arXiv subjects

Suraj Prasad

Publications and source records attributed to Suraj Prasad.

36 records · Page 2Linked to original sources

arXiv2025

Prompt and non-prompt production of charm hadrons in proton-proton collisions at the Large Hadron Collider using machine learning

In this contribution, we use machine learning (ML) based models to separate the prompt and non-prompt production of heavy flavour hadrons, such as $D^0$ and J/$ψ$, in proton-proton collisions at LHC energies. For this purpose, we use PYTHIA~8 to generate events, which provides a good qualitative agreement with experimental measurements of charm hadron production. The input features for the ML models are experimentally measurable. The prediction accuracy of the ML models used in this study reaches up to 99\%. The ML models can be useful in providing precise track-level identification, which is not possible in experiments with traditional methods. The contribution also discusses future applications of the ML models to understand the production of prompt and non-prompt heavy quark hadrons.

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arXiv2025

Exploring the effects of $α$-clustered structure of $^{16}O$ nuclei in anisotropic flow fluctuations in $^{16}O$-$^{16}O$ collisions at the LHC within a CGC+Hydro framework

In this paper, we explore the effects of the presence of clustered nuclear structure of $^{16}O$ in the final state elliptic flow fluctuations through $^{16}O$-$^{16}O$ collisions at $\sqrt{s_{\rm NN}}=7$ TeV within a hybrid model, IPGlasma+MUSIC+iSS+ UrQMD. We compare the results of elliptic flow fluctuations using $α$-clustered nuclear structure to the Woods-Saxon nuclear profile having no clustered structure. We observe a significant difference in the elliptic flow fluctuations, which arise due to the consideration of a clustered nuclear structure of $^{16}O$.

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arXiv2025

Probing strangeness with event topology classifiers in pp collisions at energies available at the CERN Large Hadron Collider with the rope hadronization mechanism in PYTHIA

In relativistic heavy-ion collisions, the formation of a deconfined and thermalized state of partons, known as quark-gluon plasma (QGP), leads to enhanced production of strange hadrons in contrast to proton-proton (pp) collisions, which are taken as baseline. This observation is known as strangeness enhancement in heavy-ion collisions and is considered one of the important signatures that can signify the formation of QGP. However, in addition to strangeness enhancement, recent measurements hint at observing several heavy-ion-like features in high multiplicity pp collisions at energies available at the CERN Large Hadron Collider. Alternatively, event shape observables, such as transverse spherocity, transverse sphericity, charged particle flattenicity, and relative transverse activity classifiers, can fundamentally separate hard interaction-dominated jetty events from soft isotropic events. These features of event shape observables can probe the observed heavy-ion-like features in pp collisions with significantly reduced selection bias and can bring all collision systems on equal footing. In this article, we present an extensive summary of the strange particle ratios to pions as a function of different event classifiers using the PYTHIA~8 model with color reconnection and rope hadronization mechanisms to understand the microscopic origin of strangeness enhancement in pp collisions and also prescribe the applicability of these event classifiers in the context of strangeness enhancement. Charged particle flattenicity is found to be most suited for the study of strangeness enhancement, and it shows a quantitative enhancement similar to that seen for the analysis based on the number of multi-parton interactions.

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arXiv2025

Investigating radial flow-like effects via pseudorapidity and transverse spherocity dependence of particle production in pp collisions at the LHC

