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Nonmonotonic Energy Dependence of Net-Proton Number Fluctuations

J. Adam1, L. Adamczyk2, J. R. Adams3, J. K. Adkins4  +357 moreInstitutions (58)
05 Mar 2021-Physical Review Letters (American Physical Society (APS))-Vol. 126, Iss: 9, pp 092301
TL;DR: In this paper, the first evidence of a non-monotonic variation in the kurtosis times variance of the net-proton number (proxy for net-baryon number) distribution as a function of collision energy was reported.
Abstract: Nonmonotonic variation with collision energy (sqrt[s_{NN}]) of the moments of the net-baryon number distribution in heavy-ion collisions, related to the correlation length and the susceptibilities of the system, is suggested as a signature for the quantum chromodynamics critical point. We report the first evidence of a nonmonotonic variation in the kurtosis times variance of the net-proton number (proxy for net-baryon number) distribution as a function of sqrt[s_{NN}] with 3.1 σ significance for head-on (central) gold-on-gold (Au+Au) collisions measured solenoidal tracker at Relativistic Heavy Ion Collider. Data in noncentral Au+Au collisions and models of heavy-ion collisions without a critical point show a monotonic variation as a function of sqrt[s_{NN}].

Summary (2 min read)

Introduction

  • Acidovorax avenae subsp. avenae (Pseudomonas avenae) and A. avenae subsp.
  • Citrulli (synonymous with P. pseudoalcaligenes subsp. citrulli) have emerged worldwide as serious pathogens on corn (Zea mays L.) [40] and watermelon (Citrullus lanatus (Thunb.) Matsumura and Nakai) [42], respectively.
  • The above nonfluorescent phytopathogenic pseudomonads together with P. pseudoalcaligenes subsp.
  • Strains of P. avenae [P. rubrilineans] from corn, sugar cane, Indian shot (Canna indica), teosinte, finger millet, and ‘‘P. setariae’’ from rice were all classified as A. avenae subsp.
  • Cattleyae; and strains of P. pseudoalcaligenes subsp. konjaci [15] from konjac were classified as A. konjaci [55].

Materials and methods

  • Source of strains and confirmation of identity.
  • All the strains used in this study were obtained from the International Collection of Phytopathogenic Bacteria (ICPB) maintained at the USDA, ARS Foreign DiseaseWeed Science Research Unit , Fort Detrick, MD or from other recognized culture collections (Table S-1).
  • Each culture was streaked onto yeast extract-dextrose CaCO3 (YDC) agar [23] and beigetan colored, transparent, round, non-mucoid, convex colonies were retained.
  • Cultures were maintained on YDC slants at room temperature and also archived at 80 1C [38].

Pathogenicity

  • All strains originating from rice were pathogenic to both rice and corn (Table S-2).
  • In contrast, strains from corn were pathogenic to corn but not rice (Table S-2).
  • Avenae strains from rice that had a mean reciprocal similarity of 97%; taxon C contained 11 A. avenae subsp.
  • FMean reciprocal % DNA relatedness calculated from pair-wise, heterologous tests between two taxa.

AFLP

  • The AFLP procedure was carried out as described previously [44].
  • Briefly, an AFLP template was prepared for PCR using a combination of MseI and EcoRI restriction endonucleases.
  • The data were analyzed with GelCompar (v. 4.2) software (Applied Maths, Kortrijk, Belgium) and dendrograms were generated using the unweighted pair group method with averages .
  • Strains of A. anthurii and A. valerianellae were not included in the AFLP analysis.

Phenotypic characters

  • Cells were grown overnight in liquid NBY shake cultures unless stated otherwise.
  • Growth at 4 and 41 1C was determined by liquid NBY shake cultures in a New Brunswick Scientific (Edison, NJ) Innova refrigerated incubator shaker with a temperature variance of 70.1 1C.
  • All tests were repeated twice and read after 7 and 14 days, unless stated otherwise.
  • The type strain and several additional strains (Table S-1) of each recognized phytopathogenic species and subspecies of Acidovorax along with the type strain of the genus, A. facilis, were included.

Fatty acid analysis

  • The procedures used to prepare, extract, and differentiate fatty acids by gas–liquid chromatography have been described previously [39].
  • The fatty acid profiles of A. facilis strains, and each phytopathogen, including the newly described A. anthurii and A. valerianellae, were compared with those in the Sherlocks Microbial Identification System MIDI database (MIDI, Inc., Newark, DE) and were used to determine the Euclidian distance to Acidovorax spp.

16S rDNA and 16S–23S rDNA internal transcribed spacer (ITS) region sequencing

  • The phytopathogenic A. avenae strains from rice, tea, corn, watermelon, and orchid, were highly related by 16S rDNA sequencing forming a tight cluster with three lineages (Fig. 2).
  • The percentage similarity values among the A. avenae strains ranged from 99.7% to 100% (1–5 nucleotides different).
  • The strains from corn and rice differed by two nucleotides (nts).
  • The newly described plant pathogenic A. anthurii formed a separate ARTICLE IN PRESS N.W. Schaad et al. / Systematic and Applied Microbiology 31 (2008) 434–446440 group more closely related to A. konjaci, whereas A. valerianellae was more distant (Fig. 2).

