Related papers: Optimal Observables for the Chiral Magnetic Effect…

The complexity of linear mixed-effects (LME) models means that traditional diagnostics are rendered less effective. This is due to a breakdown of asymptotic results, boundary issues, and visible patterns in residual plots that are…

We provide a phenomenological analysis of present experimental searches for local parity violation manifested through the Chiral Magnetic Effect. We introduce and discuss the relevant correlation functions used for the measurements. Our…

Charge separation ($\Delta S$) measurements, obtained relative to the $2^{\rm{nd}}$-order ($\Psi_2$) and $3^{\rm{rd}}$-order ($\Psi_3$) event planes with a new charge-sensitive correlator ${R_{\Psi_m}(\Delta S)}$, are presented for…

The energy dependence of the local ${\cal P}$ and ${\cal CP}$ violation in Au+Au and Cu+Cu collisions in a large energy range is estimated within a simple phenomenological model. It is expected that at LHC the chiral magnetic effect will be…

Machine Learning (ML) plays an increasingly important role in the discovery and design of new materials. In this paper, we demonstrate the potential of ML for materials research using hard-magnetic phases as an illustrative case. We build…

Using two novel methods, pair invariant mass ($m_{inv}$) and comparative measurements with respect to reaction plane ($\Psi_{\rm RP}$) and participant plane ($\Psi_{\rm PP}$), we isolate the possible chiral magnetic effect (CME) from…

The scaling properties of the $R_{\Psi_2}(\Delta S)$ correlator and the $\Delta\gamma$ correlator are used to investigate a possible chiral-magnetically-driven (CME) charge separation in $p$+Au, $d$+Au, Ru+Ru, Zr+Zr, and Au+Au collisions at…

A novel technique using machine learning (ML) to reduce the computational cost of evaluating lattice quantum chromodynamics (QCD) observables is presented. The ML is trained on a subset of background gauge field configurations, called the…

We propose the measurement of net $\Lambda$ and $\bar{\Lambda}$ helicity, correlated event-by-event with the magnitude and sign of charge separation along the event's magnetic field direction, as a probe to investigate the Chiral Magnetic…

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine…

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this…

First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…

We investigate a bias-corrected machine learning (ML) strategy for estimating traces of the inverse Dirac operator, $\text{Tr}\, M^{-n}$ ($n=1,2,3,4$), motivated by the need for higher-order cumulants of the chiral condensate near the…

Masked image modeling (MIM) has achieved promising results on various vision tasks. However, the limited discriminability of learned representation manifests there is still plenty to go for making a stronger vision learner. Towards this…

Optimal observables provide statistically powerful probes of small deformations from a reference theory, but in realistic collider measurements they are rarely available in compact analytic form. We show that interpretable event-level…

The chiral magnetic effect (CME) -- a macroscopic manifestation of the quantum chiral anomaly -- induces currents along magnetic field lines, facilitating mutual conversion between chiral asymmetry and magnetic helicity. Although the finite…

A series of new flow observables mixed harmonic multi-particle cumulants (MHC), which allow for the first time to quantify the correlations strength between different order of flow coefficients with various moments, was investigated using…

Causal learning is the key to obtaining stable predictions and answering \textit{what if} problems in decision-makings. In causal learning, it is central to seek methods to estimate the average treatment effect (ATE) from observational…

Data analyses in particle physics rely on an accurate simulation of particle collisions and a detailed simulation of detector effects to extract physics knowledge from the recorded data. Event generators together with a GEANT-based…

Purpose: We address the challenge of inaccurate parameter estimation in diffusion MRI when the signal-to-noise ratio (SNR) is very low, as in the spinal cord. The accuracy of conventional maximum-likelihood estimation (MLE) depends highly…