Trigger Optimization and Event Classification for Dark Matter Searches in the CYGNO Experiment Using Machine Learning
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| author | Amaro, F. D. Antonietti, R. Baracchini, E. Benussi, L. Capoccia, C. Caponero, M. de Carvalho, L. G. M. Cavoto, G. Costa, I. A. Croce, A. D'Astolfo, M. D'Imperio, G. Dho, G. Di Marco, E. Santos, J. M. F. dos Fiorina, D. Iacoangeli, F. Islam, Z. Kemp, E. Lima Jr, H. P. Maccarrone, G. Mano, R. D. P. Marques, D. J. G. Mazzitelli, G. Meloni, P. Messina, A. Monteiro, C. M. B. Nobrega, R. A. Oppedisano, G. M. Pains, I. F. Paoletti, E. Petrucci, F. Piacentini, S. Pierluigi, D. Pinci, D. Renga, F. Russo, A. Saviano, G. Silva, P. A. O. C. Spooner, N. J. Tesauro, R. Tomassini, S. Tozzi, D. |
| author_facet | Amaro, F. D. Antonietti, R. Baracchini, E. Benussi, L. Capoccia, C. Caponero, M. de Carvalho, L. G. M. Cavoto, G. Costa, I. A. Croce, A. D'Astolfo, M. D'Imperio, G. Dho, G. Di Marco, E. Santos, J. M. F. dos Fiorina, D. Iacoangeli, F. Islam, Z. Kemp, E. Lima Jr, H. P. Maccarrone, G. Mano, R. D. P. Marques, D. J. G. Mazzitelli, G. Meloni, P. Messina, A. Monteiro, C. M. B. Nobrega, R. A. Oppedisano, G. M. Pains, I. F. Paoletti, E. Petrucci, F. Piacentini, S. Pierluigi, D. Pinci, D. Renga, F. Russo, A. Saviano, G. Silva, P. A. O. C. Spooner, N. J. Tesauro, R. Tomassini, S. Tozzi, D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_20626 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Trigger Optimization and Event Classification for Dark Matter Searches in the CYGNO Experiment Using Machine Learning Amaro, F. D. Antonietti, R. Baracchini, E. Benussi, L. Capoccia, C. Caponero, M. de Carvalho, L. G. M. Cavoto, G. Costa, I. A. Croce, A. D'Astolfo, M. D'Imperio, G. Dho, G. Di Marco, E. Santos, J. M. F. dos Fiorina, D. Iacoangeli, F. Islam, Z. Kemp, E. Lima Jr, H. P. Maccarrone, G. Mano, R. D. P. Marques, D. J. G. Mazzitelli, G. Meloni, P. Messina, A. Monteiro, C. M. B. Nobrega, R. A. Oppedisano, G. M. Pains, I. F. Paoletti, E. Petrucci, F. Piacentini, S. Pierluigi, D. Pinci, D. Renga, F. Russo, A. Saviano, G. Silva, P. A. O. C. Spooner, N. J. Tesauro, R. Tomassini, S. Tozzi, D. Instrumentation and Detectors Machine Learning Data Analysis, Statistics and Probability 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. |
| title | Trigger Optimization and Event Classification for Dark Matter Searches in the CYGNO Experiment Using Machine Learning |
| topic | Instrumentation and Detectors Machine Learning Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2601.20626 |