Related papers: Deep learning for the R-parity violating supersymm…
With this article we introduce recent, improved machine learning methods from computer vision to the problem of event classification in particle physics. Supersymmetric scalar top decays to top quarks and weak scale bino-like neutralinos,…
A search for R-parity-violating supersymmetry in final states with high jet multiplicity is presented. The search uses $140{\,\text{fb}^{-1}}$ of proton-proton collision data at $\sqrt{s} = 13{TeV}$ collected by the ATLAS experiment during…
Results of a search for gluino pair production with subsequent R-parity-violating decays to quarks are presented. This search uses 36.1 fb$^{-1}$ of data collected by the ATLAS detector in proton-proton collisions with a center-of-mass…
We report the largest scale deep learning with High Performance Computing (HPC) to physics analysis with the CMS simulation data in proton-proton collisions at 13 TeV. We build a Convolutional Neural Network (CNN) model that takes low-level…
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…
We use modern jet-substructure techniques to propose LHC searches for multijet-resonance signals without leptons or missing energy. We focus on three-jet resonances produced by R-parity-violating decays of boosted gluinos, showing that…
Artificial intelligence offers the potential to automate challenging data-processing tasks in collider physics. To establish its prospects, we explore to what extent deep learning with convolutional neural networks can discriminate quark…
Since the machine learning techniques are improving rapidly, it has been shown that the image recognition techniques in deep neural networks can be used to detect jet substructure. And it turns out that deep neural networks can match or…
We compare the performance of a convolutional neural network (CNN) trained on jet images with dense neural networks (DNNs) trained on n-subjettiness variables to study the distinguishing power of these two separate techniques applied to top…
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic…
Higgsino in supersymmetric standard models can play the role of dark matter particle. In conjunction with the naturalness criterion, the higgsino mass parameter is expected to be around the electroweak scale. In this work, we explore the…
Jet interactions with the color-deconfined QCD medium in relativistic heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to their baselines in proton-proton…
A search for Supersymmetry involving the pair production of gluinos decaying via third-generation squarks to the lightest neutralino is reported. It uses an LHC proton--proton dataset at a center-of-mass energy $\sqrt{s} = 13$ TeV with an…
A search is conducted for hadronic three-body decays of a new massive coloured particle in sqrt(s) = 7 TeV pp collisions at the LHC using an integrated luminosity of 4.6/fb collected by the ATLAS detector. Supersymmetric gluino pair…
By representing each collider event as a point cloud, we adopt the Graphic Convolutional Network (GCN) with focal loss to reconstruct the Higgs jet in it. This method provides higher Higgs tagging efficiency and better reconstruction…
Currently, newly developed artificial intelligence techniques, in particular convolutional neural networks, are being investigated for use in data-processing and classification of particle physics collider data. One such challenging task is…
Recent literature on deep neural networks for tagging of highly energetic jets resulting from top quark decays has focused on image based techniques or multivariate approaches using high-level jet substructure variables. Here, a sequential…
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not…
Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis using a spectral function on the angular scale. The…
A search for R-parity violating supersymmetry in final states characterised by high jet multiplicity, at least one isolated light lepton and either zero or at least three $b$-tagged jets is presented. The search uses 139 fb$^{-1}$ of…