Recent observations of quark-gluon plasma (QGP) like signatures in high multiplicity proton-proton (pp) collisions, have compelled the heavy-ion physics community to re-examine small collision systems for proper baseline studies. Event-shape-based studies in pp collisions have succeeded to a certain extent in identifying the rare events mimicking such heavy-ion-like behaviour. In this study, we incorporate PYTHIA8 and AMPT to study radial flow-like signatures in pp collisions at $\sqrt{s} = 13$ TeV as a function of transverse spherocity and pseudorapidity. The selection of softer events possibly carrying heavy-ion-like features is performed using the transverse spherocity event-shape observable. As the particle production mechanism in midrapidity differs greatly from the forward rapidity, a pseudorapidity-dependent study is meaningful. Keeping ALICE 3 upgrades at the LHC in mind, this study aims to demonstrate the transverse spherocity and pseudorapidity dependence of the mean transverse momentum, particle ratios, and kinetic freezeout parameters in pp collisions at $\sqrt{s}$ = 13 TeV using PYTHIA8. We observe that the isotropic events show enhanced radial-flow effects in all multiplicity classes, however, the jetty events show signatures of the radial flow-like effects only in high-multiplicity events. For the first time, we show the transverse spherocity and pseudorapidity dependence of partonic modification factor in pp collisions, which clearly shows that by choosing transverse spherocity, one can directly probe the radial flow-like effects in pp collisions at the LHC.

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arXiv2024

Dynamics of Hot QCD Matter 2024 -- Bulk Properties

The second Hot QCD Matter 2024 conference at IIT Mandi focused on various ongoing topics in high-energy heavy-ion collisions, encompassing theoretical and experimental perspectives. This proceedings volume includes 19 contributions that collectively explore diverse aspects of the bulk properties of hot QCD matter. The topics encompass the dynamics of electromagnetic fields, transport properties, hadronic matter, spin hydrodynamics, and the role of conserved charges in high-energy environments. These studies significantly enhance our understanding of the complex dynamics of hot QCD matter, the quark-gluon plasma (QGP) formed in high-energy nuclear collisions. Advances in theoretical frameworks, including hydrodynamics, spin dynamics, and fluctuation studies, aim to improve theoretical calculations and refine our knowledge of the thermodynamic properties of strongly interacting matter. Experimental efforts, such as those conducted by the ALICE and STAR collaborations, play a vital role in validating these theoretical predictions and deepening our insight into the QCD phase diagram, collectivity in small systems, and the early-stage behavior of strongly interacting matter. Combining theoretical models with experimental observations offers a comprehensive understanding of the extreme conditions encountered in relativistic heavy-ion and proton-proton collisions.

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arXiv2024

Anisotropic flow fluctuation as a possible signature of clustered nuclear geometry in O-O collisions at the Large Hadron Collider

Nuclei having $4n$ number of nucleons are theorized to possess clusters of $α$ particles ($^4$He nucleus). The Oxygen nucleus ($^{16}$O) is a doubly magic nucleus, where the presence of an $α$-clustered nuclear structure grants additional nuclear stability. In this study, we exploit the anisotropic flow coefficients to discern the effects of an $α$-clustered nuclear geometry with respect to a Woods-Saxon nuclear distribution in O--O collisions at $\sqrt{s_{\rm NN}}=7$ TeV using a hybrid of IP-Glasma + MUSIC + iSS + UrQMD models. In addition, we use the multi-particle cumulants method to measure anisotropic flow coefficients, such as elliptic flow ($v_{2}$) and triangular flow ($v_{3}$), as a function of multiplicity class. Anisotropic flow fluctuations, which are expected to be larger in small collision systems, are also studied for the first time in O--O collisions. It is found that an $α$-clustered nuclear distribution gives rise to an enhanced value of $v_{2}$ and $v_3$ for the low-multiplicity events. Consequently, a rise in $v_3/v_2$ is also observed for the 0--10\% multiplicity class. Further, for $α$-clustered O--O collisions, fluctuations of $v_{2}$ are larger for the highest multiplicity events, which decrease as the final-state multiplicity decreases. In contrast, for a Woods-Saxon $^{16}$O nucleus, $v_{2}$ fluctuations show an opposite behavior with decreasing multiplicity. When confronted with experimental data, this study may reveal the importance of the nuclear density profile on the discussed observables and provide physics validation for the hybrid model discussed in this work.

