64 citations · 120 across the 5 of their papers we have counts for
5 papers
A Practical Guide to Unbinned Unfolding
Florencia Canelli, Kyle Cormier, Andrew Cudd +12
Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are stra…
Weakly-Supervised Anomaly Detection in the Milky Way
Mariel Pettee, Sowmya Thanvantri, Benjamin Nachman +3
Large-scale astrophysics datasets present an opportunity for new machine learning techniques to identify regions of interest that might otherwise be overlooked by traditional searc…
Learning Likelihood Ratios with Neural Network Classifiers
Shahzar Rizvi, Mariel Pettee, Benjamin Nachman
The likelihood ratio is a crucial quantity for statistical inference in science that enables hypothesis testing, construction of confidence intervals, reweighting of distributions,…
Fast Point Cloud Generation with Diffusion Models in High Energy Physics
Vinicius Mikuni, Benjamin Nachman, Mariel Pettee
Many particle physics datasets like those generated at colliders are described by continuous coordinates (in contrast to grid points like in an image), respect a number of symmetri…
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)…