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785 results on '"event reconstruction"'

3. Indirekte Orbitafrakturen nach okzipitalem Trauma (Geserick-Zeichen).

Author

Gerling, Moritz, Ondruschka, Benjamin, Kniep, Inga, and Anders, Sven

Abstract

Copyright of Rechtsmedizin is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2023
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4. Reconstructing the kinematics of deep inelastic scattering with deep learning

Author

Arratia, Miguel, Britzger, Daniel, Long, Owen, and Nachman, Benjamin

Subjects
Nuclear and Plasma Physics, Physical Sciences, Bioengineering, Rehabilitation, Machine learning, Neural networks, Event reconstruction, Deep inelastic scattering, HERA, EIC, Astronomical and Space Sciences, Atomic, Molecular, Nuclear, Particle and Plasma Physics, Other Physical Sciences, Nuclear & Particles Physics, Nuclear and plasma physics
Published
2022

5. Reconstructing invisible deviating events: A conformance checking approach for recurring events

Author

Joscha Grüger, Martin Kuhn, and Ralph Bergmann

Subjects
process mining, conformance checking, recurring events, invisible deviating events, event reconstruction, event log preprocessing, Biotechnology, TP248.13-248.65, Mathematics, QA1-939
Abstract

Conformance checking enables organizations to determine whether their executed processes are compliant with the intended process. However, if the processes contain recurring activities, state-of-the-art approaches unfortunately have difficulties calculating the conformance. The occurrence of complex temporal rules can further increase the complexity of the problem. Identifying this limitation, this paper presents a novel approach towards dealing with recurring activities in conformance checking. The core idea of the approach is to reconstruct the missing events in the event log using defined rules while incorporating specified temporal event characteristics. This approach then enables the use of native conformance checking algorithms. The paper illustrates the algorithmic approach and defines the required temporal event characteristics. Furthermore, the approach is applied and evaluated in a case study on an event log for melanoma surveillance.

Published
2022
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7. An Ontology Engineering Case Study for Advanced Digital Forensic Analysis

Author

Chikul, Pavel, Bahsi, Hayretdin, Maennel, Olaf, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Attiogbé, Christian, editor, and Ben Yahia, Sadok, editor

Published
2021
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11. Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks

Author

Mo Jia, Karan Kumar, Liam S. Mackey, Alexander Putra, Cristovao Vilela, Michael J. Wilking, Junjie Xia, Chiaki Yanagisawa, and Karan Yang

Subjects
experimental particle physics, event reconstruction, water Cherenkov detectors, generative models, convolutional neural network, Information technology, T58.5-58.64
Abstract

Large water Cherenkov detectors have shaped our current knowledge of neutrino physics and nucleon decay, and will continue to do so in the foreseeable future. These highly capable detectors allow for directional and topological, as well as calorimetric information to be extracted from signals on their photosensors. The current state-of-the-art approach to water Cherenkov reconstruction relies on maximum-likelihood estimation, with several simplifying assumptions employed to make the problem tractable. In this paper, we describe neural networks that produce probability density functions for the signals at each photosensor, given a set of inputs that characterizes a particle in the detector. The neural networks we propose allow for likelihood-based approaches to event reconstruction with significantly fewer assumptions compared to traditional methods, and are thus expected to improve on the current performance of water Cherenkov detectors.

Published
2022
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12. Digital Forensics Event Graph Reconstruction

Author

Schelkoph, Daniel J., Peterson, Gilbert L., Okolica, James S., Akan, Ozgur, Series Editor, Bellavista, Paolo, Series Editor, Cao, Jiannong, Series Editor, Coulson, Geoffrey, Series Editor, Dressler, Falko, Series Editor, Ferrari, Domenico, Series Editor, Gerla, Mario, Series Editor, Kobayashi, Hisashi, Series Editor, Palazzo, Sergio, Series Editor, Sahni, Sartaj, Series Editor, Shen, Xuemin (Sherman), Series Editor, Stan, Mircea, Series Editor, Xiaohua, Jia, Series Editor, Zomaya, Albert Y., Series Editor, Breitinger, Frank, editor, and Baggili, Ibrahim, editor

Published
2019
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13. Temporally sorting images from real-world events.

Author

Padilha, Rafael, Andaló, Fernanda A., Lavi, Bahram, Pereira, Luís A.M., and Rocha, Anderson

Subjects
*DATA augmentation, *CONCEPT learning, *VISUALIZATION, *SOCIAL media
Abstract

• A data-driven method for chronologically sorting images of real-world events by analyzing the appearance of the scene. • The duration of the event is split into successive intervals and their relationships are modeled hierarchically. • An evaluation of several data augmentation techniques in scenarios with limited data availability. • A visualization of the concepts learned by the model to sort images offers additional insights about the problem. As smartphones become ubiquitous in modern life, every major event — from musical concerts to terrorist attempts — is massively captured by multiple devices and instantly uploaded to the Internet. Once shared through social media, the chronological order between available media pieces cannot be reliably recovered, hindering the understanding and reconstruction of that event. In this work, we propose data-driven methods for temporally sorting images originated from heterogeneous sources and captured from distinct angles, viewpoints, and moments. We model the chronological sorting task as an ensemble of binary classifiers whose answers are combined hierarchically to estimate an image's temporal position within the duration of the event. We evaluate our method on images from the Notre-Dame Catedral fire and the Grenfell Tower fire events and discuss research challenges for analyzing data from real-world forensic events. Finally, we employ visualization techniques to understand what our models have learned, offering additional insights to the problem. [ABSTRACT FROM AUTHOR]

Published
2021
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14. Event Reconstruction of Indonesian E-Banking Services on Windows Phone Devices

Author

Cahyani, Niken Dwi Wahyu, Martini, Ben, Choo, Kim-Kwang Raymond, Ashman, Helen, Akan, Ozgur, Series Editor, Bellavista, Paolo, Series Editor, Cao, Jiannong, Series Editor, Coulson, Geoffrey, Series Editor, Dressler, Falko, Series Editor, Ferrari, Domenico, Series Editor, Gerla, Mario, Series Editor, Kobayashi, Hisashi, Series Editor, Palazzo, Sergio, Series Editor, Sahni, Sartaj, Series Editor, Shen, Xuemin (Sherman), Series Editor, Stan, Mircea, Series Editor, Xiaohua, Jia, Series Editor, Zomaya, Albert Y., Series Editor, Beyah, Raheem, editor, Chang, Bing, editor, Li, Yingjiu, editor, and Zhu, Sencun, editor

Published
2018
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17. Lorenzetti Showers- A general-purpose framework for supporting signal reconstruction and triggering with calorimeters

Abstract

Calorimeters play an important role in high-energy physics experiments. Their design includes electronic instrumentation, signal processing chain, computing infrastructure, and also a good understanding of their response to particle showers produced by the interaction of incoming particles. This is usually supported by full simulation frameworks developed for specific experiments so that their access is restricted to the collaboration members only. Such restrictions limit the general-purpose developments that aim to propose innovative approaches to signal processing, which may include machine learning and advanced stochastic signal processing models. This work presents the Lorenzetti Showers, a general-purpose framework that mainly targets supporting novel signal reconstruction and triggering strategies using segmented calorimeter information. This framework fully incorporates developments down to the signal processing chain level (signal shaping, energy estimation, and noise mitigation techniques) to allow advanced signal processing approaches in modern calorimetry and triggering systems. The developed framework is flexible enough to be extended in different directions. For instance, it can become a tool for the phenomenology community to go beyond the usual detector design and physics process generation approaches. Program summary Program Title: Lorenzetti Showers CPC Library link to program files: https://doi .org /10 .17632 /sy64367452 .1 Developer's repository link: https://github .com /lorenzetti -hep /lorenzetti Licensing provisions: GPLv3 Programming language: Python, C++. Nature of problem: In experimental high-energy physics, simulation is essential for experiment preparation, design and interpretations of ongoing acquisitions. Especially for calorimeters, an accurate simulation that can describe detector geometry, behavior to different physics processes and signal generation close to the readout electronics and data acquisition levels is required to prope, QC 20230321

Published
2023
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18. Geometry optimization of a muon-electron scattering detector

Author

Tommaso Dorigo

Subjects
Optimization, Particle physics, Tracking detectors, Muon anomalous magnetic moment, Event reconstruction, Physics, QC1-999
Abstract

A high-statistics determination of the differential cross section of elastic muon-electron scattering as a function of the transferred four-momentum squared, dσel(μe→μe)/dq2, has been argued to provide an effective constraint to the hadronic contribution to the running of the fine-structure constant, Δαhad, a crucial input for precise theoretical predictions of the anomalous magnetic moment of the muon. An experiment called ‘‘MUonE’’ is being planned at the north area of CERN for that purpose. We consider the geometry of the detector proposed by the MUonE collaboration and offer a few suggestions on the layout of the passive target material and on the placement of silicon strip sensors, based on a fast simulation of elastic muon-electron scattering events and the investigation of a number of possible solutions for the detector geometry. The employed methodology for detector optimization is of general interest as it may be applied to the design of task-specific detectors for high-energy physics, nuclear physics, and astro-particle physics applications.

Published
2020
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20. Reconstruction of point events in liquid-scintillator detectors subjected to total internal reflection.

Author

Dou, Wei, Xu, Benda, Zhou, Jianfeng, Wang, Zhe, and Chen, Shaomin

Subjects
*LIQUID scintillators, *DETECTORS, *SCINTILLATORS, *PHOTOMULTIPLIERS, *REFRACTIVE index, *OPTICAL reflection, *NEUTRINO detectors, *SPHERICAL harmonics
Abstract

The outer water buffer is an economic option to shield the external radiative backgrounds for liquid-scintillator neutrino detectors. Since the refractive index of the liquid scintillator is larger than that of water, the consequential total internal reflection of scintillation light at the media boundary introduces extra complexity to the detector optics. This paper develops a precise detector-response model by investigating how total internal reflection complicates photon propagation and degrades reconstruction. We first parameterize the detector response by regression, providing an unbiased energy and vertex reconstruction in the total internal reflection region while keeping the number of parameters under control. From the experience of event degeneracy at the Jinping prototype, we identify the root cause as the multimodality in the reconstruction likelihood function, determined by the refractive index of the buffer, detector scale and PMT coverage. To avoid multimodality, we propose a straightforward criterion based on the expected photo-electron-count ratios between neighboring PMTs. The criterion will be valuable for the success in future liquid-scintillator detectors by guaranteeing the effectiveness of event reconstruction. [ABSTRACT FROM AUTHOR]

Published
2023
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21. Social inferences from physical evidence via bayesian event reconstruction

Author

Michael Lopez-Brau, Julian Jara-Ettinger, and Kwon J

Subjects
Computer science, business.industry, Bayesian probability, Bayes Theorem, Experimental and Cognitive Psychology, Machine learning, computer.software_genre, Social Perception, Developmental Neuroscience, Humans, Artificial intelligence, business, computer, General Psychology, Event reconstruction
Abstract

Humans can make remarkable social inferences by watching each other's behavior. In many cases, however, people can also make social inferences about agents whose behavior they cannot see, based only on the physical evidence left behind. We hypothesized that this capacity is supported by a form of mental event reconstruction. Under this account, observers derive social inferences by reconstructing the agent's behavior, based on the physical evidence that revealed their presence. We present a computational model of this idea, embedded in a Bayesian framework for action understanding, and show that its predictions match human inferences with high quantitative accuracy. Specifically, Experiment 1 shows that people can infer where an agent came from and which goal they pursued in a room, all from a small pile of cookie crumbs. Experiment 2 shows that people can explicitly reconstruct the actions that the agent took, and these reconstructed trajectories can predict the entry point and goal inferences from Experiment 1. Finally, Experiment 3 shows that people can also infer whether one or two agents were in a room based on the position of two piles of cookie crumbs. Our results shed light on how people extract social information from the physical world. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Published
2022

23. Trusting information systems in everyday work events - effects on cognitive resources, performance, and well-being.

Author

Müller LS and Hertel G

Abstract

In today's data-intensive work environments, information systems are crucial for supporting workers. However, workers often do not rely on these systems but resort to workarounds. We argue that trust is essential for workers' reliance on information systems, positively affecting workers' cognitive resources, performance, and well-being. Moreover, we argue that the organisational context (accountability, distractions) and user-related factors qualify trust-outcome associations by affecting workers' trust calibration. In a preregistered study, we asked N  = 291 employed users of information systems to re-experience prior everyday usage events (event reconstruction method) and assess event-specific trust in the system, work outcomes, and context conditions. Results confirmed the assumed association between trust in the information system and workers' ratings of both performance and well-being. Moreover, workers' technology competence and need for cognition - but not contextual conditions - qualified trust-outcome associations. Our results offer specific suggestions for achieving successful use of information systems at work.

Published
2023
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26. A survey on forensic investigation of operating system logs.

Author

Studiawan, Hudan, Sohel, Ferdous, and Payne, Christian

Subjects
ELECTRONIC evidence, FORENSIC sciences
Abstract

Event logs are one of the most important sources of digital evidence for forensic investigation because they record essential activities on the system. In this paper, we present a comprehensive literature survey of the forensic analysis on operating system logs. We present a taxonomy of various techniques used in this area. Additionally, we discuss the tools that support the examination of the event logs. This survey also gives a review of the publicly available datasets that are used in operating system log forensics research. Finally, we suggest potential future directions on the topic of operating system log forensics. [ABSTRACT FROM AUTHOR]

Published
2019
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27. Computational modeling and forensic analysis for terrorist airplane bombing: A case study.

Author

Yeh, Jean, Chen, Goong, Gu, Cong, Thurman, James "Tom", Sergeev, Alexey, Wei, Chunqiu, Zhu, Jing, Hajaiej, Hichem, Shang, Ying-Feng, Zhu, Feng, and Tahir Mustafa, M.

Subjects
*BOMBINGS, *COMPUTER simulation, *COMPUTATIONAL mechanics, *CASE studies, *FORENSIC sciences
Abstract

• Contains practice, modeling and computation. • Dynamics is illuminated by supercomputer results of videos. • Event reconstruction and forensic assessments are made for Daallo Airlines Flight 159 bombing case. The bombing of airliners has been a tactic used by terrorists during the past 40 years. Its prevention is a major priority by homeland security officials on a worldwide basis. In efforts to aid in the investigation of such bombings, this paper provides the results of the development of mathematical modeling and computer simulation for the study of aircraft bombings and associated forensics. As an assist in the forensic study, a number of photographs are provided to depict the normally observed physical characteristics of explosives damage upon aircraft and related materials. Our study illuminates and evaluates how these characteristics can be captured by computational mechanics. Finally, we use the laptop bombing of Daallo Airlines Flight 159 as a case study to demonstrate that event reconstruction can be accomplished for the purpose of forensic investigations. Most of our supercomputer results are visualized by video animations in order to show the dynamic effects and phenomena of explosives and the associated event reconstruction. [ABSTRACT FROM AUTHOR]

Published
2019
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28. Deleted file fragment dating by analysis of allocated neighbors.

