相关论文: Optimal Observables for the Chiral Magnetic Effect…
Alignment between human brain networks and artificial models has become an active research area in vision science and machine learning. A widely adopted approach is identifying "metamers," stimuli physically different yet perceptually…
Machine-learning techniques have become fundamental in high-energy physics and, for new physics searches, it is crucial to know their performance in terms of experimental sensitivity, understood as the statistical significance of the…
Based on the chiral kinetic approach using quarks and antiquarks from a multiphase transport model as initial conditions, we study the chiral magnetic effect, i.e., the magnetic field induced separation of charged particles in the…
In this thesis, we study the Chiral Magnetic Effect (CME) and the Chiral Separation Effect (CSE) using lattice QCD simulations. We completely characterize the CSE in QCD using $2+1$ simulations of staggered quarks tuned at the physical…
We investigate the chiral magnetic effect (CME) under a strong magnetic field B = B_0 x_3 at low temperature T < T^chi_c. For this purpose, we employ the instanton vacuum configuration with the finite instanton-number fluctuation Delta,…
The estimation of causal effects with observational data continues to be a very active research area. In recent years, researchers have developed new frameworks which use machine learning to relax classical assumptions necessary for the…
Experimental searches for Chiral Magnetic Effect (CME) in heavy-ion collisions have been going on for a decade, and so far there is no conclusive evidence for its existence. Recently, the Signed Balance Function (SBF), based on the idea of…
Coronal mass ejections (CMEs) are the most geoeffective space weather phenomena, being associated with large geomagnetic storms, having the potential to cause disturbances to telecommunication, satellite network disruptions, power grid…
Under the approximate chiral symmetry restoration, quark interactions with topological gluon fields in quantum chromodynamics can induce chirality imbalance and parity violation in local domains. An electric charge separation ({\sc cs})…
In this paper Chiral Magnetic Effect (CME) in Dirac semimetals is studied by means of lattice Monte Carlo simulation. We measure conductivity of Dirac semimetals as a function of external magnetic field in parallel $\sigma_{\parallel}$ and…
In this paper the non-perturbative suppression of the Chiral Magnetic Effect (CME) is investigated in the deconfined region of the QCD phase diagram (as a model for Quark-Gluon Plasma (QGP) emerging in Heavy Ion Collisions (HIC)), using the…
In phenomenological preparation for new measurements one searches for the carriers of quality signatures. Often, the first approach quantities may be difficult to measure or to provide sufficiently precise predictions for comparisons.…
At the early stage of heavy ion collisions, non-trivial topologies of the gauge fields can be created resulting in an imbalance of axial charge density and eventually separation of electric charges along the direction of the magnetic field…
In this paper we investigate the chiral magnetic effect (CME): the generation of an electric current due to a homogeneous background magnetic field and a homogeneous chiral imbalance in QCD. We demonstrate that the leading coefficient…
The quark-gluon plasma (QGP) generated in relativistic heavy-ion collisions could be locally parity-odd. In parity-odd QGP, the electric field may induce a chiral current which is called the chiral electric separation effect (CESE). We…
Through analyzing the quark propagator under the magnetic field, we establish that the axial anomaly originates from an additional Dirac structure in quark propagator induced by the magnetic field. This Dirac structure also allows one to…
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…
Recent methods for learning unsupervised visual representations, dubbed contrastive learning, optimize the noise-contrastive estimation (NCE) bound on mutual information between two views of an image. NCE uses randomly sampled negative…
We introduce a technique to enhance the reliability of gravitational wave parameter estimation results produced by machine learning. We develop two independent machine learning models based on the Vision Transformer to estimate effective…
The system of light electroweakinos and heavy squarks gives rise to one of the most challenging signatures to detect at the LHC. It consists of missing transverse energy recoiled against a few hadronic jets originating either from QCD…