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Real-Time Wiener Deconvolution for feature reconstruction in JUNO

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Main Authors:	Lastrucci, L., Grassi, M., Triossi, A., Hu, J., Jiang, X., Brugnera, R., Garfagnini, A., Cerrone, V., D'Auria, L. V., Gavrikov, A., Guizzetti, R. M., Serafini, A., Andronico, G., Antonelli, V., Barresi, A., Basilico, D., Beretta, M., Bergnoli, A., Borghesi, M., Brigatti, A., Bruno, R., Budano, A., Caccianiga, B., Cammi, A., Caruso, R., Chiesa, D., Clementi, C., Coletta, C., Dusini, S., Fabbri, A., Felici, G., Ferrante, G., Giammarchi, M. G., Giudice, N., Guardone, N., Houria, F., Islam, A., Landini, C., Lippi, I., Loi, L., Lombardi, P., Mantovani, F., Mari, S. M., Martini, A., Miramonti, L., Montuschi, M., Nastasi, M., Orestano, D., Ortica, F., Paoloni, A., Pelicci, L., Percalli, E., Petrucci, F., Previtali, E., Ranucci, G., Re, A. C., Ricci, B., Romani, A., Sirignano, C., Sisti, M., Stanco, L., Farilla, E. Stanescu, Strati, V., Torri, M. D. C., Tuvè, C., Venettacci, C., Verde, G., Votano, L., Dong, G., Dong, J., Fan, L., Hou, S., Ning, Z., Sun, Y., Wang, Y., Wang, Z., Yan, X.
Format:	Preprint
Published:	2026
Subjects:	Instrumentation and Detectors
Online Access:	Acceder al recurso
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_version_	1866917363046154240
author	Lastrucci, L. Grassi, M. Triossi, A. Hu, J. Jiang, X. Brugnera, R. Garfagnini, A. Cerrone, V. D'Auria, L. V. Gavrikov, A. Guizzetti, R. M. Serafini, A. Andronico, G. Antonelli, V. Barresi, A. Basilico, D. Beretta, M. Bergnoli, A. Borghesi, M. Brigatti, A. Bruno, R. Budano, A. Caccianiga, B. Cammi, A. Caruso, R. Chiesa, D. Clementi, C. Coletta, C. Dusini, S. Fabbri, A. Felici, G. Ferrante, G. Giammarchi, M. G. Giudice, N. Guardone, N. Houria, F. Islam, A. Landini, C. Lippi, I. Loi, L. Lombardi, P. Mantovani, F. Mari, S. M. Martini, A. Miramonti, L. Montuschi, M. Nastasi, M. Orestano, D. Ortica, F. Paoloni, A. Pelicci, L. Percalli, E. Petrucci, F. Previtali, E. Ranucci, G. Re, A. C. Ricci, B. Romani, A. Sirignano, C. Sisti, M. Stanco, L. Farilla, E. Stanescu Strati, V. Torri, M. D. C. Tuvè, C. Venettacci, C. Verde, G. Votano, L. Dong, G. Dong, J. Fan, L. Hou, S. Ning, Z. Sun, Y. Wang, Y. Wang, Z. Yan, X.
author_facet	Lastrucci, L. Grassi, M. Triossi, A. Hu, J. Jiang, X. Brugnera, R. Garfagnini, A. Cerrone, V. D'Auria, L. V. Gavrikov, A. Guizzetti, R. M. Serafini, A. Andronico, G. Antonelli, V. Barresi, A. Basilico, D. Beretta, M. Bergnoli, A. Borghesi, M. Brigatti, A. Bruno, R. Budano, A. Caccianiga, B. Cammi, A. Caruso, R. Chiesa, D. Clementi, C. Coletta, C. Dusini, S. Fabbri, A. Felici, G. Ferrante, G. Giammarchi, M. G. Giudice, N. Guardone, N. Houria, F. Islam, A. Landini, C. Lippi, I. Loi, L. Lombardi, P. Mantovani, F. Mari, S. M. Martini, A. Miramonti, L. Montuschi, M. Nastasi, M. Orestano, D. Ortica, F. Paoloni, A. Pelicci, L. Percalli, E. Petrucci, F. Previtali, E. Ranucci, G. Re, A. C. Ricci, B. Romani, A. Sirignano, C. Sisti, M. Stanco, L. Farilla, E. Stanescu Strati, V. Torri, M. D. C. Tuvè, C. Venettacci, C. Verde, G. Votano, L. Dong, G. Dong, J. Fan, L. Hou, S. Ning, Z. Sun, Y. Wang, Y. Wang, Z. Yan, X.
