arXiv subjects

Dipankar Basak

Publications and source records attributed to Dipankar Basak.

4 recordsLinked to original sources

arXiv2025

Observation of partonic collectivity via $p_{\rm T}$-differential radial flow fluctuations in Au+Au collisions at $\sqrt{s_{\rm NN}} = 200$ GeV

We report the observation of partonic radial collectivity in Au+Au collisions at $\sqrt{s_{\rm NN}} = 200$~GeV via the $p_{\rm T}$-differential flow observable $v_{0}(p_{\rm T})$ using the \texttt{AMPT} String Melting model. For inclusive charged hadrons, we establish three signatures of collectivity: long-range pseudorapidity correlations, the factorization of two-particle correlations, and a centrality-independent scaling of $v_{0}(p_{\rm T})$ normalized by its $p_{\rm T}$-integrated value $v_{0}$, analogous to anisotropic flow. For identified particles ($\pi^{\pm}, K^{\pm}, p + \overline p$), the $v_{0}(p_{\rm T})$ spectra show mass ordering at low-$p_{\rm T}$ and meson-baryon separation at intermediate-$p_{\rm T}$. In \textit{central} collisions, $v_{0}(p_{\rm T})/n_{q}$ exhibits robust \textit{Number of Constituent Quark} (NCQ) scaling with $(m_{\rm T} - m_{0})/n_{q}$, a scaling that breaks down in \textit{peripheral} collisions and is more precise at RHIC than at LHC energies, consistent with earlier $v_{2}$ studies. These findings provide strong evidence that radial collectivity originates predominantly at the partonic stage, extending the paradigm of quark-level dynamics from anisotropic to isotropic flow.

nucl-ex↗

arXiv2025

Harnessing data-driven methods for precise model independent event shape estimation in relativistic heavy-ion collisions

This study demonstrates the application of supervised machine learning (ML) techniques to distinguish between isotropic and jet-like event topologies in heavy-ion collisions via the spherocity observable. State-of-the-art ML algorithms, optimized through systematic hyperparameter tuning, are employed to predict both traditional transverse spherocity $S_{0}$ and unweighted transverse spherocity $S_{0}^{p_{\rm T}=1}$ directly from raw event data. Moreover, the results from this study demonstrated that our approach remains largely model-independent, underscoring its potential applicability in future experimental heavy-ion physics analyses.

hep-ph↗

arXiv2024

Estimating centrality in heavy-ion collisions using Transfer Learning technique

In this study, we explore the applicability of Transfer Learning techniques for estimating collision centrality in terms of the number of participants ($N_{\rm part}$) in high-energy heavy-ion collisions. In the present work, we leverage popular pre-trained CNN models such as VGG16, ResNet50, and DenseNet121 to determine $N_{\rm part}$ in Au+Au collisions at $\sqrt{s}=200$ GeV on an event-by-event basis. Remarkably, all three models achieved good performance despite the pre-trained models being trained for databases of other domains. Particularly noteworthy is the superior performance of the VGG16 model, showcasing the potential of transfer learning techniques for extracting diverse observables from heavy-ion collision data.

hep-ph↗

arXiv2023

Estimation of collision centrality in terms of the number of participating nucleons in heavy-ion collisions using deep learning

The deep learning technique has been applied for the first time to investigate the possibility of centrality determination in terms of the number of participants ($N_{\mathrm{part}}$) in high-energy heavy-ion collisions. For this purpose, supervised learning using both deep neural network (DNN) and convolutional neural network (CNN) is performed with labeled data obtained by modeling relativistic heavy-ion collisions utilizing A Multi-phase Transport Model (AMPT). Event-by-event distributions of pseudorapidity and azimuthal angle of charged hadrons weighted by their transverse momentum are used as input to train the DL models. The DL models did remarkably well in predicting $N_{\mathrm{part}}$ values with CNN slightly outperforming the DNN model. The Mean Squared Logarithmic Error (MSLE) for the CNN model (Model-4) is determined to be 0.0592 for minimum bias collisions and 0.0114 for 0-60\% centrality class, indicating that the model performs better for semi-central and central collisions. Furthermore, the studied DL model is proven to be robust to changes in energy as well as model parameters of the input. The current study demonstrates that the data-driven technique has a distinct potential for determining centrality in terms of the number of participants in high-energy heavy-ion collision experiments.

hep-ph↗