21 – 25 de ago. de 2023
IFSC/USP
Fuso horário America/Sao_Paulo

Separation of iron and proton induced air showers using convolutional neural networks

21 de ago. de 2023 16:00
1h 30m
Salão de Eventos USP

Salão de Eventos USP

Normal 16h00 - 17h30

Descrição

The Cherenkov Telescope Array (CTA) will be the next-generation gamma-ray observatory offering better flux sensitivity than the current generation. (1) Each telescope will detect the Cherenkov emission from air showers initiated by gamma and cosmic rays. State-of-the-art deep learning algorithms, such as convolutional neural networks (CNNs), are used to reconstruct the energy and classify the primary particle. (2-3) CNNs are commonly used for image classification because they extract and learn from the features and patterns within an image. In this work, we proposed an architecture to separate iron and proton induced showers using the image recorded in the camera (Img), the depth of the shower maximum (Xmax), and the particle energy (E) associated with the air shower. We tested the CNN performance with three different inputs: (i) Img + E, (ii) Img + Xmax, and (iii) Img + Xmax + E. In addition, we used reconstructed and true values (for the Xmax and E) during the training and the prediction. This architecture takes advantage of the image patterns and the air showers' mass-sensitive parameters. Therefore, we aim to obtain significant efficiency in identifying iron and proton events.

Referências

1 ACHARYA, B. S. et al. Science with the Cherenkov telescope array. Disponível em: https://arxiv.org/pdf/1709.07997.pdf. Acesso em: 23 jan. 2023.

2 NIETO, D. et al. Reconstruction of IACT events using deep learning techniques with CTLearn. Astronomical Society of the Pacific Conference Series, v. 532, p. 191-194, July 2022.

3 ASCHERSLEBEN, J. et al. Application of pattern spectra and convolutional neural networks to the analysis of simulated Cherenkov Telescope Array Data. Proceedigns of Science, v. 395, p. 697-1-697-14, Mar. 2022. DOI: https://dx.doi.org/10.22323/1.395.0697.

Certifico que os nomes citados como autor e coautor estão cientes de suas nomeações. Sim
Palavras-chave Cosmic rays. Cherenkov telescope. Artifical neural network.
Orientador e coorientador Vitor de Souza. Manuela Vecchi.
Subárea 1 Astrofísica e Astronomia
Subárea 2 (opcional) Instrumentação e Detectores
Agência de Fomento CAPES
Número de Processo 88887.370416/2019-00
Modalidade DOUTORADO
Concessão de Direitos Autorais Sim

Autores primários

Andres Gabriel Delgado Giler (Instituto de Fisica de São Carlos - USP) Prof. Manuela Vecchi (University of Groningen) Vitor de Souza (Instituto de Fisica de São Carlos - USP)

Materiais de apresentação

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