contents	In particle physics, experiments generate substantial amounts of data that can be difficult to process without preliminary scaling. To avoid losing potentially crucial data, experimental collaborations are studying novel techniques for real-time data processing to extract features for further physics analysis. A common approach, especially in neutrino physics, is to use FPGAs for data acquisition and pre-processing. This paper presents an advanced Real-Time Wiener deconvolution algorithm designed to leverage the processing capabilities of the FPGA integrated into the readout boards of the Jiangmen Underground Neutrino Observatory (JUNO). The goal is to enable real-time reconstruction of the signal generated by photomultiplier tubes (PMTs) when neutrino interactions are detected. By exploiting online reconstruction of the signal generated by PMTs, we expect to improve the detection of low-energy depositions, such as those produced by transient astrophysical phenomena. These depositions are usually not saved because of the significant background that affects the low end of the energy spectrum, which would result in a large trigger rate, hence a large amount of data required for storage. This paper presents the features of the algorithm, including its ability to manage high-throughput data streams with minimal latency, adaptability, and resilience in discerning the characteristics of input data. Performance is evaluated on a JUNO electronic board. This study further demonstrates the potential of FPGA-based solutions for neutrino physics.
format	Preprint
id	arxiv_https___arxiv_org_abs_2603_25436
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	Real-Time Wiener Deconvolution for feature reconstruction in JUNO Lastrucci, L. Grassi, M. Triossi, A. Hu, J. Jiang, X. Brugnera, R. Garfagnini, A. Cerrone, V. D'Auria, L. V. Gavrikov, A. Guizzetti, R. M. Serafini, A. Andronico, G. Antonelli, V. Barresi, A. Basilico, D. Beretta, M. Bergnoli, A. Borghesi, M. Brigatti, A. Bruno, R. Budano, A. Caccianiga, B. Cammi, A. Caruso, R. Chiesa, D. Clementi, C. Coletta, C. Dusini, S. Fabbri, A. Felici, G. Ferrante, G. Giammarchi, M. G. Giudice, N. Guardone, N. Houria, F. Islam, A. Landini, C. Lippi, I. Loi, L. Lombardi, P. Mantovani, F. Mari, S. M. Martini, A. Miramonti, L. Montuschi, M. Nastasi, M. Orestano, D. Ortica, F. Paoloni, A. Pelicci, L. Percalli, E. Petrucci, F. Previtali, E. Ranucci, G. Re, A. C. Ricci, B. Romani, A. Sirignano, C. Sisti, M. Stanco, L. Farilla, E. Stanescu Strati, V. Torri, M. D. C. Tuvè, C. Venettacci, C. Verde, G. Votano, L. Dong, G. Dong, J. Fan, L. Hou, S. Ning, Z. Sun, Y. Wang, Y. Wang, Z. Yan, X. Instrumentation and Detectors In particle physics, experiments generate substantial amounts of data that can be difficult to process without preliminary scaling. To avoid losing potentially crucial data, experimental collaborations are studying novel techniques for real-time data processing to extract features for further physics analysis. A common approach, especially in neutrino physics, is to use FPGAs for data acquisition and pre-processing. This paper presents an advanced Real-Time Wiener deconvolution algorithm designed to leverage the processing capabilities of the FPGA integrated into the readout boards of the Jiangmen Underground Neutrino Observatory (JUNO). The goal is to enable real-time reconstruction of the signal generated by photomultiplier tubes (PMTs) when neutrino interactions are detected. By exploiting online reconstruction of the signal generated by PMTs, we expect to improve the detection of low-energy depositions, such as those produced by transient astrophysical phenomena. These depositions are usually not saved because of the significant background that affects the low end of the energy spectrum, which would result in a large trigger rate, hence a large amount of data required for storage. This paper presents the features of the algorithm, including its ability to manage high-throughput data streams with minimal latency, adaptability, and resilience in discerning the characteristics of input data. Performance is evaluated on a JUNO electronic board. This study further demonstrates the potential of FPGA-based solutions for neutrino physics.
title	Real-Time Wiener Deconvolution for feature reconstruction in JUNO
topic	Instrumentation and Detectors
url	https://arxiv.org/abs/2603.25436