Related papers: Exploring SMEFT in VH with Machine Learning
Variational inference algorithms have proven successful for Bayesian analysis in large data settings, with recent advances using stochastic variational inference (SVI). However, such methods have largely been studied in independent or…
In this paper, we propose a new Hybrid Quantum Machine Learning (HyQML) framework to improve the sensitivity of double Higgs boson searches in the $HH \to b\bar{b}\gamma\gamma$ final state at $\sqrt{s}$ = 13.6 TeV. The proposed model…
We investigate the sensitivity of a future 10 TeV muon collider to dimension-6 operators in the Standard Model Effective Field Theory (SMEFT), focusing on Higgs and top quark production processes. The analysis includes two-fermion and…
The $2\rightarrow 3$ scattering of longitudinal vector bosons (VBS) has been proven to be a complementary channel to measure the Higgs self-couplings in the Standard Model (SM) and the SM effective field theory (SMEFT) at colliders. We…
Machine learning methods are widely used in the natural sciences to model and predict physical systems from observation data. Yet, they are often used as poorly understood "black boxes," disregarding existing mathematical structure and…
Once a Higgs boson has been discovered, it is also important to know what are its properties, in particular its couplings to the other particles. At the LHC, there will be many observable channels that can be used to measure the relevant…
Our current understanding of the structure and dynamics of aqueous interfaces at the molecular level has grown substantially in the last few decades due to the continuous development of surface-specific spectroscopies, such as vibrational…
We consider the Higgs sector in extensions of the Minimal Supersymmetric Standard Model by higher-dimension operators in the superpotential and the K\"ahler potential, in the context of Higgs searches at the LHC 7 TeV run. Such an effective…
Di-Higgs ($hh$) production is crucial for probing the Higgs boson self-interaction and understanding the electroweak phase transition. Deviations from Standard Model predictions in $hh$ gluon-gluon fusion (ggF) can be systematically…
In the context of the Standard Model effective field theory (SMEFT) the next-to-next-to-leading (NNLO) QCD corrections to the Higgsstrahlungs ($Vh$) processes in hadronic collisions are calculated and matched to a parton shower (PS).…
Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates. Our aim is to…
We develop a framework for indirect discovery in the Standard Model Effective Field Theory (SMEFT) based on Bayesian model selection over operator subsets. We argue that SMEFT should be understood as a structured space of competing…
This paper presents a systematic literature review focusing on the application of machine learning techniques for deriving observational constraints in cosmology. The goal is to evaluate and synthesize existing research to identify…
We develop, discuss, and compare several inference techniques to constrain theory parameters in collider experiments. By harnessing the latent-space structure of particle physics processes, we extract extra information from the simulator.…
We build upon recent work on using Machine Learning models to estimate Hamiltonian parameters using continuous weak measurement of qubits as input. We consider two settings for the training of our model: (1) supervised learning where the…
The article is aimed to address a mutually boosting use of asymptotic analysis and machine learning, for fast stiffness design of configurations infilled with smoothly-varying graded microstructures. The discussion is conducted in the…
With the Large Hadron Collider's Run 3 in progress, the 125 GeV Higgs boson couplings are being examined in greater detail, while searching for additional scalars. Multi-Higgs frameworks allow Higgs couplings to significantly deviate from…
Support Vector Machines (SVM), a popular machine learning technique, has been applied to a wide range of domains such as science, finance, and social networks for supervised learning. Whether it is identifying high-risk patients by…
The study of modern machine learning models often necessitates storing vast quantities of gradients or Hessian vector products (HVPs). Traditional sketching methods struggle to scale under these memory constraints. We present a novel…
Most metric learning algorithms, as well as Fisher's Discriminant Analysis (FDA), optimize some cost function of different measures of within-and between-class distances. On the other hand, Support Vector Machines(SVMs) and several Multiple…