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Uncertainty quantification (UQ) to detect samples with large expected errors (outliers) is applied to reactive molecular potential energy surfaces (PESs). Three methods - Ensembles, Deep Evidential Regression (DER), and Gaussian Mixture…

Deep learning has been shown to be highly effective for automatic modulation classification (AMC), which is a pivotal technology for next-generation cognitive communications. Yet, existing deep learning methods for AMC often lack robust…

We have studied Electroweak Symmetry Breaking (EWSB) fine-tuning in the context of two unified Supersymmetry scenarios: the Constrained Minimal Supersymmetric Model (CMSSM) and models with Non-Universal Higgs Masses (NUHM), in light of…

We investigate an extension of the Standard Model (SM) with two candidates for dark matter (DM). One of them is a real scalar field and the other is an Abelian gauge field. Except for these two, there is another beyond SM field which has…

The hybrid neural differentiable models mark a significant advancement in the field of scientific machine learning. These models, integrating numerical representations of known physics into deep neural networks, offer enhanced predictive…

Machine learning interatomic potentials (MLIPs) have been widely used to facilitate large-scale molecular simulations with accuracy comparable to ab initio methods. In practice, MLIP-based molecular simulations often encounter the issue of…

Current methods commonly used for uncertainty quantification (UQ) in deep learning (DL) models utilize Bayesian methods which are computationally expensive and time-consuming. In this paper, we provide a detailed study of UQ based on…

We show that "accidental" supersymmetry is a beyond-the-Standard Model framework that naturally accommodates a thermal relic dark matter candidate and successful electroweak baryogenesis, including the needed strongly first-order character…

Uncertainty-aware deep learning (DL) models recently gained attention in fault diagnosis as a way to promote the reliable detection of faults when out-of-distribution (OOD) data arise from unseen faults (epistemic uncertainty) or the…

The search for physics beyond the Standard Model (BSM) at collider experiments requires model-independent strategies to avoid missing possible discoveries of unexpected signals. Anomaly detection (AD) techniques offer a promising approach…

The phase ordering kinetics of emergent orders in correlated electron systems is a fundamental topic in non-equilibrium physics, yet it remains largely unexplored. The intricate interplay between quasiparticles and emergent order-parameter…

We report the results of a study of electroweak parameters from observations of neutral-current $\nu$N deeply inelastic scattering in the CCFR detector at the FNAL Tevatron Quadrupole Triplet neutrino beam. An improved extraction of the…

We analyze an ensemble-based approach for uncertainty quantification (UQ) in atomistic neural networks. This method generates an epistemic uncertainty signal without requiring changes to the underlying multi-headed regression neural network…

We consider a model where neutrinos interact with weakly interacting massive particles (WIMPs) as a possible explanation for the production of NuTeV's anomalous dimuon events. This model naturally produces the features reported by NuTeV,…

The relationship between the degree of fine-tuning in Electroweak Symmetry Breaking (EWSB) and the discoverability of dark matter in current and next generation direct detection experiments is investigated in the context of two unified…

Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC). However, current experiments do not…

A wide array of deep-inelastic-scattering and hadron collider experiments have tested the predictions of the electroweak theory and measured its parameters, while also searching for new particles and processes. We summarise recent…

Electroweak precision observables are fundamentally important for testing the standard model (SM) or its extensions. The influences to observables from new physics within the electroweak sector can be expressed in terms of oblique…

Extensions of the Standard Model (SM) of Particle Physics can be probed either through precision measurements of SM observables or via direct searches for processes beyond the SM (BSM). This proceeding focuses on precision measurements in…

The Exceptional Supersymmetric Standard Model (E6SSM) is an E6 inspired model with an extra gauged U(1) symmetry, which solves the mu-problem in a similar way to the NMSSM but without the accompanying problems of singlet tadpoles or domain…