arXiv subjects

Marko Djordjevic

Publications and source records attributed to Marko Djordjevic.

At least 19 recordsLinked to original sources

arXiv2026

Joint Bayesian analysis of soft and high-$p_\perp$ probes yields tighter constraints on QGP properties

To extract bulk QGP properties, we perform a joint Bayesian calibration of bulk-medium parameters using low-$\pt$ bulk and high-$\pt$ tomography within a common medium evolution. Low-$\pt$ observables are computed with \textsc{TRENTo}+\textsc{VISHNU}; temperature profiles are passed to \textsc{DREENA-A} to predict light/heavy $R_{\mathrm{AA}}(\pt)$ and $v_2(\pt)$. Gaussian-process emulation enables Hamiltonian Monte Carlo sampling of the low-$\pt$-only and joint posteriors. The low-$\pt$-only case underpredicts high-$\pt$ anisotropy; the joint calibration matches both sectors and markedly tightens bulk-parameter constraints, demonstrating the added power of high-$\pt$ data.

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arXiv2026

Cone-Dependent Jet Collisional Energy Loss in Finite QCD Medium

We derive a compact HTL-resummed expression for the leading-order jet collisional energy loss in a finite-size, finite-temperature QCD medium. Defining the jet energy inside a cone of radius $R$, we obtain the out-of-cone elastic energy loss with an explicit separation between contributions from the primary jet parton and recoiling medium partons. The result reproduces the known partonic limit as $R\!\to\!0$, vanishes for $R\!\to\!π$, and applies to both light- and heavy-flavor jets. Numerically, the elastic component shows a pronounced non-linear $R$ dependence relative to the radiative baseline, and its importance increases with $R$, becoming comparable to or exceeding the radiative contribution for sufficiently large jet radii. The path-length dependence remains close to linear for all $R$, while the medium-response contribution can exceed $10\%$ for realistic jet radii.

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arXiv2025

Can high-$p_\perp$ theory and data constrain $η/s$?

Understanding the temperature dependence of the specific shear viscosity $(η/s)$ is crucial for characterizing the properties of the QCD matter produced in ultrarelativistic heavy-ion collisions. Since, low-$p_\perp$ theory and data are only weakly sensitive to the typical forms of $η/s(T)$, especially at high temperatures, we use high-$p_\perp$ data and theory to impose additional constraints on it. Our approach, based on dynamical radiative and collisional energy loss of high-$p_\perp$ particles, provides promising results in constraining the temperature dependence of $η/s$.

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arXiv2024

Probing the shape of the quark-gluon plasma droplet via event-by-event QGP tomography

This study investigates Quark-Gluon Plasma (QGP) in heavy-ion collisions through two avenues: high-$p_{\perp}$ frameworks and hydrodynamic modeling. Using the T$_{\text{R}}$ENTo model, we find that IP-Glasma mimicking $p=0$ value aligns well with high-$p_{\perp}$ data, in agreement with Bayesian analysis of the low-$p_{\perp}$ regime. While adjusting $p$ values may improve a fit to a particular high-$p_{\perp}$ observable, it does not permit an earlier onset of transverse expansion.

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arXiv2023

Constraining $η/s$ through high-p$_\perp$ theory and data

We study whether it is possible to use high-$p_\perp$ data/theory to constrain the temperature dependence of the shear viscosity over entropy density ratio $η/s$ of the matter formed in ultrarelativistic heavy-ion collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC). We use two approaches: i) We calculate high-$p_\perp$ $R_{AA}$ and flow coefficients $v_2$, $v_3$ and $v_4$ assuming different $(η/s)(T)$ of the fluid-dynamically evolving medium. ii) We calculate the quenching strength ($\hat{q}/T^3$) from our dynamical energy loss model and convert it to $η/s$ as a function of temperature. It turned out that the first approach can not distinguish between different $(η/s)(T)$ assumptions when the evolution is constrained to reproduce the low-$p_\perp$ data. In distinction, $(η/s)(T)$ calculated using the second approach agrees surprisingly well with the $(η/s)(T)$ inferred through state-of-the-art Bayesian analyses of the low-$p_\perp$ data even in the vicinity of $T_c$, while providing much smaller uncertainties at high temperatures.