AFLP analysis

  • Similarities between the non-phytopathogenic strains (A. facilis, C. testosteroni, and P. pseudoalcaligenes) and the phytopathogenic strains (except A. konjaci) were less than 18% (Fig. 1).
  • These AFLP data correlated highly with DNA/DNA homology groupings: strains that fell into the same DNA/DNA homology group also clustered closely with AFLP.
  • Phylogenetic analysis of AFLP patterns revealed four major host-based clusters among the phytopathogenic strains: corn (with minimal internal linkage of 68%), rice (44%), cucurbits (58%), and orchids (68%).
  • Strains from konjac clustered into a distinct group separately from the other phytopathogens at a similarity coefficient of less than 20%.

Fatty acids

  • None of the phytopathogenic strains, except A. valerianellae, A. anthurii and A. konjaci, contained the 3-hydroxyoctanoic acid (8:0 3-OH).
  • In contrast, fatty acid 8:0 3-OH was present in A. facilis (Table S-4) and the other non-phytopathogenic species [18].

Discussion

  • The results support the elevation of two subspecies of Acidovorax to species rank, and the naming of a new species.
  • Within this group, three subgroups were delineated; subgroup one contained strains of [P.] avenae, [P.] rubrilineans, and ‘‘[P.] setariae’’ which were 75–100% similar; subgroup two contained strains of [P.] cattleyae which were 95% similar; and subgroup three contained strains of [P.] pseudoalcaligenes subsp.
  • Discrepancies between the DNA/ DNA reassociation data of Willems et al. [55] and their findings are most likely due to differences in protocols used for DNA/DNA reassociation.
  • A corn strains and Group B rice strains have less than 70% DNA/DNA similarity, differ serologically [24], have distinct protein patterns [25], and can be differentiated phenotypically, the authors propose a new species, A. oryzae, for Group B strains from rice.

Protologues

  • Acidovorax oryzae can be distinguished from A. avenae, A. citrulli, and A. cattleyae by DNA/DNA reassociation assays (Table 1), AFLP analysis (Fig. 1), and several phenotypic traits (Tables 2 and S-3) [14,16].
  • A. cattleyae utilizes D-arabitol, whereas A. avenae and A. oryzae do not.

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Title
Nonmonotonic Energy Dependence of Net-Proton Number Fluctuations.
Permalink
https://escholarship.org/uc/item/19m4w50f
Journal
Physical review letters, 126(9)
ISSN
0031-9007
Authors
Adam, J
Adamczyk, L
Adams, JR
et al.
Publication Date
2021-03-01
DOI
10.1103/physrevlett.126.092301
Peer reviewed
eScholarship.org Powered by the California Digital Library
University of California

