A comparative study on different background estimation methods for extensive air shower arrays Article Swipe

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Yanjin Wang , M. Zha , Shi-Cong Hu , Chuan-Dong Gao , Jianli Zhang , Xin Zhang ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2210.00004

Background estimation is essential when studying TeV gamma-ray astronomy for extensive air shower arrays. In this work, by applying four applying four different methods including equi-zenith angle method, surrounding window method, direct integration method, and time-swapping method, the number of the background events is calculated. Based on simulation samples, the statistical significance of the excess signal from different background estimation methods is determined. Following this, we discuss the limits and the applicability of the four methods under different conditions. Under the detector stability assumption with signal, the results from the above four methods are consistent at the 1 sigma level. In the no signal condition, when the acceptance of the detector changes with both space and time, the surrounding window method is most stable and hardly affected. In this acceptance assumption, we find that the background estimation in the direct integration and time-swapping methods are sensitive to the selection of time window, and the shorter time window can reduce the impact on the background estimation to some extent.

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Window (computing) Detector Time delay and integration SIGNAL (programming language) Stability (learning theory) Air shower Estimation Selection (genetic algorithm) Statistics Computer science Algorithm Physics Mathematics Optics Artificial intelligence Engineering Machine learning Operating system Systems engineering Programming language

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2210.00004
PDF: https://arxiv.org/pdf/2210.00004
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Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2210.00004

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Title: A comparative study on different background estimation methods for extensive air shower arrays

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Language: en

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Publication year: 2022

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Publication date: 2022-09-30

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Authors: Yanjin Wang, M. Zha, Shi-Cong Hu, Chuan-Dong Gao, Jianli Zhang, Xin Zhang

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Landing page: https://arxiv.org/abs/2210.00004

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PDF URL: https://arxiv.org/pdf/2210.00004

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Concepts: Window (computing), Detector, Time delay and integration, SIGNAL (programming language), Stability (learning theory), Air shower, Estimation, Selection (genetic algorithm), Statistics, Computer science, Algorithm, Physics, Mathematics, Optics, Artificial intelligence, Engineering, Machine learning, Operating system, Systems engineering, Programming language

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Related works (count): 10

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