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arXiv2022

Utilizing high-$p_\perp$ theory and data to constrain the initial stages of quark-gluon plasma

The scarce knowledge of the initial stages of quark-gluon plasma before the thermalization is mostly inferred through the low-$p_\perp$ sector. We propose a complementary approach in this report - the use of high-$p_\perp$ probes' energy loss. We study the effects of four commonly assumed initial stages, whose temperature profiles differ only before the thermalization, on high-$p_\perp$ $R_{AA}$ and $v_2$ predictions. The predictions are based on our Dynamical Radiative and Elastic ENergy-loss Approach (DREENA) framework. We report insensitivity of $v_2$ to the initial stages, making it unable to distinguish between different cases. $R_{AA}$ displays sensitivity to the presumed initial stages, but current experimental precision does not allow resolution between these cases. We further revise the commonly accepted procedure of fitting the energy loss parameters, for each individual initial stage, to the measured $R_{AA}$. We show that the sensitivity of $v_2$ to various initial stages obtained through such procedure is mostly a consequence of fitting procedure, which may obscure the physical interpretations. Overall, the simultaneous study of high-$p_\perp$ observables, with unchanged energy loss parametrization and restrained temperature profiles, is crucial for future constraints on initial stages.

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arXiv2022

Early evolution constrained by high-$p_T$ QGP tomography

We show that high-$p_T$ $R_{AA}$ and $v_2$ are sensitive to the early expansion dynamics, and that the high-$p_T$ observables prefer delayed onset of energy loss and transverse expansion. To calculate high-$p_T$ $R_{AA}$ and $v_2$, we employ our newly developed DREENA-A framework, which combines state-of-the-art dynamical energy loss model with 3+1-dimensional hydrodynamical simulations. The model applies to both light and heavy flavor, and we predict a larger sensitivity of heavy flavor observables to the onset of transverse expansion. This presents the first time when bulk QGP behavior has been constrained by high-$p_\perp$ observables and related theory, i.e., by so-called QGP tomography.

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arXiv2021

Inferring the main drivers of SARS-CoV-2 global transmissibility by feature selection methods

Identifying the main environmental drivers of SARS-CoV-2 transmissibility in the population is crucial for understanding current and potential future outbursts of COVID-19 and other infectious diseases. To address this problem, we concentrate on the basic reproduction number $R_0$, which is not sensitive to testing coverage and represents transmissibility in an absence of social distancing and in a completely susceptible population. While many variables may potentially influence $R_0$, a high correlation between these variables may obscure the result interpretation. Consequently, we combine Principal Component Analysis with feature selection methods from several regression-based approaches to identify the main demographic and meteorological drivers behind $R_0$. We robustly obtain that country's wealth/development (GDP per capita or Human Development Index) is the most important $R_0$ predictor at the global level, probably being a good proxy for the overall contact frequency in a population. This main effect is modulated by built-up area per capita (crowdedness in indoor space), onset of infection (likely related to increased awareness of infection risks), net migration, unhealthy living lifestyle/conditions including pollution, seasonality, and possibly BCG vaccination prevalence. Also, we argue that several variables that significantly correlate with transmissibility do not directly influence $R_0$ or affect it differently than suggested by naive analysis.

