65 citations · 236 across the 15 of their papers we have counts for
14 papers · 1 filter
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)-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…
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…
Flows for Flows: Morphing one Dataset into another with Maximum Likelihood Estimation
Tobias Golling, Samuel Klein, Radha Mastandrea +2
Many components of data analysis in high energy physics and beyond require morphing one dataset into another. This is commonly solved via reweighting, but there are many advantages…
The Interplay of Machine Learning--based Resonant Anomaly Detection Methods
Tobias Golling, Gregor Kasieczka, Claudius Krause +6
Machine learning--based anomaly detection (AD) methods are promising tools for extending the coverage of searches for physics beyond the Standard Model (BSM). One class of AD metho…
Efficiently Moving Instead of Reweighting Collider Events with Machine Learning
Radha Mastandrea, Benjamin Nachman
There are many cases in collider physics and elsewhere where a calibration dataset is used to predict the known physics and / or noise of a target region of phase space. This calib…
FETA: Flow-Enhanced Transportation for Anomaly Detection
Tobias Golling, Samuel Klein, Radha Mastandrea +1
Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a poten…
Anomaly Detection under Coordinate Transformations
Gregor Kasieczka, Radha Mastandrea, Vinicius Mikuni +3
There is a growing need for machine learning-based anomaly detection strategies to broaden the search for Beyond-the-Standard-Model (BSM) physics at the Large Hadron Collider (LHC)…
Self-supervised Anomaly Detection for New Physics
Barry M. Dillon, Radha Mastandrea, Benjamin Nachman
We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural…
Hunting for vampires and other unlikely forms of parity violation at the Large Hadron Collider
Christopher G. Lester, Radha Mastandrea, Daniel Noel +1
Non-Standard-Model parity violation may be occurring in LHC collisions. Any such violation would go unseen, however, as searches are for it are not currently performed. One barrier…
Exploring the Space of Jets with CMS Open Data
Patrick T. Komiske, Radha Mastandrea, Eric M. Metodiev +2
We explore the metric space of jets using public collider data from the CMS experiment. Starting from 2.3/fb of 7 TeV proton-proton collisions collected at the Large Hadron Collide…