Neural-network-based level-1 trigger upgrade for the SuperCDMS experiment at SNOLAB

TL;DR

This paper presents a neural network-based upgrade to the level-1 trigger system of the SuperCDMS SNOLAB dark matter experiment, improving sensitivity to low-mass dark matter by enhancing trigger performance.

Contribution

It introduces a recurrent neural network implementation on FPGA for the trigger, providing improved amplitude estimation and noise discrimination capabilities.

Findings

Trigger threshold lowered by approximately 22%.

Enhanced efficiency and resolution demonstrated through simulations and noise data.

Significant noise rate reduction achieved.

Abstract

The extended physics program of the SuperCDMS SNOLAB dark matter search experiment aims to maximize the sensitivity to low-mass dark matter. To realize this, an upgrade of the existing level-1 trigger of the data acquisition system is proposed by making use of a recurrent neural network to be implemented on the trigger FPGA. This provides an improved amplitude estimator and signal-noise discriminator based on the combined information of filtered traces from individual detector channels. The architecture and configuration of this neural trigger are discussed in this article, and the improvements in key performance indicators such as the efficiency, resolution, and noise rate are quantified based on signal simulations and noise data. Based on the findings in this proof of concept, the trigger threshold is expected to be lowered by ~22%.