Cumulants and correlation functions of net-proton, proton, and antiproton multiplicity distributions in Au+Au collisions at energies available at the BNL Relativistic Heavy Ion Collider
TL;DR: Abdelallah et al. as discussed by the authors reported a systematic measurement of cumulants, Cn, for net-proton, proton, and antiproton multiplicity distributions, and correlation functions, κn, in the first phase of the Beam Energy Scan (BES) program at the BNL Relativistic Heavy Ion Collider (RHIC) facility.
Abstract: Author(s): Abdallah, MS; Adam, J; Adamczyk, L; Adams, JR; Adkins, JK; Agakishiev, G; Aggarwal, I; Aggarwal, MM; Ahammed, Z; Alekseev, I; Anderson, DM; Aparin, A; Aschenauer, EC; Ashraf, MU; Atetalla, FG; Attri, A; Averichev, GS; Bairathi, V; Baker, W; Ball Cap, JG; Barish, K; Behera, A; Bellwied, R; Bhagat, P; Bhasin, A; Bielcik, J; Bielcikova, J; Bordyuzhin, IG; Brandenburg, JD; Brandin, AV; Bunzarov, I; Butterworth, J; Cai, XZ; Caines, H; Calderon De La Barca Sanchez, M; Cebra, D; Chakaberia, I; Chaloupka, P; Chan, BK; Chang, FH; Chang, Z; Chankova-Bunzarova, N; Chatterjee, A; Chattopadhyay, S; Chen, D; Chen, J; Chen, JH; Chen, X; Chen, Z; Cheng, J; Chevalier, M; Choudhury, S; Christie, W; Chu, X; Crawford, HJ; Csanad, M; Daugherity, M; Dedovich, TG; Deppner, IM; Derevschikov, AA; Dhamija, A; Di Carlo, L; Didenko, L; Dong, X; Drachenberg, JL; Dunlop, JC; Elsey, N; Engelage, J; Eppley, G; Esumi, S; Evdokimov, O; Ewigleben, A; Eyser, O; Fatemi, R; Fawzi, FM; Fazio, S; Federic, P; Fedorisin, J; Feng, CJ; Feng, Y; Filip, P; Finch, E; Fisyak, Y; Francisco, A; Fu, C | Abstract: We report a systematic measurement of cumulants, Cn, for net-proton, proton, and antiproton multiplicity distributions, and correlation functions, κn, for proton and antiproton multiplicity distributions up to the fourth order in Au+Au collisions at sNN=7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4, and 200 GeV. The Cn and κn are presented as a function of collision energy, centrality and kinematic acceptance in rapidity, y, and transverse momentum, pT. The data were taken during the first phase of the Beam Energy Scan (BES) program (2010-2017) at the BNL Relativistic Heavy Ion Collider (RHIC) facility. The measurements are carried out at midrapidity (|y|l 0.5) and transverse momentum 0.4
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University of North Carolina at Chapel Hill1, University of Nantes2, Ohio State University3, University of Connecticut4, McGill University5, University of Houston6, Indiana University7, Stony Brook University8, Brookhaven National Laboratory9, Lawrence Berkeley National Laboratory10, University of Illinois at Chicago11, North Carolina State University12, University of Illinois at Urbana–Champaign13, University of Washington14, University of Wuppertal15, Michigan State University16, Massachusetts Institute of Technology17, Wayne State University18, University of Minnesota19, University of Chicago20, University of California, Los Angeles21, Chinese Academy of Sciences22
TL;DR: The Beam Energy Scan Theory (BEST) Collaboration was formed with the goal of providing a theoretical framework for analyzing data from the BES program at the relativistic heavy ion collider (RHIC) at Brookhaven National Laboratory as mentioned in this paper.
36 citations
01 Jan 2022
TL;DR: The Beam Energy Scan Theory (BEST) Collaboration was formed with the goal of providing a theoretical framework for analyzing data from the BES program at the relativistic heavy ion collider (RHIC) at Brookhaven National Laboratory as mentioned in this paper .