Author

Bahjat, Ahmed A. and Jones, Jim

Subjects
ELECTRONIC evidence, FORENSIC sciences, TIMESTAMPS, CONTENT analysis, FILES (Records)
Abstract

Timestamps play a substantial role during digital forensic investigations and address two main objectives. First, they serve as a primary culling criterion to reduce the amount of digital evidence subject to analysis. Second, timestamps are the sole feature that allows reliable reconstruction of time-lines and they assist in locating temporal anomalies. File fragments, typically from previously deleted or relocated content, are often useful, especially when intact files are unavailable. Such fragments rarely contain embedded timestamps or have file-system timestamp information, which renders them less useful. In this work, we investigate and propose a framework for determining a time-window for deleted file fragments that are typically found in un-allocated space and file slack. We hypothesize that using the known temporal state of neighboring clusters allows us to derive a date-and-time range for when the file fragment was first written to media until it was subsequently deleted. [ABSTRACT FROM AUTHOR]

Published
2019
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29. Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning

Author

Glombitza, J., Abreu, P., Aglietta, M., Albury, J. M., Allekotte, I., Almela, A., Alvarez-Muniz, J., Alves Batista, R., Anastasi, G. A., Anchordoqui, L., Andrada, B., Andringa, S., Aramo, C., Araujo Ferreira, P. R., Arteaga Velazquez, J. C., Asorey, H., Assis, P., Avila, G., Badescu, A. M., Bakalova, A., Balaceanu, A., Barbato, F., Barreira Luz, R. J., Becker, K. H., Bellido, J. A., Berat, C., Bertaina, M. E., Bertou, X., Biermann, P. L., Binet, V., Bismark, K., Bister, T., Biteau, J., Blazek, J., Bleve, C., Bohacova, M., Boncioli, D., Bonifazi, C., Bonneau Arbeletche, L., Borodai, N., Botti, A. M., Brack, J., Bretz, T., Brichetto Orchera, P. G., Briechle, F. L., Buchholz, P., Bueno, A., Buitink, S., Buscemi, M., Busken, M., Caballero-Mora, K. S., Caccianiga, L., Canfora, F., Caracas, I., Carceller, J. M., Caruso, R., Castellina, A., Catalani, F., Cataldi, G., Cazon, L., Cerda, M., Chinellato, J. A., Chudoba, J., Chytka, L., Clay, R. W., Cobos Cerutti, A. C., Colalillo, R., Coleman, A., Coluccia, M. R., Conceicao, R., Condorelli, A., Consolati, G., Contreras, F., Convenga, F., Correia dos Santos, D., Covault, C. E., Dasso, S., Daumiller, K., Dawson, B. R., Day, J. A., de Almeida, R. M., de Jesus, J., de Jong, S. J., De Mauro, G., de Mello Neto, J. R. T., De Mitri, I., de Oliveira, J., de Oliveira Franco, D., de Palma, F., de Souza, V., De Vito, E., del Rio, M., Deligny, O., Deval, L., di Matteo, A., Dobrigkeit, C., D'Olivo, J. C., Domingues Mendes, L. M., dos Anjos, R. C., dos Santos, D., Dova, M. T., Ebr, J., Engel, R., Epicoco, I., Erdmann, M., Escobar, C. O., Etchegoyen, A., Falcke, H., Farmer, J., Farrar, G., Fauth, A. C., Fazzini, N., Feldbusch, F., Fenu, F., Fick, B., Figueira, J. M., Filipcic, A., Fitoussi, T., Fodran, T., Freire, M. M., Fujii, T., Fuster, A., Galea, C., Galelli, C., Garcia, B., Garcia Vegas, A. L., Gemmeke, H., Gesualdi, F., Gherghel-Lascu, A., Ghia, P. L., Giaccari, U., Giammarchi, M., Gobbi, F., Gollan, F., Golup, G., Gomez Berisso, M., Gomez Vitale, P. F., Gongora, J. P., Gonzalez, J. M., Gonzalez, N., Goos, I., Gora, D., Gorgi, A., Gottowik, M., Grubb, T. D., Guarino, F., Guedes, G. P., Guido, E., Hahn, S., Hamal, P., Hampel, M. R., Hansen, P., Harari, D., Harvey, V. M., Haungs, A., Hebbeker, T., Heck, D., Hill, G. C., Hojvat, C., Horandel, J. R., Horvath, P., Hrabovsky, M., Huege, T., Insolia, A., Isar, P. G., Janecek, P., Johnsen, J. A., Jurysek, J., Kaapa, A., Kampert, K. H., Karastathis, N., Keilhauer, B., Kemp, J., Khakurdikar, A., Kizakke Covilakam, V. V., Klages, H. O., Kleifges, M., Kleinfeller, J., Kopke, M., Kunka, N., Lago, B. L., Lang, R. G., Langner, N., Leigui de Oliveira, M. A., Lenok, V., Letessier-Selvon, A., Lhenry-Yvon, I., Lo Presti, D., Lopes, L., Lopez, R., Lu, L., Luce, Q., Lundquist, J. P., Machado Payeras, A., Mancarella, G., Mandat, D., Manning, B. C., Manshanden, J., Mantsch, P., Marafico, S., Mariazzi, A. G., Maris, I. C., Marsella, G., Martello, D., Martinelli, S., Martinez Bravo, O., Mastrodicasa, M., Mathes, H. J., Matthews, J., Matthiae, G., Mayotte, E., Mazur, P. O., Medina-Tanco, G., Melo, D., Menshikov, A., Merenda, K. -D., Michal, S., Micheletti, M. I., Miramonti, L., Mollerach, S., Montanet, F., Morello, C., Mostafa, M., Muller, A. L., Muller, M. A., Mulrey, K., Mussa, R., Muzio, M., Namasaka, W. M., Nasr-Esfahani, A., Nellen, L., Niculescu-Oglinzanu, M., Niechciol, M., Nitz, D., Nosek, D., Novotny, V., Nozka, L., Nucita, A., Nunez, L. A., Palatka, M., Pallotta, J., Papenbreer, P., Parente, G., Parra, A., Pawlowsky, J., Pech, M., Pedreira, F., Pekala, J., Pelayo, R., Pena-Rodriguez, J., Pereira Martins, E. E., Perez Armand, J., Perez Bertolli, C., Perlin, M., Perrone, L., Petrera, S., Pierog, T., Pimenta, M., Pirronello, V., Platino, M., Pont, B., Pothast, M., Privitera, P., Prouza, M., Puyleart, A., Querchfeld, S., Rautenberg, J., Ravignani, D., Reininghaus, M., Ridky, J., Riehn, F., Risse, M., Rizi, V., Rodrigues de Carvalho, W., Rodriguez Rojo, J., Roncoroni, M. J., Rossoni, S., Roth, M., Roulet, E., Rovero, A. C., Ruehl, P., Saftoiu, A., Salamida, F., Salazar, H., Salina, G., Sanabria Gomez, J. D., Sanchez, F., Santos, E. M., Santos, E., Sarazin, F., Sarmento, R., Sarmiento-Cano, C., Sato, R., Savina, P., Schafer, C. M., Scherini, V., Schieler, H., Schimassek, M., Schimp, M., Schluter, F., Schmidt, D., Scholten, O., Schovanek, P., Schroder, F. G., Schroder, S., Schulte, J., Sciutto, S. J., Scornavacche, M., Segreto, A., Sehgal, S., Shellard, R. C., Sigl, G., Silli, G., Sima, O., Smida, R., Sommers, P., Soriano, J. F., Souchard, J., Squartini, R., Stadelmaier, M., Stanca, D., Stanic, S., Stasielak, J., Stassi, P., Streich, A., Suarez-Duran, M., Sudholz, T., Suomijarvi, T., Supanitsky, A. D., Szadkowski, Z., Tapia, A., Taricco, C., Timmermans, C., Tkachenko, O., Tobiska, P., Todero Peixoto, C. J., Tome, B., Torres, Z., Travaini, A., Travnicek, P., Trimarelli, C., Tueros, M., Ulrich, R., Unger, M., Vaclavek, L., Vacula, M., Valdes Galicia, J. F., Valore, L., Varela, E., Vasquez-Ramirez, A., Veberic, D., Ventura, C., Vergara Quispe, I. D., Verzi, V., Vicha, J., Vink, J., Vorobiov, S., Wahlberg, H., Watanabe, C., Watson, A. A., Weber, M., Weindl, A., Wiencke, L., Wilczynski, H., Wirtz, M., Wittkowski, D., Wundheiler, B., Yushkov, A., Zapparrata, O., Zas, E., Zavrtanik, D., Zavrtanik, M., and Zehrer, L.

Subjects
Pierre Auger Observatory, Astroparticle physics, Physics, Astrophysics::High Energy Astrophysical Phenomena, Astronomy, Detector, Astrophysics::Instrumentation and Methods for Astrophysics, Cosmic ray, Auger, Nuclear physics, Experimental High Energy Physics, ddc:530, High Energy Physics, Event (particle physics), Energy (signal processing), Event reconstruction
Abstract

The measurement of the mass composition of ultra-high energy cosmic rays constitutes a prime challenge in astroparticle physics. Most detailed information on the composition can be obtained from measurements of the depth of maximum of air showers, $X_{\mathrm{max}}$, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights. Using deep neural networks, it is now possible for the first time to perform an event-by-event reconstruction of $X_{\mathrm{max}}$ with the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on $X_{\mathrm{max}}$, and thus, the cosmic-ray composition. Since the SD operates with a duty cycle of almost $100\%$ and its event selection is less strict than for the Fluorescence Detector (FD), the gain in statistics with respect to the FD is almost a factor of 15 for energies above $10^{19.5}~\mathrm{eV}$. In this contribution, we introduce the neural network particularly designed for the SD of the Pierre Auger Observatory. We evaluate its performance using three different hadronic interaction models, verify its functionality using Auger hybrid measurements, and find that the method can extract mass information on an event level.

Published
2022

30. Algorithms and Software for Event Reconstruction in the RICH, TRD and MUCH Detectors of the CBM Experiment

Author

Lebedev, Semen, Höhne, Claudia, Kisel, Ivan, Lebedev, Andrey, Ososkov, Gennady, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Adam, Gheorghe, editor, Buša, Ján, editor, and Hnatič, Michal, editor

Published
2012
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31. Computational Challenges for the CBM Experiment

Author

Friese, Volker, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Adam, Gheorghe, editor, Buša, Ján, editor, and Hnatič, Michal, editor

Published
2012
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32. Performance of the High-Level Trigger System at CMS in LHC Run-2

Author

Somnath Choudhury

Subjects
Nuclear and High Energy Physics, Luminosity (scattering theory), Large Hadron Collider, business.industry, Computer science, Detector, Real-time computing, Collision, Set (abstract data type), Data acquisition, Software, Nuclear Energy and Engineering, Electrical and Electronic Engineering, business, Event reconstruction
Abstract

The CMS experiment at the LHC selects events with a two-level trigger system, the Level-1 (L1) trigger, and the high-level trigger (HLT). The HLT reduces the rate from 100 to about 1 kHz and has access to the full detector readout and runs a streamlined version of the offline event reconstruction. During LHC Run-2, the peak instantaneous luminosity reached values up to $2.0\times 10^{34}$ cm−2s−1, posing a challenge to the online event selection. An overview of the HLT system at CMS, the online physics object reconstruction, and the main triggers using those physics objects in 2016–2018 proton collision data-taking period is presented. The performance of a representative set of physics triggers is also discussed.

Published
2021

33. Event Reconstruction and Physics Signal Selection in the MPD Experiment at NICA

Author

V. Kolesnikov, Dmitry Zinchenko, V. Vasendina, A.I. Zinchenko, A. Mudrokh, I. Rufanov, and J. Drnoyan

Subjects
Physics, Nuclear and High Energy Physics, Vertex (computer graphics), Time projection chamber, Monte Carlo method, Hyperon, Observable, Algorithm, Signal selection, Event reconstruction, Event (probability theory)
Abstract

The event reconstruction approaches developed and implemented for the MPD experiment are described including cluster and hit reconstruction in the time projection chamber and track, primary and secondary vertex finding methods. Some results of their application to physics observables such as hyperon production are demonstrated for Monte Carlo simulated event samples.

Published
2021

34. Development and Software Implementation of Optimal Algorithms for Event Reconstruction, Evaluation of the Quality of Event Reconstruction and Simulation of Detector Components in the BM@N Experiment

Author

A. A. Iufriakova, A. V. Driuk, N. E. Kakhanovskaya, S. P. Merts, S. A. Nemnyugin, Vladimir Roudnev, and K. I. Mashitsin

Subjects
Physics, Nuclear and High Energy Physics, business.industry, media_common.quotation_subject, Detector, Particle identification, Development (topology), Software, Point (geometry), Quality (business), business, Quality assurance, Algorithm, media_common, Event reconstruction
Abstract

The BmnRoot software package plays a crucial role in the BM@N NICA experiment and is used both for simulation and event reconstruction purposes. In this article, few approaches to optimization of simulation and event reconstruction algorithms are presented. Results of performance studies of the BmnRoot modules are obtained using software dynamic analysis. Different methods of optimization including parallelization are considered. Algorithms of tracks global matching are discussed. Results of optimization of the virtual planes method are given. Machine learning methods are analyzed from the point of view of efficiency of particle identification. Flexible and optimal extension of the quality assurance module of the BmnRoot is described. Analysis of fragments for SRC studies with the BM@N experiment is also discussed.

Published
2021

35. Signature Based Detection of User Events for Post-mortem Forensic Analysis

Author

James, Joshua Isaac, Gladyshev, Pavel, Zhu, Yuandong, Akan, Ozgur, Series editor, Bellavista, Paolo, Series editor, Cao, Jiannong, Series editor, Dressler, Falko, Series editor, Ferrari, Domenico, Series editor, Gerla, Mario, Series editor, Kobayashi, Hisashi, Series editor, Palazzo, Sergio, Series editor, Sahni, Sartaj, Series editor, Shen, Xuemin (Sherman), Series editor, Stan, Mircea, Series editor, Xiaohua, Jia, Series editor, Zomaya, Albert, Series editor, Coulson, Geoffrey, Series editor, and Baggili, Ibrahim, editor

Published
2011
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36. Event reconstruction for the CBM-RICH prototype beamtest data in 2014.

