Related papers: Reconstructing Sparticle masses at the LHC using G…
Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models…
Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired…
The catalog of gravitational-wave events is growing, and so are our hopes of constraining the underlying astrophysics of stellar-mass black-hole mergers by inferring the distributions of, e.g., masses and spins. While conventional analyses…
We present a generalization of our recently proposed machine learning framework, aiming to provide new physical insights into dark matter halo formation. We investigate the impact of the initial density and tidal shear fields on the…
A huge swath of parameter space in the context of the Minimal Supersymmetric Standard Model (MSSM) has been ruled at after run I of the LHC. Various exclusion contours in the $m_{\tilde{g}}-\m_{\tilde{\chi}_{1}^{0}}$ plane were derived by…
In this study, a deep learning-based approach is applied with the aim of reconstructing high-resolution turbulent flow fields using minimal flow fields data. A multi-scale enhanced super-resolution generative adversarial network with a…
We extensively study how to combine Generative Adversarial Networks and learned compression to obtain a state-of-the-art generative lossy compression system. In particular, we investigate normalization layers, generator and discriminator…
A model based on a $U(1)_{T^3_R}$ extension of the Standard Model can address the mass hierarchy between generations of fermions, explain thermal dark matter abundance, and the muon $g - 2$, $R_{(D)}$, and $R_{(D^*)}$ anomalies. The model…
Deep generative models such as diffusion and flow matching are powerful machine learning tools capable of learning and sampling from high-dimensional distributions. They are particularly useful when the training data appears to be…
The image distortions of high-redshift galaxies caused by gravitational light deflection of foreground clusters of galaxies can be used to reconstruct the two-dimensional surface mass density of these clusters. We apply an unbiased…
We analyze the prospects for resonant di-Higgs production searches at the LHC in the $b\bar{b} W^+ W^-$ ($W^{+} \to \ell^{+} \nu_{\ell}$, $W^{-} \to \ell^{-} \bar{\nu}_{\ell}$) channel, as a probe of the nature of the electroweak phase…
In the search for exotic events involving displaced particles at HL-LHC, the triggering at the level-1 (L1) system will pose a significant challenge. This is particularly relevant in scenarios where low mass long-lived particles (LLPs) are…
We study the production of heavy neutrinos at the Large Hadron Collider (LHC) through the dominant s-channel production mode as well as the vector boson fusion (VBF) process. We consider the TeV scale minimal linear seesaw model containing…
Strong gravitational lensing is a powerful tool for probing the nature of dark matter, as lensing signals are sensitive to the dark matter substructure within the lensing galaxy. We present a comparative analysis of strong gravitational…
In Split-Supersymmetry models, where the only non-Standard Model states produceable at LHC-energies consist of a gluino plus neutralinos and charginos, it is conventionally accepted that only mass differences among these latter are…
In this work we explore multiple search strategies for higgsinos and mixed higgsino-wino states in the MSSM and project the results onto the $(\mu,M_2)$ plane. Assuming associated production of higgsino-like pairs with a $W/Z$ boson, we…
We propose a new mass reconstruction technique for SUSY processes at the LHC. The idea is to completely solve the kinematics of the SUSY cascade decay by using the assumption that the selected events satisfy the same mass shell conditions…
For many analyses in cosmology it is necessary to reconstruct the likely distribution of unobserved fields, such as dark matter or non-luminous baryons, from observed luminous tracers. The dominant approach in cosmology has been to use the…
We present a hybrid machine learning framework that combines Physics-Informed Neural Operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive…
The measurement of sparticle masses in the Minimal Supersymmetric Standard Model at the LHC is analysed, in the scenario where the lightest neutralino decays into three quarks. Such decays, occurring through the baryon-number violating…