Zaharijas, unpublished invited conference lecture

Keywords: gamma rays, dark matter, Cherenkov Telescope Array
Published in RUNG: 16.05.2020; Views: 2687; Downloads: 0
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30.
Gamma Ray Showers Observed at Ground Level in Coincidence With Downward Lightning Leaders
R.U. Abbasi, Jon Paul Lundquist, 2018, original scientific article

Abstract: Bursts of gamma ray showers have been observed in coincidence with downward propagating negative leaders in lightning flashes by the Telescope Array Surface Detector (TASD). The TASD is a 700‐km^2 cosmic ray observatory located in southwestern Utah, USA. In data collected between 2014 and 2016, correlated observations showing the structure and temporal development of three shower‐producing flashes were obtained with a 3‐D lightning mapping array, and electric field change measurements were obtained for an additional seven flashes, in both cases colocated with the TASD. National Lightning Detection Network information was also used throughout. The showers arrived in a sequence of 2–5 short‐duration (≤10 μs) bursts over time intervals of several hundred microseconds and originated at an altitude of ≃3–5 km above ground level during the first 1–2 ms of downward negative leader breakdown at the beginning of cloud‐to‐ground lightning flashes. The shower footprints, associated waveforms and the effect of atmospheric propagation indicate that the showers consist primarily of downward‐beamed gamma radiation. This has been supported by GEANT simulation studies, which indicate primary source fluxes of ≃10^12–10^14 photons for 16° half‐angle beams. We conclude that the showers are terrestrial gamma ray flashes, similar to those observed by satellites, but that the ground‐based observations are more representative of the temporal source activity and are also more sensitive than satellite observations, which detect only the most powerful terrestrial gamma ray flashes.
Keywords: gamma rays, lightning, gamma ray bursts, surface detector
Published in RUNG: 30.04.2020; Views: 3160; Downloads: 0
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