Related papers: Reconstructing Sparticle masses at the LHC using G…
We examine resonant slepton production at the LHC with gravitinos in the final state. The slepton undergoes gauge decay into a neutralino and a lepton, the neutralino decays into a photon and a gravitino. By measuring the transverse masses…
Galaxy clusters are considered as excellent probes for cosmology. For that purpose, their mass needs to be measured and their structural properties needs to be understood. We propose a method for galaxy cluster mass reconstruction which…
This paper introduces a deep learning enabled generative sensing framework which integrates low-end sensors with computational intelligence to attain a high recognition accuracy on par with that attained with high-end sensors. The proposed…
Heavy neutrinos, a key prediction of many standard model extensions, remain some of the most searched-for objects at collider experiments. In this context, we revisit the premise that the gluon fusion production mechanism, $gg \to Z^*/h^*…
We develop a machine learning (ML) framework to populate large dark matter-only simulations with baryonic galaxies. Our ML framework takes input halo properties including halo mass, environment, spin, and recent growth history, and outputs…
A conditional latent-diffusion based framework for solving the electromagnetic inverse scattering problem associated with microwave imaging is introduced. This generative machine-learning model explicitly mirrors the non-uniqueness of the…
Magnetic resonance image (MRI) reconstruction is a severely ill-posed linear inverse task demanding time and resource intensive computations that can substantially trade off {\it accuracy} for {\it speed} in real-time imaging. In addition,…
We use MasterCode to perform a frequentist analysis of the constraints on a phenomenological MSSM model with 11 parameters, the pMSSM11, including constraints from ~ 36/fb of LHC data at 13 TeV and PICO, XENON1T and PandaX-II searches for…
We propose a new method for neutrino mass generation through a modified Higgs mechanism by introducing an additional tiny vacuum breaking to the charged Higgs field. With identical particle spectrum as standard electroweak model, this…
Gravitational wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template based matched filtering method. While this method is optimal for stationary and Gaussian data…
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance for fast calorimeter shower simulations with increasing phase space dimension.…
Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of a focusing lens and limited lighting in such environments often…
The purpose of this paper is to examine the model dependence of the stringent constraints on the gluino mass obtained from the Large Hadron Collider (LHC) experiments by analyzing the Run II data using specific simplified models based on…
We develop a generative neural network for the generation of sparse data in particle physics using a permutation-invariant and physics-informed loss function. The input dataset used in this study consists of the particle constituents of…
When several sparticle masses are known, the kinematics of SUSY decay processes observed at the LHC can be solved if the cascade decays contain sufficient steps. We demonstrate four examples of this full reconstruction technique applied to…
In the coming years, strong gravitational lens discoveries are expected to increase in frequency by two orders of magnitude. Lens-modelling techniques are being developed to prepare for the coming massive influx of new lens data, and blind…
Gravitational lensing is potentially able to observe mass-selected halos, and to measure the projected cluster mass function. An optimal mass-selection requires a quantitative understanding of the noise behavior in mass maps. This paper is…
While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We show how symbolic regression trained on matrix-element information provides, for…
Continuing work presented in Li et al. (2021), we performed a series of tests to our high-resolution three-dimensional mass map reconstruction algorithm \splinv{}. We test the mass reconstruction accuracy against realistic mock catalogs…
The use of machine learning algorithms is an attractive way to produce very fast detector simulations for scattering reactions that can otherwise be computationally expensive. Here we develop a factorised approach where we deal with each…