arXiv subjects

G. M. Oppedisano

Publications and source records attributed to G. M. Oppedisano.

3 recordsLinked to original sources

arXiv2026

Upgrade of the Trigger and Data Acquisition System for Continuous Imaging and Multi-Camera Operation in CYGNO

The CYGNO experiment employs an optical readout to image particle interactions in a gaseous Time Projection Chamber (TPC), combining cameras and photomultiplier tubes (PMTs) to achieve high spatial resolution and timing information. This approach enables detailed track reconstruction but poses significant challenges for data acquisition, particularly in view of the next experimental phase, CYGNO-04, which will operate multiple cameras simultaneously. In this paper, we present an upgrade of the CYGNO Trigger and Data Acquisition (T-DAQ) system, developed starting from the LIME configuration and validated on the MANGO prototype. The upgrade introduces a continuous imaging acquisition mode, substantially reducing the camera dead time, together with an extended trigger time-tagging scheme that provides a robust global time reference for PMT signals. A synchronous multi-camera DAQ architecture is also implemented and tested, enabling coordinated operation of multiple optical sensors without a master camera. The performance of the upgraded system is validated through dedicated tests, demonstrating stable continuous acquisition, reliable time-tagging, and consistent synchronization across multiple cameras. These results establish a solid and scalable foundation for the CYGNO-04 DAQ and represent a key step toward efficient data acquisition in future large-scale optical TPC detectors.

physics.ins-det↗

arXiv2026

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

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.

physics.ins-det↗

arXiv2026

Trigger Optimization and Event Classification for Dark Matter Searches in the CYGNO Experiment Using Machine Learning

The CYGNO experiment employs an optical-readout Time Projection Chamber (TPC) to search for rare low-energy interactions using finely resolved scintillation images. While the optical readout provides rich topological information, it produces large, sparse megapixel images that challenge real-time triggering, data reduction, and background discrimination. We summarize two complementary machine-learning approaches developed within CYGNO. First, we present a fast and fully unsupervised strategy for online data reduction based on reconstruction-based anomaly detection. A convolutional autoencoder trained exclusively on pedestal images (i.e. frames acquired with GEM amplification disabled) learns the detector noise morphology and highlights particle-induced structures through localized reconstruction residuals, from which compact Regions of Interest (ROIs) are extracted. On real prototype data, the selected configuration retains (93.0 +/- 0.2)% of reconstructed signal intensity while discarding (97.8 +/- 0.1)% of the image area, with ~25 ms per-frame inference time on a consumer GPU. Second, we report a weakly supervised application of the Classification Without Labels (CWoLa) framework to data acquired with an Americium--Beryllium neutron source. Using only mixed AmBe and standard datasets (no event-level labels), a convolutional classifier learns to identify nuclear-recoil-like topologies. The achieved performance approaches the theoretical limit imposed by the mixture composition and isolates a high-score population with compact, approximately circular morphologies consistent with nuclear recoils.

physics.ins-det↗