Related papers: Accelerating the BSM interpretation of LHC data wi…
\texttt{SpaceMath v.2.0} with Machine Learning is an extension of the previous version which we implement observables related with LHC Higgs boson data and their projections for the High Luminosity and High Energy Large Hadron Collider. In…
The potential of the LHeC, a future electron-proton collider, for precision Deep Inelastic Scattering measurements is reviewed with particular emphasis on the reduction of uncertainties on the parton distribution functions (PDFs) of the…
One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies…
The Run 2 data taking period of the Large Hadron Collider (LHC) at CERN in years 2015-2018 has presented a great opportunity to search for physics beyond the standard model (BSM). It will be followed by the Run 3 period starting in 2022,…
Anomalies refer to data points or events that deviate from normal and homogeneous events, which can include fraudulent activities, network infiltrations, equipment malfunctions, process changes, or other significant but infrequent events.…
Since the top quark FCNC processes are extremely supressed in the Standard Model (SM) but could be greatly enhanced in some new physics models, they could serve as a smoking gun for new physics hunting at the LHC. In this brief review we…
Naturalness arguments suggest that the stop sector is within reach of the Large Hadron Collider (LHC). We investigate how the observation of a third generation squark signal could predict masses and discovery modes of other supersymmetric…
Search for the bottom squarks (sbottoms) at the Large Hadron Collider (LHC) has recently assumed a heightened focus in the hunt for Supersymmetry (SUSY). The popular framework of the Next-to-Minimal Supersymmetric Standard Model (NMSSM)…
This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory…
We conduct a detailed exploration of charged Higgs boson masses $M_{H^{\pm}}$ within the range of $100-190~GeV$. This investigation is grounded in the benchmark points that comply with experimental constraints, allowing us to systematically…
Anomaly detection is a key application of machine learning, but is generally focused on the detection of outlying samples in the low probability density regions of data. Here we instead present and motivate a method for unsupervised…
The Standard Model (SM) is one of the most successful theories ever conceived, representing a triumph of human intellect and collaboration. The SM provides a very good description of visible matter, with the proton being a central part of…
Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or…
We perform a comprehensive global analysis in the Minimal Supersymmetric Standard Model (MSSM) as well as in the 2-Higgs Doublet Model (2HDM) of the production and decay mechanisms of charged Higgs bosons $(H^\pm)$ at the Large Hadron…
Video anomaly detection is a challenging task because most anomalies are scarce and non-deterministic. Many approaches investigate the reconstruction difference between normal and abnormal patterns, but neglect that anomalies do not…
This paper addresses the dual challenge of improving anomaly detection and signal integrity in high-speed dynamic random access memory signals. To achieve this, we propose a joint training framework that integrates an autoencoder with a…
Extrapolations of sensitivity to new interactions and standard model parameters critically inform the programme at the Large Hadron Collider (LHC) and potential future collider cases. To this end, statistical considerations based on…
Popular Hough Transform-based object detection approaches usually construct an appearance codebook by clustering local image features. However, how to choose appropriate values for the parameters used in the clustering step remains an open…
Detecting Beyond Standard Model (BSM) signals in high-energy particle collisions presents significant challenges due to complex data and the need to differentiate rare signal events from Standard Model (SM) backgrounds. This study…
We examine production and decay of heavy neutral SUSY Higgs bosons pp-> H,\ A -> \tau\bar{\tau} within the hMSSM and compare against a perhaps more plausible natural supersymmetry scenario dubbed m_h^{125}({\rm nat}) which allows for a…