Ad-hoc Pulse Shape Simulation using Cyclic Positional U-Net

TL;DR

This paper introduces a Cyclic Positional U-Net model for precise, low-latency pulse shape simulation in HPGe detectors, enhancing the accuracy of rare-event physics experiments.

Contribution

It presents a novel Cyclic Positional U-Net architecture for ad-hoc pulse shape simulation, utilizing transfer learning to produce indistinguishable detector pulses.

Findings

High-precision pulse shape simulation achieved

Low-latency processing demonstrated

Effective transfer learning approach validated

Abstract

High-Purity Germanium~(HPGe) detectors have been a key technology for rare-event searches, such as neutrinoless double-beta decay and dark matter searches, for many decades. Pulse shape simulation is pivotal to improving the physics reach of these experiments. In this work, we propose a Cyclic Positional U-Net to achieve ad-hoc pulse shape simulations with high precision and low latency. Taking the transfer learning approach, CPU-Net translates simulated pulses to detector pulses such that they are indistinguishable. We demonstrate CPU-Net's performance on data taken from a local HPGe detector.