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Found 23 papers, 14 papers with code
Benchmarking machine learning models for quantum state classification
Quantum computing is a growing field where the information is processed by two-levels quantum states known as qubits.
Product Jacobi-Theta Boltzmann machines with score matching
The estimation of probability density functions is a non trivial task that over the last years has been tackled with machine learning techniques.
Style-based quantum generative adversarial networks for Monte Carlo events
We propose and assess an alternative quantum generator architecture in the context of generative adversarial learning for Monte Carlo event generation, used to simulate particle physics processes at the Large Hadron Collider (LHC).
A framework for quantitative analysis of Computed Tomography images of viral pneumonitis: radiomic features in COVID and non-COVID patients
A Gaussian model developed in Python language was applied to calculate quantitative metrics (QM) describing well-aerated and ill portions of the lungs from the histogram distribution of lung CT numbers in both lungs of each image and in four geometrical subdivision.
PDFFlow: hardware accelerating parton density access
We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators.
MCNNTUNES: tuning Shower Monte Carlo generators with machine learning
The parameters tuning of event generators is a research topic characterized by complex choices: the generator response to parameter variations is difficult to obtain on a theoretical basis, and numerical methods are hardly tractable due to the long computational times required by generators.
Computational Physics High Energy Physics - Experiment High Energy Physics - Phenomenology
PDFFlow: parton distribution functions on GPU
We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators.
Qibo: a framework for quantum simulation with hardware acceleration
We present Qibo, a new open-source software for fast evaluation of quantum circuits and adiabatic evolution which takes full advantage of hardware accelerators.
VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms
We present VegasFlow, a new software for fast evaluation of high dimensional integrals based on Monte Carlo integration techniques designed for platforms with hardware accelerators.
Towards hardware acceleration for parton densities estimation
In this proceedings we describe the computational challenges associated to the determination of parton distribution functions (PDFs).
Lund jet images from generative and cycle-consistent adversarial networks
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane.
Modelling conditional probabilities with Riemann-Theta Boltzmann Machines
The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units.
Jet grooming through reinforcement learning
We introduce a novel implementation of a reinforcement learning (RL) algorithm which is designed to find an optimal jet grooming strategy, a critical tool for collider experiments.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Sampling the Riemann-Theta Boltzmann Machine
We show that the visible sector probability density function of the Riemann-Theta Boltzmann machine corresponds to a gaussian mixture model consisting of an infinite number of component multi-variate gaussians.
Riemann-Theta Boltzmann Machine
Both the Boltzmann machine and the derived feedforward neural network can be successfully trained via standard gradient- and non-gradient-based optimization techniques.
QCDLoop: a comprehensive framework for one-loop scalar integrals
We present a new release of the QCDLoop library based on a modern object-oriented framework.
High Energy Physics - Phenomenology High Energy Physics - Experiment High Energy Physics - Theory
Specialized minimal PDFs for optimized LHC calculations
We present a methodology for the construction of parton distribution functions (PDFs) designed to provide an accurate representation of PDF uncertainties for specific processes or classes of processes with a minimal number of PDF error sets: specialized minimal PDF sets, or SM-PDFs.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Doped Parton Distributions
Calculations of high-energy processes involving the production of b-quarks are typically performed in two different ways, the massive four-flavour scheme (4FS) and the massless five-flavour scheme (5FS).
High Energy Physics - Phenomenology High Energy Physics - Experiment
On the Impact of Lepton PDFs
We present the implementation of the QED-corrected DGLAP evolution in the presence of photon and lepton PDFs in the APFEL program and, by means of different assumptions for the initial scale PDFs, we produce for the first time PDF sets containing charged lepton distributions.
High Energy Physics - Phenomenology High Energy Physics - Experiment
Reference results for time-like evolution up to $\mathcal{O}(α_s^3)$
We present high-precision numerical results for time-like Dokshitzer-Gribov-Lipatov-Altarelli-Parisi evolution in the $\bar{\rm MS}$ factorisation scheme, for the first time up to next-to-next-to-leading order accuracy in quantum chromodynamics.
High Energy Physics - Phenomenology
APFEL Web: a web-based application for the graphical visualization of parton distribution functions
We present APFEL Web, a web-based application designed to provide a flexible user-friendly tool for the graphical visualization of parton distribution functions (PDFs).
High Energy Physics - Phenomenology High Energy Physics - Experiment
Parton distributions with LHC data
We present the NNPDF2. 3 PDF sets, and compare them to the NNPDF2. 1 sets to assess the impact of the LHC data.
High Energy Physics - Phenomenology