Non-monotonic energy dependence of net-proton number fluctuations
J. Adam
6
, L. Adamczyk
2
, J. R. Adams
39
, J. K. Adkins
30
, G. Agakishiev
28
, M. M. Aggarwal
41
, Z. Ahammed
61
, I. Alekseev
3,35
,
D. M. Anderson
55
, A. Aparin
28
, E. C. Aschenauer
6
, M. U. Ashraf
11
, F. G. Atetalla
29
, A. Attri
41
, G. S. Averichev
28
,
V. Bairathi
53
, K. Barish
10
, A. Behera
52
, R. Bellwied
20
, A. Bhasin
27
, J. Bielcik
14
, J. Bielcikova
38
, L. C. Bland
6
,
I. G. Bordyuzhin
3
, J. D. Brandenburg
6
, A. V. Brandin
35
, J. Butterworth
45
, H. Caines
64
, M. Calder
´
on de la Barca S
´
anchez
8
,
D. Cebra
8
, I. Chakaberia
29,6
, P. Chaloupka
14
, B. K. Chan
9
, F-H. Chang
37
, Z. Chang
6
, N. Chankova-Bunzarova
28
,
A. Chatterjee
11
, D. Chen
10
, J. Chen
49
, J. H. Chen
18
, X. Chen
48
, Z. Chen
49
, J. Cheng
57
, M. Cherney
13
, M. Chevalier
10
,
S. Choudhury
18
, W. Christie
6
, X. Chu
6
, H. J. Crawford
7
, M. Csan
´
ad
16
, M. Daugherity
1
, T. G. Dedovich
28
, I. M. Deppner
19
,
A. A. Derevschikov
43
, L. Didenko
6
, X. Dong
31
, J. L. Drachenberg
1
, J. C. Dunlop
6
, T. Edmonds
44
, N. Elsey
63
, J. Engelage
7
,
G. Eppley
45
, S. Esumi
58
, O. Evdokimov
12
, A. Ewigleben
32
, O. Eyser
6
, R. Fatemi
30
, S. Fazio
6
, P. Federic
38
, J. Fedorisin
28
,
C. J. Feng
37
, Y. Feng
44
, P. Filip
28
, E. Finch
51
, Y. Fisyak
6
, A. Francisco
64
, L. Fulek
2
, C. A. Gagliardi
55
, T. Galatyuk
15
,
F. Geurts
45
, A. Gibson
60
, K. Gopal
23
, X. Gou
49
, D. Grosnick
60
, W. Guryn
6
, A. I. Hamad
29
, A. Hamed
5
, S. Harabasz
15
,
J. W. Harris
64
, S. He
11
, W. He
18
, X. H. He
26
, Y. He
49
, S. Heppelmann
8
, S. Heppelmann
42
, N. Herrmann
19
, E. Hoffman
20
,
L. Holub
14
, Y. Hong
31
, S. Horvat
64
, Y. Hu
18
, H. Z. Huang
9
, S. L. Huang
52
, T. Huang
37
, X. Huang
57
, T. J. Humanic
39
,
P. Huo
52
, G. Igo
9
, D. Isenhower
1
, W. W. Jacobs
25
, C. Jena
23
, A. Jentsch
6
, Y. JI
48
, J. Jia
6,52
, K. Jiang
48
, S. Jowzaee
63
, X. Ju
48
,
E. G. Judd
7
, S. Kabana
53
, M. L. Kabir
10
, S. Kagamaster
32
, D. Kalinkin
25
, K. Kang
57
, D. Kapukchyan
10
, K. Kauder
6
,
H. W. Ke
6
, D. Keane
29
, A. Kechechyan
28
, M. Kelsey
31
, Y. V. Khyzhniak
35
, D. P. Kikoła
62
, C. Kim
10
, B. Kimelman
8
,
D. Kincses
16
, T. A. Kinghorn
8
, I. Kisel
17
, A. Kiselev
6
, M. Kocan
14
, L. Kochenda
35
, L. K. Kosarzewski
14
, L. Kramarik
14
,
P. Kravtsov
35
, K. Krueger
4
, N. Kulathunga Mudiyanselage
20
, L. Kumar
41
, S. Kumar
26
, R. Kunnawalkam Elayavalli
63
,
J. H. Kwasizur
25
, R. Lacey
52
, S. Lan
11
, J. M. Landgraf
6
, J. Lauret
6
, A. Lebedev
6
, R. Lednicky
28
, J. H. Lee
6
, Y. H. Leung
31
,
C. Li
49
, C. Li
48
, W. Li
45
, W. Li
50
, X. Li
48
, Y. Li
57
, Y. Liang
29
, R. Licenik
38
, T. Lin
55
, Y. Lin
11
, M. A. Lisa
39
, F. Liu
11
, H. Liu
25
,
P. Liu
52
, P. Liu
50
, T. Liu
64
, X. Liu
39
, Y. Liu
55
, Z. Liu
48
, T. Ljubicic
6
, W. J. Llope
63
, R. S. Longacre
6
, N. S. Lukow
54
,
S. Luo
12
, X. Luo
11
, G. L. Ma
50
, L. Ma
18
, R. Ma
6
, Y. G. Ma
50
, N. Magdy
12
, R. Majka
64
, D. Mallick
36
, S. Margetis
29
,
C. Markert
56
, H. S. Matis
31
, J. A. Mazer
46
, N. G. Minaev
43
, S. Mioduszewski
55
, B. Mohanty
36
, I. Mooney
63
, Z. Moravcova
14
,
D. A. Morozov
43
, M. Nagy
16
, J. D. Nam
54
, Md. Nasim
22
, K. Nayak
11
, D. Neff
9
, J. M. Nelson
7
, D. B. Nemes
64
, M. Nie
49
,
G. Nigmatkulov
35
, T. Niida
58
, L. V. Nogach
43
, T. Nonaka
58
, A. S. Nunes
6
, G. Odyniec
31
, A. Ogawa
6
, S. Oh
31
,
V. A. Okorokov
35
, B. S. Page
6
, R. Pak
6
, A. Pandav
36
, Y. Panebratsev
28
, B. Pawlik
40
, D. Pawlowska
62
, H. Pei
11
, C. Perkins
7
,
L. Pinsky
20
, R. L. Pint
´
er
16
, J. Pluta
62
, J. Porter
31
, M. Posik
54
, N. K. Pruthi
41
, M. Przybycien
2
, J. Putschke
63
, H. Qiu
26
,
A. Quintero
54
, S. K. Radhakrishnan
29
, S. Ramachandran
30
, R. L. Ray
56
, R. Reed
32
, H. G. Ritter
31
, O. V. Rogachevskiy
28
,
J. L. Romero
8
, L. Ruan
6
, J. Rusnak
38
, N. R. Sahoo
49
, H. Sako
58
, S. Salur
46
, J. Sandweiss
64
, S. Sato
58
, W. B. Schmidke
6
,
N. Schmitz
33
, B. R. Schweid
52
, F. Seck
15
, J. Seger
13
, M. Sergeeva
9
, R. Seto
10
, P. Seyboth
33
, N. Shah
24
, E. Shahaliev
28
,
P. V. Shanmuganathan
6
, M. Shao
48
, A. I. Sheikh
29
, W. Q. Shen
50
, S. S. Shi
11
, Y. Shi
49
, Q. Y. Shou
50
, E. P. Sichtermann
31
,
R. Sikora
2
, M. Simko
38
, J. Singh
41
, S. Singha
26
, N. Smirnov
64
, W. Solyst
25
, P. Sorensen
6
, H. M. Spinka
4
, B. Srivastava
44
,
T. D. S. Stanislaus
60
, M. Stefaniak
62
, D. J. Stewart
64
, M. Strikhanov
35
, B. Stringfellow
44
, A. A. P. Suaide
47
, M. Sumbera
38
,
B. Summa
42
, X. M. Sun
11
, X. Sun
12
, Y. Sun
48
, Y. Sun
21
, B. Surrow
54
, D. N. Svirida
3
, P. Szymanski
62
, A. H. Tang
6
,
Z. Tang
48
, A. Taranenko
35
, T. Tarnowsky
34
, J. H. Thomas
31
, A. R. Timmins
20
, D. Tlusty
13
, M. Tokarev
28
, C. A. Tomkiel
32
,
S. Trentalange
9
, R. E. Tribble
55
, P. Tribedy
6
, S. K. Tripathy
16
, O. D. Tsai
9
, Z. Tu
6
, T. Ullrich
6
, D. G. Underwood
4
, I. Upsal
49,6
,
G. Van Buren
6
, J. Vanek
38
, A. N. Vasiliev
43
, I. Vassiliev
17
, F. Videbæk
6
, S. Vokal
28
, S. A. Voloshin
63
, F. Wang
44
, G. Wang
9
,
J. S. Wang
21
, P. Wang
48
, Y. Wang
11
, Y. Wang
57
, Z. Wang
49
, J. C. Webb
6
, P. C. Weidenkaff
19
, L. Wen
9
, G. D. Westfall
34
,
H. Wieman
31
, S. W. Wissink
25
, R. Witt
59
, Y. Wu
10
, Z. G. Xiao
57
, G. Xie
31
, W. Xie
44
, H. Xu
21
, N. Xu
31
, Q. H. Xu
49
, Y. F. Xu
50
,
Y. Xu
49
, Z. Xu
6
, Z. Xu
9
, C. Yang
49
, Q. Yang
49
, S. Yang
6
, Y. Yang
37
, Z. Yang
11
, Z. Ye
45
, Z. Ye
12
, L. Yi
49
, K. Yip
6
, Y. Yu
49
,
H. Zbroszczyk
62
, W. Zha
48
, C. Zhang
52
, D. Zhang
11
, S. Zhang
48
, S. Zhang
50
, X. P. Zhang
57
, Y. Zhang
48
, Y. Zhang
11
,
Z. J. Zhang
37
, Z. Zhang
6
, Z. Zhang
12
, J. Zhao
44
, C. Zhong
50
, C. Zhou
50
, X. Zhu
57
, Z. Zhu
49
, M. Zurek
31
, M. Zyzak
17
1
Abilene Christian University, Abilene, Texas 79699
2
AGH University of Science and Technology, FPACS, Cracow 30-059, Poland
3
Alikhanov Institute for Theoretical and Experimental Physics NRC ”Kurchatov Institute”, Moscow 117218, Russia
4
Argonne National Laboratory, Argonne, Illinois 60439
5
American University of Cairo, New Cairo 11835, New Cairo, Egypt
6
Brookhaven National Laboratory, Upton, New York 11973
7
University of California, Berkeley, California 94720
8
University of California, Davis, California 95616
9
University of California, Los Angeles, California 90095
10
University of California, Riverside, California 92521
11
Central China Normal University, Wuhan, Hubei 430079

