Related papers: Machine and deep learning techniques in heavy-ion …

ALICE (A Large Ion Collider Experiment) is one of the seven experiments at the the Large Hadron Collider (LHC) at CERN, Geneva, Switzerland. ALICE is especially designed for heavy-ion collisions but it also operates a rich proton-proton…

The electron density is a key parameter to characterize any plasma. Most of the plasma applications and research in the area of low-temperature plasmas (LTPs) are based on the accurate estimations of plasma density and plasma temperature.…

We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred,…

Azimuthal di-hadron correlations play important role in the characterization of the medium created in heavy-ion collisions at RHIC. Moreover, as a novel phenomenon, strong modification of the away-side correlation is observed in Au+Au with…

Two widely used techniques for training supervised machine learning models on small datasets are Active Learning and Transfer Learning. The former helps to optimally use a limited budget to label new data. The latter uses large pre-trained…

Deep neural networks ("deep learning") have emerged as a technology of choice to tackle problems in natural language processing, computer vision, speech recognition and gameplay, and in just a few years has led to superhuman level…

Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction…

The ALICE detector was designed to identify hadrons over a wide range of transverse momentum at mid-rapidity. Here measurements of light charged ({\pi}, K, p) and neutral ({\Lambda}, K0S) hadrons in Pb-Pb collisions at sqrt(s_NN) = 2.76 TeV…

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the…

The present document discusses plans for a compact, next-generation multi-purpose detector at the LHC as a follow-up to the present ALICE experiment. The aim is to build a nearly massless barrel detector consisting of truly cylindrical…

Short-lived hadronic resonances are unique tools for studying the hadron-gas phase that is created in the late stages of relativistic heavy-ion collisions. Measurements of the yield ratios between resonances and the corresponding stable…

We present studies of electron identification (eID) in the MPD experiment at NICA using machine learning techniques. The goal is to improve electron identification efficiency while preserving high purity, which is crucial for dielectron…

$\gamma +$jet events provide a tomographic measurement of the medium formed in heavy ion collisions at LHC energies. Tagging events with a well identified high $p_{T}$ direct photon and measuring the correlation distribution of hadrons…

The measurement of dielectron production is a fundamental piece of the puzzle in the understanding of the hot and dense matter produced in ultra-relativistic heavy-ion collisions. The dielectron spectrum provides information that penetrates…

Deep learning techniques are currently being investigated for high energy physics experiments, to tackle a wide range of problems, with quark and gluon discrimination becoming a benchmark for new algorithms. One weakness is the traditional…

The study of hadronic resonances plays an important role both in pp and in heavy-ion collisions. Since the lifetimes of short-lived resonances are comparable with the lifetime of the fireball formed in heavy-ion collisions, regeneration and…

With the great promise of deep learning, discoveries of new particles at the Large Hadron Collider (LHC) may be imminent. Following the discovery of a new Beyond the Standard model particle in an all-hadronic channel, deep learning can also…

The ALICE Collaboration measures the production of low-mass dielectrons in pp, p-Pb and Pb-Pb collisions at the LHC. The main detectors used in the analyses are the Inner Tracking System, Time Projection Chamber and Time-Of-Flight detector,…

Active learning (AL) is a prominent technique for reducing the annotation effort required for training machine learning models. Deep learning offers a solution for several essential obstacles to deploying AL in practice but introduces many…

We employ a descriptor based machine-learning approach to assess the effect of chemical alloying on formation-enthalpy of rare-earth intermetallics. Application of machine-learning approaches in rare-earth intermetallic design have been…