Related papers: Status of MadLoop/aMC@NLO
Particle Accelerators are high power complex machines. To ensure uninterrupted operation of these machines, thousands of pieces of equipment need to be synchronized, which requires addressing many challenges including design, optimization…
A key component of graph-based SLAM systems is the ability to detect loop closures in a trajectory to reduce the drift accumulated over time from the odometry. Most LiDAR-based methods achieve this goal by using only the geometric…
Modern machine learning is driving a paradigm shift in particle physics phenomenology at the Large Hadron Collider. This short review examines the transformative role of machine learning across the entire theoretical prediction pipeline,…
We describe techniques that simplify the calculation of one-loop QCD amplitudes with many external legs, which are needed for next-to-leading-order (NLO) corrections to multi-jet processes. The constraints imposed by perturbative unitarity,…
We present novel predictions for the production of $W^+W^-$ pairs in hadron collisions that are next-to-next-to-leading order accurate and consistently matched to a parton shower (NNLOPS). All diagrams that lead to the process $pp\to e^-…
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years. ML engineers, as a consequence, are in high demand across the industry, yet improving the efficiency of ML engineers…
Over the past five years, modern machine learning has been quietly revolutionizing particle physics. Old methodology is being outdated and entirely new ways of thinking about data are becoming commonplace. This article will review some…
Collimation systems are essential in particle accelerators to safely and efficiently manage unavoidable beam losses during operation. These systems rely on collimators, which are specially designed movable jaws or absorbers positioned close…
In this work we calculate for the first time the next-to-next-to leading order (NNLO) QCD corrections to identified hadron production at hadron colliders. The inclusion of the NNLO correction has an important impact on all observables…
In this paper we will describe two new optimisations implemented in MadGraph5_aMC@NLO, both of which are designed to speed-up the computation of leading-order processes (for any model). First we implement a new method to evaluate the…
This article provides a review of the literature of students' feedback papers published in recent years employing data mining techniques. In particular, the focus is to highlight those papers which are using either machine learning or deep…
The interpretation of experimental measurements at the LHC requires accurate theoretical predictions for exclusive observables, and in particular the summation of soft and collinear radiation to all orders in perturbation theory. We report…
A directed R&D program is presently underway in the U.S. to evaluate the designs and technologies required to provide muon-based high energy physics (HEP) accelerator capabilities. Such capabilities have the potential to provide unique…
We provide Next-to-Leading Order (NLO) predictions for Dark Matter (DM) production in association with either a jet or a photon at hadron colliders. In particular we study the production of a pair of fermionic DM particles through a…
In this contribution we review recent progress with fixed-order QCD predictions for the production of a vector boson in association with jets at hadron colliders, using the programs BlackHat and SHERPA. We review general features of…
The production of two isolated photons in high-energy hadron collisions poses a challenge to perturbative QCD because of large corrections through next-to-next-to-leading order (NNLO). We present novel next-to-next-to-next-to-leading order…
We review recent results in precision multiboson+jet phenomenology at the LHC. We discuss strategies how to compute these processes at NLO QCD and examine the impact of the perturbative corrections on the expected phenomenology, especially…
We briefly discuss the current status of NLO QCD automation in the Monte Carlo event generator WHIZARD. The functionality is presented for the explicit study of off-shell top quark production with associated backgrounds at a lepton…
Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. Objective: In this paper, we assess the benefits and…
This article presents an experiment focused on optimizing the MLOps (Machine Learning Operations) process, a crucial aspect of efficiently implementing machine learning projects. The objective is to identify patterns and insights to enhance…