Korea University

About: Korea University is a education organization based out in Seoul, South Korea. It is known for research contribution in the topics: Population & Catalysis. The organization has 39756 authors who have published 82424 publications receiving 1860927 citations. The organization is also known as: Bosung College & Bosung Professional College.

Study of high-p(T) charged particle suppression in PbPb compared to pp collisions at root s(NN)=2.76 TeV

Abstract: The transverse momentum spectra of charged particles have been measured in pp and PbPb collisions at sqrt(sNN) = 2.76 TeV by the CMS experiment at the LHC. In the transverse momentum range pt = 5-10 GeV/c, the charged particle yield in the most central PbPb collisions is suppressed by up to a factor of 5 compared to the pp yield scaled by the number of incoherent nucleon-nucleon collisions. At higher pt, this suppression is significantly reduced, approaching roughly a factor of 2 for particles with pt in the range pt=40-100 GeV/c.

What is the proper way to apply the multiple comparison test

Abstract: Multiple comparisons tests (MCTs) are performed several times on the mean of experimental conditions. When the null hypothesis is rejected in a validation, MCTs are performed when certain experimental conditions have a statistically significant mean difference or there is a specific aspect between the group means. A problem occurs if the error rate increases while multiple hypothesis tests are performed simultaneously. Consequently, in an MCT, it is necessary to control the error rate to an appropriate level. In this paper, we discuss how to test multiple hypotheses simultaneously while limiting type I error rate, which is caused by α inflation. To choose the appropriate test, we must maintain the balance between statistical power and type I error rate. If the test is too conservative, a type I error is not likely to occur. However, concurrently, the test may have insufficient power resulted in increased probability of type II error occurrence. Most researchers may hope to find the best way of adjusting the type I error rate to discriminate the real differences between observed data without wasting too much statistical power. It is expected that this paper will help researchers understand the differences between MCTs and apply them appropriately.

Towards exact molecular dynamics simulations with machine-learned force fields.

Abstract: Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.