Low Energy LArTPC Signal Detection Using Anomaly Detection

• DOI: 10.2172/2204657
• Publication Date: 2023-11-08T07:34:07Z
• Authors: Jovan Mitrevski
• Keywords: Autoencoder; SIGNAL (programming language)

Extracting low-energy signals from LArTPC detectors is useful, for example, detecting supernova events or calibrating the energy scale with argon-39. However, it difficult to efficiently extract because of noise. We propose using a 1DCNN select wire traces that have signal. This suppresses background while still being efficient then followed by 1D autoencoder denoise traces. At point signal waveform can be cleanly extracted. In order make this processing efficient, we implement two networks on an FPGA. particular use hls4ml produce HLS Keras models both and autoencoder. deploy them AMD/Xilinx Alveo U55C Vitis software platform.