2025/02/13 by Araz, Jack Y., Beck, Anja, Reboud, Méril +2 · 1 citation
#Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High Energy Physics - Phenomenology (hep-ph) #Machine Learning (cs.LG) #Statistics and Probability (physics.data-an)
paper · doi:10.48550/arxiv.2502.09494
We present a machine-learning-based workflow to model an unbinned likelihood from its samples. A key advancement over existing approaches is the validation of the learned likelihood using rigorous statistical tests of the joint distribution, such as the Kolmogorov-Smirnov test of the joint distribution. Our method enables the reliable communication of experimental and phenomenological likelihoods for subsequent analyses. We demonstrate its effectiveness through three case studies in high-energy physics. To support broader adoption, we provide an open-source reference implementation, nabu.