Related papers: Harnessing data-driven methods for precise model i…
The application machine learning (ML) algorithms to turbulence modeling has shown promise over the last few years, but their application has been restricted to eddy viscosity based closure approaches. In this article we discuss rationale…
Anisotropic flow is accredited to have effects from the initial state geometry and fluctuations in the nuclear overlap region. The elliptic flow ($v_2$) and triangular flow ($v_3$) coefficients of the final state particles are expected to…
Event-by-event reconstruction of the collision geometry using some incarnation of the Glauber-model is a widely accepted method in studying heavy ion collisions. While there is no known problem with the procedure when applied to the…
Surface wettability, governed by both topography and chemistry, plays a critical role in applications such as heat transfer, lubrication, microfluidics, and surface coatings. In this study, we present a machine learning (ML) framework…
Event shape analysis has been used to look for DCC signals in simulated ultra-relativistic heavy-ion collision data at SPS energy. A simple redistribution of particles, with two detectors to detect charged particles and photons, is seen to…
Coupling hadronic kinetic theory models to fluid dynamics in phenomenological studies of heavy ion collisions requires a prescription for ``particlization''. Existing particlization models are based on implicit or explicit assumptions about…
In this study, we use the maximum likelihood estimator (MLE) to explore factorization and event-plane correlations in relativistic heavy-ion collisions. Our analyses incorporate both numerical simulations and publicly available data from…
Recent observations of QGP-like conditions in high-multiplicity pp collisions from ALICE experiment at the LHC warrants an introspection whether to use pp collisions as a baseline measurement to characterize heavy-ion collisions for…
A method for selecting events with densely populated narrow regions or spikes in a given data sample is discussed. Applying this method to 200 A GeV/c 32S-AgBr and 32S-Gold collision data, a few events having "hot regions" are chosen for…
Methodology is presented for analysis of three-particle correlation functions obtained in heavy ion collisions at ultra-relativistic energies. We show that harmonic correlations can be removed and jet driven correlations reliably extracted.…
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…
Jets produced in high-energy heavy-ion collisions are modified compared to those in proton-proton collisions due to their interaction with the deconfined, strongly-coupled quark-gluon plasma (QGP). In this work, we employ machine learning…
Measurements of jet substructure in ultra-relativistic heavy ion collisions suggest that the jet showering process is modified by the interaction with quark gluon plasma. Modifications of the hard substructure of jets can be explored with…
Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate…
We present a machine learning (ML) method to calibrate hadronic jet energy in real-time trigger systems of the High-Luminosity Large Hadron Collider (HL-LHC) using an efficient implementation on field programmable gate arrays (FPGA).…
Multiple studies have now demonstrated that machine learning (ML) can give improved skill for predicting or simulating fairly typical weather events, for tasks such as short-term and seasonal weather forecasting, downscaling simulations to…
The first study of charge-dependent two-particle differential number ($B$) and momentum balance functions ($P_{2}^{CD}$) with respect to an event shape variable, transverse spherocity, is reported. Results are presented from PYTHIA8 and…
The collective expansion of the quark-gluon plasma (QGP) created in heavy-ion collisions suggests that geometry-inspired approaches can be useful in extracting information about the QGP. In this work, a systematic study of observables based…
The event-by-event analysis of multiparticle production in high energy hadron and nuclei collisions can be performed using the discrete wavelet transformation. The ring-like and jet-like structures in two-dimensional angular histograms are…
Recent developments on a deep learning feed-forward network for estimating elliptic flow ($v_2$) coefficients in heavy-ion collisions have shown us the prediction power of this technique. The success of the model is mainly the estimation of…