54 citations · 248 across the 33 of their papers we have counts for
34 papers
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…
Defining a Minimum Resolution for Unbinned Analyses
Manuel Szewc
Collider analyses combine rigorous statistical techniques with state-of-the-art Machine Learning models. However, when the latter are used directly to estimate the likelihood funct…
Vistas: A Visualization Interface for Particle Collision Simulations
Benoit Assi, Christan Bierlich, Rikab Gambhir +7
We introduce Vistas, a tool for visualizing high-energy particle physics collisions simulated by the Pythia Monte-Carlo event generator. Vistas utilizes the browser-based event dis…
Many Wrongs Make a Right: Leveraging Biased Simulations Towards Unbiased Parameter Inference
Ezequiel Alvarez, Sean Benevedes, Manuel Szewc +1
In particle physics, as in many areas of science, parameter inference relies on simulations to bridge the gap between theory and experiment. Recent developments in simulation-based…
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…
Di-Higgs to 4b with Bayesian inference: improving simulation estimates
Ezequiel Alvarez, Leandro Da Rold, Manuel Szewc +3
Measuring di-Higgs production in the four-bottom channel is challenged by overwhelming QCD backgrounds and imperfect simulations. We develop a Bayesian mixture model that simultane…
Iterative HOMER with uncertainties
Anja Butter, Ayodele Ore, Sofia Palacios Schweitzer +10
We present iHOMER, an iterative version of the HOMER method to extract Lund fragmentation functions from experimental data. Through iterations, we address the information gap betwe…
Data-Driven High-Dimensional Statistical Inference with Generative Models
Oz Amram, Manuel Szewc
Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poo…
Inferring correlated distributions: boosted top jets
Ezequiel Alvarez, Manuel Szewc, Alejandro Szynkman +2
Improving the understanding of signal and background distributions in signal-region is a valuable key to enhance any analysis in collider physics. This is usually a difficult task…
Post-hoc reweighting of hadron production in the Lund string model
Benoît Assi, Christan Bierlich, Philip Ilten +6
We present a method for reweighting flavor selection in the Lund string fragmentation model. This is the process of calculating and applying event weights enabling fast and exact v…
Characterizing the hadronization of parton showers using the HOMER method
Benoit Assi, Christian Bierlich, Philip Ilten +6
We update the HOMER method, a technique to solve a restricted version of the inverse problem of hadronization -- extracting the Lund string fragmentation function from data…
Rejection Sampling with Autodifferentiation - Case study: Fitting a Hadronization Model
Nick Heller, Phil Ilten, Tony Menzo +5
We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be use…
Describing Hadronization via Histories and Observables for Monte-Carlo Event Reweighting
Christian Bierlich, Phil Ilten, Tony Menzo +5
We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for…
Direct CKM determination from W decays at future lepton colliders
David Marzocca, Manuel Szewc, Michele Tammaro
We project the reach of future lepton colliders for measuring CKM elements from direct observations of decays. We focus our attention to and determination…
Improvement and generalization of ABCD method with Bayesian inference
Ezequiel Alvarez, Leandro Da Rold, Manuel Szewc +3
To find New Physics or to refine our knowledge of the Standard Model at the LHC is an enterprise that involves many factors. We focus on taking advantage of available information a…
Accessing CKM suppressed top decays at the LHC
Manuel Szewc
We present an strategy for measuring the off-diagonal elements of the third row of CKM matrix through the branching fractions of top quark decays , where i…
Towards a data-driven model of hadronization using normalizing flows
Christian Bierlich, Phil Ilten, Tony Menzo +5
We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Addition…
Reweighting Monte Carlo Predictions and Automated Fragmentation Variations in Pythia 8
Christan Bierlich, Philip Ilten, Tony Menzo +5
This work reports on a method for uncertainty estimation in simulated collider-event predictions. The method is based on a Monte Carlo-veto algorithm, and extends previous work on…
Flavor violating Higgs and decays at FCC-ee
Jernej F. Kamenik, Arman Korajac, Manuel Szewc +2
Recent advances in , , and quark tagging coupled with novel statistical analysis techniques will allow future high energy and high statistics electron-positron colliders,…
Exploring unsupervised top tagging using Bayesian inference
Ezequiel Alvarez, Manuel Szewc, Alejandro Szynkman +2
Recognizing hadronically decaying top-quark jets in a sample of jets, or even its total fraction in the sample, is an important step in many LHC searches for Standard Model and Bey…
Null Hypothesis Test for Anomaly Detection
Jernej F. Kamenik, Manuel Szewc
We extend the use of Classification Without Labels for anomaly detection with a hypothesis test designed to exclude the background-only hypothesis. By testing for statistical indep…
Accessing CKM suppressed top decays at the LHC
Darius A. Faroughy, Jernej F. Kamenik, Manuel Szewc +1
We propose an extension of the existing experimental strategy for measuring branching fractions of top quark decays, targeting specifically , where is a light qua…
A method for approximating optimal statistical significances with machine-learned likelihoods
Ernesto Arganda, Xabier Marcano, Víctor Martín Lozano +4
Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivi…
Unsupervised quark/gluon jet tagging with Poissonian Mixture Models
Ezequiel Alvarez, Michael Spannowsky, Manuel Szewc
The classification of jets induced by quarks or gluons is important for New Physics searches at high-energy colliders. However, available taggers usually rely on modelling the data…