What is the significance of the Bayesian inference in this analysis?

The application of Bayesian inference in this analysis represents a significant advance in quantitative understanding of jet-medium interactions in the quark-gluon plasma.

What is the first parametrization of q?

The first parametrization uses Eq. (12) to calculate q̂ in both MATTER and LBT stages, while introducing an additional parameter Q0 that represents the virtuality boundary between the two stages.

What is the value of the MATTER vacuum shower?

In the pre-equilibrium (τ0 < 0.6 fm) and the late (T < Tstop) stages, only MATTER vacuum shower is applied with the value of Q0 = 1 GeV.

Why is the covariance matrix of measurement uncertainty difficult to determine?

Because the luminosity and 〈TAA〉 uncertainties are independent of pT , it is straightforward to calculate their contribution to the off-diagonal terms of the covariance matrix.

What is the name of the second parametrization for MATTER+LBT?

To reduce the number of parameters and capture the jet physics of virtuality evolution in MATTER more precisely, the authors introduce a second q̂ parametrization for the multistage MATTER+LBT model, as follows:q̂(Q, E , T )|Q0,A,C,D T 3 = 42CR ζ (3) π( 4π9)2{A[ln ( Q ) − ln (Q0 )] [ ln ( Q )]2 θ (Q − Q0) + C [ ln ( E T ) − ln(D)][ ln ( ET 2 )]2 } . (13)This parametrization is denoted “MATTER+LBT2”.

How many training points are added to the model?

Based on their preliminary posterior distribution after the entire analysis procedure (as will be discussed in the following sections), the authors sequentially add more training points–20 points in [0, 0.4] × [0, 0.4], 40 points in [0, 1] × [0, 1], and then 20 points in [0, 0.6] × [0, 0.75]—and repeat the calibration procedure several times to ensure that sufficient training points have been sampled within the region where the peaks of the posterior distributions of their model parameters locate.

How many training points are sampled in the LBT model?

For instance, for the LBT model calibration described later, the authors start with 60 training points uniformly sampled within A × C ∈ [0, 2] × [0, 2].

What is the minimum assumption that q is proportional to the entropy density?

As discussed in [90], the minimal assumption that q̂ is proportional to the entropy density s in the local rest frame, q̂/s = q̂0/s0, is able to describe single inclusive hadron and jet RAA, but distinct values of q̂0 at a given reference point s0 are required at RHIC and the LHC.

Why is the parton shower a function of virtuality?

The motivation behind this parametrization is that the MATTER model better characterizes the parton shower as a function of virtuality.

Open AccessJournal Article10.1103/PHYSREVC.104.024905

Determining the jet transport coefficient q ̂ from inclusive hadron suppression measurements using Bayesian parameter estimation

Q: How many CPU hours have been used for this study?

Since multiple colliding systems, model setups and training points are utilized for this study, over 10 million CPU hours have been utilized in total on the Open Science Grid.

Q: What is the energy loss formalism in this paper?

The energy loss formalism in this paper involves the convolution of initial state and hard scattering distributions with energy loss calculations applied to hard partons as they propagate through the medium.

Q: What is the value of q before jets enter the thermal medium?

The value of q̂ is zero before jets enter the thermal medium (τ0 < 0.6 fm) and after they exit the QGP; in both regions only the vacuum splitting function contributes to the parton shower.

Q: What is the effect of a lower value of Tstop?

If a lower value of Tstop is used, the jet energy loss will increase and a smaller value of q̂ will be extracted from the jet quenching data, though the effect is expected to be small due to the minor enhancement of jet energy loss at such low temperature.

Q: Why is the covariance matrix of measurement uncertainty difficult to determine?

Because the luminosity and 〈TAA〉 uncertainties are independent of pT , it is straightforward to calculate their contribution to the off-diagonal terms of the covariance matrix.

Q: What is the name of the second parametrization for MATTER+LBT?

To reduce the number of parameters and capture the jet physics of virtuality evolution in MATTER more precisely, the authors introduce a second q̂ parametrization for the multistage MATTER+LBT model, as follows:q̂(Q, E , T )|Q0,A,C,D T 3 = 42CR ζ (3) π( 4π9)2{A[ln ( Q ) − ln (Q0 )] [ ln ( Q )]2 θ (Q − Q0) + C [ ln ( E T ) − ln(D)][ ln ( ET 2 )]2 } . (13)This parametrization is denoted “MATTER+LBT2”.

Q: How many training points are added to the model?

Based on their preliminary posterior distribution after the entire analysis procedure (as will be discussed in the following sections), the authors sequentially add more training points–20 points in [0, 0.4] × [0, 0.4], 40 points in [0, 1] × [0, 1], and then 20 points in [0, 0.6] × [0, 0.75]—and repeat the calibration procedure several times to ensure that sufficient training points have been sampled within the region where the peaks of the posterior distributions of their model parameters locate.

