Speaker
Description
Current statistical inference tools in high-energy physics typically focus on binned analyses and often use asymptotic approximations to draw statistical inferences. However, present and future neutrinoless double beta decay experiments, such as the Large Enriched Germanium Experiment for Neutrinoless ββ Decay (LEGEND), operate in a quasi-background free regime, where the expected number of background counts in the signal region is less than or close to one. Due to the well-established peak shape and good energy resolution of these experiments, an unbinned frequentist analysis is used to maximize the power of the statistical analysis.
For the first physics analysis of LEGEND-200 [1], a new Python-based tool (freqfit) for conducting unbinned frequentist inference was created [2], making heavy use of the existing iminuit package. This tool builds up test statistic distributions through Monte Carlo pseudoexperiments, enabling frequentist inference in the non-asymptotic, low-statistics regime in which LEGEND and other experiments operate. By allowing for user-defined likelihoods, freqfit is applicable for a broad class of experiments, not only neutrinoless double beta decay. This talk will discuss the development of freqfit, including the computing and mathematical challenges encountered, and its application to LEGEND data.
[1] H. Acharya et al., arXiv:2505.10440.
[2] L. Varriano, S. Borden, G. Song, CJ Nave, Y.-R. Lin, & J. Detwiler. (2025). cenpa/freqfit: https://github.com/cenpa/freqfit
