Year: 2026
Type: preprint
Abstract: Searches for new physics at the LHC often look for localized excesses on smoothly falling background distributions. Several classes of background models have been considered, including polynomials and other parametric families; however, these approaches can require extensive analysis-specific development as datasets grow. In this work, we motivate the finite exponential mixture as a flexible semi-parametric class of functions for approximating falling distributions, drawing on results from extreme value theory. Using two published datasets ($n=28,619,185$ and $n=5,036$), we show that the exponential mixture performance is comparable to existing methods for both small and large datasets. Finally, in simulation studies ($n = 5,036$), we find that the finite exponential mixture exhibits small bias relative to the true statistical uncertainty while maintaining consistent nominal coverage in the bulk.
Source: arXiv (Cornell University)
Institution: University of Notre Dame
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7167040106
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