19 citations · 47 across the 14 of their papers we have counts for
13 papers · 1 filter
Pythia-Contrib: a modular contribution framework for the Pythia 8 event generator
Benoit Assi, Christian Bierlich, Rikab Gambhir +6
Pythia is a general-purpose Monte Carlo event generator used in particle, astroparticle, and nuclear physics, by theorists and experimentalists, to simulate particle collisions. Be…
HDSense: An efficient method for ranking observable sensitivity
Benoît Assi, Christian Bierlich, Rikab Gambhir +6
Identifying which observables most effectively constrain model parameters can be computationally prohibitive when considering full likelihoods of many correlated observables. This…
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…
The Pareto Frontier of Resilient Jet Tagging
Rikab Gambhir, Matt LeBlanc, Yuanchen Zhou
Classifying hadronic jets using their constituents' kinematic information is a critical task in modern high-energy collider physics. Often, classifiers are designed by targeting th…
A Search for "New Physics'' "Beyond the Standard Model'' in Open Data with Machine Learning
Rikab Gambhir
In this new era of large data, it is important to make sure we do not miss any signs of new physics. Using the publicly-available open data collected by the arXiv.org experiment in…
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…
SPECTER: Efficient Evaluation of the Spectral EMD
Rikab Gambhir, Andrew J. Larkoski, Jesse Thaler
The Energy Mover's Distance (EMD) has seen use in collider physics as a metric between events and as a geometric method of defining infrared and collinear safe observables. Recentl…
Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling
Rikab Gambhir, Athis Osathapan, Jesse Thaler
Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose "…
Seeing Double: Calibrating Two Jets at Once
Rikab Gambhir, Benjamin Nachman
Jet energy calibration is an important aspect of many measurements and searches at the LHC. Currently, these calibrations are performed on a per-jet basis, i.e. agnostic to the pro…
The New Physics Case for Beam-Dump Experiments with Accelerated Muon Beams
Cari Cesarotti, Rikab Gambhir
As the field examines a future muon collider as a possible successor to the LHC, we must consider how to fully utilize not only the high-energy particle collisions, but also any lo…
SHAPER: Can You Hear the Shape of a Jet?
Demba Ba, Akshunna S. Dogra, Rikab Gambhir +2
The identification of interesting substructures within jets is an important tool for searching for new physics and probing the Standard Model at colliders. Many of these substructu…
Bias and Priors in Machine Learning Calibrations for High Energy Physics
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Machine learning offers an exciting opportunity to improve the calibration of nearly all reconstructed objects in high-energy physics detectors. However, machine learning approache…
Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics
Rikab Gambhir, Benjamin Nachman, Jesse Thaler
Calibration is a common experimental physics problem, whose goal is to infer the value and uncertainty of an unobservable quantity Z given a measured quantity X. Additionally, one…