Speaker
Description
Cryogenic detectors aiming to push detection thresholds to ever lower energies must operate close to the noise limit, where reducing the threshold further risks increasing false triggers. The DELight experiment will use a superfluid helium detector instrumented with large area cryogenic microcalorimeters to probe sub-GeV dark matter via faint quasiparticle and photon signals.
We formulate reconstruction as a noise-aware representation learning problem, aiming to improve sensitivity to faint signals and thereby extend reach to lower dark matter masses. Starting from an optimal filter, we generalize to expectation-maximization principal component analysis (EMPCA), which learns signal subspaces under the noise covariance metric, extending matched filtering to a data-driven, multi-dimensional setting.
Using simulated traces with realistic per-channel noise and controlled cross-channel correlations, we compare optimal filtering and EMPCA, suggesting improved robustness and energy resolution for faint signals. In the simplest one-component case, EMPCA reduces to the optimal filter, while multi-component models capture additional structure beyond template-based approaches. In this view, EMPCA can be interpreted as a noise-weighted linear autoencoder under quadratic loss, linking classical reconstruction to modern representation learning.
This framework naturally extends toward nonlinear representation learning, providing a pathway to integrate deep learning methods. For DELight, this enables more robust reconstruction near threshold, improving sensitivity to low-energy events. More broadly, the approach applies to rare-event experiments with multi-channel time-series data, where signal shapes vary and noise can be non-stationary and correlated across channels.
