Related papers: Reconstructing parton collisions with machine lear…
Machine learning has been applied to several problems in particle physics research, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event…
We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. Our collision detector is represented as a bilevel deep autoencoder with an attention mechanism that identifies colliding mesh sub-parts. We use…
We use a Monte Carlo approach to study hadron azimuthal angular correlations in high energy proton-proton and central nucleus-nucleus collisions at the BNL Relativistic Heavy Ion Collider (RHIC) energies at mid-rapidity. We build a hadron…
We explore the potential to use machine learning methods to search for heavy neutrinos, from their hadronic final states including a fat-jet signal, via the processes $pp \rightarrow W^{\pm *}\rightarrow \mu^{\pm} N \rightarrow \mu^{\pm}…
Machine learning plays a crucial role in enhancing and accelerating the search for new fundamental physics. We review the state of machine learning methods and applications for new physics searches in the context of terrestrial high energy…
We study the efficiency of a neural-net filter and deconvolution method for estimating jet energies and spectra in high-background reactions such as nuclear collisions at the relativistic heavy-ion collider and the large hadron collider.…
Hinging on ideas from physical-layer network coding, some promising proposals of coded random access systems seek to improve system performance (while preserving low complexity) by means of packet repetitions and decoding of linear…
A transport model based on the mean free path approach to describe pp collisions is proposed. We assume that hadrons can be treated as bags of partons similarly to the MIT bag model. When the energy density in the collision is higher than a…
To precisely measure jets over a large background such as pile up in high luminosity p+p collisions at LHC, a new generation of jet reconstruction algorithms is developed. These algorithms are also applicable to reconstruct jets in the…
Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of…
The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) will produce particle collisions with up to 200 simultaneous proton-proton interactions. These unprecedented conditions will create a combinatorial complexity for…
Collider processes with identified hadrons in the final state are widely studied in view of determining details of the proton structure and of understanding hadronization. Their theory description requires the introduction of fragmentation…
We present an implementation of an explainable and physics-aware machine learning model capable of inferring the underlying physics of high-energy particle collisions using the information encoded in the energy-momentum four-vectors of the…
The parton recombination model has turned out to be a valuable tool to describe hadronization in high energy heavy ion collisions. I review the model and revisit recent progress in our understanding of hadron correlations. I also discuss…
Non-linear effects in hadronic interactions are treated by means of enhanced pomeron diagrams, assuming that pomeron-pomeron coupling is dominated by soft partonic processes. It is shown that the approach allows to resolve a seeming…
We show that machine learning methods produce superior particle position reconstruction accuracy in scintillation-based detectors.
High-multiplicity signatures at particle colliders can arise in Standard Model processes and beyond. With such signatures, difficulties often arise from the large dimensionality of the kinematic space. For final states containing a single…
Reconstructing unstable heavy particles requires sophisticated techniques to sift through the large number of possible permutations for assignment of detector objects to the underlying partons. Anapproach based on a generalized attention…
Track reconstruction is a vital aspect of High-Energy Physics (HEP) and plays a critical role in major experiments. In this study, we delve into unexplored avenues for particle track reconstruction and hit clustering. Firstly, we enhance…
Photon-photon collisions are investigated in the framework of the two-component Dual Parton Model. The model is shown to agree well to hadron production data from hadron-hadron and photon-hadron collisions. The multiparticle production in…