Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC

TL;DR

This paper introduces an unsupervised autoencoder-based method for fast, real-time ROI detection in optical TPC images, improving data reduction for rare-event searches.

Contribution

It presents a reconstruction-based anomaly detection strategy using pedestal-trained autoencoders for efficient online ROI extraction in optical TPCs.

Findings

Retains 93% of signal while discarding 97.8% of image area.

Inference time of ~25 ms per frame on a consumer GPU.

Training objective impacts detection effectiveness.

Abstract

Optical-readout Time Projection Chambers (TPCs) produce megapixel-scale images whose fine-grained topological information is essential for rare-event searches, but whose size challenges real-time data selection. We present an unsupervised, reconstruction-based anomaly-detection strategy for fast Region-of-Interest (ROI) extraction that operates directly on minimally processed camera frames. A convolutional autoencoder trained exclusively on pedestal images learns the detector noise morphology without labels, simulation, or fine-grained calibration. Applied to standard data-taking frames, localized reconstruction residuals identify particle-induced structures, from which compact ROIs are extracted via thresholding and spatial clustering. Using real data from the CYGNO optical TPC prototype, we compare two pedestal-trained autoencoder configurations that differ only in their training objective, enabling a controlled study of its impact. The best configuration retains (93.0 +/- 0.2)% of reconstructed signal intensity while discarding (97.8 +/- 0.1)% of the image area, with an inference time of approximately 25 ms per frame on a consumer GPU. The results demonstrate that careful design of the training objective is critical for effective reconstruction-based anomaly detection and that pedestal-trained autoencoders provide a transparent and detector-agnostic baseline for online data reduction in optical TPCs.