Speaker
Description
Hybrid pixel detectors such as Timepix3 and Timepix4 enable pixel-level resolution of individual particle interactions, where each event manifests as a cluster or track spanning multiple pixels. Analyzing these clusters allows for the estimation of key particle parameters, including type, initial energy, and angle of incidence. However, such ground-truth parameters are typically unavailable during data acquisition, necessitating the use of simulations, which are often computationally intensive and may fail to capture detector imperfections and noise. A central challenge is thus leveraging unlabeled experimental data alongside simulations for training. To address this, we propose a semi-supervised approach that pretrains convolutional autoencoders—specifically U-Net architectures with EfficientNet backbones—on unlabeled measured data. These models are subsequently fine-tuned on labeled simulations for classification and regression tasks. To interpret the learned latent representations, we employ t-distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP). Finally, we show that self-supervised pertaining improves the classification of simulated protons and electrons and enhances incidence angle regression within the Space Application of Timepix Radiation Monitor (SATRAM) context.