Related papers: JetFlow: Generating Jets with Conditioned and Mass…
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…
High-dimensional generative modeling is fundamentally a manifold-learning problem: real data concentrate near a low-dimensional structure embedded in the ambient space. Effective generators must therefore balance support fidelity -- placing…
Generative modeling of high-energy collisions at the Large Hadron Collider (LHC) offers a data-driven route to simulations, anomaly detection, among other applications. A central challenge lies in the hybrid nature of particle-cloud data:…
Jet modification in heavy-ion collisions provides microscopic access to the properties of the quark-gluon plasma. However, conventional approaches based on traditional global observables, such as \(R_{AA}\), capture limited information…
We construct jet observables for energetic top quarks that can be used to determine a short distance top quark mass from reconstruction in e+ e- collisions with accuracy better than Lambda_{QCD}. Using a sequence of effective field theories…
Diffusion and flow matching approaches to generative modeling have shown promise in domains where the state space is continuous, such as image generation or protein folding & design, and discrete, exemplified by diffusion large language…
The production of jets is studied in collisions of virtual photons, gamma*-p and gamma*-gamma*, specifically for applications at HERA and LEP2. Photon flux factors are convoluted with matrix elements involving either direct or resolved…
The production of jets is studied in collisions of virtual photons, gamma*-p and gamma*-gamma*, specifically for applications at HERA and LEP2. Photon flux factors are convoluted with matrix elements involving either direct or resolved…
Recent observations as well as theoretical studies of YSO jets suggest the presence of two steady components: a disk wind type outflow needed to explain the observed high mass loss rates and a stellar wind type outflow probably accounting…
Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with quark gluon plasma. Modifications of the hard substructure of jets can be explored with…
Recent numerical simulations and analytical models of magnetically collimated plasma outflows from a uniformly rotating central gravitating object and/or a Keplerian accretion disk have shown that relatively low mass and magnetic fluxes…
Jets can become collimated as they propagate through dense environments and understanding such interactions is crucial for linking physical models of the environments to observations. In this work, we use 3D special-relativistic simulations…
In the particle-flow approach information from all available sub-detector systems is combined to reconstruct all stable particles. The global event reconstruction has been shown to improve, in particular, the resolution of jet energy and…
Wall-bounded turbulent flows are chaotic and multiscale, rendering real-time prediction at high Reynolds numbers computationally prohibitive in applications such as wind farms. Classical data assimilation methods are based on repeated…
We propose a unified model for jet formation applicable to active galactic nuclei, young stellar objects, and X-ray binaries. In this model, the binding energy released from the accretion disk is primarily stored as turbulence rather than…
In high-energy heavy-ion collisions, propagation of the energy deposited into the medium by energetic partons that traverse the quark-gluon plasma (QGP) leads to Mach-cone-like jet-induced medium response. Full simulations of such…
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of…
We present a novel integrator based on normalizing flows which can be used to improve the unweighting efficiency of Monte-Carlo event generators for collider physics simulations. In contrast to machine learning approaches based on surrogate…
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…
In this paper, we propose a simple yet effective method to represent point clouds as sets of samples drawn from a cloud-specific probability distribution. This interpretation matches intrinsic characteristics of point clouds: the number of…