240 citations · 261 across the 13 of their papers we have counts for
6 papers · 1 filter
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)-bas…
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…
Incorporating Physical Priors into Weakly-Supervised Anomaly Detection
Chi Lung Cheng, Gup Singh, Benjamin Nachman
We propose a new machine-learning-based anomaly detection strategy for comparing data with a background-only reference (a form of weak supervision). The sensitivity of previous str…
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…
The Machine Learning Landscape of Top Taggers
G. Kasieczka, T. Plehn, A. Butter +24
Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…