2
12
University of Illinois at Chicago, Chicago, Illinois 60607
13
Creighton University, Omaha, Nebraska 68178
14
Czech Technical University in Prague, FNSPE, Prague 115 19, Czech Republic
15
Technische Universit
¨
at Darmstadt, Darmstadt 64289, Germany
16
ELTE E
¨
otv
¨
os Lor
´
and University, Budapest, Hungary H-1117
17
Frankfurt Institute for Advanced Studies FIAS, Frankfurt 60438, Germany
18
Fudan University, Shanghai, 200433
19
University of Heidelberg, Heidelberg 69120, Germany
20
University of Houston, Houston, Texas 77204
21
Huzhou University, Huzhou, Zhejiang 313000
22
Indian Institute of Science Education and Research (IISER), Berhampur 760010 , India
23
Indian Institute of Science Education and Research (IISER) Tirupati, Tirupati 517507, India
24
Indian Institute Technology, Patna, Bihar 801106, India
25
Indiana University, Bloomington, Indiana 47408
26
Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou, Gansu 730000
27
University of Jammu, Jammu 180001, India
28
Joint Institute for Nuclear Research, Dubna 141 980, Russia
29
Kent State University, Kent, Ohio 44242
30
University of Kentucky, Lexington, Kentucky 40506-0055
31
Lawrence Berkeley National Laboratory, Berkeley, California 94720
32
Lehigh University, Bethlehem, Pennsylvania 18015
33
Max-Planck-Institut f
¨
ur Physik, Munich 80805, Germany
34
Michigan State University, East Lansing, Michigan 48824
35
National Research Nuclear University MEPhI, Moscow 115409, Russia
36
National Institute of Science Education and Research, HBNI, Jatni 752050, India
37
National Cheng Kung University, Tainan 70101
38
Nuclear Physics Institute of the CAS, Rez 250 68, Czech Republic
39
Ohio State University, Columbus, Ohio 43210
40
Institute of Nuclear Physics PAN, Cracow 31-342, Poland
41
Panjab University, Chandigarh 160014, India
42
Pennsylvania State University, University Park, Pennsylvania 16802
43
NRC ”Kurchatov Institute”, Institute of High Energy Physics, Protvino 142281, Russia
44
Purdue University, West Lafayette, Indiana 47907
45
Rice University, Houston, Texas 77251
46
Rutgers University, Piscataway, New Jersey 08854
47
Universidade de S
˜
ao Paulo, S
˜
ao Paulo, Brazil 05314-970
48
University of Science and Technology of China, Hefei, Anhui 230026
49
Shandong University, Qingdao, Shandong 266237
50
Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800
51
Southern Connecticut State University, New Haven, Connecticut 06515
52
State University of New York, Stony Brook, New York 11794
53
Instituto de Alta Investigaci
´
on, Universidad de Tarapac
´
a, Arica 1000000, Chile
54
Temple University, Philadelphia, Pennsylvania 19122
55
Texas A&M University, College Station, Texas 77843
56
University of Texas, Austin, Texas 78712
57
Tsinghua University, Beijing 100084
58
University of Tsukuba, Tsukuba, Ibaraki 305-8571, Japan
59
United States Naval Academy, Annapolis, Maryland 21402
60
Valparaiso University, Valparaiso, Indiana 46383
61
Variable Energy Cyclotron Centre, Kolkata 700064, India
62
Warsaw University of Technology, Warsaw 00-661, Poland
63
Wayne State University, Detroit, Michigan 48201 and
64
Yale University, New Haven, Connecticut 06520
(STAR Collaboration)
Non-monotonic variation with collision energy (
s
NN
) of the moments of the net-baryon number distribu-
tion in heavy-ion collisions, related to the correlation length and the susceptibilities of the system, is suggested
as a signature for the Quantum Chromodynamics (QCD) critical point. We report the first evidence of a non-
monotonic variation in kurtosis times variance of the net-proton number (proxy for net-baryon number) dis-
tribution as a function of
s
NN
with 3.1σ significance, for head-on (central) gold-on-gold (Au+Au) collisions
measured using the STAR detector at RHIC. Data in non-central Au+Au collisions and models of heavy-ion
collisions without a critical point show a monotonic variation as a function of
s
NN
.