Abstract: The Beam Energy Scan Theory (BEST) Collaboration was formed with the goal of providing a theoretical framework for analyzing data from the Beam Energy Scan (BES) program at the relativistic heavy ion collider (RHIC) at Brookhaven National Laboratory. The physics goal of the BES program is the search for a conjectured QCD critical point as well as for manifestations of the chiral magnetic effect. We describe progress that has been made over the previous five years. This includes studies of the equation of state and equilibrium susceptibilities, the development of suitable initial state models, progress in constructing a hydrodynamic framework that includes fluctuations and anomalous transport effects, as well as the development of freezeout prescriptions and hadronic transport models. Finally, we address the challenge of integrating these components into a complete analysis framework. This document describes the collective effort of the BEST Collaboration and its collaborators around the world.
36 citations
TL;DR: In this paper , the proton multiplicity distribution from dedicated fixed-target Au+Au collisions at 3.0 GeV is reported, which is less than unity, the Poisson baseline.
Abstract: We report cumulants of the proton multiplicity distribution from dedicated fixed-target Au+Au collisions at 3.0 GeV, measured by the STAR experiment in the kinematic acceptance of rapidity ($y$) and transverse momentum ($p_{\rm T}$) within $-0.5 < y<0$ and $0.4 < p_{\rm T} <2.0 $ GeV/$c$. In the most central 0--5\% collisions, a proton cumulant ratio is measured to be $C_4/C_2=-0.85 \pm 0.09 ~(\rm stat.) \pm 0.82 ~(\rm syst.)$, which is less than unity, the Poisson baseline. The hadronic transport UrQMD model reproduces our $C_4/C_2$ in the measured acceptance. Compared to higher energy results and the transport model calculations, the suppression in $C_4/C_2$ is consistent with fluctuations driven by baryon number conservation and indicates an energy regime dominated by hadronic interactions. These data imply that the QCD critical region, if created in heavy-ion collisions, could only exist at energies higher than 3\,GeV.
21 citations
TL;DR: In this paper , it was shown that the variance of net-baryon distribution normalized by the Skellam distribution baseline is sensitive to the possible modification of (anti)baryons yields due to annihilation in the hadronic phase.
12 citations
TL;DR: In this article , the experimental status of the search for the QCD critical point via the measurements of cumulants of net-particle distributions in heavy ion collisions is reviewed and various experimental challenges and associated corrections in such fluctuation measurements.
11 citations
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TL;DR: In this article, the authors discuss the problem of estimating the sampling distribution of a pre-specified random variable R(X, F) on the basis of the observed data x.
Abstract: We discuss the following problem given a random sample X = (X 1, X 2,…, X n) from an unknown probability distribution F, estimate the sampling distribution of some prespecified random variable R(X, F), on the basis of the observed data x. (Standard jackknife theory gives an approximate mean and variance in the case R(X, F) = \(\theta \left( {\hat F} \right) - \theta \left( F \right)\), θ some parameter of interest.) A general method, called the “bootstrap”, is introduced, and shown to work satisfactorily on a variety of estimation problems. The jackknife is shown to be a linear approximation method for the bootstrap. The exposition proceeds by a series of examples: variance of the sample median, error rates in a linear discriminant analysis, ratio estimation, estimating regression parameters, etc.
14,483 citations
TL;DR: The American Statistical Association (ASA) released a policy statement on p-values and statistical significance in 2015 as discussed by the authors, which was based on a discussion with the ASA Board of Trustees and concerned with reproducibility and replicability of scientific conclusions.