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arXiv2024

A Deep Learning Based Estimator for Light Flavour Elliptic Flow in Heavy Ion Collisions at LHC Energies

We developed a deep learning feed-forward network for estimating elliptic flow ($v_2$) coefficients in heavy-ion collisions from RHIC to LHC energies. The success of our model is mainly the estimation of $v_2$ from final state particle kinematic information and learning the centrality and the transverse momentum ($p_{\rm T}$) dependence of $v_2$ in wide $p_{\rm T}$ regime. The deep learning model is trained with AMPT-generated Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events. We present $v_2$ estimates for $π^{\pm}$, $\rm K^{\pm}$, and $\rm p+\bar{p}$ in heavy-ion collisions at various LHC energies. These results are compared with the available experimental data wherever possible.

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arXiv2024

MedPromptExtract (Medical Data Extraction Tool): Anonymization and Hi-fidelity Automated data extraction using NLP and prompt engineering

Introduction: The labour-intensive nature of data extraction from sources like discharge summaries (DS) poses significant obstacles to the digitisation of medical records particularly for low- and middle-income countries (LMICs). In this paper we present a completely automated method MedPromptExtract to efficiently extract data from DS while maintaining confidentiality. Methods: The source of data was Discharge Summaries (DS) from Kokilaben Dhirubhai Ambani Hospital (KDAH) of patients having Acute Kidney Injury (AKI). A pre-existing tool EIGEN which leverages semi-supervised learning techniques for high-fidelity information extraction was used to anonymize the DS, Natural Language Processing (NLP) was used to extract data from regular fields. We used Prompt Engineering and Large Language Model(LLM) to extract custom clinical information from free flowing text describing the patients stay in the hospital. Twelve features associated with occurrence of AKI were extracted. The LLM responses were validated against clinicians annotations. Results: The MedPromptExtracttool first subjected DS to the anonymization pipeline which took three seconds per summary. Successful anonymization was verified by clinicians, thereafter NLP pipeline extracted structured text from the anonymized pdfs at the rate of 0.2 seconds per summary with 100% accuracy.Finally DS were analysed by the LLM pipeline using Gemini Pro for the twelve features. Accuracy metrics were calculated by comparing model responses to clinicians annotations with seven features achieving AUCs above 0.9, indicating high fidelity of the extraction process. Conclusion: MedPromptExtract serves as an automated adaptable tool for efficient data extraction from medical records with a dynamic user interface. Keywords: Digitizing Medical Records, Automated Anonymisation, Information Retrieval, Large Language Models, Prompt Engineering

cs.AI↗

arXiv2024

A machine learning-based study of open-charm hadrons in proton-proton collisions at the Large Hadron Collider

n proton-proton and heavy-ion collisions, the study of charm hadrons plays a pivotal role in understanding the QCD medium and provides an undisputed testing ground for the theory of strong interaction, as they are mostly produced in the early stages of collisions via hard partonic interactions. The lightest open-charm, $D^{0}$ meson ($c\Bar{u}$), can originate from two separate sources. The prompt $D^{0}$ originates from either direct charm production or the decay of excited open charm states, while the nonprompt stems from the decay of beauty hadrons. In this paper, using different machine learning (ML) algorithms such as XGBoost, CatBoost, and Random Forest, an attempt has been made to segregate the prompt and nonprompt production modes of $D^{0}$ meson signal from its background. The ML models are trained using the invariant mass through its hadronic decay channel, i.e., $D^{0}\rightarrowπ^{+} K^{-}$, pseudoproper time, pseudoproper decay length, and distance of closest approach of $D^{0}$ meson, using PYTHIA8 simulated $pp$ collisions at $\sqrt{s}=13~\rm{TeV}$. The ML models used in this analysis are found to retain the pseudorapidity, transverse momentum, and collision energy dependence. In addition, we report the ratio of nonprompt to prompt $D^{0}$ yield, the self-normalized yield of prompt and nonprompt $D^{0}$ and explore the charmonium, $J/ψ$ to open-charm, $D^{0}$ yield ratio as a function of transverse momenta and normalized multiplicity. The observables studied in this manuscript are well predicted by all the ML models compared to the simulation.