Author

Adamczewski-Musch, J., Akishin, P., Becker, K.-H., Belogurov, S., Bendarouach, J., Boldyreva, N., Deveaux, C., Dobyrn, V., Dürr, M., Eschke, J., Förtsch, J., Heep, J., Höhne, C., Kampert, K.-H., Kochenda, L., Kopfer, J., Kravtsov, P., Kres, I., Lebedev, S., and Lebedeva, E.

Subjects
*BARYONS, *PHASE diagrams, *QUANTUM chromodynamics, *COLLISIONS (Physics), *TEMPERATURE effect, *CHERENKOV radiation
Abstract

The Compressed Baryonic Matter (CBM) experiment at the future FAIR facility will investigate the QCD phase diagram at high net baryon densities and moderate temperatures in A+A collisions from 2 to 11 A GeV (SIS100). Electron identification in CBM will be performed by a Ring Imaging Cherenkov (RICH) detector and Transition Radiation Detectors (TRD). A real size prototype of the RICH detector was tested together with other CBM groups at the CERN PS/T9 beam line in 2014. For the first time the data format used the FLESnet protocol from CBM delivering free streaming data. The analysis was fully performed within the CBMROOT framework. In this contribution the data analysis and the event reconstruction methods which were used for obtained data are discussed. Rings were reconstructed using an algorithm based on the Hough Transform method and their parameters were derived with high accuracy by circle and ellipse fitting procedures. We present results of the application of the presented algorithms. In particular we compare results with and without Wavelength shifting (WLS) coating. [ABSTRACT FROM AUTHOR]

Published
2017
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37. Simulation of Event Reconstruction of Cosmic Particles With a Radio Network.

Author

Badescu, A. M.

Abstract

In this paper, the possibility to accurately reconstruct the energy and direction of high-energy cosmic rays using a network of 192 radio detectors, assembled on an area of about 20 km2, is investigated. Measurements are influenced by the properties of the detecting equipment; therefore, its behavior and limiting effects are discussed. The responses of the detection chain to radio pulses generated by cosmic particles with different characteristics are analyzed, and it is concluded that individual stations can serve as first estimators for the types of cosmic particles. The proposed radio network can resolve events (with energies higher than \10^18\,eV) coming from N–NW direction, with zenith angles higher than 44°. Simulations have also shown that lighter particles that interact closer to the Earth are more likely to be detected. [ABSTRACT FROM PUBLISHER]

Published
2017
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41. High-Energy Neutrino Follow-up at the Baikal-GVD Neutrino Telescope

Author

Z. Bardačová, T. I. Gress, M. S. Katulin, K. V. Konishchev, K.V. Golubkov, Lukas Fajt, Mark Shelepov, E. V. Ryabov, A. V. Skurikhin, M.M. Kolbin, W. Noga, B. A. Tarashchansky, V. A. Kozhin, D. N. Zaborov, A.P. Koshechkin, T. V. Elzhov, V. Ya. Dik, G.B. Safronov, A.V. Avrorin, Aleksandr Gafarov, E.N. Pliskovsky, O.G. Kebkal, V.D. Rushay, M.I. Rozanov, A. A. Doroshenko, Zh.-A.M. Dzhilkibaev, R. R. Mirgazov, A.G. Solovjev, V. Nazari, V. B. Brudanin, V.F. Kulepov, N. M. Budnev, S. V. Fialkovski, E.V. Khramov, Fedor Šimkovic, D. P. Petukhov, V. M. Aynutdinov, Yu. V. Yablokova, Sergey Yakovlev, K. A. Kopański, N.S. Gorshkov, R. Bannasch, E. O. Sushenok, R. Dvornicky, A.V. Korobchenko, I. A. Belolaptikov, Konstantin Kebkal, I. Stekl, R. Ivanov, V.A. Tabolenko, Dmitry V. Naumov, A.D. Avrorin, G.V. Domogatsky, B.A. Shaybonov, A. N. Dyachok, E. Eckerová, Olga Suvorova, M.K. Kryukov, M. V. Milenin, M.N. Sorokovikov, and M.V. Kruglov

Subjects
Physics, Physics::Instrumentation and Detectors, 010308 nuclear & particles physics, Astrophysics::High Energy Astrophysical Phenomena, Detector, Neutrino telescope, Astrophysics::Instrumentation and Methods for Astrophysics, Astronomy, Astronomy and Astrophysics, 01 natural sciences, Monitoring program, law.invention, Telescope, Data acquisition, Space and Planetary Science, law, 0103 physical sciences, Neutrino, Underwater, 010303 astronomy & astrophysics, Event reconstruction
Abstract

The Baikal-GVD deep underwater neutrino experiment participates in the international multi-messenger program to detect the astrophysical sources of high- and ultrahigh-energy cosmic-ray particles, being at the stage of array deployment and a step-by-step increase of the telescope’s effective volume to the scale of a cubic kilometer. At present, the telescope consists of seven clusters containing 2016 photodetectors. The effective volume of the detector has reached 0.35 km $${}^{3}$$ for the selection of shower events from neutrino interactions in Baikal water. The experimental data have been accumulated in a continuous exposure mode since 2015, allowing a prompt data analysis and a celestial-sphere monitoring program to be implemented in real time. We discuss the structure of the data acquisition system, describe the physical event reconstruction procedure in the mode of fast response to alerts, and present the results of our analysis of nine alerts from the polar IceCube telescope from early September to late October 2020.

Published
2021

43. Invisible Higgs search through vector boson fusion: a deep learning approach

Author

Partha Konar, Aruna Kumar Nayak, Vishal S. Ngairangbam, and Akanksha Bhardwaj

Subjects
Particle physics, Physics and Astronomy (miscellaneous), Event (relativity), Physics beyond the Standard Model, FOS: Physical sciences, Parton, lcsh:Astrophysics, 01 natural sciences, High Energy Physics - Experiment, Vector boson, High Energy Physics - Experiment (hep-ex), High Energy Physics - Phenomenology (hep-ph), 0103 physical sciences, lcsh:QB460-466, lcsh:Nuclear and particle physics. Atomic energy. Radioactivity, 010306 general physics, Engineering (miscellaneous), Event reconstruction, Physics, 010308 nuclear & particles physics, business.industry, Deep learning, High Energy Physics::Phenomenology, High Energy Physics - Phenomenology, Higgs boson, lcsh:QC770-798, Artificial intelligence, business, Fusion mechanism
Abstract

Vector boson fusion proposed initially as an alternative channel for finding heavy Higgs has now established itself as a crucial search scheme to probe different properties of the Higgs boson or for new physics. We explore the merit of deep-learning entirely from the low-level calorimeter data in the search for invisibly decaying Higgs. Such an effort supersedes decades-old faith in the remarkable event kinematics and radiation pattern as a signature to the absence of any color exchange between incoming partons in the vector boson fusion mechanism. We investigate among different neural network architectures, considering both low-level and high-level input variables as a detailed comparative analysis. To have a consistent comparison with existing techniques, we closely follow a recent experimental study of CMS search on invisible Higgs with 36 fb$^{-1}$ data. We find that sophisticated deep-learning techniques have the impressive capability to improve the bound on invisible branching ratio by a factor of three, utilizing the same amount of data. Without relying on any exclusive event reconstruction, this novel technique can provide the most stringent bounds on the invisible branching ratio of the SM-like Higgs boson. Such an outcome has the ability to constraint many different BSM models severely., Included estimation of pixelwise energy uncertainty, minor changes in text and updated references. Accepted for publication in EPJC

Published
2020

44. Characterization of Photosensitive Devices and Design of an Innovative Large-Sized Gamma-ray Telescope Camera

Author

Medina Miranda, Luis David

Subjects
Cherenkov Telescope Array, SiPM, Silicon photomultiplier, Gamma hadron separation, SST-1M, ctapipe, Machine Learning, SiPM camera, Extensivve Air Shower, Sensitivity, Hillas' parameterization, Photomultiplier tube, Mu3e, Imagining Atmospheric Cherenkov Telescope, Angular resolution, Single Mirror Small-Sized Telescope, Energy resolution, Sci-Fi, PMT camera, LST, Event reconstruction, Gamma-ray astronomy, Random Forest, CTA, Scintillating fiber, IACT, PMT, EAS, lstchain, Energy bias, Large-Sized Telescope
Abstract

The work for a doctoral thesis is presented in two parts concerning two different projects, connected by the usage of photosensitive devices, in particular, silicon photomultipliers (SiPMs). The most extensive part of this thesis is dedicated to my work on the application of SiPMs in the future generation of Imagining Atmospheric Cherenkov Telescopes (IACTs). While the remaining part is dedicated to the characterization of scintillating fibers using SIPMs as fiber trackers in the 𝜇3𝑒 experiment. The work focusing on IACTs is divided into two different projects: • The design of an innovative Cherenkov telescope camera for the Large-Sized Telescope (LST) as part of the Cherenkov Telescope Array (CTA). • The characterization of an external light source (LUMP Calibration box) foreseen as a possible tool to help in the calibration of the Single Mirror Small-Sized Telescope (SST-1M), whose camera was built by our group at the University of Geneva. The remaining part of the thesis belongs to a project in the 𝜇3e experiment whose goal is to search for flavor lepton violation in the Standard Model: • Characterization of single scintillating fibers for the Scintillating Fiber detector (Sci-Fi)., Les travaux de thèse de doctorat sont présentés en deux parties concernant deux projets différents reliés par l’utilisation de dispositifs photosensibles, en particulier, les photomultiplicateurs de silicium (SiPMs). La partie la plus importante de cette thèse est consacrée à mes travaux sur l’application des SiPMs aux télescopes imageurs à effet Cherenkov atmosphérique (IACTs, pour Imaging Atmospheric Cherenkov Telescope) de génération future. La partie restante est consacrée à la caractérisation des fibres scintillantes à l’aide de SiPMs pour leur utilisation come trajectographe dans l’expérience 𝜇3𝑒. Le travail axé sur les IACTs est divisé en deux projets différents : • La conception d’une caméra innovante à effet Cherenkov atmosphérique pour le télescope de grand taille (LST, pour Large-Sized Telescope) dans le cadre du réseau de télescopes imageurs à effet Cherenkov atmosphérique (CTA, pour Cherenkov Telescope Array). • La caractérisation d’une source de lumière externe (boîte de calibration LUMP) en tant qu’outil possible pour aider à la calibration du télescope à miroir unique de petite taille (SST- 1M, Single Mirror Small-Sized Telescope), dont la caméra a été construite par notre groupe à l’Université de Genève. Le reste de la thèse appartient au projet de l’expérience 𝜇3𝑒, dont le but est de rechercher la violation de conservation des saveurs leptoniques dans le modèle standard : • Caractérisation des fibres scintillantes uniques pour le détecteur à fribres scintillantes (Sci-Fi, pour Scintillating Fiber detector).

Published
2022
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45. Prototype Open Event Reconstruction Pipeline for the Cherenkov Telescope Array

Author

Maximilian Nöthe, Lukas Nickel, Michele Peresano, Karl Kosack, API (FNWI), High Energy Astrophys. & Astropart. Phys (API, FNWI), and GRAPPA (ITFA, IoP, FNWI)

Subjects
Event (computing), Astrophysics::High Energy Astrophysical Phenomena, Astrophysics::Instrumentation and Methods for Astrophysics, FOS: Physical sciences, IACT, Cherenkov Telescope Array, Pipeline (software), law.invention, Telescope, Observatory, law, Data analysis, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Remote sensing, Event reconstruction
Abstract

The Cherenkov Telescope Array (CTA) is the next-generation gamma-ray observatory currently under construction. It will improve over the current generation of imaging atmospheric Cherenkov telescopes (IACTs) by a factor of five to ten in sensitivity and it will be able to observe the whole sky from a combination of two sites: a northern site in La Palma, Spain, and a southern one in Paranal, Chile. CTA will also be the first open gamma-ray observatory. Accordingly, the data analysis pipeline is developed as open-source software. The event reconstruction pipeline accepts raw data of the telescopes and processes it to produce suitable input for the higher-level science tools. Its primary tasks include reconstructing the physical properties of each recorded shower and providing the corresponding instrument response functions. ctapipe is a framework providing algorithms and tools to facilitate raw data calibration, image extraction, image parameterization and event reconstruction. Its main focus is currently the analysis of simulated data but it has also been successfully applied for the analysis of data obtained with the first CTA prototype telescopes, such as the Large-Sized Telescope 1 (LST-1). pyirf is a library to calculate IACT instrument response functions, needed to obtain physics results like spectra and light curves, from the reconstructed event lists. Building on these two, protopipe is a prototype for the event reconstruction pipeline for CTA. Recent developments in these software packages will be presented., Comment: In proceedings of the 37th International Cosmic Ray Conference (ICRC 2021)

