Related papers: Accelerating LHC event generation with simplified …
Machine learning has recently gained traction as a way to overcome the slow accelerator generation and implementation process on an FPGA. It can be used to build performance and resource usage models that enable fast early-stage design…
One approach to improving the running time of kernel-based machine learning methods is to build a small sketch of the input and use it in lieu of the full kernel matrix in the machine learning task of interest. Here, we describe a version…
We perform a detailed phenomenological study of high-energy neutrino deep inelastic scattering (DIS) focused on LHC far-forward experiments such as FASER$\nu$ and SND@LHC. To this aim, we parametrise the neutrino fluxes reaching these LHC…
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector…
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton-proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run…
We study the impact of new set of cuts, proposed in our previous works, on the improvement of accuracy of the jet energy calibration with 'p p ->photon+Jet+X' process at LHC. Monte Carlo events produced by the PYTHIA 5.7 generator are used…
The AcerMC Monte Carlo generator is dedicated to the generation of Standard Model background processes which were recognised as critical for the searches at LHC, and generation of which was either unavailable or not straightforward so far.…
The effect of full $7$ sets of LHC ATLAS jet cross sections data at $ \sqrt{s} = 7$ TeV on the proton parton distribution functions (PDFs) up to next-to-next-to-next-to-leading order (NNNLO or N3LO) corrections is investigated for the first…
The matrix element technique provides a superior statistical sensitivity for precision measurements of important parameters at hadron colliders, such as the mass of the top quark or the cross section for the production of Higgs bosons. The…
The LHCb experiment stores around $10^{11}$ collision events per year. A typical physics analysis deals with a final sample of up to $10^7$ events. Event preselection algorithms (lines) are used for data reduction. Since the data are stored…
Principal component analysis (PCA) is one of the most powerful tools in machine learning. The simplest method for PCA, the power iteration, requires $\mathcal O(1/\Delta)$ full-data passes to recover the principal component of a matrix with…
A "meta-analysis" is a method for comparison and combination of nonperturbative parton distribution functions (PDFs) in a nucleon obtained with heterogeneous procedures and assumptions. Each input parton distribution set is converted into a…
Supervised artificial neural networks with the rapidity-mass matrix (RMM) inputs were studied using several Monte Carlo event samples for various pp collision processes. The study shows the usability of this approach for general event…
Determinations of the proton's collinear parton distribution functions (PDFs) are emerging with growing precision due to increased experimental activity at facilities like the Large Hadron Collider. While this copious information is…
At high energy physics experiments, processing billions of records of structured numerical data from collider events to a few statistical summaries is a common task. The data processing is typically more complex than standard query…
This paper presents updated Monte Carlo configurations used to model the production of single electroweak vector bosons (W, Z/$\gamma^{*}$) in association with jets in proton-proton collisions for the ATLAS experiment at the Large Hadron…
Ensuring the reproducibility of physics results is one of the crucial challenges in high-energy physics (HEP). In this study, we develop a proof-of-concept system that uses large language models (LLMs) to extract analysis procedures from…
We present an automated implementation for the calculation of one-loop double and single Sudakov logarithms stemming from electroweak radiative corrections within the Sherpa event generation framework, based on the derivation in[1]. At high…
The project, aimed at the theoretical support of experiments at modern and future accelerators -- TEVATRON, LHC, electron Linear Colliders (TESLA, NLC, CLIC) and muon factories, is presented. Within this project a four-level computer system…
We present a novel approach that is being developed at DZero for extracting information from data through a direct comparison of all measured variables in an event with a matrix element that describes the entire production process. The…