3
One of the fundamental goals in physics is to understand
the properties of matter when subjected to variations in tem-
perature and pressure. Currently, the study of the phases of
strongly interacting nuclear matter is the focus of many re-
search activities worldwide, both theoretically and experimen-
tally [1, 2]. The theory that governs the strong interactions
is Quantum Chromodynamics (QCD), and the correspond-
ing phase diagram is called the QCD phase diagram. From
different examples of condensed-matter systems, experimen-
tal progress in mapping out phase diagrams is achieved by
changing the material doping, adding more holes than elec-
trons. Similarly it is suggested for the QCD phase diagram,
that adding more quarks than antiquarks (the energy required
is defined by the baryonic chemical potential, µ
B
), through
changing the heavy-ion collision energy, enables a search for
new emergent properties and a possible critical point in the
phase diagram. The phase diagram of QCD has at least two
distinct phases: a Quark Gluon Plasma (QGP) at higher tem-
peratures, and a state of confined quarks and gluons at lower
temperatures called the hadronic phase [3–5]. It is inferred
from lattice QCD calculations [6] that the transition is consis-
tent with being a cross over at small µ
B
, and that the transi-
tion temperature is about 155 MeV [7–9]. An important pre-
dicted feature of the QCD phase structure is a critical point
[10, 11], followed at higher µ
B
by a first order phase transi-
tion. Attempts are being made to locate the predicted critical
point both experimentally and theoretically. Current theoreti-
cal calculations are highly uncertain about the location of the
critical point. Lattice QCD calculations at finite µ
B
face nu-
merical challenges in computing [12, 13]. Within these lim-
itations, the current best estimate from lattice QCD is that if
there is a critical point, its location is likely above µ
B
300
MeV [12, 13]. The goal of this work is to search for possible
signatures of the critical point by varying the collision energy
in heavy ion collisions to cover a wide range in effective tem-
perature (T ) and µ
B
in the QCD phase diagram [14].
Another key aspect of investigating the QCD phase diagram
is to determine whether the system has attained thermal equi-
librium. Several theoretical interpretations of experimental
data have the underlying assumption that the system produced
in the collisions should have come to local thermal equilib-
rium during its evolution. Experimental tests of thermaliza-
tion for these femto-scale expanding systems are non-trivial.
However, the yields of produced hadrons and fluctuations of
multiplicity distributions related to conserved quantities have
been studied and shown to have characteristics of thermody-
namic equilibrium for higher collision energies [12, 15–20].
Upon approaching a critical point, the correlation length di-
verges and thus renders, to a large extent, microscopic details
irrelevant. Hence observables like the moments of the con-
served net-baryon number distribution, which are sensitive to
the correlation length, are of interest when searching for a crit-
ical point. A non-monotonic variation of these moments as a
function of
s
NN
has been proposed as an experimental sig-
nature of a critical point [10, 14]. However, considering the
complexity of the system formed in heavy-ion collisions, sig-
natures of a critical point are detectable only if they can sur-
vive the evolution of the system, including the effects of finite
size and time [21]. Hence, it was proposed to study higher
moments of distributions of conserved quantities (N) due to
their stronger dependence on the correlation length [11]. The
promising higher moments are the skewness, S =
(δN)
3
/σ
3
,
and kurtosis, κ = [
(δN)
4
/σ
4
] 3, where δN = N M, M
is the mean and σ is the standard deviation. The magnitude
and the sign of the moments, which quantify the shape of
the multiplicity distributions, are important for understanding
the critical point [14, 22]. An additional crucial experimental
challenge is to measure, on an event-by-event basis, all of the
baryons produced within the acceptance of a detector [23–25].
However, theoretical calculations have shown that the proton-
number fluctuations can also reflect the baryon-number fluc-
tuations at the critical point [23, 26].
The measurements reported here are from Au+Au colli-
sions recorded by the STAR detector [27] at RHIC from
the years 2010 to 2017. The data is presented for
s
NN
=
7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4 and 200 GeV as
part of phase-I of the Beam Energy Scan (BES) program at
RHIC [15]. These
s
NN
values correspond to µ
B
values rang-
ing from 420 MeV to 20 MeV at chemical freeze-out [15].
All valid Au+Au collisions occurring within 60 cm (80 cm
for
s
NN
= 7.7 GeV) of the nominal interaction point along
the beam axis are selected. For the results presented here, the
number of minimum bias Au+Au collisions ranges between
3 million for
s
NN
= 7.7 GeV and 585 million at
s
NN
=
54.4 GeV. These statistics are found to be adequate to make
the measurements of the moments of the net-proton distribu-
tions up to the fourth order [28]. The collisions are further
divided into centrality classes characterised by their impact
parameter, which is the closest distance between the centroid
of two nuclei passing by. In practice, the impact parame-
ter is determined indirectly from the measured multiplicity of
charged particles other than protons (p) and anti-protons ( ¯p) in
the pseudo-rapidity range |η| < 1, where η = ln[tan(θ/2)],
with θ being the angle between the momentum of the parti-
cle and the positive direction of the beam axis. We exclude
p and ¯p while classifying events based on impact parame-
ter specifically to avoid self-correlation effects [29]. The ef-
fect of self-correlation potentially arising due to the decay of
heavier hadrons into p( ¯p) and other charged particles has been
checked to be negligible from a study using standard heavy-
ion collision event generators, HIJING [30] and UrQMD [31].
The effect of resonance decays and the pseudo-rapidity range
for centrality determination have been understood and opti-
mized using model calculations [32, 33]. The results pre-
sented here correspond to two event classes: central collisions
(impact parameters 0-3 fm, obtained from the top 5% of
the above-mentioned multiplicity distribution) and peripheral
collisions (impact parameters 12-13 fm, obtained from the
70-80% region of the multiplicity distribution).
The protons and anti-protons are identified, along with their
momenta, by reconstructing their tracks in the Time Projec-
tion Chamber (TPC) placed within a solenoidal magnetic field
of 0.5 Tesla, and by measuring their ionization energy loss
(dE/dx) in the sensitive gas-filled volume of the chamber.
The selected kinematic region for protons covers all azimuthal
angles for the rapidity range |y| < 0.5, where rapidity y is the

