Related papers: How Invisible: Regressing The Key Model Parameter …
Identifying jets originating from bottom quarks is vital in collider experiments for new physics searches. This paper proposes a novel approach based on Retentive Networks (RetNet) for b-jet tagging using low-level features of jet…
Tiny object detection is one of the key challenges in the field of object detection. The performance of most generic detectors dramatically decreases in tiny object detection tasks. The main challenge lies in extracting effective features…
The present work deals with the late time evolution of the linear density contrast in the dark energy models reconstructed from the jerk parameter. It is found that the non-interacting models are favoured compared to the models where an…
A search is performed for narrow resonances decaying to final states of two jets, with at least one jet originating from a b quark, in proton-proton collisions at $\sqrt{s}$ = 13 TeV. The data set corresponds to an integrated luminosity of…
We investigate the consequences of models where dark sector quarks could be produced at the LHC, which subsequently undergo a dark parton shower, generating jets of dark hadrons that ultimately decay back to Standard Model hadrons. This…
In this work, we propose a deep learning-based method to perform semiparametric regression analysis for spatially dependent data. To be specific, we use a sparsely connected deep neural network with rectified linear unit (ReLU) activation…
A strongly interacting dark sector can give rise to a class of signatures dubbed dark showers, where in analogy to the strong sector in the Standard Model, the dark sector undergoes its own showering and hadronization, before decaying into…
We examine the singlet-doublet fermionic dark matter model, where the non-thermal production of the dark matter in light of a non-standard cosmology demands a significantly large interaction rate than the typical radiation-dominated…
With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to…
Support vector machine modeling is a new approach in machine learning for classification showing good performance on forecasting problems of small samples and high dimensions. Later, it promoted to Support Vector Regression (SVR) for…
We present a set of Lorentz invariant kinematic variables for reconstructing mass of semi-invisible decaying particles pair-produced at lepton colliders, $m_{\rm RC}^{\rm min}$, $m_{\rm RC}^{\rm max}$ and $m_{\rm LSP}^{\rm max}$, with…
Searches for dark photons provide serendipitous discovery potential for other types of vector particles. We develop a framework for recasting dark photon searches to obtain constraints on more general theories, which includes a data-driven…
We describe an approach to detect dark matter and other invisible particles with mass below a GeV, exploiting missing energy-momentum measurements and other kinematic features of fixed-target production. In the case of an invisibly decaying…
The scotogenic model is a well motivated scenario that provides both an explanation for neutrino masses and for dark matter. We focus on a real scalar dark matter candidate in this model, produced through standard thermal freeze-out. We…
Parameter reduction can enable otherwise infeasible design and uncertainty studies with modern computational science models that contain several input parameters. In statistical regression, techniques for sufficient dimension reduction…
A search is presented for new particles produced at the LHC in proton-proton collisions at $\sqrt{s} =$ 13 TeV, using events with energetic jets and large missing transverse momentum. The analysis is based on a data sample corresponding to…
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…
Simplified dark matter models have been recently advocated as a powerful tool to exploit the complementarity between dark matter direct detection, indirect detection and LHC experimental probes. Focusing on pseudoscalar mediators between…
In this work, we use a recast of the Run II search for invisible Higgs decays within Vector Boson Fusion to constrain the parameter space of the Inert Doublet model, a two Higgs doublet model with a dark matter candidate. When including all…
In this paper an identification method for state-space LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using…