Shanshan Cao, +46 more

- 01 Aug 2021

- Physical Review C

- Vol. 104, Iss: 2, pp 024905

100

TL;DR: Cao et al. as mentioned in this paper used the JETSCAPE framework, which incorporates a novel multistage theoretical approach to in-medium jet evolution and Bayesian inference for parameter extraction.

Abstract: Author(s): Cao, S; Chen, Y; Coleman, J; Mulligan, J; Jacobs, PM; Soltz, RA; Angerami, A; Arora, R; Bass, SA; Cunqueiro, L; Dai, T; Du, L; Ehlers, R; Elfner, H; Everett, D; Fan, W; Fries, RJ; Gale, C; Garza, F; He, Y; Heffernan, M; Heinz, U; Jacak, BV; Jeon, S; Ke, W; Kim, B; Kordell, I; Kumar, A; Majumder, A; Mak, S; McNelis, M; Nattrass, C; Oliinychenko, D; Park, C; Paquet, JF; Putschke, JH; Roland, G; Silva, A; Schenke, B; Schwiebert, L; Shen, C; Sirimanna, C; Tachibana, Y; Vujanovic, G; Wang, XN; Wolpert, RL; Xu, Y | Abstract: We report a new determination of q, the jet transport coefficient of the quark-gluon plasma. We use the JETSCAPE framework, which incorporates a novel multistage theoretical approach to in-medium jet evolution and Bayesian inference for parameter extraction. The calculations, based on the Matter and Lbt jet quenching models, are compared to experimental measurements of inclusive hadron suppression in Au+Au collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and Pb+Pb collisions at the CERN Large Hadron Collider (LHC). The correlation of experimental systematic uncertainties is accounted for in the parameter extraction. The functional dependence of q on jet energy or virtuality and medium temperature is based on a perturbative picture of in-medium scattering, with components reflecting the different regimes of applicability of Matter and Lbt. In the multistage approach, the switch between Matter and Lbt is governed by a virtuality scale Q0. Comparison of the posterior model predictions to the RHIC and LHC hadron suppression data shows reasonable agreement, with moderate tension in limited regions of phase space. The distribution of q/T3 extracted from the posterior distributions exhibits weak dependence on jet momentum and medium temperature T, with 90% credible region (CR) depending on the specific choice of model configuration. The choice of Matter+Lbt, with switching at virtuality Q0, has 90% CR of 240 GeV/c. The value of Q0, determined here for the first time, is in the range 2.0-2.7 GeV.

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Most frequently asked questions

1. How many CPU hours have been used for this study?

Since multiple colliding systems, model setups and training points are utilized for this study, over 10 million CPU hours have been utilized in total on the Open Science Grid.

2. What is the energy loss formalism in this paper?

The energy loss formalism in this paper involves the convolution of initial state and hard scattering distributions with energy loss calculations applied to hard partons as they propagate through the medium.

3. What is the value of q before jets enter the thermal medium?

The value of q̂ is zero before jets enter the thermal medium (τ0 < 0.6 fm) and after they exit the QGP; in both regions only the vacuum splitting function contributes to the parton shower.

4. What is the effect of a lower value of Tstop?

If a lower value of Tstop is used, the jet energy loss will increase and a smaller value of q̂ will be extracted from the jet quenching data, though the effect is expected to be small due to the minor enhancement of jet energy loss at such low temperature.

FIG. 16. 90% CR regions for the quark-jet transport coefficient q̂ using MATTER+LBT2 (a) as function of medium temperature, and (b) as function of quark energy. The lines at the center of the bands indicate their median values. The data points (black circles with vertical error bars) show the result from the JET Collaboration [66]; dotted boxes indicate the range of that analysis.

TABLE I. Median values of posterior parameter distributions for the various model parametrizations. Note that the median values do not take account of correlations between the parameters.

FIG. 4. Example closure test for a single design point. The truth (solid line) is compared to the inferred 90% range of q̂ (band delineated by dashed lines), as a function of (a) temperature and (b) jet momentum.

FIG. 5. Distribution of p values from the closure tests performed using all the design points.

FIG. 11. Posterior predictive distributions of MATTER+LBT1 compared to the same data as Fig. 6.

FIG. 12. Posterior distribution of the four-dimensional space for MATTER+LBT1. Off-diagonal panels show correlations of posterior distributions for RHIC+LHC (lower left, red) and LHC only (upper right, blue).

Citations

•Journal Article•10.1088/1674-1137/aca4c1

Effects of the formation time of parton shower on jet quenching in heavy-ion collisions

Mengxue 梦雪 Zhang 张, +3 more

- 01 Feb 2023

- Chinese Physics C

TL;DR: In this article , the effects of jet formation time on jet quenching in relativistic nuclear collisions were analyzed using a Linear Boltzmann transport (LBT) model that incorporates both elastic and inelastic scatterings between jet partons and the thermal medium.

