Related papers: Reconstructing parton collisions with machine lear…
We investigate whether artificial intelligence can autonomously recover known structures of the Standard Model of particle physics using only experimental data and without theoretical inputs. By applying unsupervised machine learning…
A formalism for hadron production at high \pt in heavy-ion collisions has been developed such that all partons hadronize by recombination. The fragmentation of a hard parton is accounted for by the recombination of shower partons that it…
Back-to-back dihadron spectra in high-energy heavy-ion collisions are studied within the next-to-leading order (NLO) perturbative QCD parton model with jet quenching incorporated via modified jet fragmentation functions due to radiative…
Machine Learning techniques can be used to represent high-dimensional potential energy surfaces for reactive chemical systems. Two such methods are based on a reproducing kernel Hilbert space representation or on deep neural networks. They…
We have developed a neural network model to perform event reconstruction of Compton telescopes. This model reconstructs events that consist of three or more interactions in a detector. It is essential for Compton telescopes to determine the…
In this article we will discuss the basic calculational concepts to simulate particle physics events at high energy colliders. We will mainly focus on the physics in hadron colliders and particularly on the simulation of the perturbative…
We discuss how the main features of high-energy `soft' and `semihard' pp collisions may be described in terms of parton cascades and multi-Pomeron exchange. The interaction between Pomerons produces an effective infrared cutoff, k_sat, by…
Soft pp interactions are considered in the framework of the phenomenological model with color strings formation. Under the assumption, that the elementary collision is realized as interaction of two color dipoles, the total inelastic cross…
We derive mass corrections for semi-inclusive deep inelastic scattering of leptons from nucleons using a collinear factorization framework which incorporates the initial state mass of the target nucleon and the final state mass of the…
The dynamics of partons and hadrons in relativistic nucleus-nucleus collisions is analyzed within the novel Parton-Hadron-String Dynamics (PHSD) transport approach, which is based on a dynamical quasiparticle model for the partonic phase…
Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics…
The CMS apparatus was identified, a few years before the start of the LHC operation at CERN, to feature properties well suited to particle-flow (PF) reconstruction: a highly-segmented tracker, a fine-grained electromagnetic calorimeter, a…
Jets can be used to probe the physical properties of the high energy density matter created in collisions at the Relativistic Heavy Ion Collider (RHIC). Measurements of strong suppression of inclusive hadron distributions and di-hadron…
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in…
The use of machine learning algorithms in theoretical and experimental high-energy physics has experienced an impressive progress in recent years, with applications from trigger selection to jet substructure classification and detector…
We calculate the production of real photons in relativistic nuclear collisions at RHIC, consistently with the quenching of fast partons. We go beyond one-body observables, and evaluate photon-triggered fragmentation functions, in the…
In many inertial confinement fusion experiments, the neutron yield and other parameters cannot be completely accounted for with one and two dimensional models. This discrepancy suggests that there are three dimensional effects which may be…
Accurate observation of two or more particles within a very narrow time window has always been a challenge in modern physics. It creates the possibility of correlation experiments, such as the ground-breaking Hanbury Brown-Twiss experiment,…
To fully exploit the physics potential of current and future high energy particle colliders, machine learning (ML) can be implemented in detector electronics for intelligent data processing and acquisition. The implementation of ML in…
We present a method for resolving the combinatorial issues in the \ttbar lepton+jets events occurring at the Tevatron collider. By incorporating multiple information into an artificial neural network, we introduce a novel event…