Density-Guided Conditional Neural Processes for Detector Efficiency Estimation

Abstract

Selection efficiency connects observed counts to physical rates and rare-event search limits. With scarce calibration data, strong smoothing can erase sharp physical changes, while highly flexible models can overfit the data and mistake statistical fluctuations for physical structure. We introduce a density-guided conditional neural process (CNP) that uses spectral concentrations to control where high-frequency features are available. On MAJORANA data, it reconstructs local structure while matching measured efficiencies within twice their binomial statistical uncertainty in an average of 89% of bins across two continuum windows, versus 37% for Attentive CNP with positional encoding. With 5,000 efficiency-model training events and 500 context events, it exceeds CNP and Attentive CNP trained on twice as many events in both peak-core and continuum summaries, and also exceeds context-only KDE and Bernoulli GP. Our method thus balances the reconstruction of sharp physical features with resistance to statistical overfitting, and achieves this with fewer training events.