Abstract: Cobb’s concern was a long-worrisome circularity in the sociology of science based on the use of bright lines such as p< 0.05: “We teach it because it’s what we do; we do it because it’s what we teach.” This concern was brought to the attention of the ASA Board. The ASA Board was also stimulated by highly visible discussions over the last few years. For example, ScienceNews (Siegfried 2010) wrote: “It’s science’s dirtiest secret: The ‘scientific method’ of testing hypotheses by statistical analysis stands on a flimsy foundation.” A November 2013, article in Phys.org Science News Wire (2013) cited “numerous deep flaws” in null hypothesis significance testing. A ScienceNews article (Siegfried 2014) on February 7, 2014, said “statistical techniques for testing hypotheses...havemore flaws than Facebook’s privacy policies.” Aweek later, statistician and “Simply Statistics” blogger Jeff Leek responded. “The problem is not that people use P-values poorly,” Leek wrote, “it is that the vast majority of data analysis is not performed by people properly trained to perform data analysis” (Leek 2014). That same week, statistician and science writer Regina Nuzzo published an article in Nature entitled “Scientific Method: Statistical Errors” (Nuzzo 2014). That article is nowone of the most highly viewedNature articles, as reported by altmetric.com (http://www.altmetric.com/details/2115792#score). Of course, it was not simply a matter of responding to some articles in print. The statistical community has been deeply concerned about issues of reproducibility and replicability of scientific conclusions. Without getting into definitions and distinctions of these terms, we observe that much confusion and even doubt about the validity of science is arising. Such doubt can lead to radical choices, such as the one taken by the editors of Basic andApplied Social Psychology, who decided to ban p-values (null hypothesis significance testing) (Trafimow and Marks 2015). Misunderstanding or misuse of statistical inference is only one cause of the “reproducibility crisis” (Peng 2015), but to our community, it is an important one. When the ASA Board decided to take up the challenge of developing a policy statement on p-values and statistical significance, it did so recognizing this was not a lightly taken step. The ASA has not previously taken positions on specific matters of statistical practice. The closest the association has come to this is a statement on the use of value-added models (VAM) for educational assessment (Morganstein and Wasserstein 2014) and a statement on risk-limiting post-election audits (American Statistical Association 2010). However, these were truly policy-related statements. The VAM statement addressed a key educational policy issue, acknowledging the complexity of the issues involved, citing limitations of VAMs as effective performance models, and urging that they be developed and interpreted with the involvement of statisticians. The statement on election auditing was also in response to a major but specific policy issue (close elections in 2008), and said that statistically based election audits should become a routine part of election processes. By contrast, the Board envisioned that the ASA statement on p-values and statistical significance would shed light on an aspect of our field that is too often misunderstood and misused in the broader research community, and, in the process, provides the community a service. The intended audience would be researchers, practitioners, and science writers who are not primarily statisticians. Thus, this statementwould be quite different from anything previously attempted. The Board tasked Wasserstein with assembling a group of experts representing a wide variety of points of view. On behalf of the Board, he reached out to more than two dozen such people, all of whom said theywould be happy to be involved. Several expressed doubt about whether agreement could be reached, but those who did said, in effect, that if there was going to be a discussion, they wanted to be involved. Over the course of many months, group members discussed what format the statement should take, tried to more concretely visualize the audience for the statement, and began to find points of agreement. That turned out to be relatively easy to do, but it was just as easy to find points of intense disagreement. The time came for the group to sit down together to hash out these points, and so in October 2015, 20 members of the group met at the ASA Office in Alexandria, Virginia. The 2-day meeting was facilitated by Regina Nuzzo, and by the end of the meeting, a good set of points around which the statement could be built was developed. The next 3 months saw multiple drafts of the statement, reviewed by group members, by Board members (in a lengthy discussion at the November 2015 ASA Board meeting), and by members of the target audience. Finally, on January 29, 2016, the Executive Committee of the ASA approved the statement. The statement development process was lengthier and more controversial than anticipated. For example, there was considerable discussion about how best to address the issue of multiple potential comparisons (Gelman and Loken 2014). We debated at some length the issues behind the words “a p-value near 0.05 taken by itself offers only weak evidence against the null
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