Object Detection with Deep Learning for Rare Event Search in the GADGET II TPC

TL;DR

This paper develops a multi-modal deep learning framework using 2D CNNs and 1D algorithms to detect rare two-particle events in TPC data, achieving perfect recall in experimental conditions.

Contribution

It introduces a novel approach combining 2D CNNs with 1D peak detection for rare event identification in TPC data, leveraging simulated training with perturbations to handle real data uncertainties.

Findings

Achieved 100% recall for rare events in FRIB data

Demonstrated effective use of simulated data with perturbations for training

Combined 2D CNNs and 1D algorithms for robust detection

Abstract

In the pursuit of identifying rare two-particle events within the GADGET II Time Projection Chamber (TPC), this paper presents a comprehensive approach for leveraging Convolutional Neural Networks (CNNs) and various data processing methods. To address the inherent complexities of 3D TPC track reconstructions, the data is expressed in 2D projections and 1D quantities. This approach capitalizes on the diverse data modalities of the TPC, allowing for the efficient representation of the distinct features of the 3D events, with no loss in topology uniqueness. Additionally, it leverages the computational efficiency of 2D CNNs and benefits from the extensive availability of pre-trained models. Given the scarcity of real training data for the rare events of interest, simulated events are used to train the models to detect real events. To account for potential distribution shifts when predominantly depending on simulations, significant perturbations are embedded within the simulations. This produces a broad parameter space that works to account for potential physics parameter and detector response variations and uncertainties. These parameter-varied simulations are used to train sensitive 2D CNN object detectors. When combined with 1D histogram peak detection algorithms, this multi-modal detection framework is highly adept at identifying rare, two-particle events in data taken during experiment 21072 at the Facility for Rare Isotope Beams (FRIB), demonstrating a 100% recall for events of interest. We present the methods and outcomes of our investigation and discuss the potential future applications of these techniques.