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arXiv2024

Inclusive, prompt and non-prompt $\rm{J}/ψ$ identification in proton-proton collisions at the Large Hadron Collider using machine learning

Studies related to $\rm{J}/ψ$ meson, a bound state of charm and anti-charm quarks ($c\bar{c}$), in heavy-ion collisions, provide genuine testing grounds for the theory of strong interaction, quantum chromodynamics (QCD). To better understand the underlying production mechanism, cold nuclear matter effects, and influence from the quark-gluon plasma, baseline measurements are also performed in proton-proton ($pp$) and proton-nucleus ($p$--A) collisions. The inclusive $\rm{J}/ψ$ measurement has contributions from both prompt and non-prompt productions. The prompt $\rm{J}/ψ$ is produced directly from the hadronic interactions or via feed-down from directly produced higher charmonium states, whereas non-prompt $\rm{J}/ψ$ comes from the decay of beauty hadrons. In experiments, $\rm{J}/ψ$ is reconstructed through its electromagnetic decays to lepton pairs, in either $e^{+}+e^{-}$ or $μ^{+}+μ^{-}$ decay channels. In this work, for the first time, machine learning techniques are implemented to separate the prompt and non-prompt dimuon pairs from the background to obtain a better identification of the $\rm{J}/ψ$ signal for different production modes. The study has been performed in $pp$ collisions at $\sqrt{s} = 7$ and 13 TeV simulated using PYTHIA8. Machine learning models such as XGBoost and LightGBM are explored. The models could achieve up to 99\% prediction accuracy. The transverse momentum ($p_{\rm T}$) and rapidity ($y$) differential measurements of inclusive, prompt, and non-prompt $\rm{J}/ψ$, its multiplicity dependence, and the $p_{\rm T}$ dependence of fraction of non-prompt $\rm{J}/ψ$ ($f_{\rm B}$) are shown. These results are compared to experimental findings wherever possible.

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arXiv2023

Effects of clustered nuclear geometry on the anisotropic flow in O-O collisions at the LHC within a multiphase transport model framework

To understand the true origin of flowlike signatures and applicability of hydrodynamics in small collision systems, effects of soft QCD dynamics, the sensitivity of jetlike correlations, and nonequilibrium effects, efforts are being made to perform \textit{p}-O and O-O collisions at the LHC and RHIC energies. It is equally interesting to look into the possible signatures of an $α$-clustered nuclear geometry in $^{16}$O-$^{16}$O collisions by studying the initial-state effects on the final-state observables. In this work, within a multiphase transport model, we implement an $α$-cluster tetrahedral density profile in the oxygen nucleus along with the default Woods-Saxon density profile. We study the eccentricity ($ε_2$), triangularity ($ε_3$), normalized symmetric cumulants [NSC(2,3)], elliptic flow ($v_2$), and triangular flow ($v_3$) in $^{16}$O-$^{16}$O collisions at $\sqrt{s_{\rm NN}} = 7~$TeV. The constituent quark number scaling of the elliptic flow is also reported. For the most central collisions, enhanced effects in $\langle ε_3 \rangle/ \langle ε_2 \rangle$ and $\langle v_3 \rangle/ \langle v_2 \rangle$ with a negative value of NSC(2,3), and an away-side broadening in the two-particle azimuthal correlation function [$C(Δϕ)$] of the identified particles are observed in the presence of an $α$-clustered geometry.

hep-ph↗

arXiv2023

Deep learning predicted elliptic flow of identified particles in heavy-ion collisions at the RHIC and LHC energies

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 $v_2$ from final state particle kinematic information and learning the centrality and the transverse momentum ($p_{\rm T}$) dependence of $v_2$. The deep learning model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with a multiphase transport model (AMPT). We extend this work to estimate $v_2$ for light-flavor identified particles such as $π^{\pm}$, $\rm K^{\pm}$, and $\rm p+\bar{p}$ in heavy-ion collisions at RHIC and LHC energies. The number of constituent quark (NCQ) scaling is also shown. The evolution of $p_{\rm T}$-crossing point of $v_2(p_{\rm T})$, depicting a change in meson-baryon elliptic flow at intermediate-$p_{\rm T}$, is studied for various collision systems and energies. The model is further evaluated by training it for different $p_{\rm T}$ regions. These results are compared with the available experimental data wherever possible.

