Related papers: Reconstructing Sparticle masses at the LHC using G…
We consider the collider phenomenology of split-supersymmetry models. We show that despite the challenging nature of the signals in these models the long-lived gluino can be discovered with masses in excess of 2 TeV at the LHC. At a future…
Machine learning (ML) can facilitate efficient thermoelectric (TE) material discovery essential to address the environmental crisis. However, ML models often suffer from poor experimental generalizability despite high metrics. This study…
The reconstruction of 3D microstructures from 2D slices is considered to hold significant value in predicting the spatial structure and physical properties of materials.The dimensional extension from 2D to 3D is viewed as a highly…
Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a GEANT-based…
Within the minimal supersymmetric extension of the Standard Model, the best fit to the most recent precision-measurement data requires charginos and neutralinos, with dominant Higgsino components and with masses within the reach of LEP1.5…
In this work we consider the HL-LHC discovery potential in the 3 inverse ab data set for gluinos in the gluino-weakino associated production channel. We propose a search in the jets plus missing energy channel which exploits kinematic edge…
We present a machine learning method for the reconstruction of the undistorted images of background sources in strongly lensed systems. This method treats the source as a pixelated image and utilizes the Recurrent Inference Machine (RIM) to…
Unsegmented, large-volume liquid scintillator (LS) neutrino detectors have proven to be a key technology for low-energy neutrino physics. The efficient rejection of radionuclide background induced by cosmic muon interactions is of paramount…
In this work, by using the machine learning methods, we study the sensitivities of heavy pseudo-Dirac neutrino $N$ in the inverse seesaw at the high-energy hadron colliders. The production process for the signal is $pp \to \ell N \to 3 \ell…
Deep learning can be used to drastically decrease the processing time of parameter estimation for coalescing binaries of compact objects including black holes and neutron stars detected in gravitational waves (GWs). As a first step, we…
The maximum-entropy method is applied to the problem of reconstructing the projected mass density of a galaxy cluster using its gravitational lensing effects on background galaxies. We demonstrate the method by reconstructing the mass…
The lightest neutralino, assumed to be the lightest supersymmetric particle, is proposed to be a dark matter (DM) candidate for the mass $\cal{O}$(100) GeV. Constraints from various direct dark matter detection experiments and Planck…
We consider the approximation of functions by 2-layer neural networks with a small number of hidden weights based on the squared loss and small datasets. Due to the highly non-convex energy landscape, gradient-based training often suffers…
Latent diffusion models (LDMs) dominate high-quality image generation, yet integrating representation learning with generative modeling remains a challenge. We introduce a novel generative image modeling framework that seamlessly bridges…
This paper presents a novel method for the reconstruction of interaction vertices in particle collision data. The algorithm is an agglomerative clustering technique designed for high-luminosity environments in current and future…
Particles at the TeV scale with lifetimes of a year or longer could have been abundantly produced at the LHC yet escaped detection because of backgrounds, and could still be trapped within detector materials. With gluinos in…
In R-parity violating supersymmetric models both leptogenesis and the correct neutrino masses are hard to achieve together. The presence of certain soft nonholomorphic R-parity violating terms helps to resolve this problem. We consider a…
Machine unlearning has emerged as a new paradigm to deliberately forget data samples from a given model in order to adhere to stringent regulations. However, existing machine unlearning methods have been primarily focused on classification…
We consider machine learning techniques associated with the application of a Boosted Decision Tree (BDT) to searches at the Large Hadron Collider (LHC) for pair-produced lepton partners which decay to leptons and invisible particles. This…
Two analyses searching for the production of supersymmetric particles through the electroweak interaction are presented: the chargino search, targeting the pair production of charginos decaying into W bosons and neutralinos, and the…