14 citations · 15 across the 4 of their papers we have counts for
7 papers
Robust resonant anomaly detection with NPLM
Gaia Grosso, Debajyoti Sengupta, Tobias Golling +1
In this study, we investigate the application of the New Physics Learning Machine (NPLM) algorithm as an alternative to the standard CWoLa method with Boosted Decision Trees (BDTs)…
RODEM Jet Datasets
Knut Zoch, John Andrew Raine, Debajyoti Sengupta +1
We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in pa…
Accelerating template generation in resonant anomaly detection searches with optimal transport
Matthew Leigh, Debajyoti Sengupta, Benjamin Nachman +1
We introduce Resonant Anomaly Detection with Optimal Transport (RAD-OT), a method for generating signal templates in resonant anomaly detection searches. RAD-OT leverages the fact…
PIPPIN: Generating variable length full events from partons
Guillaume Quétant, John Andrew Raine, Matthew Leigh +2
This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To ad…
SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data
Debajyoti Sengupta, Stephen Mulligan, David Shih +2
We present SkyCURTAINs, a data driven and model agnostic method to search for stellar streams in the Milky Way galaxy using data from the Gaia telescope. SkyCURTAINs is a weakly su…
Improving new physics searches with diffusion models for event observables and jet constituents
Debajyoti Sengupta, Matthew Leigh, John Andrew Raine +2
We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background…