Related papers: Multimodal Generative Flows for LHC Jets
Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schr\"odinger…
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the…
In many interfacial flow systems, variations of surface properties lead to novel and interesting behaviors. In this work a three-dimensional model of flow dynamics for multicomponent vesicles is presented. The surface composition is modeled…
We train a generative diffusion model (DM) to simulate ultra-relativistic heavy-ion collisions from end to end. The model takes initial entropy density profiles as input and produces two-dimensional final particle spectra, successfully…
We review results on hadron multiplicities in high energy particle collisions. Both theory and experiment are discussed. The general procedures used to describe particle multiplicity in Quantum Chromodynamics (QCD) are summarized. The QCD…
This paper focuses on a novel generative approach for 3D point clouds that makes use of invertible flow-based models. The main idea of the method is to treat a point cloud as a probability density in 3D space that is modeled using a…
The rapid deployment of computing hardware different from the traditional CPU+RAM model in data centers around the world mandates a change in the design of event generators for the Large Hadron Collider, in order to provide economically and…
We present data-driven methods for the full reconstruction of jets in heavy ion collisions, for inclusive and co-incidence jet measurements at both RHIC and LHC. The complex structure of heavy ion events generates a large background of…
Generative networks are an exciting tool for fast LHC event fixed number of particles. Autoregressive transformers allow us to generate events containing variable numbers of particles, very much in line with the physics of QCD jet…
High-energy collisions at the Large Hadron Collider (LHC) provide valuable insights into open questions in particle physics. However, detector effects must be corrected before measurements can be compared to certain theoretical predictions…
The quark gluon plasma (QGP) is one of the most interesting forms of matter providing us with insight on quantum chromodynamics (QCD) and the early universe. It is believed that the heavy-ion collision experiments at the Relativistic Heavy…
The simple method for simulation of ``thermal'' hadron spectra in ultrarelativistic heavy ion collisions including longitudinal, transverse and elliptic flow is developed. The model is realized as fast Monte-Carlo event generator.
In this note a study of hadronic event shapes in QCD events at the Large Hadron Collider (LHC) is presented. Calorimetric jet momenta, determined by various jet clustering algorithms, are used as input for calculating various event-shape…
Generative models have demonstrated remarkable success in domains such as text, image, and video synthesis. In this work, we explore the application of generative models to fluid dynamics, specifically for turbulence simulation, where…
In nucleus--nucleus collisions, high-pT partons interact with a dense medium, which possesses strong collective flow components. Here, we demonstrate that the resulting medium-induced gluon radiation does not depend solely on the energy…
We study how to use Deep Variational Autoencoders for a fast simulation of jets of particles at the LHC. We represent jets as a list of constituents, characterized by their momenta. Starting from a simulation of the jet before detector…
We developed the full magnetohydrodynamical analytical jet model that allows accurate reproducing of a transversal and longitudinal structure for a highly collimated relativistic jets. This model can be used as a setup for convenient…
A key question for machine learning approaches in particle physics is how to best represent and learn from collider events. As an event is intrinsically a variable-length unordered set of particles, we build upon recent machine learning…
We explore the metric space of jets using public collider data from the CMS experiment. Starting from 2.3/fb of 7 TeV proton-proton collisions collected at the Large Hadron Collider in 2011, we isolate a sample of 1,690,984 central jets…
Large transverse momentum jets provide unique tools to study dense QCD matter in high-energy heavy-ion collisions. Results from RHIC on suppression of high transverse momentum particles in Au+Au collisions indicate a significant energy loss…