Camera Calibration of the CTA-LST prototype

Kobayashi, Yukiho; Okumura, A.; Cassol, Franca; Katagiri, H.; Sitarek, J.; Gliwny, Paweł; Nozaki, Seiya; Abe, Hyuga; Aguasca, Arnau; Agudo, I.; Antonelli, L. A.; Aramo, C.; Armstrong, T. P.; Artero, Manuel; Asano, Katsuaki; Ashkar, Halim; Aubert, Pierre; Baktash, Ali; Bamba, Aya; Larriva, Andres Baquero; Baroncelli, Leonardo; Almeida, U. Barres de; Barrio, J. A.; Batković, Ivana; González, J. Becerra; Bernardos, Mabel; Berti, Alessio; Biederbeck, Noah; Bigongiari, C.; Blanch, O.; Bonnoli, Giacomo; Bordas, P.; Bose, D.; Bulgarelli, A.; Burelli, Irene; Buscemi, Mario; Cardillo, M.; Caroff, S.; Carosi, A.; Cerruti, M.; Chai, Yating; Cheng, K. S.; Chikawa, M.; Chytka, L.; Contreras, J. L.; Cortina, J.; Costantini, H.; Dalchenko, M.; Angelis, A. De; Lavergne, Mathieu de Bony de; Deleglise, Guillaume; Delgado, C.; Mengual, Jordi Delgado; Volpe, D. della; Depaoli, Davide; Pierro, F. Di; Venere, L. Di; Díaz, C.; Dominik, Rune Michael; Prester, D. Dominis; Donini, Alice; Dorner, D.; Doro, M.; Elsässer, Dominik; Emery, Gabriel; Escudero, Juan Pablo; Fiaßon, A.; Foffano, Luca; Fonseca, M. V.; Coromina, Lluis Freixas; Fukami, Satoshi; Fukazawa, Yasushi; García, Enrique; López, Ramon Garcia; Giglietto, N.; Giordano, F.; Godinović, N.; Green, David; Grespan, Pietro; Gunji, Shuichi; Hackfeld, Jonas; Hadasch, D.; Hahn, Alexander; Hassan, Tarek; Hayashi, Kohei; Heckmann, Lea; Heller, M.; Llorente, Javier Herrera; Hirotani, Kouichi; Hoffmann, D.; Horns, D.; Houles, Julien; Hrabovský, M.; Hrupec, Dario; Hui, D. C. Y.; Hütten, Moritz; Inada, Tomohiro; Inome, Yusuke; Iori, M.; Ishio, Kazuma; Iwamura, Y.; Jacquemont, Mikaël; Martínez, Irene Jiménez; Jouvin, L.; Jurýšek, Jakub; Kagaya, Mika; Karas, Vladimír; Kataoka, J.; Kerszberg, Daniel; Kong, A. K. H.; Kubo, H.; Kushida, J.; Lamanna, G.; Lamastra, Alessandra; Flour, T. Le; Longo, F.; López-Coto, R.; López-Moya, Marcos; López-Oramas, Alicia; Luque-Escamilla, Pedro L.; Majumdar, P.; Makariev, M.; Mandát, Dušan; Manganaro, Marina; Mannheim, K.; Mariotti, M.; Marquez, Patricia; Marsella, G.; Martı́, Josep; Martinez, Oibar; Martínez, G.; Martı́nez, M.; Marusevec, Petra; Mas, Alvaro; Maurin, G.; Mazin, D.; Guillen, Enrique Mestre; Mićanović, Saša; Miceli, Davide; Miener, Tjark; Miranda, Jose Miguel; Miranda, Luis David Medina; Mirzoyan, R.; Mizuno, T.; Molina, Edgar; Montaruli, T.; Monteiro, Inocencio; Moralejo, A.; Morcuende, Daniel; Moretti, E.; Morselli, A.; Mrakovcic, Karlo; Murase, Kohta; Nagai, A.; Nakamori, Takeshi; Nickel, Lukas; Nieto, D.; Nievas, Mireia; Nishijima, K.; Noda, K.; Nosek, D.; Nöthe, Maximilian; Ohishi, M.; Ohtani, Yoshiki; Oka, Tomohiko; Okazaki, N.; Orito, R.; Otero-Santos, Jorge; Palatiello, M.; Paneque, D.; Paoletti, R.; Paredes, J. M.; Pavletić, Lovro; Pech, M.; Pecimotika, Mario; Poireau, V.; Polo, Miguel Angel Molinero; Prandini, E.; Prast, J.; Priyadarshi, Chaitanya; Prouza, M.; Rando, R.; Rhode, W.; Ribó, M.; Rizi, Vincenzo; Rugliancich, A.; Ruíz, José Enrique; Saito, Takayuki; Sakurai, Shunsuke; Sánchez, David; Šarić, Toni; Saturni, Francesco Gabriele; Scherpenberg, Juliane van; Schleicher, Bernd; Schubert, Jan Lukas; Schüßler, F.; Schweizer, T.; Arroyo, Monica Seglar; Shellard, R. C.; Sliusar, Vitalii; Spolon, Alessia; Strišković, Jelena; Strzys, M.; Suda, Y.; Sunada, Yuji; Tajima, H.; Takahashi, Mitsunari; Takahashi, H.; Takata, Jumpei; Takeishi, Ryuji; Tam, Pak-Hin Thomas; Tanaka, Shuta J.; Tateishi, Dai; Tejedor, Luis; Temnikov, P.; Terada, Y.; Terzić, T.; Teshima, M.; Tluczykont, M.; Tokanai, Fuyuki; Torres, D. F.; Trávničék, P.; Truzzi, Stefano; Vacula, Martin; Acosta, Mónica Vázquez; Verguilov, V.; Verna, Gaia; Viale, Ilaria; Vigorito, C.; Vitale, V.; Vovk, Ievgen; Vuillaume, Thomas; Walter, R.; Will, Martin; Yamamoto, Tokonatsu; Yamazaki, Ryo; Yoshida, Tatsuo; Yoshikoshi, T.; Zarić, Darko

doi:10.22323/1.395.0720

Cited by 2 publications

(3 citation statements)

