497 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Massachusetts Institute of TechnologyUS149 papers
- Center for Astrophysics Harvard & SmithsonianUS76 papers
- Las Cumbres Observatory Global Telescope NetworkUS48 papers
- University of California, Santa BarbaraUS43 papers
- University of ArizonaUS36 papers
- Institute of Space SciencesES27 papers
- Université Bourgogne Franche-ComtéFR26 papers
- Harvard UniversityUS24 papers
- University of California, BerkeleyUS24 papers
- Flatiron Health (United States)US23 papers
- University of California, DavisUS23 papers
- University of WashingtonUS23 papers
39 papers · 1 filter
Analysis of Nuclear Fragmentation Functions for Pions with and Dependence
Mengyang Li, Zijian Ye, Jun Gao +3
We present a QCD analysis of pion nuclear fragmentation functions (nFFs), which encode nuclear modifications to hadronization in high-energy nuclear collisions. Within this framewo…
Observable Optimization for Precision Theory: Machine Learning Energy Correlators
Arindam Bhattacharya, Katherine Fraser, Matthew D. Schwartz
The practice of collider physics typically involves the marginalization of multi-dimensional collider data to uni-dimensional observables relevant for some physics task. In any cas…
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…
A Lorentz-Equivariant Transformer for All of the LHC
Johann Brehmer, Víctor Bresó, Pim de Haan +4
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Colli…
A Field Guide to Event-Shape Observables Using Optimal Transport
Cari Cesarotti, Matt LeBlanc
We lay out the phenomenological behavior of event-shape observables evaluated by solving optimal transport problems between collider events and reference geometries -- which we nam…
Moment Unfolding
Krish Desai, Benjamin Nachman, Jesse Thaler
Deconvolving ("unfolding'') detector distortions is a critical step in the comparison of cross section measurements with theoretical predictions in particle and nuclear physics. Ho…
Re-Simulation-based Self-Supervised Learning for Pre-Training Foundation Models
Philip Harris, Michael Kagan, Jeffrey Krupa +2
Self-Supervised Learning (SSL) is at the core of training modern large machine learning models, providing a scheme for learning powerful representations that can be used in a varie…
Real-time Dynamics of the Schwinger Model as an Open Quantum System with Neural Density Operators
Joshua Lin, Di Luo, Xiaojun Yao +1
Ab-initio simulations of multiple heavy quarks propagating in a Quark-Gluon Plasma are computationally difficult to perform due to the large dimension of the space of density matri…
Safe but Incalculable: Energy-weighting is not all you need
Samuel Bright-Thonney, Benjamin Nachman, Jesse Thaler
Infrared and collinear (IRC) safety has long been used a proxy for robustness when developing new jet substructure observables. This guiding philosophy has been carried into the de…
Goodness of fit by Neyman-Pearson testing
Gaia Grosso, Marco Letizia, Maurizio Pierini +1
The Neyman-Pearson strategy for hypothesis testing can be employed for goodness of fit if the alternative hypothesis is selected from data by exploring a rich parametrised family o…
Pileup and Infrared Radiation Annihilation (PIRANHA): A Paradigm for Continuous Jet Grooming
Samuel Alipour-Fard, Patrick T. Komiske, Eric M. Metodiev +1
Jet grooming is an important strategy for analyzing relativistic particle collisions in the presence of contaminating radiation. Most jet grooming techniques introduce hard cutoffs…
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…
EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
Erik Buhmann, Gregor Kasieczka, Jesse Thaler
With the vast data-collecting capabilities of current and future high-energy collider experiments, there is an increasing demand for computationally efficient simulations. Generati…
Non-perturbative strong coupling at timelike momenta
Jan Horak, Jan M. Pawlowski, Jonas Turnwald +3
We compute the strong coupling constant of Landau gauge QCD in the full complex momentum plane, both directly and via spectral reconstruction. In particular, we consider the Taylor…
Comparing Point Cloud Strategies for Collider Event Classification
Peter Onyisi, Delon Shen, Jesse Thaler
In this paper, we compare several event classification architectures defined on the point cloud representation of collider events. These approaches, which are based on the framewor…
Yang-Mills glueball masses from spectral reconstruction
Jan M. Pawlowski, Coralie S. Schneider, Jonas Turnwald +2
We compute masses of the two lightest glueballs from spectral reconstructions of timelike interaction channels of the four-gluon vertex in Landau gauge Yang-Mills theory. The Eucli…
Neural Embedding: Learning the Embedding of the Manifold of Physics Data
Sang Eon Park, Philip Harris, Bryan Ostdiek
In this paper, we present a method of embedding physics data manifolds with metric structure into lower dimensional spaces with simpler metrics, such as Euclidean and Hyperbolic sp…
Modeling early-universe energy injection with Dense Neural Networks
Yitian Sun, Tracy R. Slatyer
We show that Dense Neural Networks can be used to accurately model the cooling of high-energy particles in the early universe, in the context of the public code package DarkHistory…
A Stimulating Explanation of the Extragalactic Radio Background
Andrea Caputo, Hongwan Liu, Siddharth Mishra-Sharma +2
Despite an intense theoretical and experimental effort over the past decade, observations of the extragalactic radio background at multiple frequencies below 10 GHz are not underst…
Power Counting Energy Flow Polynomials
Pedro Cal, Jesse Thaler, Wouter J. Waalewijn
Power counting is a systematic strategy for organizing collider observables and their associated theoretical calculations. In this paper, we use power counting to characterize a cl…
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…
Disentangling Quarks and Gluons with CMS Open Data
Patrick T. Komiske, Serhii Kryhin, Jesse Thaler
We study quark and gluon jets separately using public collider data from the CMS experiment. Our analysis is based on 2.3/fb of proton-proton collisions at 7 TeV, collected at the…
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…
Machine Learning and Cosmology
Cora Dvorkin, Siddharth Mishra-Sharma, Brian Nord +8
Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data colle…