Accurate and robust methods for direct background estimation in resonant anomaly detection
Resonant anomaly detection methods have great potential for enhancing the sensitivity of traditional bump hunt searches. A key component of these methods is a high quality background template used to produce an anomaly score. Using the LHC Olympics R&D dataset, we demonstrate that this background template can also be repurposed to directly estimate the background expectation in a simple cut and count setup. In contrast to a traditional bump hunt, no fit to the invariant mass distribution is needed, thereby avoiding the potential problem of background sculpting. Furthermore, direct background estimation allows working with large background rejection rates, where resonant anomaly detection methods typically show their greatest improvement in significance.
Authors
- Ranit Das
- Gregor Kasieczka (ORCID: https://orcid.org/0000-0003-3457-2755)
- Thorben Finke (ORCID: https://orcid.org/0000-0003-1948-7077)
- David Shih (ORCID: https://orcid.org/0000-0003-3408-3871)
- Alexander Mück (ORCID: https://orcid.org/0000-0002-0215-720X)
- Marie Hein (ORCID: https://orcid.org/0009-0005-2150-6179)
- Michael Krämer (ORCID: https://orcid.org/0000-0002-3089-6827)
Institutions
- Rutgers, The State University of New Jersey (US)
- Universität Hamburg (DE)
- Rutgers Sexual and Reproductive Health and Rights (NL)
- RWTH Aachen University (DE)
Publication Details
- Journal
- Physical review. D/Physical review. D.
- Published
- 2025-05-29
- DOI
- https://doi.org/10.1103/physrevd.111.094041
- Primary Topic
- Particle physics theoretical and experimental studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- U.S. Department of Energy
- Aspen Center for Physics
- Rutgers, The State University of New Jersey
- National Energy Research Scientific Computing Center
- Deutsche Forschungsgemeinschaft
- RWTH Aachen University
- Office of Science
- Office of Advanced Research Computing, Rutgers, The State University of New Jersey