The large size telescope of the Cherenkov Telescope Array

Ambrosi, G.; Awane, Y.; Baba, Hajime; Bamba, Aya; Barceló, M.; Almeida, U. Barres de; Barrio, J. A.; Blanch-Bigas, O.; Boix, J.; Brunetti, L.; Carmona, E.; Chabanne, E.; Chikawa, M.; Colin, Réginald; Cortina, J.; Contreras, J. L.; Dazzi, F.; Angelis, A. De; Deleglise, Guillaume; Mendez, C. Delgado; Díaz, C.; Fiaßon, A.; Fink, David J.; Fouque, N.; Freixas, Lluís; Fruck, C.; Gadola, A.; García, R. Luna; Gascon, D.; Geffroy, N.; Giglietto, N.; Giordano, F.; Grañena, F.; Gunji, Shuichi; Hagiwara, Rika; Hamer, N.; Hanabata, Y.; Hassan, Tarek; Hatanaka, K.; Hirotani, Kouichi; Inoue, Susumu; Inoue, Y.; Ioka, Kunihito; Jablonski, C.; Kagaya, Mika; Katagiri, H.; Kishimoto, T.; Kodani, K.; Kohri, Kazunori; Konno, Y.; Koyama, Shu; Kubo, H.; Kushida, J.; Lamanna, G.; Flour, T. Le; Lorenz, E.; López, R.; López, M.; Majumdar, P.; Manalaysay, A.; Mariotti, M.; Martínez, G.; Martı́nez, M.; Mazin, D.; Miranda, J. M.; Mirzoyan, R.; Monteiro, Inocencio; Moralejo, A.; Murase, Kohta; Nagataki, Shigehiro; Nakajima, D.; Nakamori, Takeshi; Nishijima, K.; Noda, K.; Nozato, A.; Ohira, Yutaka; Ohishi, M.; Ohoka, H.; Okumura, A.; Orito, R.; Panazol, J. L.; Paneque, D.; Paoletti, R.; Paredes, J. M.; Pauletta, G.; Podkladkin, S.; Prast, J.; Rando, R.; Reimann, O.; Ribó, M.; Rosier‐Lees, S.; Saito, Koji; Saito, Takayuki; Saito, Yuji; Sakaki, N.; Sakonaka, R.; Sanuy, A.; Sekiya, Hiroshi; Sawada, Makoto; Scalzotto, V.; Schultz, S.; Schweizer, T.; Shibata, T.; Shu, Shiwei; Sieiro, J.; Stamatescu, V.; Steiner, S.; Straumann, U.; Sugawara, R.; Tajima, H.; Takami, H.; Tanaka, S.; Tanaka, Masahito; Tejedor, Luis; Terada, Y.; Teshima, M.; Totani, Tomonori; Ueno, H.; Umehara, K.; Vollhardt, A.; Wagner, R. M.; Wetteskind, H.; Yamamoto, T.; Yamazaki, Ryo; Yoshida, A.; Yoshida, Tatsuo; Yoshikoshi, T.

doi:10.1117/12.2054605

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…To reach this goal, 10s of IACTs will be deployed, which will provide the improvement of angular and energy resolution with respect to nowadays instruments as well. As a result, the number of detected VHE gamma-ray astrophysical sources will increase considerably, from about 280 today [26] to more than 1000 [27] [28]. The operation of CTA will have an estimated duration of 30 years [9] [29].…”

Section: Methods and Resultsmentioning

confidence: 99%

Useful Life of Astrophysical Scientific Facilities

Márquez¹,

Martinez²,

Miranda³

2021

WJET

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Section: Methods and Resultsmentioning

confidence: 99%

Useful Life of Astrophysical Scientific Facilities

Márquez¹,

Martinez²,

Miranda³

2021

WJET

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“…The already existing LST-North (LST-N) [1] is the baseline for the realization of these telescopes also in the south (LST-S). Nevertheless, because of more stringent anti-seismic requirements in the southern site, the LST-N structure can not be directly adopted for the LST-S. We are currently discussing some options, e.g., Option (a): employing the LST-N telescope structure with foundations including damping systems, Option (b): strengthening the mechanical structure, or other options between (a) and (b).…”