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arXiv2021

COVID-19 severity determinants inferred through ecological and epidemiological modeling

Determinants of COVID-19 clinical severity are commonly assessed by transverse or longitudinal studies of the fatality counts. However, the fatality counts depend both on disease clinical severity and transmissibility, as more infected also lead to more deaths. Moreover, fatality counts (and related measures such as Case Fatality Rate) are dynamic quantities, as they appear with a delay to infections, while different geographic regions generally belong to different points on the epidemics curve. Instead, we use epidemiological modeling to propose a disease severity measure, which accounts for the underlying disease dynamics. The measure corresponds to the ratio of population averaged mortality and recovery rates (m/r). It is independent of the disease transmission dynamics (i.e., the basic reproduction number) and has a direct mechanistic interpretation. We use this measure to assess demographic, medical, meteorological and environmental factors associated with the disease severity. For this, we employ an ecological regression study design and analyze different US states during the first disease outbreak. Principal Component Analysis, followed by univariate and multivariate analyses based on machine learning techniques, is used for selecting important predictors. Without using prior knowledge from clinical studies, we recover significant predictors known to influence disease severity, in particular age, chronic diseases, and racial factors. Additionally, we identify long-term pollution exposure and population density as not widely recognized (though for the pollution previously hypothesized) predictors of the disease severity. Overall, the proposed measure is useful for inferring severity determinants of COVID-19 and other infectious diseases, and the obtained results may aid a better understanding of COVID-19 risks.

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arXiv2021

PM$_{2.5}$ as a major predictor of COVID-19 basic reproduction number in the USA

Many studies have proposed a relationship between COVID-19 transmissibility and ambient pollution levels. However, a major limitation in establishing such associations is to adequately account for complex disease dynamics, influenced by e.g. significant differences in control measures and testing policies. Another difficulty is appropriately controlling the effects of other potentially important factors, due to both their mutual correlations and a limited dataset. To overcome these difficulties, we will here use the basic reproduction number ($R_0$) that we estimate for USA states using non-linear dynamics methods. To account for a large number of predictors (many of which are mutually strongly correlated), combined with a limited dataset, we employ machine-learning methods. Specifically, to reduce dimensionality without complicating the variable interpretation, we employ Principal Component Analysis on subsets of mutually related (and correlated) predictors. Methods that allow feature (predictor) selection, and ranking their importance, are then used, including both linear regressions with regularization and feature selection (Lasso and Elastic Net) and non-parametric methods based on ensembles of weak-learners (Random Forest and Gradient Boost). Through these substantially different approaches, we robustly obtain that PM$_{2.5}$ is a major predictor of $R_0$ in USA states, with corrections from factors such as other pollutants, prosperity measures, population density, chronic disease levels, and possibly racial composition. As a rough magnitude estimate, we obtain that a relative change in $R_0$, with variations in pollution levels observed in the USA, is typically ~30%, which further underscores the importance of pollution in COVID-19 transmissibility.

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arXiv2021

A systems biology approach to COVID-19 progression in a population

A number of models in mathematical epidemiology have been developed to account for control measures such as vaccination or quarantine. However, COVID-19 has brought unprecedented social distancing measures, with a challenge on how to include these in a manner that can explain the data but avoid overfitting in parameter inference. We here develop a simple time-dependent model, where social distancing effects are introduced analogous to coarse-grained models of gene expression control in systems biology. We apply our approach to understand drastic differences in COVID-19 infection and fatality counts, observed between Hubei (Wuhan) and other Mainland China provinces. We find that these unintuitive data may be explained through an interplay of differences in transmissibility, effective protection, and detection efficiencies between Hubei and other provinces. More generally, our results demonstrate that regional differences may drastically shape infection outbursts. The obtained results demonstrate the applicability of our developed method to extract key infection parameters directly from publically available data so that it can be globally applied to outbreaks of COVID-19 in a number of countries. Overall, we show that applications of uncommon strategies, such as methods and approaches from molecular systems biology research to mathematical epidemiology, may significantly advance our understanding of COVID-19 and other infectious diseases.

