Related papers: Quality assurance for the ALICE Monte Carlo proced…
ALICE, which stands for A Large Ion Collider Experiment, is designed to study hadronic collisions at ultrarelativistic energies at the LHC. The primary objective of ALICE is to investigate the properties of quark-gluon plasma (QGP), a state…
Recent years have seen a rise in the application of machine learning techniques to aid the simulation of hard-to-sample systems that cannot be studied using traditional methods. Despite the introduction of many different architectures and…
The performance of the missing transverse momentum (E$_{T}^{miss}$) reconstruction with the ATLAS detector is evaluated using data collected in proton-proton collisions at the LHC at a center-of-mass energy of 13 TeV in 2015. To reconstruct…
Because of properties of QED, the bremsstrahlung corrections to decays of particles or resonances can be calculated, with a good precision, separately from other effects. Thanks to the widespread use of event records such calculations can…
Monte Carlo techniques play a central role in statistical mechanics approaches for connecting macroscopic thermodynamic and kinetic properties to the electronic structure of a material. This paper describes the implementation of Monte Carlo…
The basic problem in equilibrium statistical mechanics is to compute phase space average, in which Monte Carlo method plays a very important role. We begin with a review of nonlocal algorithms for Markov chain Monte Carlo simulation in…
A numerical method to implement a linearized Coulomb collision operator in the two-weight $\delta f$ Monte Carlo method for multi-ion-species neoclassical transport simulation is developed. The conservation properties and the adjointness…
At LHC energy, heavy quarks will be abundantly produced and the design of the ALICE detector will allow us to study their production using several channels. The expected heavy-quark in-medium energy loss in nucleus-nucleus collisions at the…
We present a Bayesian approach to particle identification (PID) within the ALICE experiment. The aim is to more effectively combine the particle identification capabilities of its various detectors. After a brief explanation of the adopted…
We revisit the problem of assigning a score (a quality of fit) to candidate geometric models -- one of the key components of RANSAC for robust geometric fitting. In a non-robust setting, the ``gold standard'' scoring function, known as the…
In this article we describe the implementation of Artificial Intelligence models in track reconstruction software for the CLAS12 detector at Jefferson Lab. The Artificial Intelligence based approach resulted in improved track reconstruction…
In the framework of detector development, Monte Carlo simulations play a key role in the evaluation of the expected performance and the full understanding of the behavior in beam conditions. In particular, a software which simulates the…
Quantum Monte Carlo integration, a quantum algorithm for calculating expectations that provides a quadratic speed-up compared to its classical counterpart, is now attracting increasing interest in the context of its industrial and…
In recent years, code security has become increasingly important, especially with the rise of interconnected technologies. Detecting vulnerabilities early in the software development process has demonstrated numerous benefits. Consequently,…
We introduce the EMC algorithm for reconstructing a particle's 3D diffraction intensity from very many photon shot-noise limited 2D measurements, when the particle orientation in each measurement is unknown. The algorithm combines a…
A free industry-grade education tool is developed for bulk-power-system reliability assessment. The software architecture is illustrated using a high-level flowchart. Three main algorithms of this tool, i.e., sequential Monte Carlo…
Monte Carlo event generators are an essential tool for data analysis in collider physics. To include subleading quantum corrections, these generators often need to produce negative weight events, which leads to statistical dilution of the…
We describe an algorithm to reduce the cost of auxiliary-field quantum Monte Carlo (AFQMC) calculations for the electronic structure problem. The technique uses a nested low-rank factorization of the electron repulsion integral (ERI). While…
The use of ensemble Monte Carlo (EMC) methods for the simulation of transport in semiconductor devices has become extensive over the past few decades. This method allows for simulation utilizing particles while addressing the full physics…
Several pool-based active learning algorithms (AL) were employed to model potential energy surfaces (PESs) with a minimum number of electronic structure calculations. Theoretical and empirical results suggest that superior strategies can be…