Real-Time Wiener Deconvolution for feature reconstruction in JUNO
Abstract
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.
Keywords
Cite
@article{arxiv.2603.25436,
title = {Real-Time Wiener Deconvolution for feature reconstruction in JUNO},
author = {L. Lastrucci and M. Grassi and A. Triossi and J. Hu and X. Jiang and R. Brugnera and A. Garfagnini and V. Cerrone and L. V. D'Auria and A. Gavrikov and R. M. Guizzetti and A. Serafini and G. Andronico and V. Antonelli and A. Barresi and D. Basilico and M. Beretta and A. Bergnoli and M. Borghesi and A. Brigatti and R. Bruno and A. Budano and B. Caccianiga and A. Cammi and R. Caruso and D. Chiesa and C. Clementi and C. Coletta and S. Dusini and A. Fabbri and G. Felici and G. Ferrante and M. G. Giammarchi and N. Giudice and N. Guardone and F. Houria and A. Islam and C. Landini and I. Lippi and L. Loi and P. Lombardi and F. Mantovani and S. M. Mari and A. Martini and L. Miramonti and M. Montuschi and M. Nastasi and D. Orestano and F. Ortica and A. Paoloni and L. Pelicci and E. Percalli and F. Petrucci and E. Previtali and G. Ranucci and A. C. Re and B. Ricci and A. Romani and C. Sirignano and M. Sisti and L. Stanco and E. Stanescu Farilla and V. Strati and M. D. C. Torri and C. Tuvè and C. Venettacci and G. Verde and L. Votano and G. Dong and J. Dong and L. Fan and S. Hou and Z. Ning and Y. Sun and Y. Wang and Z. Wang and X. Yan},
journal= {arXiv preprint arXiv:2603.25436},
year = {2026}
}