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arXiv2021

Effects of demographic and weather parameters on COVID-19 basic reproduction number

Timely prediction of the COVID-19 progression is not possible without a comprehensive understanding of environmental factors that may affect the infection transmissibility. Studies addressing parameters that may influence COVID-19 progression relied on either the total numbers of detected cases and similar proxies and/or a small number of analyzed factors, including analysis of regions that display a narrow range of these parameters. We here apply a novel approach, exploiting widespread growth regimes in COVID-19 detected case counts. By applying nonlinear dynamics methods to the exponential regime, we extract basic reproductive number R0 (i.e., the measure of COVID-19 inherent biological transmissibility), applying to the completely naive population in the absence of social distancing, for 118 different countries. We then use bioinformatics methods to systematically collect data on a large number of demographics and weather parameters from these countries, and seek their correlations with the rate of COVID-19 spread. In addition to some of the already reported tendencies, we show a number of both novel results and those that help settle existing disputes: the absence of dependence on wind speed and air pressure, negative correlation with precipitation; significant positive correlation with society development level (human development index) irrespective of testing policies, and percent of the urban population, but an absence of correlation with population density per se. We find a strong positive correlation of transmissibility on alcohol consumption, and the absence of correlation on refugee numbers, contrary to some widespread beliefs. Significant tendencies with health-related factors are reported, including a detailed analysis of the blood type group showing consistent tendencies on Rh factor, and a strong positive correlation of transmissibility with cholesterol levels.

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arXiv2021

Understanding infection progression under strong control measures through universal COVID-19 growth signatures

Widespread growth signatures in COVID-19 confirmed case counts are reported, with sharp transitions between three distinct dynamical regimes (exponential, superlinear and sublinear). Through analytical and numerical analysis, a novel framework is developed that exploits information in these signatures. An approach well known to physics is applied, where one looks for common dynamical features, independently from differences in other factors. These features and associated scaling laws are used as a powerful tool to pinpoint regions where analytical derivations are effective, get an insight into qualitative changes of the disease progression, and infer the key infection parameters. The developed framework for joint analytical and numerical analysis of empirically observed COVID-19 growth patterns can lead to a fundamental understanding of infection progression under strong control measures, applicable to outbursts of both COVID-19 and other infectious diseases.

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arXiv2020

From high $p_\perp$ theory and data to inferring anisotropy of Quark-Gluon Plasma

High $p_\perp$ theory and data are commonly used to study high $p_\perp$ parton interactions with QGP, while low $p_\perp$ data and corresponding models are employed to infer QGP bulk properties. On the other hand, with a proper description of high $p_\perp$ parton-medium interactions, high $p_\perp$ probes become also powerful tomography tools, since they are sensitive to global QGP features, such as different temperature profiles or initial conditions. This tomographic role of high $p_\perp$ probes can be utilized to assess the spatial anisotropy of the QCD matter. With our dynamical energy loss formalism, we show that a (modified) ratio of $R_{AA}$ and $v_2$ presents a reliable and robust observable for straightforward extraction of initial state anisotropy. We analytically estimated the proportionality between the $v_2/(1-R_{AA})$ and anisotropy coefficient $ε_{2L}$, and found surprisingly good agreement with full-fledged numerical calculations. Within the current error bars, the extraction of the anisotropy from the existing data using this approach is still inaccessible. However, with the expected accuracy improvement in the upcoming LHC runs, the anisotropy of the QGP formed in heavy ion collisions can be straightforwardly derived from the data. Such a data-based anisotropy parameter would present an important test to models describing the initial stages of heavy-ion collision and formation of QGP, and demonstrate the usefulness of high $p_\perp$ theory and data in obtaining QGP properties.

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arXiv2020

Dynamical energy loss formalism: from describing suppression patterns to implications for future experiments