4
10 0 10 20 30 40
0
0.02
0.04
0.06
0.08
0.1
7.7
11.5
14.5
19.6
27
39
54.4
62.4
200
(GeV)
NN
s
Au+Au Collisions
0-5% Central
< 2.0 (GeV/c), |y| < 0.5
T
0.4 < p
Normalized Number of Events
Net-proton (N
p
= N
p
- N
p
)
FIG. 1. Event-by-event net-proton number distributions for head-on
(0-5% central) Au+Au collisions for nine
s
NN
values measured by
STAR. The distributions are normalized to the total number of events
at each
s
NN
. The statistical uncertainties are smaller than the sym-
bol sizes and the lines are shown to guide the eye. The distributions
in this figure are not corrected for proton and anti-proton detection
efficiency. The deviation of the distribution for
s
NN
= 54.4 GeV
from the general energy dependence trend is understood to be due to
the reconstruction efficiency of protons and anti-protons being dif-
ferent compared to other energies.
inverse hyperbolic tangent of the component of speed parallel
to the beam direction in units of the speed of light. The pre-
cise measurement of dE/dx with a resolution of 7% in Au+Au
collisions allows for a clear identification of protons up to 800
MeV/c in transverse momentum (p
T
). The identification for
larger p
T
(up to 2 GeV/c, with purity above 97%) is made by
a Time Of Flight detector (TOF) [34] having a timing resolu-
tion of better than 100 ps. A minimum p
T
threshold of 400
MeV/c and a maximum distance of closest approach to the
collision vertex of 1 cm for each p( ¯p) candidate track is used
to suppress contamination from secondaries and other back-
grounds [15, 35]. This p
T
acceptance accounts for approx-
imately 80% of the total p + ¯p multiplicity at mid-rapidity.
This is a significant improvement from the results previously
reported [35] which only had the p + ¯p measured using the
TPC. The observation of non-monotonic variation of the kur-
tosis times variance (κσ
2
) with energy is much more signif-
icant with the increased acceptance. For the rapidity depen-
dence of the observable see Supplemental Material [34].
Figure 1 shows the event-by-event net-proton (N
p
N
¯p
=
N
p
) distributions obtained by measuring the number of pro-
tons (N
p
) and anti-protons (N
¯p
) at mid-rapidity (|y| < 0.5) in
the transverse momentum range 0.4 < p
T
(GeV/c)< 2.0 for
Au+Au collisions at various
s
NN
. To study the shape of
the event-by-event net-proton distribution in detail, cumulants
(C
n
) of various orders are calculated, where C
1
= M, C
2
= σ
2
,
C
3
= Sσ
3
and C
4
= κσ
4
.
Figure 2 shows the net-proton cumulants (C
n
) as a function
of
s
NN
for central and peripheral (see Supplemental Mate-
rial [34] for a zoomed version). Au+Au collisions. The cumu-
lants are corrected for the multiplicity variations arising due
to finite impact parameter range for the measurements [32].
These corrections suppress the volume fluctuations consid-
erably [32, 36]. A different volume fluctuation correction
method [37] has been applied to the 0-5% central Au+Au col-
lision data and the results were found to be consistent with
those shown in Fig 2 . The cumulants are also corrected for
finite track reconstruction efficiencies of the TPC and TOF
0
10
20
30
40
(1) C
1
0 - 5%
70 - 80%
5 10 20 50 100 200
0
10
20
30
40
(3) C
3
0
10
20
30
40
(2) C
2
0
20
40
60
80
100
5 10 20 50 100 200
(4) C
4
Stat. uncertainty
Syst. uncertainty
Net-proton Cumulants
(GeV)
NN
sCollision Energy
Au+Au CollisionsAu+Au Collisions
Net-proton
< 2.0 (GeV/c)
T
|y| < 0.5, 0.4 < p
FIG. 2. Cumulants (C
n
) of the net-proton distributions for central
(0-5%) and peripheral (70-80%) Au+Au collisions as a function of
collision energy. The transverse momentum (p
T
) range for the mea-
surements is from 0.4 to 2 GeV/c and the rapidity (y) range is -0.5 <
y < 0.5.
detectors. This is done by assuming a binomial response of
the two detectors [35, 38]. A cross-check using a different
method based on unfolding [34] of the distributions for central
Au+Au collisions at
s
NN
= 200 GeV has been found to give
values consistent with the cumulants shown in Fig. 2. Further,
the efficiency correction method used has been verified in a
Monte Carlo calculation. Typical values for the efficiencies
in the TPC (TOF-matching) for the momentum range stud-
ied in 0-5% central Au+Au collisions at
s
NN
= 7.7 GeV are
83%(72%) and 81%(70%) for the protons and anti-protons,
respectively. The corresponding efficiencies for
s
NN
= 200
GeV collisions are 62%(69%) and 60%(68%) for the protons
and anti-protons, respectively. The statistical uncertainties
are obtained using both a bootstrap approach [28, 38] and
the Delta theorem [28, 38, 39] method. The systematic un-
certainties are estimated by varying the experimental require-
ments to reconstruct p ( ¯p) in the TPC and TOF. These require-
ments include the distance of the proton and anti-proton tracks
from the primary vertex position, track quality reflected by the
number of TPC space points used in the track reconstruction,
the particle identification criteria passing certain selection cri-
teria, and the uncertainties in estimating the reconstruction ef-
ficiencies. The systematic uncertainties at different collision
energies are uncorrelated.
The large values of C
3
and C
4
for central Au+Au collisions
show that the distributions have non-Gaussian shapes, a possi-
ble indication of enhanced fluctuations arising from a possible
critical point [11, 22]. The corresponding values for periph-
eral collisions are small and close to zero. For central colli-
sions, the C
1
and C
3
monotonically decrease with increasing
s
NN
.
We employ ratios of cumulants in order to cancel volume
variations to first order. Further, these ratios of cumulants
are related to the ratio of baryon-number susceptibilities. The