Published
2022

46. Sensitivity of the Cherenkov Telescope Array to a dark matter signal from the Galactic centre

Author

Abdalla, H., Abe, H., Abe, S., Abusleme, A., Acero, F., Acharyya, A., Acín Portella, V., Ackley, K., Adam, R., Adams, C., Adhikari, S. S., Aguado-Ruesga, I., Agudo, I., Aguilera, R., Aguirre-Santaella, A., Aharonian, F., Alberdi, A., Alfaro, R., Alfaro, J., Alispach, C., Aloisio, R., Alves Batista, R., Amans, J. -P, Amati, L., Amato, E., Ambrogi, L., Ambrosi, G., Ambrosio, M., Ammendola, R., Anderson, J., Anduze, M., Angüner, E. O., Antonelli, L. A., Antonuccio, V., Antoranz, P., Anutarawiramkul, R., Aragunde Gutierrez, J., Aramo, C., Araudo, A., Araya, M., Arbet-Engels, A., Arcaro, C., Arendt, V., Armand, C., Armstrong, T., Arqueros, F., Arrabito, L., Arsioli, B., Artero, M., Asano, K., Ascasíbar, Y., Aschersleben, J., Ashley, M., Attinà, P., Aubert, P., Singh, C. B., Baack, D., Babic, A., Backes, M., Baena, V., Bajtlik, S., Baktash, A., Balazs, C., Balbo, M., Ballester, O., Ballet, J., Balmaverde, B., Bamba, A., Bandiera, R., Baquero Larriva, A., Barai, P., Barbier, C., Barbosa Martins, V., Barcelo, M., Barkov, M., Barnard, M., Baroncelli, L., Barres Almeida, U., Barrio, J. A., Bastieri, D., Batista, P. I., Batkovic, I., Bauer, C., Bautista-González, R., Baxter, J., Becciani, U., Becerra González, J., Becherini, Y., Beck, G., Becker Tjus, J., Bednarek, W., Belfiore, A., Bellizzi, L., Belmont, R., Benbow, W., Berge, D., Bernardini, E., Bernardos, M. I., Bernlöhr, K., Berti, A., Berton, M., Bertucci, B., Beshley, V., Bhatt, N., Bhattacharyya, S., Bhattacharyya, W., Bi, B., Bicknell, G., Biederbeck, N., Bigongiari, C., Biland, A., Bird, R., Bissaldi, E., Biteau, J., Bitossi, M., Blanch, O., Blank, M., Blazek, J., Bobin, J., Boccato, C., Bocchino, F., Boehm, C., Bohacova, M., Boisson, C., Boix, J., Bolle, J. -P, Bolmont, J., Bonanno, G., Bonavolontà, C., Bonneau Arbeletche, L., Bonnoli, G., Bordas, P., Borkowski, J., Bórquez, S., Bose, R., Bose, D., Bosnjak, Z., Bottacini, E., Böttcher, M., Botticella, M. T., Boutonnet, C., Bouyjou, F., Bozhilov, V., Bozzo, E., Brahimi, L., Braiding, C., Brau-Nogué, S., Breen, S., Bregeon, J., Breuhaus, M., Brill, A., Brisken, W., Brocato, E., Brown, A. M., Brügge, K., Brun, P., Brun, F., Brunetti, L., Brunetti, G., Bruno, P., Bruno, A., Bruzzese, A., Bucciantini, N., Buckley, J., Bühler, R., Bulgarelli, A., Bulik, T., Bünning, M., Bunse, M., Burton, M., Burtovoi, A., Buscemi, M., Buschjäger, S., Busetto, G., Buss, J., Byrum, K., Caccianiga, A., Cadoux, F., Calanducci, A., Calderón, C., Calvo Tovar, J., Cameron, R., Campaña, P., Canestrari, R., Cangemi, F., Cantlay, B., Capalbi, M., Capasso, M., Cappi, M., Caproni, A., Capuzzo-Dolcetta, R., Caraveo, P., Cárdenas, V., Cardiel, L., Cardillo, M., Carlile, C., Caroff, S., Carosi, R., Carosi, A., Carquín, E., Carrère, M., Casandjian, J. -M, Casanova, S., Cascone, E., Cassol, F., Castro-Tirado, A. J., Catalani, F., Catalano, O., Cauz, D., Ceccanti, A., Celestino Silva, C., Celli, S., Cerny, K., Cerruti, M., Chabanne, E., Chadwick, P., Chai, Y., Chambery, P., Champion, C., Chandra, S., Chaty, S., Chen, A., Cheng, K., Chernyakova, M., Chiaro, G., Chiavassa, A., Chikawa, M., Chitnis, V. R., Chudoba, J., Chytka, L., Cikota, S., Circiello, A., Clark, P., Çolak, M., Colombo, E., Colome, J., Colonges, S., Comastri, A., Compagnino, A., Conforti, V., Congiu, E., Coniglione, R., Conrad, J., Conte, F., Contreras, J. L., Coppi, P., Cornat, R., Coronado-Blazquez, J., Cortina, J., Costa, A., Costantini, H., Cotter, G., Courty, B., Covino, S., Crestan, S., Cristofari, P., Crocker, R., Croston, J., Cubuk, K., Cuevas, O., Cui, X., Cusumano, G., Cutini, S., D’aì, A., D’amico, G., D’ammando, F., D’avanzo, P., Da Vela, P., Dadina, M., Dai, S., Dalchenko, M., Dall’ Ora, M., Daniel, M. K., Dauguet, J., Davids, I., Davies, J., Dawson, B., Angelis, A., Araújo Carvalho, A. E., Bony Lavergne, M., Caprio, V., Cesare, G., Frondat, F., Gouveia Dal Pino, E. M., La Calle, I., Lotto, B., Luca, A., Martino, D., Menezes, R. M., Naurois, M., Oña Wilhelmi, E., Palma, F., Persio, F., Simone, N., Souza, V., Del Santo, M., Del Valle, M. V., Delagnes, E., Deleglise, G., Delfino Reznicek, M., Delgado, C., Delgado Giler, A. G., Delgado Mengual, J., Della Ceca, R., Della Valle, M., Della Volpe, D., Depaoli, D., Depouez, D., Devin, J., Di Girolamo, T., Di Giulio, C., Di Piano, A., Di Pierro, F., Di Venere, L., Díaz, C., Díaz-Bahamondes, C., Dib, C., Diebold, S., Digel, S., Dima, R., Djannati-Ataï, A., Djuvsland, J., Dmytriiev, A., Docher, K., Domínguez, A., Dominis Prester, D., Donath, A., Donini, A., Dorner, D., Doro, M., Dos Anjos, R. D. C., Dournaux, J. -L, Downes, T., Drake, G., Drass, H., Dravins, D., Duangchan, C., Duara, A., Dubus, G., Ducci, L., Duffy, C., Dumora, D., Dundas Morå, K., Durkalec, A., Dwarkadas, V. V., Ebr, J., Eckner, C., Eder, J., Ederoclite, A., Edy, E., Egberts, K., Einecke, S., Eisch, J., Eleftheriadis, C., Elsässer, D., Emery, G., Emmanoulopoulos, D., Ernenwein, J. -P, Errando, M., Escarate, P., Escudero, J., Espinoza, C., Ettori, S., Eungwanichayapant, A., Evans, P., Evoli, C., Fairbairn, M., Falceta-Goncalves, D., Falcone, A., Fallah Ramazani, V., Falomo, R., Farakos, K., Fasola, G., Fattorini, A., Favre, Y., Fedora, R., Fedorova, E., Fegan, S., Feijen, K., Feng, Q., Ferrand, G., Ferrara, G., Ferreira, O., Fesquet, M., Fiandrini, E., Fiasson, A., Filipovic, M., Fink, D., Finley, J. P., Fioretti, V., Fiorillo, D. F. G., Fiorini, M., Flis, S., Flores, H., Foffano, L., Föhr, C., Fonseca, M. V., Font, L., Fontaine, G., Fornieri, O., Fortin, P., Fortson, L., Fouque, N., Fournier, A., Fraga, B., Franceschini, A., Franco, F. J., Franco Ordovas, A., Freixas Coromina, L., Fresnillo, L., Fruck, C., Fugazza, D., Fujikawa, Y., Fujita, Y., Fukami, S., Fukazawa, Y., Fukui, Y., Fulla, D., Funk, S., Furniss, A., Gabella, O., Gabici, S., Gaggero, D., Galanti, G., Galaz, G., Galdemard, P., Gallant, Y., Galloway, D., Gallozzi, S., Gammaldi, V., Garcia, R., Garcia, E., García, E., Garcia López, R., Garczarczyk, M., Gargano, F., Gargano, C., Garozzo, S., Gascon, D., Gasparetto, T., Gasparrini, D., Gasparyan, H., Gaug, M., Geffroy, N., Gent, A., Germani, S., Gesa, L., Ghalumyan, A., Ghedina, A., Ghirlanda, G., Gianotti, F., Giarrusso, S., Giarrusso, M., Giavitto, G., Giebels, B., Giglietto, N., Gika, V., Gillardo, F., Gimenes, R., Giordano, F., Giovannini, G., Giro, E., Giroletti, M., Giuliani, A., Giunti, L., Gjaja, M., Glicenstein, J. -F, Gliwny, P., Godinovic, N., Göksu, H., Goldoni, P., Gómez, J. L., Gómez-Vargas, G., González, M. M., González, J. M., Gothe, K. S., Götz, D., Goulart Coelho, J., Gourgouliatos, K., Grabarczyk, T., Graciani, R., Grandi, P., Grasseau, G., Grasso, D., Green, A. J., Green, D., Green, J., Greenshaw, T., Grenier, I., Grespan, P., Grillo, A., Grondin, M. -H, Grube, J., Guarino, V., Guest, B., Gueta, O., Gündüz, M., Gunji, S., Gusdorf, A., Gyuk, G., Hackfeld, J., Hadasch, D., Haga, J., Hagge, L., Hahn, A., Hajlaoui, J. E., Hakobyan, H., Halim, A., Hamal, P., Hanlon, W., Hara, S., Harada, Y., Hardcastle, M. J., Harvey, M., Hashiyama, K., Hassan Collado, T., Haubold, T., Haupt, A., Hautmann, U. A., Havelka, M., Hayashi, K., Hayashida, M., He, H., Heckmann, L., Heller, M., Helo, J. C., Henault, F., Henri, G., Hermann, G., Hermel, R., Hernández Cadena, S., Herrera Llorente, J., Herrero, A., Hervet, O., Hinton, J., Hiramatsu, A., Hiroshima, N., Hirotani, K., Hnatyk, B., Hnatyk, R., Hoang, J. K., Hoffmann, D., Hofmann, W., Hoischen, C., Holder, J., Holler, M., Hona, B., Horan, D., Hörandel, J., Horns, D., Horvath, P., Houles, J., Hovatta, T., Hrabovsky, M., Hrupec, D., Huang, Y., Huet, J. -M, Hughes, G., Hui, D., Hull, G., Humensky, T. B., Hütten, M., Iaria, R., Iarlori, M., Illa, J. M., Imazawa, R., Impiombato, D., Inada, T., Incardona, F., Ingallinera, A., Inome, Y., Inoue, S., Inoue, T., Inoue, Y., Insolia, A., Iocco, F., Ioka, K., Ionica, M., Iori, M., Iovenitti, S., Iriarte, A., Ishio, K., Ishizaki, W., Iwamura, Y., Jablonski, C., Jacquemier, J., Jacquemont, M., Jamrozy, M., Janecek, P., Jankowsky, F., Jardin-Blicq, A., Jarnot, C., Jean, P., Jiménez Martínez, I., Jin, W., Jocou, L., Jordana, N., Josselin, M., Jouvin, L., Jung-Richardt, I., Junqueira, F. J. P. A., Juramy-Gilles, C., Jurysek, J., Kaaret, P., Kadowaki, L. H. S., Kagaya, M., Kalekin, O., Kankanyan, R., Kantzas, D., Karas, V., Karastergiou, A., Karkar, S., Kasai, E., Kasperek, J., Katagiri, H., Kataoka, J., Katarzyński, K., Katsuda, S., Katz, U., Kawanaka, N., Kazanas, D., Kerszberg, D., Khélifi, B., Kherlakian, M. C., Kian, T. P., Kieda, D. B., Kihm, T., Kim, S., Kimeswenger, S., Kisaka, S., Kissmann, R., Kleijwegt, R., Kleiner, T., Kluge, G., Kluźniak, W., Knapp, J., Knödlseder, J., Kobakhidze, A., Kobayashi, Y., Koch, B., Kocot, J., Kohri, K., Kokkotas, K., Komin, N., Kong, A., Kosack, K., Kowal, G., Krack, F., Krause, M., Krennrich, F., Krumholz, M., Kubo, H., Kudryavtsev, V., Kunwar, S., Kuroda, Y., Kushida, J., Kushwaha, P., La Barbera, A., La Palombara, N., La Parola, V., La Rosa, G., Lahmann, R., Lamanna, G., Lamastra, A., Landoni, M., Landriu, D., Lang, R. G., Lapington, J., Laporte, P., Lason, P., Lasuik, J., Lazendic-Galloway, J., Le Flour, T., Le Sidaner, P., Leach, S., Leckngam, A., Lee, S. -H, Lee, W. H., Lee, S., Leigui Oliveira, M. A., Lemière, A., Lemoine-Goumard, M., Lenain, J. -P, Leone, F., Leray, V., Leto, G., Leuschner, F., Levy, C., Lindemann, R., Lindfors, E., Linhoff, L., Liodakis, I., Lipniacka, A., Lloyd, S., Lobo, M., Lohse, T., Lombardi, S., Longo, F., Lopez, A., López, M., López-Coto, R., Loporchio, S., Louis, F., Louys, M., Lucarelli, F., Lucchesi, D., Ludwig Boudi, H., Luque-Escamilla, P. L., Lyard, E., Maccarone, M. C., Maccarone, T., Mach, E., Maciejewski, A. J., Mackey, J., Madejski, G. M., Maeght, P., Maggio, C., Maier, G., Majczyna, A., Majumdar, P., Makariev, M., Mallamaci, M., Malta Nunes Almeida, R., Maltezos, S., Malyshev, D., Mandat, D., Maneva, G., Manganaro, M., Manicò, G., Manigot, P., Mannheim, K., Maragos, N., Marano, D., Marconi, M., Marcowith, A., Marculewicz, M., Marčun, B., Marín, J., Marinello, N., Marinos, P., Mariotti, M., Markoff, S., Marquez, P., Marsella, G., Martí, J., Martin, J. -M, Martin, P., Martinez, O., Martínez, M., Martínez, G., Martínez, O., Martínez-Huerta, H., Marty, C., Marx, R., Masetti, N., Massimino, P., Mastichiadis, A., Matsumoto, H., Matthews, N., Maurin, G., Max-Moerbeck, W., Maxted, N., Mazin, D., Mazziotta, M. N., Mazzola, S. M., Mbarubucyeye, J. D., Mc Comb, L., Mchardy, I., Mckeague, S., Mcmuldroch, S., Medina, E., Medina Miranda, D., Melandri, A., Melioli, C., Melkumyan, D., Menchiari, S., Mender, S., Mereghetti, S., Merino Arévalo, G., Mestre, E., Meunier, J. -L, Meures, T., Meyer, M., Micanovic, S., Miceli, M., Michailidis, M., Michałowski, J., Miener, T., Mievre, I., Miller, J., Minaya, I. A., Mineo, T., Minev, M., Miranda, J. M., Mirzoyan, R., Mitchell, A., Mizuno, T., Mode, B., Moderski, R., Mohrmann, L., Molina, E., Molinari, E., Teresa Montaruli, Monteiro, I., Moore, C., Moralejo, A., Morcuende-Parrilla, D., Moretti, E., Morganti, L., Mori, K., Moriarty, P., Morik, K., Morlino, G., Morris, P., Morselli, A., Mosshammer, K., Moya, P., Mukherjee, R., Muller, J., Mundell, C., Mundet, J., Murach, T., Muraczewski, A., Muraishi, H., Murase, K., Musella, I., Musumarra, A., Nagai, A., Nagar, N., Nagataki, S., Naito, T., Nakamori, T., Nakashima, K., Nakayama, K., Nakhjiri, N., Naletto, G., Naumann, D., Nava, L., Navarro, R., Nawaz, M. A., Ndiyavala, H., Neise, D., Nellen, L., Nemmen, R., Newbold, M., Neyroud, N., Ngernphat, K., Nguyen Trung, T., Nicastro, L., Nickel, L., Niemiec, J., Nieto, D., Nievas, M., Nigro, C., Nikołajuk, M., Ninci, D., Nishijima, K., Noda, K., Nogami, Y., Nolan, S., Nomura, R., Norris, R., Nosek, D., Nöthe, M., Novosyadlyj, B., Novotny, V., Nozaki, S., Nunio, F., O’brien, P., Obara, K., Oger, R., Ohira, Y., Ohishi, M., Ohm, S., Ohtani, Y., Oka, T., Okazaki, N., Okumura, A., Olive, J. -F, Oliver, C., Olivera, G., Olmi, B., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, S., Orlando, E., Osborne, J. P., Ostrowski, M., Otte, N., Ovcharov, E., Owen, E., Oya, I., Ozieblo, A., Padovani, M., Pagano, I., Pagliaro, A., Paizis, A., Palatiello, M., Palatka, M., Palazzi, E., Panazol, J. -L, Paneque, D., Panes, B., Panny, S., Pantaleo, F. R., Panter, M., Paoletti, R., Paolillo, M., Papitto, A., Paravac, A., Paredes, J. M., Pareschi, G., Park, N., Parmiggiani, N., Parsons, R. 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J., Rando, R., Razzaque, S., Rebert, E., Recchia, S., Reichherzer, P., Reimer, O., Reimer, A., Reisenegger, A., Remy, Q., Renaud, M., Reposeur, T., Reville, B., Reymond, J. -M, Reynolds, J., Rhode, W., Ribeiro, D., Ribó, M., Richards, G., Richtler, T., Rico, J., Rieger, F., Riitano, L., Ripepi, V., Riquelme, M., Riquelme, D., Rivoire, S., Rizi, V., Roache, E., Röben, B., Roche, M., Rodriguez, J., Rodriguez Fernandez, G., Rodriguez Ramirez, J. C., Rodríguez Vázquez, J. J., Roepke, F., Rojas, G., Romanato, L., Romano, P., Romeo, G., Romero Lobato, F., Romoli, C., Roncadelli, M., Ronda, S., Rosado, J., Rosales Leon, A., Rowell, G., Rudak, B., Rugliancich, A., Ruíz Del Mazo, J. 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P., Veyssiere, C., Viale, I., Viana, A., Viaux, N., Vicha, J., Vignatti, J., Vigorito, C. F., Villanueva, J., Vink, J., Vitale, V., Vittorini, V., Vodeb, V., Voelk, H., Vogel, N., Voisin, V., Vorobiov, S., Vovk, I., Vrastil, M., Vuillaume, T., Wagner, S. J., Wagner, R., Wagner, P., Wakazono, K., Wakely, S. P., Walter, R., Ward, M., Warren, D., Watson, J., Webb, N., Wechakama, M., Wegner, P., Weinstein, A., Weniger, C., Werner, F., Wetteskind, H., White, M., White, R., Wierzcholska, A., Wiesand, S., Wijers, R., Wilkinson, M., Will, M., Williams, D. A., Williams, J., Williamson, T., Wolter, A., Wong, Y. W., Wood, M., Wunderlich, C., Yamamoto, T., Yamamoto, H., Yamane, Y., Yamazaki, R., Yanagita, S., Yang, L., Yoo, S., Yoshida, T., Yoshikoshi, T., Yu, P., Yusafzai, A., Zacharias, M., Zaharijas, G., Zaldivar, B., Zampieri, L., Zanmar Sanchez, R., Zaric, D., Zavrtanik, M., Zavrtanik, D., Zdziarski, A. A., Zech, A., Zechlin, H., Zenin, A., Zerwekh, A., Zhdanov, V. 