Real-time Position Reconstruction for the KamLAND-Zen Experiment using Hardware-AI Co-design

TL;DR

This paper presents a hardware-AI co-designed pipeline using PointNet on FPGA for real-time event position and energy reconstruction in the KamLAND-Zen neutrino detector, enabling immediate data analysis during experiments.

Contribution

It introduces the first hardware-AI co-design implementation for real-time event reconstruction in a neutrinoless double-beta decay experiment.

Findings

Successful FPGA implementation of PointNet for event reconstruction

Real-time processing of detector data during physics events

Demonstrated reliable and rapid position and energy estimation

Abstract

Monolithic liquid scintillator detector technology is the workhorse for detecting neutrinos and exploring new physics. The KamLAND-Zen experiment exemplifies this detector technology and has yielded top results in the quest for neutrinoless double-beta ($0\nu\beta\beta$) decay. To understand the physical events that occur in the detector, experimenters must reconstruct each event's position and energy from the raw data produced. Traditionally, this information has been obtained through a time-consuming offline process, meaning that event position and energy would only be available days after data collection. This work introduces a new pipeline to acquire this information quickly by implementing a machine learning model, PointNet, onto a Field Programmable Gate Array (FPGA). This work outlines a successful demonstration of the entire pipeline, showing that event position and energy information can be reliably and quickly obtained as physics events occur in the detector. This marks one of the first instances of applying hardware-AI co-design in the context of $0\nu\beta\beta$ decay experiments.