References 7 publications

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“…Two such waveforms (corresponding to the high-and low-gain branches) are stored per pixel, each having 40 samples taken at 1 ns intervals. The raw waveforms are calibrated as described in Kobayashi et al (2022). In most cases, the calibrated high-gain waveform is that used for further analysis; only when it has one or more raw samples above 3500 counts is the high gain discarded and the low gain is used instead (the transition occurs at a charge of around 200 p.e.…”

Section: Pixel-wise Charge Integrationmentioning

confidence: 99%

“…where s max is the sample with the highest value in the calibrated waveform. The integrated charge is converted to photoelectrons using conversion factors determined (using the so-called F-factor method) in the analysis of dedicated calibration runs (Kobayashi et al 2022). The conversion factors include a correction for the (average) pulse tails beyond the integration window, which typically contains around 84% of the total charge.…”

Section: Pixel-wise Charge Integrationmentioning

confidence: 99%

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Observations of the Crab Nebula and Pulsar with the Large-sized Telescope Prototype of the Cherenkov Telescope Array

Abe,

Abe

et al. 2023

ApJ

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The Cherenkov Telescope Array (CTA) is a next-generation ground-based observatory for gamma-ray astronomy at very high energies. The Large-Sized Telescope prototype (LST-1) is located at the CTA-North site, on the Canary Island of La Palma. LSTs are designed to provide optimal performance in the lowest part of the energy range covered by CTA, down to ≃20 GeV. LST-1 started performing astronomical observations in 2019 November, during its commissioning phase, and it has been taking data ever since. We present the first LST-1 observations of the Crab Nebula, the standard candle of very-high-energy gamma-ray astronomy, and use them, together with simulations, to assess the performance of the telescope. LST-1 has reached the expected performance during its commissioning period—only a minor adjustment of the preexisting simulations was needed to match the telescope’s behavior. The energy threshold at trigger level is around 20 GeV, rising to ≃30 GeV after data analysis. Performance parameters depend strongly on energy, and on the strength of the gamma-ray selection cuts in the analysis: angular resolution ranges from 0.°12–0.°40, and energy resolution from 15%–50%. Flux sensitivity is around 1.1% of the Crab Nebula flux above 250 GeV for a 50 hr observation (12% for 30 minutes). The spectral energy distribution (in the 0.03–30 TeV range) and the light curve obtained for the Crab Nebula agree with previous measurements, considering statistical and systematic uncertainties. A clear periodic signal is also detected from the pulsar at the center of the Nebula.

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Section: Pixel-wise Charge Integrationmentioning

confidence: 99%

Section: Pixel-wise Charge Integrationmentioning

confidence: 99%

Observations of the Crab Nebula and Pulsar with the Large-sized Telescope Prototype of the Cherenkov Telescope Array

Abe,

Abe

et al. 2023

ApJ

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“…Here we will compare the standard approach using engineered parameters and random forests with the CNN we developed specifically for the tasks at hand, 𝛾-PhysNet DA. The first step of calibration plus charge integration is common for both approaches and presented in further details in [11]. It is performed using the lstchain v0.7.3 library [12] based on ctapipe v10.5 [13,14].…”

Section: Event Reconstructionmentioning

confidence: 99%

Analysis of the Cherenkov Telescope Array first Large-Sized Telescope real data using convolutional neural networks

Vuillaume¹,

Jacquemont²,

Lavergne³

et al. 2021

Preprint

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The Cherenkov Telescope Array (CTA) is the future ground-based gamma-ray observatory and will be composed of two arrays of imaging atmospheric Cherenkov telescopes (IACTs) located in the Northern and Southern hemispheres respectively. The first CTA prototype telescope built on-site, the Large-Sized Telescope (LST-1), is under commissioning in La Palma and has already taken data on numerous known sources. IACTs detect the faint flash of Cherenkov light indirectly produced after a very energetic gamma-ray photon has interacted with the atmosphere and generated an atmospheric shower. Reconstruction of the characteristics of the primary photons is usually done using a parameterization up to the third order of the light distribution of the images. In order to go beyond this classical method, new approaches are being developed using state-of-the-art methods based on convolutional neural networks (CNN) to reconstruct the properties of each event (incoming direction, energy and particle type) directly from the telescope images. While promising, these methods are notoriously difficult to apply to real data due to differences (such as different levels of night sky background) between Monte Carlo (MC) data used to train the network and real data. The GammaLearn project, based on these CNN approaches, has already shown an increase in sensitivity on MC simulations for LST-1 as well as a lower energy threshold. This work applies the GammaLearn network to real data acquired by LST-1 and compares the results to the classical approach that uses random forests trained on extracted image parameters. The improvements on the background rejection, event direction, and energy reconstruction are discussed in this contribution.

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Camera Calibration of the CTA-LST prototype

Cited by 2 publications

References 7 publications

Observations of the Crab Nebula and Pulsar with the Large-sized Telescope Prototype of the Cherenkov Telescope Array

Observations of the Crab Nebula and Pulsar with the Large-sized Telescope Prototype of the Cherenkov Telescope Array

Analysis of the Cherenkov Telescope Array first Large-Sized Telescope real data using convolutional neural networks

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