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Software Compensation for Highly Granular Calorimeters using Machine Learning

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Lai, S., Utehs, J., Wilhahn, A., Bach, O., Brianne, E., Ebrahimi, A., Gadow, K., Göttlicher, P., Hartbrich, O., Heuchel, D., Irles, A., Krüger, K., Kvasnicka, J., Lu, S., Neubüser, C., Provenza, A., Reinecke, M., Sefkow, F., Schuwalow, S., De Silva, M., Sudo, Y., Tran, H. L., Buhmann, E., Garutti, E., Huck, S., Kasieczka, G., Martens, S., Rolph, J., Wellhausen, J., Blazey, G. C., Dyshkant, A., Francis, K., Zutshi, V., Bilki, B., Northacker, D., Onel, Y., Hummer, F., Simon, F., Kawagoe, K., Onoe, T., Suehara, T., Tsumura, S., Yoshioka, T., Fouz, M. C., Emberger, L., Graf, C., Wagner, M., Pöschl, R., Richard, F., Zerwas, D., Boudry, V., Brient, J-C., Nanni, J., Videau, H., Liu, L., Masuda, R., Murata, T., Ootani, W., Takatsu, T., Tsuji, N., Chadeeva, M., Danilov, M., Korpachev, S., Rusinov, V.
Formato:	Preprint
Publicado:	2024
Materias:	Instrumentation and Detectors
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

_version_	1866913257209462784
author	Lai, S. Utehs, J. Wilhahn, A. Bach, O. Brianne, E. Ebrahimi, A. Gadow, K. Göttlicher, P. Hartbrich, O. Heuchel, D. Irles, A. Krüger, K. Kvasnicka, J. Lu, S. Neubüser, C. Provenza, A. Reinecke, M. Sefkow, F. Schuwalow, S. De Silva, M. Sudo, Y. Tran, H. L. Buhmann, E. Garutti, E. Huck, S. Kasieczka, G. Martens, S. Rolph, J. Wellhausen, J. Blazey, G. C. Dyshkant, A. Francis, K. Zutshi, V. Bilki, B. Northacker, D. Onel, Y. Hummer, F. Simon, F. Kawagoe, K. Onoe, T. Suehara, T. Tsumura, S. Yoshioka, T. Fouz, M. C. Emberger, L. Graf, C. Wagner, M. Pöschl, R. Richard, F. Zerwas, D. Boudry, V. Brient, J-C. Nanni, J. Videau, H. Liu, L. Masuda, R. Murata, T. Ootani, W. Takatsu, T. Tsuji, N. Chadeeva, M. Danilov, M. Korpachev, S. Rusinov, V.
author_facet	Lai, S. Utehs, J. Wilhahn, A. Bach, O. Brianne, E. Ebrahimi, A. Gadow, K. Göttlicher, P. Hartbrich, O. Heuchel, D. Irles, A. Krüger, K. Kvasnicka, J. Lu, S. Neubüser, C. Provenza, A. Reinecke, M. Sefkow, F. Schuwalow, S. De Silva, M. Sudo, Y. Tran, H. L. Buhmann, E. Garutti, E. Huck, S. Kasieczka, G. Martens, S. Rolph, J. Wellhausen, J. Blazey, G. C. Dyshkant, A. Francis, K. Zutshi, V. Bilki, B. Northacker, D. Onel, Y. Hummer, F. Simon, F. Kawagoe, K. Onoe, T. Suehara, T. Tsumura, S. Yoshioka, T. Fouz, M. C. Emberger, L. Graf, C. Wagner, M. Pöschl, R. Richard, F. Zerwas, D. Boudry, V. Brient, J-C. Nanni, J. Videau, H. Liu, L. Masuda, R. Murata, T. Ootani, W. Takatsu, T. Tsuji, N. Chadeeva, M. Danilov, M. Korpachev, S. Rusinov, V.
contents	A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_04632
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Software Compensation for Highly Granular Calorimeters using Machine Learning Lai, S. Utehs, J. Wilhahn, A. Bach, O. Brianne, E. Ebrahimi, A. Gadow, K. Göttlicher, P. Hartbrich, O. Heuchel, D. Irles, A. Krüger, K. Kvasnicka, J. Lu, S. Neubüser, C. Provenza, A. Reinecke, M. Sefkow, F. Schuwalow, S. De Silva, M. Sudo, Y. Tran, H. L. Buhmann, E. Garutti, E. Huck, S. Kasieczka, G. Martens, S. Rolph, J. Wellhausen, J. Blazey, G. C. Dyshkant, A. Francis, K. Zutshi, V. Bilki, B. Northacker, D. Onel, Y. Hummer, F. Simon, F. Kawagoe, K. Onoe, T. Suehara, T. Tsumura, S. Yoshioka, T. Fouz, M. C. Emberger, L. Graf, C. Wagner, M. Pöschl, R. Richard, F. Zerwas, D. Boudry, V. Brient, J-C. Nanni, J. Videau, H. Liu, L. Masuda, R. Murata, T. Ootani, W. Takatsu, T. Tsuji, N. Chadeeva, M. Danilov, M. Korpachev, S. Rusinov, V. Instrumentation and Detectors A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.
title	Software Compensation for Highly Granular Calorimeters using Machine Learning
topic	Instrumentation and Detectors
url	https://arxiv.org/abs/2403.04632