hep-ph↗

arXiv2023

Probing initial geometrical anisotropy and final azimuthal anisotropy in heavy-ion collisions at Large Hadron Collider energies through event-shape engineering

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 have influenced by eccentricity ($\varepsilon_2$) and triangularity ($\varepsilon_3$) of the participants, respectively. In this work, we study $v_2$, $v_3$, $\varepsilon_2$, $\varepsilon_3$ and the correlations among them with respect to event topology in the framework of a multi-phase transport model (AMPT). We use transverse spherocity and reduced flow vector as event shape classifiers in this study. Transverse spherocity has the unique ability to separate events based on geometrical shapes, i.e., jetty and isotropic, which pertain to pQCD and non-pQCD domains of particle production in high-energy physics, respectively. We use the two-particle correlation method to study different anisotropic flow coefficients. We confront transverse spherocity with a more widely used event shape classifier -- reduced flow vector ($q_n$) and they are found to have significant (anti-)correlations among them. We observe significant spherocity dependence on $v_2$, $v_3$ and $\varepsilon_2$. This work also addresses transverse momentum dependent crossing points between $v_2$ and $v_3$, which varies for different centrality and spherocity percentiles.

hep-ph↗

arXiv2022

Unveiling the effects of multiple soft partonic interactions in pp collisions at $\sqrt{\textit s}=13.6$ TeV using charged-particle flattenicity

Event classifiers based either on the charged-particle multiplicity or the event shape have been extensively used in proton-proton (pp) collisions by the ALICE collaboration at the LHC. The use of these tools became very instrumental since the observation of fluid-like behavior in high-multiplicity pp collisions. In particular, the study as a function of the charged-particle multiplicity registered in the forward V0 ALICE detector allowed for the discovery of strangeness enhancement in high-multiplicity pp collisions. However, one drawback of the multiplicity-based event classifiers is that requiring a high charged-particle multiplicity biases the sample towards hard processes like multi-jet final states. These biases make it difficult to perform jet-quenching searches in high-multiplicity pp collisions. In this context, the present paper explores the use of the new event classifier, flattenicity; which uses the multiplicity calculated in the forward pseudorapidity region. To illustrate how this tool works, pp collisions at $\sqrt{s}=13.6$ TeV simulated with PYTHIA~8 are explored. The sensitivity of flattencity to multi-partonic interactions as well as to the ``hardness'' of the collision are discussed. PYTHIA 8 predictions for the transverse momentum spectra of light- and heavy-flavored hadrons as a function of flattenicity are presented.

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arXiv2022

Predictions on global properties in O+O collisions at the Large Hadron Collider using a multi-phase transport model

Oxygen ($^{16}$O) ions are planned to be injected at the Large Hadron Collider (LHC) in its next runs, and a day of physics run is anticipated for O+O collisions at $\sqrt{s_{\rm{NN}}}$ = 7 TeV. As the system size of O+O collisions has the final state multiplicity overlap with those produced in pp, p+Pb and Pb+Pb collisions, the study of global properties in O+O collisions may provide a deeper insight into the heavy-ion-like behavior observed in small collision systems and its similarities/differences with a larger system like Pb+Pb collisions. In the present work, we report the predictions for global properties in O+O collisions at $\sqrt{s_{\rm{NN}}}$ = 7 TeV using a multi-phase transport model (AMPT). We report the mid-rapidity charged-particle multiplicity, transverse mass, Bjorken energy density, pseudo-rapidity distributions, squared speed of sound, transverse momentum ($p_{\rm T}$) spectra, the kinetic freeze-out parameters, and $p_{\rm T}$-differential particle ratio as a function of collision centrality. Further, we have studied the transverse momentum-dependent elliptic flow of charged particles. The results are shown for Woods-Saxon and harmonic oscillator nuclear density profiles. In addition, we have compared the results with an $α$-clustered structure incorporated inside the oxygen nucleus. Average charged-particle multiplicity and the Bjorken energy density show a significant increase in most central collisions for the harmonic oscillator density profile, while other global properties show less dependence on the density profiles considered in this work. The results from the $α$-clustered structure incorporated inside the oxygen nucleus show similar initial energy density and final charged-particle multiplicity as observed for the harmonic oscillator density profile.

