Related papers: Status of MadLoop/aMC@NLO

The $B_c$ meson pair, including pairs of pseudoscalar states and vector states, productions in proton-proton collisions are investigated at the next-to-leading order (NLO) accuracy in the nonrelativistic quantum chromodynamics factorization…

We present MadAnalysis 5, a new framework for phenomenological investigations at particle colliders. Based on a C++ kernel, this program allows to efficiently perform, in a straightforward and user-friendly fashion, sophisticated physics…

For the Run 2 of the LHC next-to-leading order electroweak corrections will play an important role. Even though they are typically moderate at the level of total cross sections they can lead to substantial deviations in the shapes of…

Background. The rapid and growing popularity of machine learning (ML) applications has led to an increasing interest in MLOps, that is, the practice of continuous integration and deployment (CI/CD) of ML-enabled systems. Aims. Since changes…

We use aMC@NLO to predict the lv+ 2-jet cross section at the NLO accuracy in QCD matched to parton shower simulations. We find that the perturbative expansion is well behaved for all the observables we study, and in particular for those…

This is the user's manual of MC@NLO 3.2. This package is a practical implementation, based upon the HERWIG event generator, of the MC@NLO formalism, which allows one to incorporate NLO QCD matrix elements consistently into a parton shower…

We present MadFlow, a first general multi-purpose framework for Monte Carlo (MC) event simulation of particle physics processes designed to take full advantage of hardware accelerators, in particular, graphics processing units (GPUs). The…

An MC@NLO for heavy quarks in photoproduction is presented. This is the first lepton-hadron process to be included into MC@NLO. To construct an MC@NLO process dependent so called MC-subtraction terms need to be calculated. The resulting…

Quantum machine learning offers the ability to capture complex correlations in high-dimensional feature spaces, crucial for the challenge of detecting beyond the Standard Model physics in collider events, along with the potential for…

We present the computational framework MATRIX which allows us to evaluate fully differential cross sections for a wide class of processes at hadron colliders in next-to-next-to-leading order (NNLO) QCD. The processes we consider are $2\to…

This article introduces the importance of machine learning in real-world applications and explores the rise of MLOps (Machine Learning Operations) and its importance for solving challenges such as model deployment and performance…

I present a master formula for the next-to-next-to-leading order (NNLO) soft and virtual QCD corrections for any process in hadron-hadron and lepton-hadron colliders. The formula is derived from a unified threshold resummation formalism.…

This contribution lists challenges of Monte Carlo event generators for future lepton, especially linear colliders. A lot of the recent development benefits from the achievements at the Large Hadron Collider (LHC), but several aspects are…

The rapid development and large body of literature on machine learning interatomic potentials (MLIPs) can make it difficult to know how to proceed for researchers who are not experts but wish to use these tools. The spirit of this review is…

We calculate the inclusive production of charged hadrons in $\pbp$ collisions to next-to-leading order (NLO) in the QCD improved parton model using a new set of NLO fragmentation functions for charged pions and kaons. We predict…

We review the status of, and prospects for, real-time data processing for collider experiments in experimental High Energy Physics. We discuss the historical evolution of data rates and volumes in the field and place them in the context of…

We present a new application and covering number bound for the framework of "Machine Learning with Operational Costs (MLOC)," which is an exploratory form of decision theory. The MLOC framework incorporates knowledge about how a predictive…

In this work, we provide a survey of active learning (AL) for its applications in natural language processing (NLP). In addition to a fine-grained categorization of query strategies, we also investigate several other important aspects of…

A next-to-leading order (NLO) analysis of hadron-jet momentum correlations in p-p collisions at the LHC is carried out. We show that the inclusive charged hadron momentum distributions inside jets is a very sensitive observable which allows…

In the present work we consider the assignment of the factorization and renormalization scales in hadron collider processes with associated jet production, at next-to-leading order (NLO) in perturbation theory. We propose a simple, definite…