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<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi mathvariant="normal">Λ</mml:mi><mml:mi mathvariant="normal">Λ</mml:mi></mml:math>Correlation Function in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:mi>Au</mml:mi><mml:mo>+</mml:mo><mml:mi>Au</mml:mi></mml:mrow></mml:math>Collisions at<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msqrt><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:mi>N</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msqrt><mml:mo>=</mml:mo><mml:mn>200</mml:mn><mml:mtext> </mml:mtext><mml:mi>GeV</mml:mi></mml:mrow></mml:math>
L. Adamczyk, J. K. Adkins, G. Agakishiev, M. M. Aggarwal, Z. Ahammed, I. Alekseev, J. Alford, C. Anson, A. Aparin, D. Arkhipkin, E. C. Aschenauer, G. S. Averichev, A. Banerjee, D. R. Beavis, R. Bellwied, A. Bhasin, A. K. Bhati, P. Bhattarai, H. Bichsel, J. Bielčík, J. Bielčíková, L. C. Bland, I. G. Bordyuzhin, W. Borowski, J. Bouchet, A. V. Brandin, S. G. Brovko, S. Bültmann, I. Bunzarov, T. P. Burton, J. M. Butterworth, H. Caines, M. Calderon De La Barca Sanchez, J. M. Campbell, D. Cebra, R. Cendejas, M. C. Cervantes, P. Chaloupka, Z. Chang, S. Chattopadhyay, H. F. Chen, J. H. Chen, L. Chen, J. Cheng, M. Cherney, A. Chikanian, W. Christie, J. J. Chwastowski, M. J. M. Codrington, G. Contin, J. G. Cramer, H. J. Crawford, X.Y. Cui, S. Das, A. Davila Leyva, L. C. De Silva, R. R. Debbe, T. G. Dedovich, J. Deng, A. A. Derevschikov, R. Derradi de Souza, B. Di Ruzza, L. Didenko, C. Dilks, F. Ding, P. Djawotho, X. Dong, J. L. Drachenberg, J. E. Draper, C. M. Du, L. E. Dunkelberger, J. C. Dunlop, L. G. Efimov, J. Engelage, K. S. Engle, G. Eppley, L. Eun, O. Evdokimov, O. Eyser, S. Fazio, J. Fedorišin, P. Filip, Y. Fisyak, C. E. Flores, C. A. Gagliardi, D. R. Gangadharan, D. Garand, F. J. M. Geurts, S. M. Gibson, M. Girard, S. Gliske, L. Greiner, D. Grosnick, D. S. Gunarathne, Y. Guo, A. Gupta, S. K. Gupta, W. Guryn, B. Haag, A. Hamed, L. Han, M. R. Haque, J. W. Harris, S. Heppelmann, A. Hirsch, G. W. Hoffmann, D. J. Hofman, S. Horvat, B. Huang, H. Z. Huang, X. Huang, Patrick Huck, T. J. Humanic, G. Igo, W. W. Jacobs, H. J. Jang, E. G. Judd, S. Kabana, D. Kalinkin, K. Kang, K. Kauder, H. W. Ke, D. Keane, A. Kechechyan, A. Kesich, Z. H. Khan, D. P. Kikoła, I. Kisel, A. Kisiel, D. D. Koetke, T. Kollegger, J. Konzer, I. Koralt, L. K. Kosarzewski, L. Kotchenda, A. F. Kraishan, П. Кравцов, K. Krueger, I. Kulakov, L. Kumar, R. A. Kycia, M. A. C. Lamont, J. M. Landgraf, K. D. Landry, J. Lauret, A. Lebedev, R. Lednický, J. H. Lee, C. Li, W. Li, Y. Li, ZeHao Li, M. A. Lisa, F. Liu, T. Ljubičić, W. J. Llope, M. Lomnitz, R. S. Longacre, X. Luo, G. L., Y. G., D. P. Mahapatra, R. Majka, S. Margetis, C. Markert, H. Masui, H. S. Matis, D. McDonald, T. S. McShane, N. G. Minaev, S. Mioduszewski, B. Mohanty, M. M. Mondal, D. A. Morozov, M. K. Mustafa, B. K. Nandi, Md. Nasim, T. K. Nayak, J. M. Nelson, G. Nigmatkulov, L. V. Nogach, S. Y. Noh, J. Novák, S. B. Nurushev, G. Odyniec, A. Ogawa, K. Oh, A. Ohlson, В. А. Окороков, E. W. Oldag, D. Olvitt, B. S. Page, Y. X. Pan, Y. Pandit, Y. Panebratsev, T. Pawlak, B. Pawlik, H. Pei, C. Perkins, P. Pile, M. Planinić, J. Pluta, N. Poljak, K. Poniatowska, J. Porter, A. M. Poskanzer, N. K. Pruthi, M. Przybycień, J. Putschke, H. Qiu, A. Quintero, S. Ramachandran, R. Raniwala, R. L. Ray, C. K. Riley, H. G. Ritter, J. B. Roberts, O. V. Rogachevskiy, J. L. Romero, J. F. Ross, A. Roy, J. Rusňák, O. Rusnakova, N. R. Sahoo, P. K. Sahu, I. Sakrejda, S. Salur, J. Sandweiss, E. Sangaline, D. Sarkar, R. P. Scharenberg, Alexander