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Vaiana', PSL University, IEEC-UB, Sofia University, IN2P3, Université Paris-Saclay, INAF - Osservatorio Astrofisico di Catania, College Hill, INAF - Osservatorio Astronomico di Padova, INFN Sezione di Catania, Stanford University, Núcleo de Astrofísica Teórica (NAT/UCS), INAF - Istituto di Astrofisica Spaziale e Fisica Cosmica di Milano, INFN Sezione di Pisa, Polish Academy of Sciences, INAF - Osservatorio Astronomico di Capodimonte, Università degli Studi di Udine, Sorbonne Paris Cité, DAp, Dublin City University, Universitá degli Studi di Torino, Denys Wilkinson Building, INAF - Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo, INAF - Istituto di Radioastronomia, Cherenkov Telescope Array Observatory, Universidade Federal do Paraná (UFPR), Institució Catalana de Recerca I Estudis Avançats (ICREA), CIEMAT, Complesso Universitario di Monte Sant'Angelo, INFN Sezione di Bari, Universität Tübingen, University of Nova Gorica, TU Dortmund University, Astronomical Observatory of Taras Shevchenko National University of Kyiv, Western Sydney University, Nagoya University, Yerevan Physics Institute, Università degli Studi di Bari, Astroparticule et Cosmologie, Open University of Israel, Yamagata University, University of Delaware, Radboud University Nijmegen, RCPTM, University of Turku, Josip Juraj Strossmayer University of Osijek, Max-Planck-Institut für Physik, INFN Sezione di Roma La Sapienza, Jagiellonian University, Gallalee Hall, Nicolaus Copernicus University, Thessaloniki, Leopold-Franzens-Universität, Université Paul Sabatier, KEK (High Energy Accelerator Research Organization), Kyoto University, Tokai University, University of Leicester, Universidade Federal do ABC (UFABC), LPNHE, Humboldt University Berlin, Università degli Studi di Trieste, Universidad de Jaén, Universitat Autònoma de Barcelona, Edifici C3, Faculty of Physics, University of Amsterdam, Erlangen Centre for Astroparticle Physics (ECAP), Bulgarian Academy of Sciences, Hiroshima University, Madison, INFN Sezione di Roma Tor Vergata, University of Bath, Institute of Particle and Nuclear Physics, University of California, Tokushima University, INAF - Osservatorio Astronomico di Brera, INAF - Istituto di Astrofisica e Planetologia Spaziali (IAPS), Universität Potsdam, Federal University of Rio Grande do Norte, Zentrum für Astronomie der Universität Heidelberg, University of Johannesburg, Universidad de Concepción, Universidade Federal de São Carlos (UFSCar), Dept of Physics and Astronomy, National Astronomical Research Institute of Thailand, Academic Computer Centre CYFRONET AGH, University of Geneva, Universität Zürich, INAF - Osservatorio Astrofisico di Torino, University of Hawai'i at Manoa, KVI - Center for Advanced Radiation Technology, Sezione di Fisica, Santa Cruz, Ibaraki University, FESB, Universidad Metropolitana de Ciencias de la Educación, Georgia Institute of Technology, AlbaNova, Sun Yat-sen University, Agenzia Spaziale Italiana (ASI), Acharyya, A., Adam, R., Adams, C., Agudo, I., Aguirre-Santaella, A., Alfaro, R., Alfaro, J., Alispach, C., Aloisio, R., Alves Batista, R., Amati, L., Ambrosi, G., Ang??ner, E. O., Antonelli, L. A., Aramo, C., Araudo, A., Armstrong, T., Arqueros, F., Asano, K., Ascas??bar, Y., Ashley, M., Balazs, C., Ballester, O., Baquero Larriva, A., Barbosa Martins, V., Barkov, M., Barres de Almeida, U., Barrio, J. A., Bastieri, D., Becerra, J., Beck, G., Becker Tjus, J., Benbow, W., Benito, M., Berge, D., Bernardini, E., Bernl??hr, K., Berti, A., Bertucci, B., Beshley, V., Biasuzzi, B., Biland, A., Bissaldi, E., Biteau, J., Blanch, O., Blazek, J., Bocchino, F., Boisson, C., Bonneau Arbeletche, L., Bordas, P., Bosnjak, Z., Bottacini, E., Bozhilov, V., Bregeon, J., Brill, A., Bringmann, T., Brown, A. M., Brun, P., Brun, F., Bruno, P., Bulgarelli, A., Burton, M., Burtovoi, A., Buscemi, M., Cameron, R., Capasso, M., Caproni, A., Capuzzo-Dolcetta, R., Caraveo, P., Carosi, R., Carosi, A., Casanova, S., Cascone, E., Cassol, F., Catalani, F., Cauz, D., Cerruti, M., Chadwick, P., Chaty, S., Chen, A., Chernyakova, M., Chiaro, G., Chiavassa, A., Chikawa, M., Chudoba, J., olak, M., Conforti, V., Coniglione, R., Conte, F., Contreras, J. L., Coronado-Blazquez, J., Costa, A., Costantini, H., Cotter, G., Cristofari, P., D'A��, A., D'Ammando, F., Damone, L. A., Daniel, M. K., Dazzi, F., De Angelis, A., De Caprio, V., de C??ssia dos Anjos, R., de Gouveia Dal Pino, E. M., De Lotto, B., De Martino, D., de O??a Wilhelmi, E., De Palma, F., de Souza, V., Delgado, C., Delgado Giler, A. G., della Volpe, D., Depaoli, D., Di Girolamo, T., Di Pierro, F., Di Venere, L., Diebold, S., Dmytriiev, A., Dom??nguez, A., Donini, A., Doro, M., Ebr, J., Eckner, C., Edwards, T. D. P., Ekoume, T. R. N., Els??sser, D., Evoli, C., Falceta-Goncalves, D., Fedorova, E., Fegan, S., Feng, Q., Ferrand, G., Ferrara, G., Fiandrini, E., Fiasson, A., Filipovic, M., Fioretti, V., Fiori, M., Foffano, L., Fontaine, G., Fornieri, O., Franco, F. J., Fukami, S., Fukui, Y., Gaggero, D., Galaz, G., Gammaldi, V., Garcia, E., Garczarczyk, M., Gascon, D., Gent, A., Ghalumyan, A., Gianotti, F., Giarrusso, M., Giavitto, G., Giglietto, N., Giordano, F., Giuliani, A., Glicenstein, J., Gnatyk, R., Goldoni, P., Gonz??lez, M. M., Gourgouliatos, K., Granot, J., Grasso, D., Green, J., Grillo, A., Gueta, O., Gunji, S., Halim, A., Hassan, T., Heller, M., Hern??ndez Cadena, S., Hiroshima, N., Hnatyk, B., Hofmann, W., Holder, J., Horan, D., H??randel, J., Horvath, P., Hovatta, T., Hrabovsky, M., Hrupec, D., Hughes, G., Humensky, T. B., H??tten, M., Iarlori, M., Inada, T., Inoue, S., Iocco, F., Iori, M., Jamrozy, M., Janecek, P., Jin, W., Jouvin, L., Jurysek, J., Karukes, E., Katarzy??ski, K., Kazanas, D., Kerszberg, D., Kherlakian, M. C., Kissmann, R., Kn??dlseder, J., Kobayashi, Y., Kohri, K., Komin, N., Kubo, H., Kushida, J., Lamanna, G., Lapington, J., Laporte, P., Leigui de Oliveira, M. A., Lenain, J., Leone, F., Leto, G., Lindfors, E., Lohse, T., Lombardi, S., Longo, F., Lopez, A., L??pez, M., L??pez-Coto, R., Loporchio, S., Luque-Escamilla, P. L., Mach, E., Maggio, C., Maier, G., Mallamaci, M., Malta Nunes de Almeida, R., Mandat, D., Manganaro, M., Mangano, S., Manic??, G., Marculewicz, M., Mariotti, M., Markoff, S., Marquez, P., Mart??, J., Martinez, O., Mart??nez, M., Mart??nez, G., Mart??nez-Huerta, H., Maurin, G., Mazin, D., Mbarubucyeye, J. D., Medina Miranda, D., Meyer, M., Miceli, M., Miener, T., Minev, M., Miranda, J. M., Mirzoyan, R., Mizuno, T., Mode, B., Moderski, R., Mohrmann, L., Molina, E., Montaruli, T., Moralejo, A., Morcuende-Parrilla, D., Morselli, A., Mukherjee, R., Mundell, C., Nagai, A., Nakamori, T., Nemmen, R., Niemiec, J., Nieto, D., Niko??ajuk, M., Ninci, D., Noda, K., Nosek, D., Nozaki, S., Ohira, Y., Ohishi, M., Ohtani, Y., Oka, T., Okumura, A., Ong, R. A., Orienti, M., Orito, R., Orlandini, M., Orlando, S., Orlando, E., Ostrowski, M., Oya, I., Pagano, I., Pagliaro, A., Palatiello, M., Pantaleo, F. R., Paredes, J. M., Pareschi, G., Parmiggiani, N., Patricelli, B., Pavleti??, L., Pe'Er, A., Pecimotika, M., P??rez-Romero, J., Persic, M., Petruk, O., Pfrang, K., Piano, G., Piatteli, P., Pietropaolo, E., Pillera, R., Pilszyk, B., Pintore, F., Pohl, M., Poireau, V., Prado, R. R., Prandini, E., Prast, J., Principe, G., Prokoph, H., Prouza, M., Przybilski, H., P??hlhofer, G., Pumo, M. L., Queiroz, F., Quirrenbach, A., Rain??, S., Rando, R., Razzaque, S., Recchia, S., Reimer, O., Reisenegger, A., Renier, Y., Rhode, W., Ribeiro, D., Rib??, M., Richtler, T., Rico, J., Rieger, F., Rinchiuso, L., Rizi, V., Rodriguez, J., Rodriguez Fernandez, G., Rodriguez Ramirez, J. C., Rojas, G., Romano, P., Romeo, G., Rosado, J., Rowell, G., Rudak, B., Russo, F., Sadeh, I., S??ther Hatlen, E., Safi-Harb, S., Salesa Greus, F., Salina, G., Sanchez, D., S??nchez-Conde, M., Sangiorgi, P., Sano, H., Santander, M., Santos, E. M., Santos-Lima, R., Sanuy, A., Sarkar, S., Saturni, F. G., Sawangwit, U., Schussler, F., Schwanke, U., Sciacca, E., Scuderi, S., Seglar-Arroyo, M., Sergijenko, O., Servillat, M., Seweryn, K., Shalchi, A., Sharma, P., Shellard, R. C., Siejkowski, H., Silk, J., Siqueira, C., Sliusar, V., S??owikowska, A., Sokolenko, A., Sol, H., Spencer, S., Stamerra, A., Stani??, S., Starling, R., Stolarczyk, T., Straumann, U., Stri??kovi??, J., Suda, Y., Suomijarvi, T., wierk, P., Tavecchio, F., Taylor, L., Tejedor, L. A., Teshima, M., Testa, V., Tibaldo, L., Todero Peixoto, C. J., Tokanai, F., Tonev, D., Tosti, G., Tosti, L., Tothill, N., Truzzi, S., Travnicek, P., Vagelli, V., Vallage, B., Vallania, P., van Eldik, C., Vandenbroucke, J., Varner, G. S., Vassiliev, V., V??zquez Acosta, M., Vecchi, M., Ventura, S., Vercellone, S., Vergani, S., Verna, G., Viana, A., Vigorito, C. F., Vink, J., Vitale, V., Vorobiov, S., Vovk, I., Vuillaume, T., Wagner, S. J., Walter, R., Watson, J., Weniger, C., White, R., White, M., Wiemann, R., Wierzcholska, A., Will, M., Williams, D. A., Wischnewski, R., Yanagita, S., Yang, L., Yoshikoshi, T., Zacharias, M., Zaharijas, G., Zakaria, A. A., Zampieri, L., Zanin, R., Zaric, D., Zavrtanik, M., Zavrtanik, D., Zdziarski, A. A., Zech, A., Zechlin, H., Zhdanov, V. I., ivec, M., ITA, USA, GBR, FRA, DEU, ESP, AUT, BEL, BRA, HRV, DNK, JPN, IRL, NLD, POL, SVN, CHE, High Energy Astrophys. & Astropart. Phys (API, FNWI), GRAPPA (ITFA, IoP, FNWI), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Diderot - Paris 7 (UPD7), Université Montpellier 2 - Sciences et Techniques (UM2)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Astrophysique Interprétation Modélisation (AIM (UMR_7158 / UMR_E_9005 / UM_112)), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris Diderot - Paris 7 (UPD7), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Iocco, Fabio, Anguner, E. O., Ascasibar, Y., Bernlohr, K., Colak, M., D'Ai, A., de Angelis, A., de Caprio, V., de Cassia dos Anjos, R., de Lotto, B., de Martino, D., de Ona Wilhelmi, E., de Palma, F., Dominguez, A., Elsasser, D., Gonzalez, M. M., Hernandez Cadena, S., Horandel, J., Hutten, M., Katarzynski, K., Knodlseder, J., Lopez, M., Lopez-Coto, R., Manico, G., Marti, J., Martinez, M., Martinez, G., Martinez-Huerta, H., Nikolajuk, M., Pavletic, L., Perez-Romero, J., Puhlhofer, G., Raino, S., Ribo, M., Saether Hatlen, E., Sanchez-Conde, M., Slowikowska, A., Stanic, S., Striskovic, J., Swierk, P., Vazquez Acosta, M., Zivec, M., Consortium, The CTA, Acharyya A., Adam R., Adams C., Agudo I., Aguirre-Santaella A., Alfaro R., Alfaro J., Alispach C., Aloisio R., Alves Batista R., Amati L., Ambrosi G., Anguner E.O., Antonelli L.A., Aramo C., Araudo A., Armstrong T., Arqueros F., Asano K., Ascasibar Y., Ashley M., Balazs C., Ballester O., Baquero Larriva A., Barbosa Martins V., Barkov M., Barres de Almeida U., Barrio J.A., Bastieri D., Becerra J., Beck G., Becker Tjus J., Benbow W., Benito M., Berge D., Bernardini E., Bernlohr K., Berti A., Bertucci B., Beshley V., Biasuzzi B., Biland A., Bissaldi E., Biteau J., Blanch O., Blazek J., Bocchino F., Boisson C., Bonneau Arbeletche L., Bordas P., Bosnjak Z., Bottacini E., Bozhilov V., Bregeon J., Brill A., Bringmann T., Brown A.M., Brun P., Brun F., Bruno P., Bulgarelli A., Burton M., Burtovoi A., Buscemi M., Cameron R., Capasso M., Caproni A., Capuzzo-Dolcetta R., Caraveo P., Carosi R., Carosi A., Casanova S., Cascone E., Cassol F., Catalani F., Cauz D., Cerruti M., Chadwick P., Chaty S., Chen A., Chernyakova M., Chiaro G., Chiavassa A., Chikawa M., Chudoba J., Colak M., Conforti V., Coniglione R., Conte F., Contreras J.L., Coronado-Blazquez J., Costa A., Costantini H., Cotter G., Cristofari P., D'Ai A., D'Ammando F., Damone L.A., Daniel M.K., Dazzi F., de Angelis A., de Caprio V., de Cassia dos Anjos R., de Gouveia Dal Pino E.M., de Lotto B., de Martino D., de Ona Wilhelmi E., de Palma F., de Souza V., Delgado C., Delgado Giler A.G., della Volpe D., Depaoli D., Di Girolamo T., Di Pierro F., Di Venere L., Diebold S., Dmytriiev A., Dominguez A., Donini A., Doro M., Ebr J., Eckner C., Edwards T.D.P., Ekoume T.R.N., Elsasser D., Evoli C., Falceta-Goncalves D., Fedorova E., Fegan S., Feng Q., Ferrand G., Ferrara G., Fiandrini E., Fiasson A., Filipovic M., Fioretti V., Fiori M., Foffano L., Fontaine G., Fornieri O., Franco F.J., Fukami S., Fukui Y., Gaggero D., Galaz G., Gammaldi V., Garcia E., Garczarczyk M., Gascon D., Gent A., Ghalumyan A., Gianotti F., Giarrusso M., Giavitto G., Giglietto N., Giordano F., Giuliani A., Glicenstein J., Gnatyk R., Goldoni P., Gonzalez M.M., Gourgouliatos K., Granot J., Grasso D., Green J., Grillo A., Gueta O., Gunji S., Halim A., Hassan T., Heller M., Hernandez Cadena S., Hiroshima N., Hnatyk B., Hofmann W., Holder J., Horan D., Horandel J., Horvath P., Hovatta T., Hrabovsky M., Hrupec D., Hughes G., Humensky T.B., Hutten M., Iarlori M., Inada T., Inoue S., Iocco F., Iori M., Jamrozy M., Janecek P., Jin W., Jouvin L., Jurysek J., Karukes E., Katarzynski K., Kazanas D., Kerszberg D., Kherlakian M.C., Kissmann R., Knodlseder J., Kobayashi Y., Kohri K., Komin N., Kubo H., Kushida J., Lamanna G., Lapington J., Laporte P., Leigui de Oliveira M.A., Lenain J., Leone F., Leto G., Lindfors E., Lohse T., Lombardi S., Longo F., Lopez A., Lopez M., Lopez-Coto R., Loporchio S., Luque-Escamilla P.L., Mach E., Maggio C., Maier G., Mallamaci M., Malta Nunes de Almeida R., Mandat D., Manganaro M., Mangano S., Manico G., Marculewicz M., Mariotti M., Markoff S., Marquez P., Marti J., Martinez O., Martinez M., Martinez G., Martinez-Huerta H., Maurin G., Mazin D., Mbarubucyeye J.D., Medina Miranda D., Meyer M., Miceli M., Miener T., Minev M., Miranda J.M., Mirzoyan R., Mizuno T., Mode B., Moderski R., Mohrmann L., Molina E., Montaruli T., Moralejo A., Morcuende-Parrilla D., Morselli A., Mukherjee R., Mundell C., Nagai A., Nakamori T., Nemmen R., Niemiec J., Nieto D., Nikolajuk M., Ninci D., Noda K., Nosek D., Nozaki S., Ohira Y., Ohishi M., Ohtani Y., Oka T., Okumura A., Ong R.A., Orienti M., Orito R., Orlandini M., Orlando S., Orlando E., Ostrowski M., Oya I., Pagano I., Pagliaro A., Palatiello M., Pantaleo F.R., Paredes J.M., Pareschi G., Parmiggiani N., Patricelli B., Pavletic L., Pe'Er A., Pecimotika M., Perez-Romero J., Persic M., Petruk O., Pfrang K., Piano G., Piatteli P., Pietropaolo E., Pillera R., Pilszyk B., Pintore F., Pohl M., Poireau V., Prado R.R., Prandini E., Prast J., Principe G., Prokoph H., Prouza M., Przybilski H., Puhlhofer G., Pumo M.L., Queiroz F., Quirrenbach A., Raino S., Rando R., Razzaque S., Recchia S., Reimer O., Reisenegger A., Renier Y., Rhode W., Ribeiro D., Ribo M., Richtler T., Rico J., Rieger F., Rinchiuso L., Rizi V., Rodriguez J., Rodriguez Fernandez G., Rodriguez Ramirez J.C., Rojas G., Romano P., Romeo G., Rosado J., Rowell G., Rudak B., Russo F., Sadeh I., Saether Hatlen E., Safi-Harb S., Salesa Greus F., Salina G., Sanchez D., Sanchez-Conde M., Sangiorgi P., Sano H., Santander M., Santos E.M., Santos-Lima R., Sanuy A., Sarkar S., Saturni F.G., Sawangwit U., Schussler F., Schwanke U., Sciacca E., Scuderi S., Seglar-Arroyo M., Sergijenko O., Servillat M., Seweryn K., Shalchi A., Sharma P., Shellard R.C., Siejkowski H., Silk J., Siqueira C., Sliusar V., Slowikowska A., Sokolenko A., Sol H., Spencer S., Stamerra A., Stanic S., Starling R., Stolarczyk T., Straumann U., Striskovic J., Suda Y., Suomijarvi T., Swierk P., Tavecchio F., Taylor L., Tejedor L.A., Teshima M., Testa V., Tibaldo L., Todero Peixoto C.J., Tokanai F., Tonev D., Tosti G., Tosti L., Tothill N., Truzzi S., Travnicek P., Vagelli V., Vallage B., Vallania P., van Eldik C., Vandenbroucke J., Varner G.S., Vassiliev V., Vazquez Acosta M., Vecchi M., Ventura S., Vercellone S., Vergani S., Verna G., Viana A., Vigorito C.F., Vink J., Vitale V., Vorobiov S., Vovk I., Vuillaume T., Wagner S.J., Walter R., Watson J., Weniger C., White R., White M., Wiemann R., Wierzcholska A., Will M., Williams D.A., Wischnewski R., Yanagita S., Yang L., Yoshikoshi T., Zacharias M., Zaharijas G., Zakaria A.A., Zampieri L., Zanin R., Zaric D., Zavrtanik M., Zavrtanik D., Zdziarski A.A., Zech A., Zechlin H., Zhdanov V.I., and Zivec M.

