相关论文: Jet energy calibration with deep learning as a Kub…
The Large Hadron Collider (LHC) experiment has successfully completed data taking at center of mass (COM) energy 7 TeV in 2011 and very recently for 8 TeV. Measurement of cross sections predicted by the standard model were the main tasks in…
Predictive and real-time inference capability for the upstream separatrix electron density, $n_\text{e, sep}$, is essential for design and control of core-edge integrated plasma scenarios. In this study, both supervised and semi-supervised…
Fatigue life prediction is essential in both the design and operational phases of any aircraft, and in this sense safety in the aerospace industry requires early detection of fatigue cracks to prevent in-flight failures. Robust and precise…
Energy correlators have recently attracted significant attention in the study of heavy ion collisions due to their potential to robustly connect experimental measurements with an underlying quantum field theoretic description. While…
Jet substructure observables, designed to identify specific features within jets, play an essential role at the Large Hadron Collider (LHC), both for searching for signals beyond the Standard Model and for testing QCD in extreme phase space…
Clustering is one of the most frequent problems in many domains, in particular, in particle physics where jet reconstruction is central in experimental analyses. Jet clustering at the CERN's Large Hadron Collider (LHC) is computationally…
Machine-learning assisted jet substructure tagging techniques have the potential to significantly improve searches for new particles and Standard Model measurements in hadronic final states. Techniques with simple analytic forms are…
Recently, machine learning Hamiltonian (MLH) models have gained traction as fast approximations of electronic structures such as orbitals and electron densities, while also enabling direct evaluation of energies and forces from their…
Jet classification in high-energy particle physics is important for understanding fundamental interactions and probing phenomena beyond the Standard Model. Jets originate from the fragmentation and hadronization of quarks and gluons, and…
In this paper, we propose a new end-to-end methodology to optimize the energy performance and the comfort, air quality and hygiene of large buildings. A metamodel based on a Transformer network is introduced and trained using a dataset…
Pions constitute nearly $70\%$ of final state particles in ultra high energy collisions. They act as a probe to understand the statistical properties of Quantum Chromodynamics (QCD) matter i.e. Quark Gluon Plasma (QGP) created in such…
Jets of high energy collimated particles provide a rich phenomenology to study quantum chromodynamics, from first-principles tests of perturbative calculations to investigations of the emergent properties of the strongly-coupled quark-gluon…
We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It…
"photon+Jet" events, based on the q~q-> g+photon and qg-> q+photon subprocesses, are proposed for jet energy scale setting and hadron calorimeter calibration at LHC energies. General features and selection criteria of "photon+Jet" events…
I explore many aspects of jet substructure at the Large Hadron Collider, ranging from theoretical techniques for jet calculations, to phenomenological tools for better searches with jets, to software for implementing and comparing such…
Jet substructure observable basis is a systematic and powerful tool for analyzing the internal energy distribution of constituent particles within a jet. In this work, we propose a novel method to insert neural networks into jet…
The Higgs boson decay modes to $b$ and $c$ quarks are crucial for many Higgs precision measurements. The presence of semileptonic decays in the jets originating from $b$ and $c$ quarks causes missing energy due to the undetectable…
Energetic jets that traverse the quark-gluon plasma created in heavy-ion collisions serve as excellent probes to study this new state of deconfined QCD matter. Presently, however, our ability to achieve a crisp theoretical interpretation of…
We perform a comparison of two jet clusterization algorithms. The first one is the standard Durham algorithm and the second one is a global optimization scheme, Deterministic Annealing, often used in clusterization problems, and adapted to…
Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learning seismology have been used to improve tasks within…