We present FlameNEST, a framework providing explicit likelihood evaluations in noble element particle detectors using data-driven models from the Noble Element Simulation Technique. FlameNEST provides a way to perform statistical analyses on real data with no dependence on large, computationally expensive Monte Carlo simulations by evaluating the likelihood on an event-by-event basis using analytic probability elements convolved together in a single TensorFlow multiplication. Furthermore, this robust framework creates opportunities for simple inter-collaborative analyses which will be fundamental for the future of experimental dark matter physics.
Cite
@article{arxiv.2204.13621,
title = {FlameNEST: Explicit Profile Likelihoods with the Noble Element Simulation Technique},
author = {R. S. James and J. Palmer and A. Kaboth and C. Ghag and J. Aalbers},
journal= {arXiv preprint arXiv:2204.13621},
year = {2022}
}