arXiv subjects

Dusan Zigic

Publications and source records attributed to Dusan Zigic.

16 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.

hep-ph↗

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

Importance of higher harmonics and $v_4$ puzzle in quark-gluon plasma tomography

QGP tomography aims to constrain the parameters characterizing the properties and evolution of Quark-Gluon Plasma (QGP) formed in heavy-ion collisions, by exploiting low and high-$p_\perp$ theory and data. Higher-order harmonics $v_n$ ($n>2$) are an important -- but seldom explored -- part of this approach. However, to take full advantage of them, several issues have to be addressed: i) consistency of different methods for calculating $v_n$, ii) importance of event-by-event fluctuations to high-$p_\perp$ $R_{AA}$ and $v_2$ predictions, iii) sensitivity of higher harmonics to the initial state of fluid-dynamical evolution. We obtain that i) several methods for calculating harmonics are compatible with each other, ii) event-by-event calculations are important in mid-central collisions, and iii) various initializations of the evolution of the medium lead to quantitatively and qualitatively different predictions, likely to be distinguished by future measurements. We also find that the present high-$p_\perp$ $v_4$ data cannot be reproduced using initial states for fluid-dynamical evolution given by state-of-the-art models. We call this discrepancy high-$p_\perp$ $v_4$ puzzle at the LHC.

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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

DREENA-A framework as a QGP tomography tool

We present a fully optimised framework DREENA-A based on a state-of-the-art energy loss model. The framework can include any, in principle arbitrary, temperature profile within the dynamical energy loss formalism. Thus, 'DREENA' stands for Dynamical Radiative and Elastic ENergy loss Approach, while 'A' stands for Adaptive. DREENA-A does not use fitting parameters within the energy loss model, allowing it to fully exploit differences in temperature profiles which are the only input in the framework. The framework applies to light and heavy flavor observables, different collision energies, and large and smaller systems. This, together with the ability to systematically compare data and predictions within the same formalism and parameter set, makes DREENA-A a unique multipurpose QGP tomography tool.

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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.

q-bio.PE↗

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.

q-bio.PE↗

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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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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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.

hep-ph↗

arXiv2019

DREENA-B framework: first predictions of $R_{AA}$ and $v_2$ within dynamical energy loss formalism in evolving QCD medium

Dynamical energy loss formalism allows generating state-of-the-art suppression predictions in finite size QCD medium, employing a sophisticated model of high-$p_\perp$ parton interactions with QGP. We here report a major step of introducing medium evolution in the formalism though $1+1D$ Bjorken (``B'') expansion, while preserving all complex features of the original dynamical energy loss framework. We use this framework to provide joint $R_{AA}$ and $v_2$ predictions, for the first time within the dynamical energy loss formalism in evolving QCD medium. The predictions are generated for a wide range of observables, i.e. for all types of probes (both light and heavy) and for all centrality regions in both $Pb+Pb$ and $Xe+Xe$ collisions at the LHC. Where experimental data are available, DREENA-B framework leads to a good joint agreement with $v_2$ and $R_{AA}$ data. Such agreement is encouraging, i.e. may lead us closer to resolving $v_2$ puzzle (difficulty of previous models to jointly explain $R_{AA}$ and $v_2$ data), though this still remains to be thoroughly tested by including state-of-the-art medium evolution within DREENA framework. While introducing medium evolution significantly changes $v_2$ predictions, $R_{AA}$ predictions remain robust and moreover in a good agreement with the experimental data; $R_{AA}$ observable is therefore suitable for calibrating parton-medium interaction model, independently from the medium evolution. Finally, for heavy flavor, we observe a strikingly similar signature of the dead-cone effect on both $R_{AA}$ and $v_2$ - we also provide a simple analytical understanding behind this result. Overall, the results presented here indicate that DREENA framework is a reliable tool for QGP tomography.

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arXiv2018

How to test path-length dependence in energy loss mechanisms: analysis leading to a new observable

When traversing QCD medium, high $p_\perp$ partons lose energy, which is typically measured by suppression, and also predicted by various energy loss models. A crucial test of different energy loss mechanisms is how the energy loss depends on the length of traversed medium (so-called path-length dependence). The upcoming experimental results will allow to, at least in principle, for the first time, clearly observe how the energy loss changes with the size of the medium, in particular, by comparing already available $Pb+Pb$ measurements with now upcoming $Xe+Xe$ data at the LHC. However, in practice, to actually perform such test, it becomes crucial to chose an optimal observable. With respect to this, a ratio of observed suppression for the two systems may seem a natural (and frequently mentioned) choice. We, however, show that extracting the path-length dependence from this observable would not be possible. We here provide an analytical derivation based on simple scaling arguments, as well as detailed numerical calculations based on our advanced energy loss framework, showing that a different observable is suitable for this purpose. We call this observable path-length sensitive suppression ratio ($R_L^{AB}$) and provide our predictions before experimental data become available. This predictions also clearly show that this observable will allow a simple comparison of the related theoretical models with the experimental data, and consequently to distinguish between different (underlying) energy loss mechanisms, which is in turn crucial for understanding properties of created QCD medium.

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arXiv2018

Joint $R_{AA}$ and $v_2$ predictions for $Pb+Pb$ collisions at the LHC within DREENA-C framework

In this paper, we presented our recently developed DREENA-C framework, which is a fully optimized computational suppression procedure based on our state-of-the-art dynamical energy loss formalism in constant temperature finite size QCD medium. With this framework, we for the first time, generated joint $R_{AA}$ and $v_2$ predictions within our dynamical energy loss formalism. The predictions are generated for both light and heavy flavor probes, and different centrality regions in $Pb+Pb$ collisions at the LHC, and compared with the available experimental data. Despite the fact that DREENA-C does not contain medium evolution (to which $v_2$ is largely sensitive) and the fact that other approaches faced difficulties in explaining $v_2$ data, we find that DREENA-C leads to qualitatively good agreement with this data, though quantitatively, the predictions are visibly above the experimental data. Intuitive explanation behind such results is presented, supporting the validity of our model, and it is expected that introduction of evolution in the ongoing improvement of DREENA framework, will lead to better joint agreement with $R_{AA}$ and $v_2$ data, and allow better understanding of the underlying QCD medium.

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