Application of artificial intelligence in the determination of impact parameter in heavy-ion collisions at intermediate energies

Abstract

The impact parameter is one of the crucial physical quantities of heavy-ion collisions, and can affect obviously many observables at the final state, such as the multifragmentation and the collective flow. Usually, it cannot be measured directly in experiments but might be inferred from observables at the final state. Artificial intelligence has had great success in learning complex representations of data, which enables novel modeling and data processing approaches in physical sciences. In this article, we employ two of commonly used algorithms in the field of artificial intelligence, the convolutional neural networks (CNN) and light gradient boosting machine (LightGBM), to improve the accuracy of determining impact parameter by analyzing the proton spectra in transverse momentum and rapidity on the event-by-event basis. Au + Au collisions with the impact parameter of 0 ⩽ b ⩽ 10 fm at intermediate energies (E lab = 0.2–1.0 GeV/nucleon) are simulated with the ultrarelativistic quantum molecular dynamics model to generate the proton spectra data. It is found that the average difference between the true impact parameter and the estimated one can be smaller than 0.1 fm. The LightGBM algorithm shows an improved performance with respect to the CNN on the task in this work. By using the LightGBM’s visualization algorithm, one can obtain the important feature map of the distribution of transverse momentum and rapidity, which may be helpful in inferring the impact parameter or centrality in heavy-ion experiments.

An equation-of-state-meter for CBM using PointNet

“…It employs multiple layered Artificial Neural Networks to learn higher dimensional correlations in the data. Machine learning and Deep Learning methods have been widely used both in theory [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] and in experimental high energy physics [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. Previous studies [18,20] on identifying the QCD phase transitions have shown that Convolutional Neural Network (CNN) based models can accurately classify the underlying equation of state from a hydrodynamic evolution using the p tφ spectra of pions (differential transverse and angular distributions in the transverse plane).…”

Deep Learning Based Impact Parameter Determination for the CBM Experiment

“…Machine learning techniques are widely used in high energy physics, both in experiments and theory to develop models that can replace conventional analysis techniques . Naturally, such techniques have also been proposed as tool for impact parameter determination in [31][32][33][34][35][36]. However, previous studies were using shallow neural networks or other traditional Machine Learning algorithms with simplified experimental constraints based on detector acceptance and event selection.…”

Determination of impact parameter in high-energy heavy-ion collisions via deep learning

“…For * huangxuguang@fudan.edu.cn heavy ion collisions, the impact parameter can be viewed as one of the IDs of an event. Several works have proved the effectiveness of DL methods on impact parameter 'recognition' [9][10][11][12][13][14][15][16][17]. From a simple neural network [9] to a PointNet model [13] and to boosted decision trees [17], with the development of machine learning algorithms, more and more appropriate learning methods have been proposed to improve the performance of 'recognition' and to satisfy experimental requests.…”

“…From a simple neural network [9] to a PointNet model [13] and to boosted decision trees [17], with the development of machine learning algorithms, more and more appropriate learning methods have been proposed to improve the performance of 'recognition' and to satisfy experimental requests. However, most of these researches only involve collisions at low or intermediate energies [9][10][11][12][13][14][15][16]. Though a recent work [17] considered LHC energies, the adopted machine learning model is not a deep neural network.…”