Citations
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Journal ArticleDOI
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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References
More filters
Journal ArticleDOI
12 Oct 2006-Nature
TL;DR: Finite-size scaling analysis shows that the finite-temperature QCD transition in the hot early Universe was not a real phase transition, but an analytic crossover (involving a rapid change, as opposed to a jump, as the temperature varied).
Abstract: The standard model of particle physics predicts two phase transitions that are relevant for the evolution of the early Universe. One, the quantum chromodynamics transition, involves the strong force that binds quarks into protons and neutrons. Despite much theoretical effort, the nature of this transition remains ambiguous. Now Aoki et al. report computationally demanding calculations that suggest that there was no true phase transition. Instead, an analytic crossover took place, involving a rapid, continuous change with temperature as opposed to a jump. This means that it will be difficult to find experimental evidence of a transition from astronomical observations. The standard model of particle physics predicts two transitions that are relevant for the evolution of the early Universe. Computationally demanding calculations now reveal that a real phase transition did not occur, but rather an analytic crossover, involving a rapid change (as opposed to a jump) as the temperature varies. Quantum chromodynamics (QCD) is the theory of the strong interaction, explaining (for example) the binding of three almost massless quarks into a much heavier proton or neutron—and thus most of the mass of the visible Universe. The standard model of particle physics predicts a QCD-related transition that is relevant for the evolution of the early Universe. At low temperatures, the dominant degrees of freedom are colourless bound states of hadrons (such as protons and pions). However, QCD is asymptotically free, meaning that at high energies or temperatures the interaction gets weaker and weaker1,2, causing hadrons to break up. This behaviour underlies the predicted cosmological transition between the low-temperature hadronic phase and a high-temperature quark–gluon plasma phase (for simplicity, we use the word ‘phase’ to characterize regions with different dominant degrees of freedom). Despite enormous theoretical effort, the nature of this finite-temperature QCD transition (that is, first-order, second-order or analytic crossover) remains ambiguous. Here we determine the nature of the QCD transition using computationally demanding lattice calculations for physical quark masses. Susceptibilities are extrapolated to vanishing lattice spacing for three physical volumes, the smallest and largest of which differ by a factor of five. This ensures that a true transition should result in a dramatic increase of the susceptibilities. No such behaviour is observed: our finite-size scaling analysis shows that the finite-temperature QCD transition in the hot early Universe was not a real phase transition, but an analytic crossover (involving a rapid change, as opposed to a jump, as the temperature varied). As such, it will be difficult to find experimental evidence of this transition from astronomical observations.