hep-ph↗

arXiv2022

Dynamics of Hot QCD Matter -- Current Status and Developments

The discovery and characterization of hot and dense QCD matter, known as Quark Gluon Plasma (QGP), remains the most international collaborative effort and synergy between theorists and experimentalists in modern nuclear physics to date. The experimentalists around the world not only collect an unprecedented amount of data in heavy-ion collisions, at Relativistic Heavy Ion Collider (RHIC), at Brookhaven National Laboratory (BNL) in New York, USA, and the Large Hadron Collider (LHC), at CERN in Geneva, Switzerland but also analyze these data to unravel the mystery of this new phase of matter that filled a few microseconds old universe, just after the Big Bang. In the meantime, advancements in theoretical works and computing capability extend our wisdom about the hot-dense QCD matter and its dynamics through mathematical equations. The exchange of ideas between experimentalists and theoreticians is crucial for the progress of our knowledge. The motivation of this first conference named "HOT QCD Matter 2022" is to bring the community together to have a discourse on this topic. In this article, there are 36 sections discussing various topics in the field of relativistic heavy-ion collisions and related phenomena that cover a snapshot of the current experimental observations and theoretical progress. This article begins with the theoretical overview of relativistic spin-hydrodynamics in the presence of the external magnetic field, followed by the Lattice QCD results on heavy quarks in QGP, and finally, it ends with an overview of experiment results.

nucl-th↗

arXiv2022

Estimating Elliptic Flow Coefficient in Heavy Ion Collisions using Deep Learning

Machine Learning (ML) techniques have been employed for the high energy physics (HEP) community since the early 80s to deal with a broad spectrum of problems. This work explores the prospects of using Deep Learning techniques to estimate elliptic flow ($v_2$) in heavy-ion collisions at the RHIC and LHC energies. A novel method is developed to process the input observables from particle kinematic information. The proposed DNN model is trained with Pb-Pb collisions at $\sqrt{s_{\rm NN}} = 5.02$ TeV minimum bias events simulated with AMPT model. The predictions from the ML technique are compared to both simulation and experiment. The Deep Learning model seems to preserve the centrality and energy dependence of $v_2$ for the LHC and RHIC energies. The DNN model is also quite successful in predicting the $p_{\rm T}$ dependence of $v_2$. When subjected to event simulation with additional noise, the proposed DNN model still keeps the robustness and prediction accuracy intact up to a reasonable extent.

hep-ph↗

arXiv2022

Event topology and global observables in heavy-ion collisions at the Large Hadron Collider

Particle production and event topology are very strongly correlated in high-energy hadronic and nuclear collisions. Event topology is decided by the underlying particle production dynamics and medium effects. Transverse spherocity is an event shape observable, which has been used in pp and heavy-ion collisions to separate the events based on their geometrical shapes. It has the unique capability to distinguish between jetty and isotropic events. In this work, we have implemented transverse spherocity in Pb-Pb collisions at $\sqrt{s_{\rm NN}}$ = 5.02 TeV using A Multi-Phase Transport Model (AMPT). While awaiting for experimental explorations, we perform a feasibility study of dependence of transverse spherocity on some of the global observables in heavy-ion collisions at the Large Hadron Collider energies. These global observables include the Bjorken energy density ($ε_{\rm B_j}$), speed of sound ($c_{\rm s}^2$) in the medium and the kinetic freeze-out properties for different collision centralities. The present study reveals about the usefulness of event topology dependent measurements in heavy-ion collisions in contrast to proton-proton collisions.

hep-ph↗