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.