Subjects
Cherenkov Telescope Array, MATÉRIA ESCURA, scale: TeV, Astronomy, atmosphere [Cherenkov counter], dark matter experiment, Dark matter theory, energy resolution, Gamma ray experiments, Particle, Astrophysics, cosmic background radiation, 01 natural sciences, 7. Clean energy, High Energy Physics - Phenomenology (hep-ph), benchmark, WIMP, HESS, energy: flux, TeV [scale], relativistic [charged particle], gamma ray experiment, MAGIC (telescope), Monte Carlo, Event reconstruction, Physics, High Energy Astrophysical Phenomena (astro-ph.HE), Contraction, spatial distribution, track data analysis, density [dark matter], Clumpy, Astrophysics::Instrumentation and Methods for Astrophysics, imaging, High Energy Physics - Phenomenology, dark matter experiments, dark matter theory, gamma ray experiments, galaxy morphology, Dark matter experiments, Física nuclear, VERITAS, Astrophysics - High Energy Astrophysical Phenomena, Simulations, noise, Astrophysics::High Energy Astrophysical Phenomena, Dark matter, satellite, Cosmic background radiation, FOS: Physical sciences, Annihilation, dark matter: density, Astrophysics::Cosmology and Extragalactic Astrophysics, Cherenkov counter: atmosphere, heavy [dark matter], annihilation [dark matter], GLAST, Galaxy morphology, cosmic radiation [p], 0103 physical sciences, Cherenkov [radiation], Candidates, ddc:530, AGN, Cherenkov radiation, Radiative Processes, thermal [cross section], 010308 nuclear & particles physics, Física, dark matter: annihilation, Gamma-Ray Signals, dark matter, Galactic Center, TeV gamma-ray astronomy, Astronomy and Astrophysics, Mass, radiation: Cherenkov, sensitivity, MAGIC, Galaxy, Astronomía, dark matter: heavy, gamma ray, p: cosmic radiation, [PHYS.HPHE]Physics [physics]/High Energy Physics - Phenomenology [hep-ph], correlation, charged particle: relativistic, flux [energy], galaxy, supersymmetry, [PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph], cross section: thermal
Abstract