Section: Pos(icrc2023)755mentioning

confidence: 99%

The CTA LST South as part of the CTA+ program

Antonelli¹

2023

Proceedings of 38th International Cosmic Ray Conference — PoS(ICRC2023)

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In the context of the Italian Resilience and Recovery Plan (PNRR), INAF and INFN proposed the "CTA+" Program aimed at extending the scope and strengthen the scientific return of the Southern Cherenkov Telescope Array Observatory (CTAO) site. The main objective of this program is to realize and implement two end-to-end Large-Sized Telescopes (LSTs) at CTAO-S as part of the LST Collaboration. The approved and full-funded program has formally began on January 1st, 2023 and has to be completed no later than December 31st, 2025. A similar procurement and implementation process is currently underway for CTAO's northern site, but some adjustments will need to be made to meet CTAO-S requirements. The baseline design of the mechanical structure will be based on that of the northern LSTs, apart from some possible changes to fulfil the environmental specifications of the southern site and further reduce the construction risks and costs. The mirrors will be produced using the same process as LST-N, a technology invented in Italy by INAF, via a cold replica of glass slabs to make the 2 m diameter mirrors. The camera will be almost identical to those of LST-N. The production of the cameras, mirrors and mechanical structures will be realized through large industrial contracts that will be supervised by the CTA+ management with the full support of the LST Collaboration.

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“…At this scale, the attention module mainly focuses on the shower pixels, and strongly rescales their value. The value of the signal pixels is multiplied by up to 1.8 in average, while, by construction, the rescaling factor computed by the spatial attention path of Dual Attention modules ranges in [1,2], as explained in Section 2. However, the attention maps are quite noisy at this scale, as also highlighted by the deviation measures.…”

Section: Modelmentioning

confidence: 99%

“…The Cherenkov Telescope Array (CTA) 1 is the next generation of Imaging Atmospheric Cherenkov Telescopes (IACTs). Composed of ∼ 100 telescopes of different sizes, it will improve sensitivity and accuracy in gamma-ray analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we first propose a new deep multi-task architecture, named γ-PhysNet, taking into account physics considerations and designed for single telescope gamma event analysis. We evaluate this model on the simulation data of the Large Size Telescopes 1 (LST1 [1]), the first prototype installed at the Northern CTA site in La Palma. In a second step, with the help of a visual explanation method for neural networks, we analyze the impact of attention (mechanism that reinforces relevant features) on the variability introduced by model weights initialization, and show that augmenting the network with attention improves the robustness of the model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Astrophysics, Understanding the Impact of Attention on Variability Induced by Parameter Initialization

Jacquemont

Vuillaume

Benoît

et al. 2021

Pattern Recognition. ICPR International Workshops and Challenges

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In the astrophysics domain, the detection and description of gamma rays is a research direction for our understanding of the universe. Gamma-ray reconstruction from Cherenkov telescope data is multi-task by nature. The image recorded in the Cherenkov camera pixels relates to the type, energy, incoming direction and distance of a particle from a telescope observation. We propose γ-PhysNet, a physically inspired multi-task deep neural network for gamma/proton particle classification, and gamma energy and direction reconstruction. As ground truth does not exist for real data, γ-PhysNet is trained and evaluated on large-scale Monte Carlo simulations. Robustness is then crucial for the transfer of the performance to real data. Relying on a visual explanation method, we evaluate the influence of attention on the variability due to weight initialization, and how it helps improve the robustness of the model. All the experiments are conducted in the context of single telescope analysis for the Cherenkov Telescope Array simulated data analysis.

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The large size telescope of the Cherenkov Telescope Array

Cited by 5 publications

References 3 publications

Useful Life of Astrophysical Scientific Facilities

Useful Life of Astrophysical Scientific Facilities

The CTA LST South as part of the CTA+ program

Deep Learning for Astrophysics, Understanding the Impact of Attention on Variability Induced by Parameter Initialization

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