422 citations · 2.4k across the 71 of their papers we have counts for
74 papers
Unbinned Inference with Correlated Events
Krish Desai, Owen Long, Benjamin Nachman
Modern machine learning has enabled parameter inference from event-level data without the need to first summarize all events with a histogram. All of these unbinned inference metho…
Measurement of the 1-jettiness event shape observable in deep-inelastic electron-proton scattering at HERA
The H1 collaboration, V. Andreev, M. Arratia +146
The H1 Collaboration reports the first measurement of the 1-jettiness event shape observable in neutral-current deep-inelastic electron-proton scattering (DIS). The observa…
Measurement of groomed event shape observables in deep-inelastic electron-proton scattering at HERA
The H1 collaboration, V. Andreev, M. Arratia +145
The H1 Collaboration at HERA reports the first measurement of groomed event shape observables in deep inelastic electron-proton scattering (DIS) at GeV, using data r…
Observation and differential cross section measurement of neutral current DIS events with an empty hemisphere in the Breit frame
The H1 collaboration, V. Andreev, M. Arratia +146
The Breit frame provides a natural frame to analyze lepton-proton scattering events. In this reference frame, the parton model hard interactions between a quark and an exchanged bo…
Model-independent extraction of form factors and in with hadronic tagging at BaBar
BaBar Collaboration, J. P. Lees, V. Poireau +208
Using the entire BaBar data set, the first two-dimensional unbinned angular analysis of the semileptonic decay is perfor…
Designing Observables for Measurements with Deep Learning
Owen Long, Benjamin Nachman
Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference i…