Kitchen Sink Anomaly Detection

arXiv (Cornell University)

Abstract

An enormous amount of R&D effort has resulted in many new resonant anomaly detection methods being proposed in recent years. However, the vast majority of previous R&D studies have suffered from two limitations: they have focused on a very small set of simulated signal benchmark models; and they have either used small sets of carefully crafted high-level jet substructure observables, which can be highly performant but are prone to model dependence, or the full collider event phase space, which is more agnostic but suffers from reduced sensitivity. In this work, we address both limitations: we formulate a number of new simulated signal benchmarks, which we make publicly available in a format fully compatible with the LHCO R&D benchmark; and we explore a high-level, yet highly agnostic, observable set consisting of Energy Flow Polynomials in addition to the usual subjettiness variables. We evaluate this "kitchen sink" observable set for both an idealized anomaly detector and the CWoLa hunting task, along with three baseline observable sets (the Baseline LHC Olympics set, subjettiness observables, and Energy Flow Polynomials). We find that our kitchen sink approach is the most sensitive to a broad range of signal types. Furthermore, we show that an attribute bagging variant, in which each ensemble member is trained on a random subset of substructure observables, yields comparable anomaly detection performance while significantly reducing training cost.

Authors 9

1. Ranit Das

   Rutgers, The State University of New Jersey · Heidelberg University · Environmental and Occupational Health Sciences Institute

   Affiliation as printed

   Institute for Theoretical Physics , Universität Heidelberg , Germany

   NHETC , Dept. of Physics and Astronomy , Rutgers University , Piscataway , NJ 08854 , USA

2. Marie Hein Aachen Institute for Theoretical Particle Physics and Cosmology

   RWTH Aachen University

   Affiliation as printed

   Institute for Theoretical Particle Physics and Cosmology , RWTH Aachen University , Germany

3. Gregor Kasieczka

   Universität Hamburg

   Affiliation as printed

   Institut für Experimentalphysik , Universität Hamburg , 22761 Hamburg , Germany

4. Michael Krämer Aachen Institute for Theoretical Particle Physics and Cosmology

   RWTH Aachen University

   Affiliation as printed

   Institute for Theoretical Particle Physics and Cosmology , RWTH Aachen University , Germany

5. Lukas Lang Aachen Institute for Theoretical Particle Physics and Cosmology

   RWTH Aachen University

   Affiliation as printed

   Institute for Theoretical Particle Physics and Cosmology , RWTH Aachen University , Germany

6. Radha Mastandrea

   University of Chicago

   Affiliation as printed

   Data Science Institute , The University of Chicago , Chicago , IL 60637 , USA

   Enrico Fermi Institute , The University of Chicago , Chicago , IL 60637 , USA

7. Louis Moureaux

   Universität Hamburg

   Affiliation as printed

   Institut für Experimentalphysik , Universität Hamburg , 22761 Hamburg , Germany

8. Alexander Mück Aachen Institute for Theoretical Particle Physics and Cosmology

   RWTH Aachen University

   Affiliation as printed

   Institute for Theoretical Particle Physics and Cosmology , RWTH Aachen University , Germany

9. David Shih

   Rutgers, The State University of New Jersey · Environmental and Occupational Health Sciences Institute

   Affiliation as printed

   NHETC , Dept. of Physics and Astronomy , Rutgers University , Piscataway , NJ 08854 , USA