相关论文: Extrapolating Jet Radiation with Autoregressive Tr…

Generative models are trained with the simple objective of imitating the conditional probability distribution induced by the data they are trained on. Therefore, when trained on data generated by humans, we may not expect the artificial…

A simple look at the steady high-energy Universe reveals a clear correlation with outflows generated around compact objects (winds and jets). In the case of relativistic jets, they are thought to be produced as a consequence of the…

Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learning (ML) efforts at building physics event generators. We…

Two types of penetrating probes, direct photon and QCD jets, are investigated in the background of a small and rapidly expanding droplet of quark-gluon plasma. The additional thermal electromagnetic radiation results in a $\sim$50\%…

We introduce a novel variational autoencoder (VAE) architecture that can generate realistic and diverse high energy physics events. The model we propose utilizes several techniques from VAE literature in order to simulate high fidelity jet…

In this article we combine a recently proposed method for factorisation-aware matrix element surrogates with an unbiased unweighting algorithm. We show that employing a sophisticated neural network emulation of QCD multijet matrix elements…

Jet shapes have the potential to play a role in many LHC analyses, for example in quark-gluon discrimination or jet substructure analyses for hadronic decays of boosted heavy objects. Most shapes, however, are significantly affected by…

A significant challenge in the tagging of boosted objects via machine-learning technology is the prohibitive computational cost associated with training sophisticated models. Nevertheless, the universality of QCD suggests that a large…

We extend the re-simulation-based self-supervised learning approach to learning representations of hadronic jets in colliders by exploiting the Markov property of the standard simulation chain. Instead of masking, cropping, or other forms…

At the extreme energies of the Large Hadron Collider, massive particles can be produced at such high velocities that their hadronic decays are collimated and the resulting jets overlap. Deducing whether the substructure of an observed jet…

Using machine learning, we explore the utility of various deep neural networks (NN) when applied to high harmonic generation (HHG) scenarios. First, we train the NNs to predict the time-dependent dipole and spectra of HHG emission from…

A key challenge in metasurface design is the development of algorithms that can effectively and efficiently produce high performance devices. Design methods based on iterative optimization can push the performance limits of metasurfaces,…

We perform the numerical simulations of axisymmetric, relativistic, optically thin jets under the influence of the radiation field of an accretion disk. We show that starting from a very low injection velocity at the base, jets can be…

We consider the problem of training generative models with deep neural networks as generators, i.e. to map latent codes to data points. Whereas the dominant paradigm combines simple priors over codes with complex deterministic models, we…

The event-by-event analysis of multiparticle production in high energy hadron and nuclei collisions can be performed using the discrete wavelet transformation. The ring-like and jet-like structures in two-dimensional angular histograms are…

We compute the next-to-leading order corrections in $\alpha_s$ to prompt diphoton production in association with a jet at hadron colliders. We use a next-to-leading order general-purpose partonic Monte Carlo event generator that allows the…

We discuss the theory of jet events in high-energy photon-proton interactions using a model which gives a good description of the data available on total inelastic $\gamma p$ cross sections up to $\sqrt{s}$=210 GeV. We show how to calculate…

Jet substructure provides one of the most exciting new approaches for searching for physics in and beyond the Standard Model at the Large Hadron Collider. Modern jet substructure searches are often performed with Neural Network (NN) taggers…

We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets…

We propose a general approach for the description of multijet events in the framework of QCD event generators. We introduce a new algorithm to match parton showers and arbitrary matrix elements for the production of any number of jets via…