Full list of authors: Acharyya, A.; Adam, R.; Adams, C.; Agudo, I.; Aguirre-Santaella, A.; Alfaro, R.; Alfaro, J.; Alispach, C.; Aloisio, R.; Alves Batista, R.; Amati, L.; Ambrosi, G.; Angüner, E. O.; Antonelli, L. A.; Aramo, C.; Araudo, A.; Armstrong, T.; Arqueros, F.; Asano, K.; Ascasíbar, Y. Ashley, M.; Balazs, C.; Ballester, O.; Baquero Larriva, A.; Barbosa Martins, V.; Barkov, M.; Barres de Almeida, U.; Barrio, J. A.; Bastieri, D.; Becerra, J.; Beck, G.; Becker Tjus, J.; Benbow, W.; Benito, M.; Berge, D.; Bernardini, E.; Bernlöhr, K.; Berti, A.; Bertucci, B.; Beshley, V.; Biasuzzi, B.; Biland, A.; Bissaldi, E.; Biteau, J.; Blanch, O.; Blazek, J.; Bocchino, F.; Boisson, C.; Bonneau Arbeletche, L.; Bordas, P.; Bosnjak, Z.; Bottacini, E.; Bozhilov, V.; Bregeon, J.; Brill, A.; Bringmann, T.; Brown, A. M.; Brun, P.; Brun, F.; Bruno, P.; Bulgarelli, A.; Burton, M.; Burtovoi, A.; Buscemi, M.; Cameron, R.; Capasso, M.; Caproni, A.; Capuzzo-Dolcetta, R.; Caraveo, P.; Carosi, R.; Carosi, A.; Casanova, S.; Cascone, E.; Cassol, F.; Catalani, F.; Cauz, D.; Cerruti, M.; Chadwick, P.; Chaty, S.; Chen, A.; Chernyakova, M.; Chiaro, G.; Chiavassa, A.; Chikawa, M.; Chudoba, J.; Çolak, M.; Conforti, V.; Coniglione, R.; Conte, F.; Contreras, J. L.; Coronado-Blazquez, J.; Costa, A.; Costantini, H.; Cotter, G.; Cristofari, P.; D'Aimath, A.; D'Ammando, F.; Damone, L. A.; Daniel, M. K.; Dazzi, F.; De Angelis, A.; De Caprio, V.; de Cássia dos Anjos, R.; de Gouveia Dal Pino, E. M.; De Lotto, B.; De Martino, D.; de Oña Wilhelmi, E.; De Palma, F.; de Souza, V.; Delgado, C.; Delgado Giler, A. G.; della Volpe, D.; Depaoli, D.; Di Girolamo, T.; Di Pierro, F.; Di Venere, L.; Diebold, S.; Dmytriiev, A.; Domínguez, A.; Donini, A.; Doro, M.; Ebr, J.; Eckner, C.; Edwards, T. D. P.; Ekoume, T. R. N.; Elsässer, D.; Evoli, C.; Falceta-Goncalves, D.; Fedorova, E.; Fegan, S.; Feng, Q.; Ferrand, G.; Ferrara, G.; Fiandrini, E.; Fiasson, A.; Filipovic, M.; Fioretti, V.; Fiori, M.; Foffano, L.; Fontaine, G.; Fornieri, O.; Franco, F. J.; Fukami, S.; Fukui, Y.; Gaggero, D.; Galaz, G.; Gammaldi, V.; Garcia, E.; Garczarczyk, M.; Gascon, D.; Gent, A.; Ghalumyan, A.; Gianotti, F.; Giarrusso, M.; Giavitto, G.; Giglietto, N.; Giordano, F.; Giuliani, A.; Glicenstein, J.; Gnatyk, R.; Goldoni, P.; González, M. M.; Gourgouliatos, K.; Granot, J.; Grasso, D.; Green, J.; Grillo, A.; Gueta, O.; Gunji, S.; Halim, A.; Hassan, T.; Heller, M.; Hernández Cadena, S.; Hiroshima, N.; Hnatyk, B.; Hofmann, W.; Holder, J.; Horan, D.; Hörandel, J.; Horvath, P.; Hovatta, T.; Hrabovsky, M.; Hrupec, D.; Hughes, G.; Humensky, T. B.; Hütten, M.; Iarlori, M.; Inada, T.; Inoue, S.; Iocco, F.; Iori, M.; Jamrozy, M.; Janecek, P.; Jin, W.; Jouvin, L.; Jurysek, J.; Karukes, E.; Katarzyński, K.; Kazanas, D.; Kerszberg, D.; Kherlakian, M. C.; Kissmann, R.; Knödlseder, J.; Kobayashi, Y.; Kohri, K.; Komin, N.; Kubo, H.; Kushida, J.; Lamanna, G.; Lapington, J.; Laporte, P.; Leigui de Oliveira, M. A.; Lenain, J.; Leone, F.; Leto, G.; Lindfors, E.; Lohse, T.; Lombardi, S.; Longo, F.; Lopez, A.; López, M.; López-Coto, R.; Loporchio, S.; Luque-Escamilla, P. L.; Mach, E.; Maggio, C.; Maier, G.; Mallamaci, M.; Malta Nunes de Almeida, R.; Mandat, D.; Manganaro, M.; Mangano, S.; Manicò, G.; Marculewicz, M.; Mariotti, M.; Markoff, S.; Marquez, P.; Martí, J.; Martinez, O.; Martínez, M.; Martínez, G.; Martínez-Huerta, H.; Maurin, G.; Mazin, D.; Mbarubucyeye, J. D.; Medina Miranda, D.; Meyer, M.; Miceli, M.; Miener, T.; Minev, M.; Miranda, J. M.; Mirzoyan, R.; Mizuno, T.; Mode, B.; Moderski, R.; Mohrmann, L.; Molina, E.; Montaruli, T.; Moralejo, A.; Morcuende-Parrilla, D.; Morselli, A.; Mukherjee, R.; Mundell, C.; Nagai, A.; Nakamori, T.; Nemmen, R.; Niemiec, J.; Nieto, D.; Nikołajuk, M.; Ninci, D.; Noda, K.; Nosek, D.; Nozaki, S.; Ohira, Y.; Ohishi, M.; Ohtani, Y.; Oka, T.; Okumura, A.; Ong, R. A.; Orienti, M.; Orito, R.; Orlandini, M.; Orlando, S.; Orlando, E.; Ostrowski, M.; Oya, I.; Pagano, I.; Pagliaro, A.; Palatiello, M.; Pantaleo, F. R.; Paredes, J. M.; Pareschi, G.; Parmiggiani, N.; Patricelli, B.; Pavletić, L.; Pe'er, A.; Pecimotika, M.; Pérez-Romero, J.; Persic, M.; Petruk, O.; Pfrang, K.; Piano, G.; Piatteli, P.; Pietropaolo, E.; Pillera, R.; Pilszyk, B.; Pintore, F.; Pohl, M.; Poireau, V.; Prado, R. R.; Prandini, E.; Prast, J.; Principe, G.; Prokoph, H.; Prouza, M.; Przybilski, H.; Pühlhofer, G.; Pumo, M. L.; Queiroz, F.; Quirrenbach, A.; Rainò, S.; Rando, R.; Razzaque, S.; Recchia, S.; Reimer, O.; Reisenegger, A.; Renier, Y.; Rhode, W.; Ribeiro, D.; Ribó, M.; Richtler, T.; Rico, J.; Rieger, F.; Rinchiuso, L.; Rizi, V.; Rodriguez, J.; Rodriguez Fernandez, G.; Rodriguez Ramirez, J. C.; Rojas, G.; Romano, P.; Romeo, G.; Rosado, J.; Rowell, G.; Rudak, B.; Russo, F.; Sadeh, I.; Sæther Hatlen, E.; Safi-Harb, S.; Salesa Greus, F.; Salina, G.; Sanchez, D.; Sánchez-Conde, M.; Sangiorgi, P.; Sano, H.; Santander, M.; Santos, E. M.; Santos-Lima, R.; Sanuy, A.; Sarkar, S.; Saturni, F. G.; Sawangwit, U.; Schussler, F.; Schwanke, U.; Sciacca, E.; Scuderi, S.; Seglar-Arroyo, M.; Sergijenko, O.; Servillat, M.; Seweryn, K.; Shalchi, A.; Sharma, P.; Shellard, R. C.; Siejkowski, H.; Silk, J.; Siqueira, C.; Sliusar, V.; Słowikowska, A.; Sokolenko, A.; Sol, H.; Spencer, S.; Stamerra, A.; Stanič, S.; Starling, R.; Stolarczyk, T.; Straumann, U.; Strišković, J.; Suda, Y.; Suomijarvi, T.; Świerk, P.; Tavecchio, F.; Taylor, L.; Tejedor, L. A.; Teshima, M.; Testa, V.; Tibaldo, L.; Todero Peixoto, C. J.; Tokanai, F.; Tonev, D.; Tosti, G.; Tosti, L.; Tothill, N.; Truzzi, S.; Travnicek, P.; Vagelli, V.; Vallage, B.; Vallania, P.; van Eldik, C.; Vandenbroucke, J.; Varner, G. S.; Vassiliev, V.; Vázquez Acosta, M.; Vecchi, M.; Ventura, S.; Vercellone, S.; Vergani, S.; Verna, G.; Viana, A.; Vigorito, C. F.; Vink, J.; Vitale, V.; Vorobiov, S.; Vovk, I.; Vuillaume, T.; Wagner, S. J.; Walter, R.; Watson, J.; Weniger, C.; White, R.; White, M.; Wiemann, R.; Wierzcholska, A.; Will, M.; Williams, D. A.; Wischnewski, R.; Yanagita, S.; Yang, L.; Yoshikoshi, T.; Zacharias, M.; Zaharijas, G.; Zakaria, A. A.; Zampieri, L.; Zanin, R.; Zaric, D.; Zavrtanik, M.; Zavrtanik, D.; Zdziarski, A. A.; Zech, A.; Zechlin, H.; Zhdanov, V. I.; Živec, M.-- This is an open access article published by IOP Publishing Ltd on behalf of Sissa Medialab. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI., We provide an updated assessment of the power of the Cherenkov Telescope Array (CTA) to search for thermally produced dark matter at the TeV scale, via the associated gamma-ray signal from pair-annihilating dark matter particles in the region around the Galactic centre. We find that CTA will open a new window of discovery potential, significantly extending the range of robustly testable models given a standard cuspy profile of the dark matter density distribution. Importantly, even for a cored profile, the projected sensitivity of CTA will be sufficient to probe various well-motivated models of thermally produced dark matter at the TeV scale. This is due to CTA's unprecedented sensitivity, angular and energy resolutions, and the planned observational strategy. The survey of the inner Galaxy will cover a much larger region than corresponding previous observational campaigns with imaging atmospheric Cherenkov telescopes. CTA will map with unprecedented precision the large-scale diffuse emission in high-energy gamma rays, constituting a background for dark matter searches for which we adopt state-of-the-art models based on current data. Throughout our analysis, we use up-to-date event reconstruction Monte Carlo tools developed by the CTA consortium, and pay special attention to quantifying the level of instrumental systematic uncertainties, as well as background template systematic errors, required to probe thermally produced dark matter at these energies. © 2021 The Author(s)., We gratefully acknowledge financial support from the following agencies and organisations: State Committee of Science of Armenia, Armenia; The Australian Research Council, Astronomy Australia Ltd, The University of Adelaide, Australian National University, Monash University, The University of New South Wales, The University of Sydney, Western Sydney University, Australia; Federal Ministry of Education, Science and Research, and Innsbruck University, Austria; Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq), Fundacao de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP), Ministry of Science, Technology, Innovations and Communications (MCTIC), and Instituto Serrapilheira, Brasil; Ministry of Education and Science, National RI Roadmap Project DO1-153/28.08.2018, Bulgaria; The Natural Sciences and Engineering Research Council of Canada and the Canadian Space Agency, Canada; CONICYT-Chile grants CATA AFB 170002, ANID PIA/APOYO AFB 180002, ACT 1406, FONDECYT-Chile grants, 1161463, 1170171, 1190886, 1171421, 1170345, 1201582, Gemini-ANID 32180007, Chile; Croatian Science Foundation, Rudjer Boskovic Institute, University of Osijek, University of Rijeka, University of Split, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia; Ministry of Education, Youth and Sports, MEYS LM2015046, LM2018105, LTT17006, EU/MEYS CZ.02.1.01/0.0/0.0/16_013/0001403, CZ.02.1.01/0.0/0.0/18_046/0016007 and CZ.02.1.01/0.0/0.0/16_019/0000754, Czech Republic; Academy of Finland (grant nr.317636, 320045, 317383 and 320085), Finland; Ministry of Higher Education and Research, CNRS-INSU and CNRS-IN2P3, CEA-Irfu, ANR, Regional Council Ile de France, Labex ENIGMASS, OSUG2020, P2IO and OCEVU, France; Max Planck Society, BMBF, DESY, Helmholtz Association, Germany; Department of Atomic Energy, Department of Science and Technology, India; Istituto Nazionale di Astrofisica (INAF), Istituto Nazionale di Fisica Nucleare (INFN), MIUR, Istituto Nazionale di Astrofisica (INAF-OABRERA) Grant Fondazione Cariplo/Regione Lombardia ID 2014-1980/RST_ERC, Italy; ICRR, University of Tokyo, JSPS, MEXT, Japan; Netherlands Research School for Astronomy (NOVA), Netherlands Organization for Scientific Research (NWO), Netherlands; University of Oslo, Norway; Ministry of Science and Higher Education, DIR/WK/2017/12, the National Centre for Research and Development and the National Science Centre, UMO-2016/22/M/ST9/00583, Poland; Slovenian Research Agency, grants P1-0031, P1-0385, I0-0033, J1-9146, J1-1700, N1-0111, and the Young Researcher program, Slovenia; South African Department of Science and Technology and National Research Foundation through the South African Gamma-Ray Astronomy Programme, South Africa; The Spanish Ministry of Science and Innovation and the Spanish Research State Agency (AEI) through grants AYA2016-79724-C4-1-P, AYA2016-80889-P, AYA2016-76012-C3-1-P, BES-2016-076342, ESP2017-87055-C2-1-P, FPA2017-82729-C6-1-R, FPA2017-82729-C6-2-R, FPA2017-82729-C6-3-R, FPA2017-82729-C6-4-R, FPA2017-82729-C6-5-R, FPA2017-82729-C6-6-R, PGC2018-095161-B-I00, PGC2018-095512-B-I00; the \Centro de Excelencia Severo Ochoa"program through grants no. SEV-2015-0548, SEV-2016-0597, SEV-2016-0588, SEV-2017-0709; the "Unidad de Excelencia Maria de Maeztu" program through grant no. MDM-2015-0509; the "Ramon y Cajal" programme through grants RYC-2013-14511, RyC-2013-14660, RYC-2017-22665; and the MultiDark Consolider Network FPA2017-90566-REDC. Atraccion de Talento contract no. 2016-T1/TIC-1542 granted by the Comunidad de Madrid; the "Postdoctoral Junior Leader Fellowship" programme from La Caixa Banking Foundation, grants no. LCF/BQ/LI18/11630014 and LCF/BQ/PI18/11630012; the "Programa Operativo" FEDER2014-2020, Consejeria de Economia y Conocimiento de la Junta de Andalucia (ref. 1257737), PAIDI 2020 (ref. P18-FR-1580), and Universidad de Jaen; the Spanish AEI EQC2018-005094-P FEDER 2014-2020; the European Union's "Horizon 2020" research and innovation programme under Marie Sklodowska-Curie grant agreement no. 665919; and the ESCAPE project with grant no. GA:824064, Spain; Swedish Research Council, Royal Physiographic Society of Lund, Royal Swedish Academy of Sciences, The Swedish National Infrastructure for Computing (SNIC) at Lunarc (Lund), Sweden; State Secretariat for Education, Research and Innovation (SERI) and Swiss National Science Foundation (SNSF), Switzerland; Durham University, Leverhulme Trust, Liverpool University, University of Leicester, University of Oxford, Royal Society, Science and Technology Facilities Council, U.K.; U.S. National Science Foundation, U.S. Department of Energy, Argonne National Laboratory, Barnard College, University of California, University of Chicago, Columbia University, Georgia Institute of Technology, Institute for Nuclear and Particle Astrophysics (INPAC-MRPI program), Iowa State University, the Smithsonian Institution, Washington University McDonnell Center for the Space Sciences, The University of Wisconsin and the Wisconsin Alumni Research Foundation, U.S.A. The research leading to these results has received funding from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreements No 262053 and No 317446. This project is receiving funding from the European Union's Horizon 2020 research and innovation programs under agreement No 676134.

