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Igor Salom

Publications and source records attributed to Igor Salom.

At least 19 recordsLinked to original sources

Reinterpreting Supersymmetry

The mathematically elegant and promising idea of supersymmetry faces severe challenges as its conventional weak-scale realizations are increasingly constrained by the lack of experimental evidence of superpartners. However, we point out that the prediction of new (seemingly non-existing) supersymmetric particles is not a necessity: if we slightly relax our expectations from the supersymmetric models, generators $Q^a_\alpha$ of $N$-extended supersymmetry can be readily interpreted as carrying a quantum number of the internal gauge group, thus connecting particles with different gauge properties. In this view, operators $Q^a_\alpha$ remain ``square roots'' of translations, but do not by themselves generate symmetries of the model. They merely represent transformations that mathematically relate bosonic and fermionic fields; it is only the gauge-invariant combinations of the form $\sum_a \{Q^a_\alpha, \bar Q_{a\beta}\}$ that correspond to spacetime momenta and thus represent symmetries of the model. With this conceptual modification, the simplest Yang-Mills supersymmetric model no longer connects vector bosons with hypothetical gauginos, but with far less exotic chiral fermions, e.g.\ with left-handed leptons. Further adding an $SU(2)$ doublet of Higgs scalars no longer introduces Higgsinos, but a fermion that naturally corresponds to the right-handed lepton, with the familiar Yukawa term showing up as a mathematical necessity. Despite giving up the requirement that operators $Q^a_\alpha$ alone generate symmetries of the action, this approach to supersymmetry is strikingly mathematically similar to standard SUSY, raising hopes that many of the favorable properties of standard supersymmetry can be retained, while potentially reconciling the idea of supersymmetry with experimental data. The approach is still quite restrictive: the relative coefficients of such models are strongly determined.

hep-ph

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

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

Understanding the temperature dependence of the specific shear viscosity $(\eta/s)$ 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 $\eta/s(T)$, 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 $\eta/s$.

hep-ph

2022 Nobel Prize in Physics and the End of Mechanistic Materialism

The ideas and results that are in the background of the 2022 Nobel Prize in physics had an immense impact on our understanding of reality. Therefore, it is crucial that these implications reach also the general public, not only the scientists in the related fields of quantum mechanics. The purpose of this review is to attempt to elucidate these revolutionary changes in our worldview that were eventually acknowledged also by the Nobel's committee, and to do it with very few references to mathematical details (which could be even ignored without undermining the take-away essence of the text). We first look into the foundational disputes between Einstein and Bohr about the nature of quantum mechanics, which culminated in the so-called EPR paradox -- the main impetus for all the research that would ensue in this context. Next, we try to explain the statement of the famous Bell's theorem -- the theorem that relocated the Einstain-Bohr discussions from the realm of philosophy and metaphysics to hard-core physics verifiable by experiments (we also give a brief derivation of the theorem's proof). Then we overview the experimental work of the last year's laureates, that had the final say about who was right in the debate. The outcome of these experiments forced us to profoundly revise our understanding of the universe. Finally, we discuss in more detail the implications of such outcomes, and what are the possible ways that our worldviews can be modified to account for the experimental facts. As we will see, the standard mechanist picture of the universe is no longer a viable option, and can be never again. Nowadays, we know this with certainty unusual for physics, that only a strict mathematical theorem could provide.

physics.gen-ph

Constraining $\eta /s$ 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 $\eta/s$ 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 $(\eta/s)(T)$ 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 $\eta/s$ as a function of temperature. It turned out that the first approach can not distinguish between different $(\eta/s)(T)$ assumptions when the evolution is constrained to reproduce the low-$p_\perp$ data. In distinction, $(\eta/s)(T)$ calculated using the second approach agrees surprisingly well with the $(\eta/s)(T)$ 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.

hep-ph

Importance of higher orders in opacity in QGP tomography

We consider the problem of including a finite number of scattering centers in dynamical energy loss and classical DGLV formalism. Previously, either one or an infinite number of scattering centers were considered in energy loss calculations, while attempts to relax such approximations were largely inconclusive or incomplete. In reality, however, the number of scattering centers is generally estimated to be 4-5 at RHIC and the LHC, making the above approximations (a priori) inadequate and this theoretical problem significant for QGP tomography. We derived explicit analytical expressions for dynamical energy loss and DGLV up to the $4^{th}$ order in opacity, resulting in complex mathematical expressions that were, to our knowledge, obtained for the first time. These expressions were then implemented into an appropriately generalized DREENA framework to calculate the effects of higher orders in opacity on a wide range of high-$p_\perp$ light and heavy flavor predictions. Results of extensive numerical analysis, together with interpretations of nonintuitive results, are presented. We find that, for both RHIC and the LHC, higher-order effects on high-$p_\perp$ observables are small, and the approximation of a single scattering center is adequate for dynamical energy loss and DGLV formalisms.

nucl-th

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.

hep-ph

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.

nucl-th

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.

q-bio.PE

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.

q-bio.PE

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

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

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.

q-bio.PE

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.

q-bio.PE

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.

nucl-th

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.

nucl-th

The hard problem and the measurement problem: a no-go theorem and potential consequences

The "measurement problem" of quantum mechanics, and the "hard problem" of cognitive science are the most profound open problems of the two research fields, and certainly among the deepest of all unsettled conundrums in contemporary science in general. Occasionally, scientists from both fields have suggested some sort of interconnectedness of the two problems. Here we revisit the main motives behind such expectations and try to put them on more formal grounds. We argue not only that such a relation exists, but that it also bears strong implications both for the interpretations of quantum mechanics and for our understanding of consciousness. The paper consists of three parts. In the first part, we formulate a "no-go-theorem" stating that a brain, functioning solely on the principles of classical physics, cannot have any greater ability to induce subjective experience than a process of writing (printing) a certain sequence of digits. The goal is to show, with an attempt to mathematical rigor, why the physicalist standpoint based on classical physics is not likely to ever explain the phenomenon of consciousness -- justifying the tendency to look beyond the physics of the 19th century. In the second part, we aim to establish a clear relation, with a sort of correspondence mapping, between attitudes towards the hard problem and interpretations of quantum mechanics. Then we discuss these connections in the light of the no-go theorem, pointing out that the existence of subjective experience might differentiate between otherwise experimentally indistinguishable interpretations. Finally, the third part is an attempt to illustrate how quantum mechanics could take us closer to the solution of the hard problem and break the constraints set by the no-go theorem.

physics.hist-ph

Quantum dynamics of the small-polaron formation in a superconducting analog simulator

We propose a scheme for investigating the nonequilibrium aspects of small-polaron physics using an array of superconducting qubits and microwave resonators. This system, which can be realized with transmon or gatemon qubits, serves as an analog simulator for a lattice model describing a nonlocal coupling of a quantum particle (excitation) to dispersionless phonons. We study its dynamics following an excitation-phonon (qubit-resonator) interaction quench using a numerically exact approach based on a Chebyshev-moment expansion of the time-evolution operator of the system. We thereby glean heretofore unavailable insights into the process of the small-polaron formation resulting from strongly momentum-dependent excitation-phonon interactions, most prominently about its inherent dynamical timescale. To further characterize this complex process, we evaluate the excitation-phonon entanglement entropy and show that initially prepared bare-excitation Bloch states here dynamically evolve into small-polaron states that are close to being maximally entangled. Finally, by computing the dynamical variances of the phonon position and momentum quadratures, we demonstrate a pronounced non-Gaussian character of the latter states, with a strong antisqueezing in both quadratures.

quant-ph