...read moreread less

•Journal Article•10.1103/PHYSREVD.104.L071501

n / s − q ^ / T 3 relation at next-to-leading order in QCD

Berndt Müller

- 07 Oct 2021

- Physical Review D

TL;DR: The relation between the specific shear viscosity and the dimensionless jet quenching parameter in perturbative QCD is explored at next-to-leading order in the coupling constant as discussed by the authors.

...read moreread less

•Proceedings Article•10.22323/1.398.0298

Jet substructure measurements in heavy-ion collisions with ALICE

Amanda L Brown

- 01 Mar 2022

TL;DR: The ALICE detector is particularly well-suited to jet substructure measurements in heavy-ion collisions due to its high-precision tracking system as discussed by the authors , which provides exciting new opportunities to study jet quenching in heavy ion collisions and to reveal the nature of the quark-gluon plasma.

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•Journal Article•10.1088/1674-1137/acb8a4

Atomic mass, Bjorken variable, and scale dependence of quark transport coefficient in Drell-Yan process for proton incident on nucleus

W. Xu, +2 more

- 01 Apr 2023

- Chinese Physics C

TL;DR: In this paper , the nuclear parton distributions were determined without the fixed-target Drell-Yan experimental data and the analytic expression of quenching weight based on the BDMPS formalism, next-to-leading order analyses were performed on the DrellYan differential cross section ratios from the Fermilab E906 and E866 collaborations.

...read moreread less

•Posted Content•10.48550/arxiv.2212.12188

Comprehensive Study of Multi-scale Jet-medium Interaction

23 Dec 2022

TL;DR: In this article , the authors explore jet-medium interactions at various scales in high-energy heavy-ion collisions using the JETSCAPE framework, and the physics of the multi-stage modeling and the coherence effect at high virtuality are discussed through the results of multiple jet and high-p_{\mathrm{T} particle observables, compared with experimental data.

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References

•Journal Article•10.1086/670067

emcee: The MCMC Hammer

Daniel Foreman-Mackey, +4 more

- 25 Feb 2013

- Publications of the Astronomical Society...

TL;DR: The emcee algorithm as mentioned in this paper is a Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (2010).

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PYTHIA 6.4 Physics and Manual

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- 09 May 2006

- Journal of High Energy Physics

TL;DR: The Pythia program as mentioned in this paper can be used to generate high-energy-physics ''events'' (i.e. sets of outgoing particles produced in the interactions between two incoming particles).

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The design and analysis of computer experiments

Thomas J. Santner, +2 more

- 01 Nov 1989

- Statistical Science

TL;DR: This paper presents a meta-modelling framework for estimating Output from Computer Experiments-Predicting Output from Training Data and Criteria Based Designs for computer Experiments.

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Marc C. Kennedy, +1 more

- 01 Jan 2001

- Journal of The Royal Statistical Society...

TL;DR: A Bayesian calibration technique which improves on this traditional approach in two respects and attempts to correct for any inadequacy of the model which is revealed by a discrepancy between the observed data and the model predictions from even the best‐fitting parameter values is presented.

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Determining the jet transport coefficient q ̂ from inclusive hadron suppression measurements using Bayesian parameter estimation

Chat with Paper

AI Agents for this Paper

Most frequently asked questions

1. How many CPU hours have been used for this study?

2. What is the energy loss formalism in this paper?

3. What is the value of q before jets enter the thermal medium?

4. What is the effect of a lower value of Tstop?

5. What is the significance of the Bayesian inference in this analysis?

6. What is the first parametrization of q?

7. What is the value of the MATTER vacuum shower?

8. Why is the covariance matrix of measurement uncertainty difficult to determine?

9. What is the name of the second parametrization for MATTER+LBT?

10. How many training points are added to the model?

11. How many training points are sampled in the LBT model?

12. What is the minimum assumption that q is proportional to the entropy density?

13. Why is the parton shower a function of virtuality?

Figures

Citations

Effects of the formation time of parton shower on jet quenching in heavy-ion collisions

n / s − q ^ / T 3 relation at next-to-leading order in QCD

Jet substructure measurements in heavy-ion collisions with ALICE

Atomic mass, Bjorken variable, and scale dependence of quark transport coefficient in Drell-Yan process for proton incident on nucleus

Comprehensive Study of Multi-scale Jet-medium Interaction

References

Scikit-learn: Machine Learning in Python

emcee: The MCMC Hammer

PYTHIA 6.4 Physics and Manual

The design and analysis of computer experiments

Bayesian Calibration of computer models

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