Schmah, W. B. Schmidke, N. Schmitz, J. Seger, P. Seyboth, N. Shah, E. Shahaliev, P. V. Shanmuganathan, M. Shao, B. Sharma, W. Q. Shen, S. S. Shi, Q. Y. Shou, E. P. Sichtermann, M. Simko, M. J. Skoby, D. Smirnov, N. Smirnov, D. Solanki, P. Sørensen, H. Spinka, B. Srivastava, T. D. S. Stanislaus, J. R. Stevens, R. Stock, M. Strikhanov, B. Stringfellow, M. Šumbera, X. Sun, Y. Sun, Z. Sun, B. Surrow, D. N. Svirida, T. J. M. Symons, M. A. Szelezniak, J. Takahashi, Z. Tang, T. Tarnowsky, J. H. Thomas, A. R. Timmins, D. Tlusty, M. Tokarev, S. Trentalange, R. E. Tribble, P. Tribedy, B. A. Trzeciak, O. D. Tsai, J. Turnau, T. Ullrich, D. G. Underwood, G. Van Buren, G. van Nieuwenhuizen, M. Vandenbroucke, J. A. Vanfossen, R. Varma, G. M. S. Vasconcelos, A. N. Vasiliev, Róbert Vértesi, F. Videbæk, Y. P. Viyogi, S. Vokál, A. Vossen, M. Wada, F. Wang, G. Wang, Haoyu Wang, J. S. Wang, Xiaogang Wang, Yu Wang, G. Webb, J. C. Webb, G. D. Westfall, H. Wieman, S. W. Wissink, R. Witt, Yue-Liang Wu, Zhigang Xiao, W. Xie, K. Xin, H. Xu, Jingzhong Xu, N. Xu, Q. Xu, Y. Xu, W. B. Yan, C. Yang, Yang Yu, Z. Ye, P. Yepes, L. Yi, K. Yip, I.-K. Yoo, N. Yu, H. Zbroszczyk, W. Zha, J. B. Zhang, J. L. Zhang, S. Zhang, X. P. Zhang, Y. Zhang, Z. P. Zhang, F. Zhao, J. Zhao, C. Zhong, X. Zhu, Yunhai Zhu, Y. Zoulkarneeva, M. Zyzak
Bibliographic record
VenuePhysical Review Letters · 2015
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicHigh-Energy Particle Collisions Research
Canadian institutionsnot available
FundersLawrence Berkeley National LaboratoryInstitut National de Physique Nucléaire et de Physique des ParticulesOffice of ScienceMinistry of Education, IndiaNarodowym Centrum NaukiMinistry of Education of the People's Republic of ChinaCentre National de la Recherche ScientifiqueDepartment of Atomic Energy, Government of IndiaConselho Nacional de Desenvolvimento Científico e TecnológicoNational Natural Science Foundation of ChinaMinistry of Science and Technology of the People's Republic of ChinaDepartment of Science and Technology, Ministry of Science and Technology, IndiaCouncil of Scientific and Industrial Research, IndiaNational Research FoundationFrankfurt Institute for Advanced StudiesMinistry of Education and Science of the Russian FederationCanadian Anesthesiologists' SocietyNational Energy Research Scientific Computing CenterKorea Institute of Science and Technology InformationFundação de Amparo à Pesquisa do Estado de São PauloU.S. Department of EnergyNational Science Foundation
KeywordsMathematicsAlgorithm
Abstract
We present ΛΛ correlation measurements in heavy-ion collisions for Au+Au collisions at sqrt[s_{NN}]=200 GeV using the STAR experiment at the Relativistic Heavy-Ion Collider. The Lednický-Lyuboshitz analytical model has been used to fit the data to obtain a source size, a scattering length and an effective range. Implications of the measurement of the ΛΛ correlation function and interaction parameters for dihyperon searches are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.509
Threshold uncertainty score0.701
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.509 | 0.617 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
Opus teacher head0.021
GPT teacher head0.258
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
Study designBench or experimental
Domainnot available
GenreEmpirical
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".
Quick stats
Citations142
Published2015
Admission routes1
Has abstractyes