Published
2022

47. Combining Maximum-Likelihood with Deep Learning for Event Reconstruction in IceCube

Author

IceCube Collaboration, Huennefeld, Mirco, Amin, Najia Moureen Binte, Finley, Chad, Fischer, Leander, Fox, Derek B., Franckowiak, Anna, Friedman, Elizabeth, Fritz, Alexander, Fürst, Philipp, Gaisser, T. K., Gallagher, Jay, Ganster, Erik, An, Rui, Garcia, Alfonso, Garrappa, Simone, Gerhardt, L., Ghadimi, Ava, Glaser, Christian, Glauch, Theo, Glusenkamp, Thorsten, Goldschmidt, A., Gonzalez, Javier, Goswami, Sreetama, Andeen, Karen, Grant, Darren, Gregoire, Timothee, Griswold, Spencer, Gunduz, Mehmet, Günther, Christoph, Haack, Christian, Hallgren, Allan, Halliday, R., Halve, Lasse Yannik, Halzen, F., Anderson, Tyler, Ha Minh, Martin, Hanson, Kael, Hardin, John, Harnisch, Alexander A., Haungs, Andreas, Hauser, Simon, Hebecker, Dustin, Helbing, K., Henningsen, Felix, Hettinger, Emma C., Anton, Gisela, Hickford, Stephanie, Hignight, Joshua, Hill, Colton, Hill, G. C., Hoffman, Kara, Hoffmann, Ruth, Hoinka, Tobias, Hokanson-Fasig, Benjamin, Hoshina, K., Huang, Feifei, Arguelles, Carlos, Huber, Matthias, Huber, Thomas, Hultqvist, Klas, Hunnefeld, Mirco, Hussain, Raamis, In, Seongjin, Iovine, Nadege, Ishihara, Aya, Jansson, Matti, Japaridze, George, Ashida, Yosuke, Jeong, Minjin, Jones, Ben, Kang, Donghwa, Kang, Woosik, Kang, Xinyue, Kappes, Alexander, Kappesser, David, Karg, Timo, Karl, Martina, Karle, A., Axani, Spencer, Katz, U., Kauer, M., Kellermann, Moritz, Kelley, J. L., Kheirandish, Ali, Kin, Ken'ichi, Kintscher, Thomas, Kiryluk, Joanna, Klein, Spencer, Koirala, Ramesh, Bai, Xinhua, Kolanoski, Hermann, Kontrimas, Tomas, Kopke, Lutz, Kopper, Claudio, Kopper, Sandro, Koskinen, D. J., Koundal, Paras, Kovacevich, Michael, Kowalski, Marek, Kozynets, Tetiana, Balagopal V., Aswathi, Kun, Emma, Kurahashi, Naoko, Lad, Neha, Lagunas Gualda, Cristina, Lanfranchi, Justin, Larson, Michael J., Lauber, Frederik Hermann, Lazar, Jeffrey, Lee, Jiwoong, Leonard, Kayla, Abbasi, Rasha, Barbano, Anastasia Maria, Leszczynska, Agnieszka, Li, Yijia, Lincetto, Massimiliano, Liu, Qinrui, Liubarska, Maria, Lohfink, Elisa, Lozano Mariscal, Cristian Jesus, Lu, Lu, Lucarelli, Francesco, Ludwig, Andrew, Barwick, S. W., Luszczak, William, Lyu, Yang, Ma, Wing Yan, Madsen, James, Mahn, Kendall, Makino, Yuya, Mancina, Sarah, Maris, Ioana Codrina, Maruyama, Reina H., Mase, K., Bastian, Benjamin, McElroy, Thomas, McNally, Frank, Mead, James Vincent, Meagher, K., Medina, Andres, Meier, Maximilian, Meighen-Berger, Stephan, Micallef, Jessie, Mockler, Daniela, Montaruli, Teresa, Basu, Vedant, Moore, Roger, Morse, R., Moulai, Marjon, Naab, Richard, Nagai, Ryo, Naumann, Uwe, Necker, Jannis, Nguyen, Le Viet, Niederhausen, Hans, Nisa, Mehr, Baur, Sebastian, Nowicki, Sarah, Nygren, Dave, Obertacke Pollmann, Anna, Oehler, Marie, Olivas, A., O'Sullivan, Erin, Pandya, Hershal, Pankova, Daria, Park, Nahee, Parker, Grant, Bay, R. C., Paudel, Ek Narayan, Paul, Larissa, Perez de los Heros, Carlos, Peters, Lilly, Peterson, Josh, Philippen, Saskia, Pieloth, Damian, Pieper, Sarah, Pittermann, Martin, Pizzuto, A., Beatty, J. J., Plum, M., Popovych, Yuiry, Porcelli, Alessio, Prado Rodriguez, Maria, Price, P. Buford, Pries, Brandom, Przybylski, Gerald, Raab, Christoph, Raissi, Amirreza, Rameez, Mohamed, Becker, K. H., Rawlins, K., Rea, Immacolata Carmen, Rehman, Abdul, Reichherzer, Patrick, Reimann, René, Renzi, Giovanni, Resconi, Elisa, Reusch, Simeon, Rhode, Wolfgang, Richman, Mike, Becker Tjus, Julia, Riedel, Benedikt, Roberts, Ella, Robertson, Sally, Roellinghoff, Gerrit, Rongen, Martin, Rott, Carsten, Ruhe, Tim, Ryckbosch, Dirk, Rysewyk Cantu, Devyn, Safa, Ibrahim, Bellenghi, Chiara, Saffer, Julian, Sanchez Herrera, Sebastian, Sandrock, Alexander, Sandroos, Joakim, Santander, Marcos, Sarkar, Subir, Sarkar, Sourav, Satalecka, Konstancja, Scharf, Maximilian Karl, Schaufel, Merlin, Ackermann, Markus, BenZvi, Segev, Schieler, Harald, Schindler, Sebastian, Schlunder, P., Schmidt, Torsten, Schneider, Austin, Schneider, Judith, Schröder, Frank G., Schumacher, Lisa Johanna, Schwefer, Georg, Sclafani, Steve, Berley, D., Seckel, D., Seunarine, Surujhdeo, Sharma, Ankur, Shefali, Shefali, Silva, Manuel, Skrzypek, Barbara, Smithers, Ben, Snihur, Robert, Soedingrekso, Jan, Soldin, Dennis, Bernardini, Elisa, Spannfellner, Christian, Spiczak, Glenn, Spiering, Christian, Stachurska, Juliana, Stamatikos, Michael, Stanev, T., Stein, Robert, Stettner, Jöran Benjamin, Steuer, A., Stezelberger, T., Besson, D. Z., Sturwald, Timo, Stuttard, Thomas, Sullivan, G. W., Taboada, I., Tenholt, Frederik, Ter-Antonyan, Samvel, Tilav, S., Tischbein, Franziska, Tollefson, Kirsten, Tomankova, Lenka, Binder, Gary, Tonnis, Christoph, Toscano, Simona, Tosi, Delia, Trettin, Alexander, Tselengidou, Maria, Tung, Chunfai, Turcati, Andrea, Turcotte, Roxanne, Turley, Colin, Twagirayezu, Jean Pierre, Bindig, Daniel, Ty, Bunheng, Unland Elorrieta, Martin, Valtonen-Mattila, Nora, Vandenbroucke, Justin, van Eijndhoven, Nick, Vannerom, David, van Santen, Jakob, Verpoest, Stef, Vraeghe, Matthias, Walck, C., Blaufuss, E., Watson, Timothyblake, Weaver, Chris, Weigel, Philip, Weindl, Andreas, Weiss, Matthew, Weldert, Jan, Wendt, Chris, Werthebach, Johannes, Weyrauch, Mark, Whitehorn, Nathan, Blot, Summer, Wiebusch, Christopher, Williams, Dawn, Wolf, Martin, Woschnagg, Kurt, Wrede, Gerrit, Wulff, Johan, Xu, Xianwu, Xu, Yiqian, Yanez, Juan Pablo, Yoshida, S., Boddenberg, Matthias, Yu, Shiqi, Yuan, Tianlu, Zhang, Zelong, Bontempo, Federico, Adams, Jenni, Borowka, Jürgen, Boser, S., Botner, Olga, Böttcher, Jakob, Bourbeau, Etienne, Bradascio, Federica, Braun, J., Bron, Stephanie, Brostean-Kaiser, Jannes, Browne, Sally-Ann, Aguilar, Juanan, Burgman, Alexander, Burley, Ryan, Busse, Raffaela, Campana, Michael, Carnie-Bronca, Erin, Chen, Chujie, Chirkin, Dmitry, Choi, Koun, Clark, Brian, Clark, Kenneth, Ahlers, M., Classen, Lew, Coleman, Alan, Collin, Gabriel, Conrad, J. M., Coppin, Paul, Correa, Pablo, Cowen, D. F., Cross, R., Dappen, Christian, Dave, Pranav, Ahrens, Maryon, De Clercq, Catherine, DeLaunay, James, Dembinski, Hans, Deoskar, Kunal, De Ridder, Sam, Desai, Abhishek, Desiati, Paolo, de Vries, Krijn, de Wasseige, Gwenhael, de With, Meike, Alispach, Cyril Martin, DeYoung, Tyce, Dharani, Sukeerthi, Diaz, Alejandro, Diaz-Velez, Juan Carlos, Dittmer, Markus, Dujmovic, Hrvoje, Dunkman, Matt, DuVernois, Michael, Dvorak, Emily, Ehrhardt, Thomas, Alves Junior, Antonio Augusto, Eller, Philipp, Engel, Ralph, Erpenbeck, Hannah, Evans, John, Evenson, P. A., Fan, Kwok Lung, Fazely, A. R., Fiedlschuster, Sebastian, Fienberg, Aaron, and Filimonov, Kirill

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computational complexity theory, neural network, FOS: Physical sciences, Context (language use), Machine learning, computer.software_genre, Field (computer science), Machine Learning (cs.LG), IceCube, ddc:530, Event reconstruction, High Energy Astrophysical Phenomena (astro-ph.HE), Physics, hybrid, Artificial neural network, business.industry, Event (computing), Deep learning, Domain knowledge, Artificial intelligence, Astrophysics - High Energy Astrophysical Phenomena, business, computer
Abstract

The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and symmetries they abide by are usually well understood. Most commonly used deep learning architectures fail at utilizing this available information. In contrast, more traditional likelihood-based methods are capable of exploiting domain knowledge, but they are often limited by computational complexity. In this contribution, a hybrid approach is presented that utilizes generative neural networks to approximate the likelihood, which may then be used in a traditional maximum-likelihood setting. Domain knowledge, such as invariances and detector characteristics, can easily be incorporated in this approach. The hybrid approach is illustrated by the example of event reconstruction in IceCube., Comment: Presented at the 37th International Cosmic Ray Conference (ICRC 2021). See arXiv:2107.06966 for all IceCube contributions

Published
2022

48. A flexible event reconstruction based on machine learning and likelihood principles.

Author

Eller, Philipp, Fienberg, Aaron T., Weldert, Jan, Wendel, Garrett, Böser, Sebastian, and Cowen, D.F.

Subjects
*MACHINE learning, *LIQUID scintillators, *PARTICLE physics, *PHYSICS experiments, *CHERENKOV counters, *NEUTRINO detectors
Abstract

Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding likelihood function is often intractable, and approximations need to be constructed. In our work, we first show how the full likelihood for a many-sensor detector can be broken apart into smaller terms, and secondly how we can train neural networks to approximate all terms solely based on forward simulation. Our technique results in a fast, flexible, and close-to-optimal surrogate model proportional to the likelihood and can be used in conjunction with standard inference techniques allowing for a consistent treatment of uncertainties. We illustrate our technique for parameter inference in neutrino telescopes based on maximum likelihood and Bayesian posterior sampling. Given its great flexibility, we also showcase our method for geometry optimization enabling to learn optimal detector designs. Lastly, we apply our method to realistic simulation of a ton-scale water-based liquid scintillator detector. [ABSTRACT FROM AUTHOR]

Published
2023
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49. Reconstruction of multiple Compton scattering events in MeV gamma-ray Compton telescopes towards GRAMS: The physics-based probabilistic model.

Author

Yoneda, Hiroki, Odaka, Hirokazu, Ichinohe, Yuto, Takashima, Satoshi, Aramaki, Tsuguo, Aoyama, Kazutaka, Asaadi, Jonathan, Fabris, Lorenzo, Inoue, Yoshiyuki, Karagiorgi, Georgia, Khangulyan, Dmitry, Kimura, Masato, Leyva, Jonathan, Mukherjee, Reshmi, Nakasone, Taichi, Perez, Kerstin, Sakurai, Mayu, Seligman, William, Tanaka, Masashi, and Tsuji, Naomi

Subjects
*MULTIPLE scattering (Physics), *COMPTON scattering, *COMPTON imaging, *LIQUID argon, *IMAGE reconstruction, *GAMMA rays, *ASTRONOMY, *NEUTRINO detectors
Abstract

Aimed at progress in mega-electron volt (MeV) gamma-ray astronomy, which has not yet been well-explored, Compton telescope missions with a variety of detector concepts have been proposed so far. One of the key techniques for these future missions is an event reconstruction algorithm that is able to determine the scattering orders of multiple Compton scattering events and to identify events in which gamma rays escape from the detectors before they deposit all of their energies. We revisit previous event reconstruction methods and propose a modified algorithm based on a probabilistic method. First, we present a general formalism of the probabilistic model of Compton scattering describing physical interactions inside the detector and measurement processes. Then, we also introduce several approximations in the calculation of the probability functions for efficient computation. For validation, the developed algorithm has been applied to simulation data of a Compton telescope using a liquid argon time projection chamber, which is a new type of Compton telescope proposed for the GRAMS project. We have confirmed that it works successfully for up to 8-hit events, including correction of incoming gamma-ray energies for escape events. The proposed algorithm can be used for next-generation MeV gamma-ray missions featured by large-volume detectors, e.g., GRAMS. [ABSTRACT FROM AUTHOR]

Published
2023
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50. TRASGOS: Towards a New Standard for the Regular Measurement of Cosmic Rays

Author

Yanis FONTENLA-BARBA, Juan Jose Blanco Avalos, and Pablo Cabanelas Eiras

Subjects
Physics, Nuclear and High Energy Physics, Muon, 010308 nuclear & particles physics, Detector, Astronomy, Cosmic ray, Electron, Tracking (particle physics), 01 natural sciences, Atomic and Molecular Physics, and Optics, 0103 physical sciences, 010306 general physics, Event reconstruction
Abstract

A new family of high accuracy multitracking detectors called TRASGOs, sensitive to both electrons and muons is proposed for multidisciplinary cosmic ray research. The corresponding tools for monitoring, tracking, event reconstruction and analysis are being developed. The present status of the project is presented and discussed.

Published
2020

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