Related papers: GRANIITTI: towards a deep learning-enhanced Monte …
Apprentice is a tool developed for event generator tuning. It contains a range of conceptual improvements and extensions over the tuning tool Professor. Its core functionality remains the construction of a multivariate analytic surrogate…
ExDiff2.0 is a Monte Carlo event generator for simulation of Exclusive Diffractive processes in proton-proton collisions. The present version includes reactions: elastic scattering $pp\to pp$ at 7, 8, 13, 14~TeV; $pp\to p+R+p$, $R =…
We present the Monte Carlo event generator {\tt WOPPER} for pair production of $W$'s and their decays at high energy $e^+e^-$ colliders. {\tt WOPPER} includes the effects from finite $W$ width and focusses on the calculation of higher order…
The rapid development of generative artificial intelligence (GenAI) tools such as ChatGPT has intensified interest in their role in higher education, particularly in how students perceive and use them and how these perceptions may relate to…
We present a Monte Carlo event generator specifically developed for the study of hadronic two-photon fusion events in two-photon scattering at electron-positron colliders. The code enables the generation of events with exact leading-order…
The possibility of distinguishing between two higgs production mechanisms, W fusion and gluon fusion, is investigated using the Monte Carlo event generator PYTHIA. It is shown that, considering the designed CM energy and luminosity for the…
Highly reliable Monte-Carlo event generators and detector simulation programs are important for the precision measurement in the high energy physics. Huge amounts of computing resources are required to produce a sufficient number of…
Recent work incorporating geometric ideas in Markov chain Monte Carlo is reviewed in order to highlight these advances and their possible application in a range of domains beyond Statistics. A full exposition of Markov chains and their use…
With large volumes of data available from LHC, it has become possible to study the multiplicity distributions for the various possible behaviours of the multiparticle production in collisions of relativistic heavy ion collisions, where a…
Sartre has been extensively used for describing photon-nuclei processes at the electron-ion collider (EIC) as well as ultra-peripheral collisions (UPC) at LHC and RHIC. Sartre is an event generator which implements the dipole model for DIS,…
Performing reliable Bayesian inference on a big data scale is becoming a keystone in the modern era of machine learning. A workhorse class of methods to achieve this task are Markov chain Monte Carlo (MCMC) algorithms and their design to…
The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the…
We develop a recently proposed importance-sampling Monte Carlo algorithm for sampling rare events and quenched variables in random disordered systems. We apply it to a two dimensional bond-diluted Ising model and study the Griffiths…
We have written the Exclusive Hadronic Monte Carlo Event (ExHuME) generator. ExHuME implements the perturbative QCD calculation of Khoze, Martin and Ryskin of the process pp-> p+X+p, where X is a centrally produced colour singlet system.
We study lithium systems over a range of number of atoms, e.g., atomic anion, dimer, metallic cluster, and body-centered cubic crystal by the diffusion Monte Carlo method. The calculations include both core and valence electrons in order to…
This contribution provides brief descriptions the current status of the basic Monte Carlo event generators for top-quark production and decays.
Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The…
We present a new quantum Monte Carlo algorithm suitable for generically complex problems, such as systems coupled to external magnetic fields or anyons in two spatial dimensions. We find that the choice of gauge plays a nontrivial role, and…
The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted…
A diffusion Monte Carlo algorithm is introduced that can determine the correct nodal structure of the wave function of a few-fermion system and its ground-state energy without an uncontrolled bias. This is achieved by confining signed…