1,606 citations

Journal ArticleDOI
TL;DR: In this paper, hadron-hadron collisions at high energies are investigated in the ultra-relativistic-quantum-molecular-dynamics approach (UrQMD), designed to study pp, pA and A+A collisions.
Abstract: Hadron-hadron collisions at high energies are investigated in the Ultra-relativistic-Quantum-Molecular-Dynamics approach (UrQMD). This microscopic transport model is designed to study pp, pA and A+A collisions. It describes the phenomenology of hadronic interactions at low and intermediate energies ($\sqrt s 5$ GeV, the excitation of color strings and their subsequent fragmentation into hadrons dominates the multiple production of particles in the UrQMD model. The model shows a fair overall agreement with a large body of experimental h-h data over a wide range of h-h center-of-mass energies. Hadronic reaction data with higher precision would be useful to support the use of the UrQMD model for relativistic heavy ion collisions.

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TL;DR: A Monte Carlo event generator HIJING is developed to study jet and multiparticle production in high energy {ital pp, {ital pA}, and {ital AA} collisions, and a schematic mechanism of jet interactions in dense matter is described.
Abstract: Combining perturbative-QCD inspired models for multiple jet production with low ${p}_{T}$ multistring phenomenology, we develop a Monte Carlo event generator hijing to study jet and multiparticle production in high energy $\mathrm{pp}$, $\mathrm{pA}$, and $\mathrm{AA}$ collisions. The model includes multiple minijet production, nuclear shadowing of parton distribution functions, and a schematic mechanism of jet interactions in dense matter. Glauber geometry for multiple collisions is used to calculate $\mathrm{pA}$ and $\mathrm{AA}$ collisions. The phenomenological parameters are adjusted to reproduce essential features of $\mathrm{pp}$ multiparticle production data for a wide energy range ($\sqrt{s}=5\ensuremath{-}2000$ GeV). Illustrative tests of the model on $p+A$ and light-ion $B+A$ data at $\sqrt{s}=20$ GeV/nucleon and predictions for Au+Au at energies of the BNL Relativistic Heavy Ion Collider ($\sqrt{s}=200$ GeV/nucleon) are given.

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Abstract: Hadron-hadron (h-h) collisions at high energies are investigated in the ultra-relativistic quantum molecular dynamics (UrQMD) approach. This microscopic transport model describes the phenomenology of hadronic interactions at low and intermediate energies ( 5 GeV, the excitation of colour strings and their subsequent fragmentation into hadrons dominates the multiple production of particles in the UrQMD model. The model shows a fair overall agreement with a large body of experimental h-h data over a wide range of h-h centre-of-mass energies. Hadronic reaction data with higher precision would be useful to support the use of the UrQMD model for relativistic heavy-ion collisions.

1,151 citations

Journal ArticleDOI
TL;DR: In this article, the chiral and deconfinement properties of the QCD transition at finite temperature were investigated using the p4, asqtad, and HISQ/tree actions.
Abstract: We present results on the chiral and deconfinement properties of the QCD transition at finite temperature. Calculations are performed with $2+1$ flavors of quarks using the p4, asqtad, and HISQ/tree actions. Lattices with temporal extent ${N}_{\ensuremath{\tau}}=6$, 8, and 12 are used to understand and control discretization errors and to reliably extrapolate estimates obtained at finite lattice spacings to the continuum limit. The chiral transition temperature is defined in terms of the phase transition in a theory with two massless flavors and analyzed using $O(N)$ scaling fits to the chiral condensate and susceptibility. We find consistent estimates from the HISQ/tree and asqtad actions and our main result is ${T}_{c}=154\ifmmode\pm\else\textpm\fi{}9\text{ }\text{ }\mathrm{MeV}$.

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