24 citations · 87 across the 22 of their papers we have counts for
23 papers
HEPTAPOD: Orchestrating High Energy Physics Workflows Towards Autonomous Agency
Tony Menzo, Alexander Roman, Sergei Gleyzer +5
Many theoretical and experimental workflows in high-energy-physics (HEP) stand to benefit from recent advances in transformer-based large language models (LLMs). While early applic…
Comment on "An implementation of neural simulation-based inference for parameter estimation in ATLAS''
Prasanth Shyamsundar
The paper titled "An implementation of neural simulation-based inference for parameter estimation in ATLAS" by the ATLAS collaboration (arXiv:2412.01600v1 [hep-ex]) describes the i…
CV4Quantum: Reducing the Sampling Overhead in Probabilistic Error Cancellation Using Control Variates
Prasanth Shyamsundar, Wern Yeen Yeong
Quasiprobabilistic decompositions (QPDs) play a key role in maximizing the utility of near-term quantum hardware. For example, Probabilistic Error Cancellation (PEC) (an error miti…
A Demonstration of ARCANE Reweighting: Reducing the Sign Problem in the MC@NLO Generation of Events
Prasanth Shyamsundar
Negatively weighted events, which appear in the simulation of particle collisions, significantly increase the computational requirements of collider experiments. A new technique ca…
ARCANE Reweighting: A Monte Carlo Technique to Tackle the Negative Weights Problem in Collider Event Generation
Prasanth Shyamsundar
Negatively weighted events, which appear in the Monte Carlo (MC) simulation of particle collisions, significantly increases the computational resource requirements of current and f…
EFT Workshop at Notre Dame
Nick Smith, Daniel Spitzbart, Jennet Dickinson +27
The LPC EFT workshop was held April 25-26, 2024 at the University of Notre Dame. The workshop was organized into five thematic sessions: "how far beyond linear" discusses issues of…
Fermion determinants on a quantum computer
George T. Fleming, Prasanth Shyamsundar, Judah Unmuth-Yockey
We present a quantum algorithm to compute the logarithm of the determinant of the fermion matrix, assuming access to a classical lattice gauge field configuration. The algorithm us…
Optimal Mass Variables for Semivisible Jets
Kevin Pedro, Prasanth Shyamsundar
Strongly coupled hidden sector theories predict collider production of invisible, composite dark matter candidates mixed with standard model hadrons in the form of semivisible jets…
Quantum circuit fidelity estimation using machine learning
Avi Vadali, Rutuja Kshirsagar, Prasanth Shyamsundar +1
The computational power of real-world quantum computers is limited by errors. When using quantum computers to perform algorithms which cannot be efficiently simulated classically,…
New Machine Learning Techniques for Simulation-Based Inference: InferoStatic Nets, Kernel Score Estimation, and Kernel Likelihood Ratio Estimation
Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna +1
We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in…
Kinematic Variables and Feature Engineering for Particle Phenomenology
Roberto Franceschini, Doojin Kim, Kyoungchul Kong +3
Kinematic variables have been playing an important role in collider phenomenology, as they expedite discoveries of new particles by separating signal events from unwanted backgroun…
Qubit assignment using time reversal
Evan Peters, Prasanth Shyamsundar, Andy C. Y. Li +1
As the number of qubits available on noisy quantum computers grows, it will become necessary to efficiently select a subset of physical qubits to use in a quantum computation. For…
Deep-Learned Event Variables for Collider Phenomenology
Doojin Kim, Kyoungchul Kong, Konstantin T. Matchev +2
The choice of optimal event variables is crucial for achieving the maximal sensitivity of experimental analyses. Over time, physicists have derived suitable kinematic variables for…
Non-Boolean Quantum Amplitude Amplification and Quantum Mean Estimation
Prasanth Shyamsundar
This paper generalizes the quantum amplitude amplification and amplitude estimation algorithms to work with non-boolean oracles. The action of a non-boolean oracle on an eige…
InClass Nets: Independent Classifier Networks for Nonparametric Estimation of Conditional Independence Mixture Models and Unsupervised Classification
Konstantin T. Matchev, Prasanth Shyamsundar
We introduce a new machine-learning-based approach, which we call the Independent Classifier networks (InClass nets) technique, for the nonparameteric estimation of conditional ind…
OASIS: Optimal Analysis-Specific Importance Sampling for event generation
Konstantin T. Matchev, Prasanth Shyamsundar
We propose a technique called Optimal Analysis-Specific Importance Sampling (OASIS) to reduce the number of simulated events required for a high-energy experimental analysis to rea…
Finding Wombling Boundaries in LHC Data with Voronoi and Delaunay Tessellations
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
We address the problem of finding a wombling boundary in point data generated by a general Poisson point process, a specific example of which is an LHC event sample distributed in…
A Quantum Algorithm for Model-Independent Searches for New Physics
Konstantin T. Matchev, Prasanth Shyamsundar, Jordan Smolinsky
We propose a novel quantum technique to search for unmodeled anomalies in multidimensional binned collider data. We propose associating an Ising lattice spin site with each bin, wi…
Uncertainties associated with GAN-generated datasets in high energy physics
Konstantin T. Matchev, Alexander Roman, Prasanth Shyamsundar
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets a…
Optimal event selection and categorization in high energy physics, Part 1: Signal discovery
Konstantin K. Matchev, Prasanth Shyamsundar
We provide a prescription to train optimal machine-learning-based event selectors and categorizers that maximize the statistical significance of a potential signal excess in high e…
Singularity Variables for Missing Energy Event Kinematics
Konstantin T. Matchev, Prasanth Shyamsundar
We discuss singularity variables which are properly suited for analyzing the kinematics of events with missing transverse energy at the LHC. We consider six of the simplest event t…
Kinematic Focus Point Method for Particle Mass Measurements in Missing Energy Events
Doojin Kim, Konstantin T. Matchev, Prasanth Shyamsundar
We investigate the solvability of the event kinematics in missing energy events at hadron colliders, as a function of the particle mass ansatz. To be specific, we reconstruct the n…
Measuring the mass, width, and couplings of semi-invisible resonances with the Matrix Element Method
Amalia Betancur, Dipsikha Debnath, James S. Gainer +2
We demonstrate the use of the Matrix Element Method (MEM) for the measurement of masses, widths, and couplings in the case of single or pair production of semi-invisibly decaying r…