65 citations · 236 across the 15 of their papers we have counts for
6 papers · 1 filter
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…
Tracing Milky Way substructure with an RR Lyrae hierarchical clustering forest
Brian T. Cook, Deborah F. Woods, Jessica D. Ruprecht +6
RR Lyrae variable stars have long been reliable standard candles used to discern structure in the Local Group. With this in mind, we present a routine to identify groupings contain…