6 papers
Kitchen Sink Anomaly Detection
Ranit Das, Marie Hein, Gregor Kasieczka +6
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…
Resummed Distribution Functions: Making Perturbation Theory Positive and Normalized
Rikab Gambhir, Radha Mastandrea
Fixed-order perturbative calculations for differential cross sections can suffer from non-physical artifacts: they can be non-positive, non-normalizable, and non-finite, none of wh…
Generator Based Inference (GBI)
Chi Lung Cheng, Ranit Das, Runze Li +5
Statistical inference in physics is often based on samples from a generator (sometimes referred to as a ``forward model") that emulate experimental data and depend on parameters of…
Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data
Rikab Gambhir, Radha Mastandrea, Benjamin Nachman +1
We present the first study of anti-isolated Upsilon decays to two muons () in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-…
Constraining the Higgs Potential with Neural Simulation-based Inference for Di-Higgs Production
Radha Mastandrea, Benjamin Nachman, Tilman Plehn
Determining the form of the Higgs potential is one of the most exciting challenges of modern particle physics. Higgs pair production directly probes the Higgs self-coupling and sho…
Non-resonant Anomaly Detection with Background Extrapolation
Kehang Bai, Radha Mastandrea, Benjamin Nachman
Complete anomaly detection strategies that are both signal sensitive and compatible with background estimation have largely focused on resonant signals. Non-resonant new physics sc…