| LDR | | 02690nam u200481 4500 |
| 001 | | 000000421798 |
| 005 | | 20190215165619 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780355869255 |
| 035 | |
▼a (MiAaPQ)AAI10750560 |
| 035 | |
▼a (MiAaPQ)duke:14617 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 539 |
| 100 | 1 |
▼a Bernhard, Jonah E. |
| 245 | 10 |
▼a Bayesian Parameter Estimation for Relativistic Heavy-ion Collisions. |
| 260 | |
▼a [S.l.]:
▼b Duke University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 220 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B. |
| 500 | |
▼a Adviser: Steffen A. Bass. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Duke University, 2018. |
| 506 | |
▼a This item is not available from ProQuest Dissertations & Theses. |
| 520 | |
▼a I develop and apply a Bayesian method for quantitatively estimating properties of the quark-gluon plasma (QGP), an extremely hot and dense state of fluid-like matter created in relativistic heavy-ion collisions. |
| 520 | |
▼a The QGP cannot be directly observed---it is extraordinarily tiny and ephemeral, about 10-14 meters in size and living 10 -23 seconds before freezing into discrete particles---but it can be indirectly characterized by matching the output of a com |
| 520 | |
▼a In this dissertation, I construct a specific computational model of heavy-ion collisions and formulate the Bayesian parameter estimation method, which is based on general statistical techniques. I then apply these tools to estimate fundamental Q |
| 520 | |
▼a Perhaps most notably, I report the most precise estimate to date of the temperature-dependent specific shear viscosity eta/s, the measurement of which is a primary goal of heavy-ion physics. The estimated minimum value is eta/ s = 0.085(+0.026)( |
| 520 | |
▼a Other estimated quantities include the temperature-dependent bulk viscosity zeta/s, the scaling of initial state entropy deposition, and the duration of the pre-equilibrium stage that precedes QGP formation. |
| 590 | |
▼a School code: 0066. |
| 650 | 4 |
▼a Nuclear physics and radiation. |
| 650 | 4 |
▼a Computational physics. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0756 |
| 690 | |
▼a 0216 |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Duke University.
▼b Physics. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-09B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0066 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997109
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |