Related papers: From Optimal Observables to Machine Learning: an E…
A fermion can be chiral or vectorlike with respect to a given symmetry, depending on its coupling to the corresponding gauge boson. Vectorlike fermions have a distinct property that their left-handed and right-handed components behave in…
At future e+- e- linear colliders, the event rates and clean signals of scalar fermion production - in particular for the scalar leptons - allow very precise measurements of their masses and couplings and the determination of their quantum…
The use of effective field theory offers a systematic way to improve calculations of nuclear reactions and the properties of atomic nuclei. Its successes have led to the widespread belief that the predictions of this approach are model…
Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of…
The photon flux resulting from high-energy electron beam interactions with high field systems, such as in the upcoming FACET-II experiments at SLAC National Accelerator Laboratory, may give deep insight into the electron beam's underlying…
The future linear collider will collide dense $e^+e^-$ bunches at high energies up to 1 TeV, generating very intense electromagnetic fields at the interaction point (IP). These fields are strong enough to lead to nonlinear effects which…
In the context of high-energy physics, a reliable description of the parton-level kinematics plays a crucial role for understanding the internal structure of hadrons and improving the precision of the calculations. Here, we study the…
Many machine learning tasks can be formulated in terms of predicting structured outputs. In frameworks such as the structured support vector machine (SVM-Struct) and the structured perceptron, discriminative functions are learned by…
Understanding the source of the universe's asymmetry between matter and antimatter is one of the major open questions in particle physics. In this work, the sensitivity of novel machine-learning-based inference techniques to CP-odd and…
Effective field theory provides a perturbative framework to study the evolution of cosmological large-scale structure. We investigate the underpinnings of this approach, and suggest new ways to compute correlation functions of cosmological…
Advanced machine learning models have recently achieved high predictive accuracy for weather and climate prediction. However, these complex models often lack inherent transparency and interpretability, acting as "black boxes" that impede…
We perform a model-independent analysis of the magnetic and electric dipole moments of the muon and electron. We give expressions for the dipole moments in terms of operator coefficients of the low-energy effective field theory (LEFT) and…
We study the sensitivity of the processes `e+ e- -> lepton (l) neutrino (v) quark (u) antiquark (d)', where the lepton is an electron or a muon, on the non-standard trilinear gauge couplings (TGC), using the optimal observables method at…
We study the sensitivity to the Z' couplings of the processes $e^+e^-\to l^+l^-,\bar{b}b$ and $\bar{c}c$ at the linear collider with $\sqrt{s}=500 GeV$ with initial beam polarization, for typical extended model examples. To this aim, we use…
We revisit the global fit to electroweak precision observables in the Standard Model and present model-independent bounds on several general new physics scenarios. We present a projection of the fit based on the expected experimental…
When a new heavy particle is discovered at the LHC or at a future high-energy collider, it will be interesting to study its decays into Standard Model particles using an effective field-theory framework. We point out that the proper…
We develop a \pi-electron effective field theory (\pi-EFT) wherein the two-body Hamiltonian for a \pi-electron system is expressed in terms of three effective parameters: the \pi-orbital quadrupole moment, the on-site repulsion, and a…
Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables…
Machine learning has emerged as a significant approach to efficiently tackle electronic structure problems. Despite its potential, there is less guarantee for the model to generalize to unseen data that hinders its application in real-world…
To address the computational challenges of ab initio molecular dynamics and the accuracy limitations of empirical force fields, the introduction of machine learning force fields has proven effective in various systems including metals and…