Understanding properties of Quark-Gluon Plasma requires an unbiased comparison of experimental data with theoretical predictions. To that end, we developed the dynamical energy loss formalism which, in distinction to most other methods, takes into account a realistic medium composed of dynamical scattering centers. The formalism also allows making numerical predictions for a wide number of observables with the same parameter set fixed to standard literature values. In this proceedings, we overview our recently developed DREENA-C and DREENA-B frameworks, where DREENA is a computational implementation of the dynamical energy loss formalism, and where C stands for constant temperature QCD medium, while B stands for the medium modeled by 1+1D Bjorken expansion. At constant temperature our predictions overestimate $v_2$, in contrast to other models, but consistent with simple analytical estimates. With Bjorken expansion, we have a good agreement of the predictions with both $R_{AA}$ and $v_2$ measurements. We find that introducing medium evolution has a larger effect on $v_2$ predictions, but for precision predictions it has to be taken into account in $R_{AA}$ predictions as well. Based on numerical calculations and simple analytical derivations, we also propose a new observable, which we call path length sensitive suppression ratio, for which we argue that the path length dependence can be assessed in a straightforward manner. We also argue that $Pb+Pb$ vs. $Xe+Xe$ measurements make a good system to assess the path length dependence. As an outlook, we expect that introduction of more complex medium evolution (beyond Bjorken expansion) in the dynamical energy loss formalism can provide a basis for a state of the art QGP tomography tool - e.g. to jointly constrain the medium properties from the point of both high pt and low pt data.

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arXiv2020

Extracting the temperature dependence in high-$p_\perp$ particle energy loss

The suppression of high-$p_\perp$ particles is one of the main signatures of parton energy loss during its passing through the QGP medium, and is reasonably reproduced by different theoretical models. However, a decisive test of the reliability of a certain energy loss mechanism, apart from its path-length, is its temperature dependence. Despite its importance and comprehensive dedicated studies, this issue is still awaiting for more stringent constraints. To this end, we here propose a novel observable to extract temperature dependence exponent of high-$p_{\perp}$ particle's energy loss, based on $R_{AA}$. More importantly, by combining analytical arguments, full-fledged numerical calculations and comparison with experimental data, we argue that this observable is highly suited for testing (and rejecting) the long-standing $ΔE/E \propto L^2 T^3$ paradigm. The anticipated significant reduction of experimental errors will allow direct extraction of temperature dependence, by considering different centrality pair in $A + A$ collisions (irrespective of the nucleus size) in high-$p_\perp$ region. Overall, our results imply that this observable, which reflects the underlying energy loss mechanism, is very important to distinguish between different theoretical models.

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arXiv2019

Shape of the quark gluon plasma droplet reflected in the high-p_T data

We show, through analytic arguments, numerical calculations, and comparison with experimental data, that the ratio of the high-p_T observables v_2/(1-R_AA) reaches a well-defined saturation value at high p_T, and that this ratio depends only on the spatial anisotropy of the quark gluon plasma (QGP) formed in ultrarelativistic heavy-ion collisions. With expected future reduction of experimental errors, the anisotropy extracted from experimental data will further constrain the calculations of initial particle production in heavy-ion collisions and thus test our understanding of QGP physics.

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arXiv2019

Exploring the initial stages in heavy-ion collisions with high-pT RAA and v2 theory and data

Traditionally, low-pT sector is used to infer the features of initial stages before QGP thermalization. On the other hand, recently acquired wealth of high-pT experimental data paves the way to utilize the high-pT particles energy loss in exploring the initial stages. We here study how four different commonly considered initial-stage scenarios which have the same temperature profile after, but differ in the 'temperature' profile before thermalization affect predictions of high-pT RAA and v2 observables. Contrary to common expectations, we obtain that high-pT v2 is insensitive to the initial stages of medium evolution, being unable to discriminate between different conditions. On the other hand, RAA is sensitive to these conditions, however, within the current errorbars, the sensitivity is not sufficient to distinguish between different initial stages. Moreover, we also reconsider the validity of widely-used procedure of fitting the energy loss parameters, individually for different initial-stage cases, to reproduce the experimentally observed RAA. We here find that previously reported sensitivity of v2 to different initial states is mainly a consequence of the RAA fitting procedure, which may lead to incorrect conclusions. On the other hand, if a global property, in particular the same average temperature, is imposed to tested temperature profiles, high sensitivity of high-pT v2 is again obtained. We however show that this sensitivity would not be a consequence of differences in initial, but rather final, stages. Consequently, the simultaneous study of high-pT RAA and v2, with consistent energy loss parametrization and stringently controlled temperature profiles, is necessary to assess sensitivity of different variables to differences in initial and final stages.

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