Related papers: Autoencoders for Real-Time SUEP Detection
Anomaly detection relies on designing a score to determine whether a particular event is uncharacteristic of a given background distribution. One way to define a score is to use autoencoders, which rely on the ability to reconstruct certain…
Selected recent quantum chromodynamics (QCD) measurements are reviewed for Fermilab Run II Tevatron proton-antiproton collisions studied by the Collider Detector at Fermilab (CDF) and DZero Collaborations at a centre-of-mass energy of…
Optimizing molecular design and discovering novel chemical structures to meet certain objectives, such as quantitative estimates of the drug-likeness score (QEDs), is NP-hard due to the vast combinatorial design space of discrete molecular…
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…
We consider the physics and collider phenomenology of quirks that transform nontrivially under QCD color, SU(2)_W as well as an SU(N)_{ic} infracolor group. Our main motivation is to show that the recent Wjj excess observed by CDF naturally…
Unsupervised anomaly-based intrusion detection requires models that can generalize to attack patterns not observed during training. This work presents the first large-scale evaluation of hybrid quantum-classical (HQC) autoencoders for this…
A search for electroweak production of supersymmetric particles in scenarios with compressed mass spectra in final states with two low-momentum leptons and missing transverse momentum is presented. This search uses proton-proton collision…
A complete next-to-leading order QCD calculation of deep inelastic electron-photon scattering including direct and resolved real photon components is presented. Soft and collinear singularities are extracted using the phase space slicing…
Searches for supersymmetry (SUSY) are performed using a sample of hadronic events produced in 8 TeV pp collisions at the CERN LHC. The searches are based on the MT2 variable, which is a measure of the transverse momentum imbalance in an…
Exotic dark matter together with the vacuum energy (associated with the cosmological constant) seem to dominate in the Universe. Thus its direct detection is central to particle physics and cosmology. Supersymmetry provides a natural dark…
Jet shapes have been measured in inclusive jet production in proton-proton collisions at sqrt(s) = 7 TeV using 3 pb^{-1} of data recorded by the ATLAS experiment at the LHC. Jets are reconstructed using the anti-kt algorithm with transverse…
Soft photons produced in heavy ion collisions are an important tool for probing the properties of the quark-gluon plasma. It is therefore crucial to understand the background - soft photons produced in elementary collisions. Low theorem…
Detecting anomalous regions in images is a frequently encountered problem in industrial monitoring. A relevant example is the analysis of tissues and other products that in normal conditions conform to a specific texture, while defects…
Understanding the properties of the quark-gluon plasma (QGP) that is produced in ultra-relativistic nucleus-nucleus collisions has been one of the top priorities of the heavy ion program at the LHC. Energetic jets are produced and…
As no evidence for classic WIMP-based signatures of dark matter have been found at the LHC, several phenomenological studies have raised the possibility of accessing a strongly-interacting dark sector through new collider-event topologies.…
A deep convolutional neural network (CNN) is developed to study symmetry energy $E_{\rm sym}(\rho)$ effects by learning the mapping between the symmetry energy and the two-dimensional (transverse momentum and rapidity) distributions of…
Autoencoders are an effective analysis tool for the LHC, as they represent one of its main goal of finding physics beyond the Standard Model. The key challenge is that out-of-distribution anomaly searches based on the compressibility of…
We present a machine learning-based anomaly detection strategy designed to identify anomalous physics in events containing resonant Standard Model physics and demonstrate this method on the final state of a Higgs boson decaying to two…
The dark matter may be a composite particle that is accessible via a weakly coupled portal. If these hidden-sector states are produced at the Large Hadron Collider (LHC), they would undergo a QCD-like shower. This would result in a spray of…
Anomaly detection is a prominent data preprocessing step in learning applications for correction and/or removal of faulty data. Automating this data type with the use of autoencoders could increase the quality of the dataset by isolating…