Related papers: Machine Learning Electroweakino Production

In this work, we examine the sensitivity of monojet searches at the LHC to directly produced charginos and neutralinos (electroweakinos) in the limit of small mass splitting, where the traditional multilepton plus missing energy searches…

Weak scale supersymmetry is one of the best motivated and studied Standard Model extensions. It predicts the existence of new heavy coloured particles called squarks and gluinos which are the supersymmetric partners of the quarks and…

Two machine learning techniques for jet measurements at the LHCb experiment are presented: a regression-based method for jet-energy calibration and a deep neural network algorithm for jet flavour tagging, distinguishing between $b$-quark,…

We present a comparison of several Difference Image Analysis (DIA) techniques, in combination with Machine Learning (ML) algorithms, applied to the identification of optical transients associated with gravitational wave events. Each…

The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of…

We make a systematic and comparative study for the LHC and ILC for the electroweakino searches in the Minimal Supersymmetric Standard Model. We adopt a general bottom-up approach and scan over the parameter regions for all the three cases…

We study the signal of long-lived sterile neutrino at the LHC produced through the decay of the $W$ boson. It decays into charged lepton and jets. The characteristic signature is a hard prompt lepton and a lepton from the displaced decay of…

We investigate a WIMP dark matter (DM) candidate in the form of a singlino-dominated lightest supersymmetric particle (LSP) within the $Z_3$-symmetric Next-to-Minimal Supersymmetric Standard Model (NMSSM). This framework gives rise to…

Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile…

The rapid advancement of observational capabilities in astronomy has led to an exponential growth in the volume of light curve (LC) data, creating both opportunities and challenges for time-domain astronomy. Traditional analytical methods…

We present an approach to identifying and characterizing unresolved, very low mass spectral blend binaries composed of late-M, L, and T dwarfs using machine learning methodologies. We generated and evaluated a series of hierarchical random…

Jets plus missing transverse energy is one of the main search channels for new physics at the LHC. A major limitation lies in our understanding of QCD backgrounds. Using jet merging we can describe the number of jets in typical background…

Understanding excitonic effects in two-dimensional (2D) materials is critical for advancing their potential in next-generation electronic and photonic devices. In this study, we introduce a machine learning (ML)-based framework to predict…

Among the electroweakinos, the weak gauginos have the largest production rate at the LHC and should therefore be the primary focus of searches. In this work, we examine the LHC sensitivity to weak gauginos in light of the latest constraints…

In this thesis, we explore the phenomenology of scalar particles within Beyond Standard Model frameworks, using Machine Learning (ML) techniques to enhance sensitivity and discovery potential at current and future collider experiments, the…

Machine learning (ML) can process large sets of data generated from complex systems, which is ideal for classification tasks as often appeared in critical phenomena. Meanwhile ML techniques have been found effective in detecting critical…

Supersymmetric models with low electroweak finetuning are expected to be more prevalent on the string landscape than finetuned models. We assume a fertile patch of landscape vacua containing the minimal supersymmetric standard model (MSSM)…

In this work, we perform a comprehensive study of the machine learning (ML) methods for the purpose of characterising the quantum set of correlations. As our main focus is on assessing the usefulness and effectiveness of the ML approach, we…

Despite significant efforts, the realization of the hybrid quantum-classical algorithms has predominantly been confined to proof-of-principles, mainly due to the hardware noise. With fault-tolerant implementation being a long-term goal,…

Calorimeters operating in high-radiation environments are susceptible to damage, leading to increased noise that can significantly degrade energy resolution. A common way to mitigate noise is to apply a higher energy threshold on the cells,…