Korea Institute of Science and Technology Information

About: Korea Institute of Science and Technology Information is a facility organization based out in Daejeon, South Korea. It is known for research contribution in the topics: Gravitational wave & LIGO. The organization has 1152 authors who have published 2319 publications receiving 93849 citations. The organization is also known as: Korea Institute of Science and Technology Information & KISTI.

Measurement of deuteron spectra and elliptic flow in Pb-Pb collisions at √sNN = 2.76 TeV at the LHC

Abstract: The transverse momentum ( $$p_\mathrm{T} $$ ) spectra and elliptic flow coefficient ( $$v_{2}$$ ) of deuterons and anti-deuterons at mid-rapidity ( $$|y|<0.5$$ ) are measured with the ALICE detector at the LHC in Pb–Pb collisions at $$\sqrt{s_{\mathrm {NN}}}$$ = 2.76 TeV. The measurement of the $$p_\mathrm{T} $$ spectra of (anti-)deuterons is done up to 8 GeV $$/c$$ in 0–10% centrality class and up to 6 GeV $$/c$$ in 10–20% and 20–40% centrality classes. The $$v_{2}$$ is measured in the 0.8 < $$p_\mathrm{T} $$ $$<~$$ 5 GeV $$/c$$ interval and in six different centrality intervals (0–5, 5–10, 10–20, 20–30, 30–40 and 40–50%) using the scalar product technique. Measured $$\pi $$ $$^{\pm }$$ , K $$^{\pm }$$ and p+ $$\overline{\mathrm {p}}$$ transverse-momentum spectra and $$v_{2}$$ are used to predict the deuteron $$p_\mathrm{T} $$ spectra and $$v_{2}$$ within the Blast-Wave model. The predictions are able to reproduce the $$v_{2}$$ coefficient in the measured $$p_\mathrm{T} $$ range and the transverse-momentum spectra for $$p_\mathrm{T} $$ > 1.8 GeV $$/c$$ within the experimental uncertainties. The measurement of the coalescence parameter $$B_2$$ is performed, showing a $$p_\mathrm{T} $$ dependence in contrast with the simplest coalescence model, which fails to reproduce also the measured $$v_{2}$$ coefficient. In addition, the coalescence parameter $$B_2$$ and the elliptic flow coefficient in the 20–40% centrality interval are compared with the AMPT model which is able, in its version without string melting, to reproduce the measured $$v_{2}$$ ( $$p_\mathrm{T} $$ ) and the $$B_2$$ ( $$p_\mathrm{T} $$ ) trend.

Inclusive quarkonium production at forward rapidity in pp collisions at root s=8 TeV

Abstract: We report on the inclusive production cross sections of J/$\psi$, $\psi$(2S), $\Upsilon$(1S), $\Upsilon$(2S) and $\Upsilon$(3S), measured at forward rapidity with the ALICE detector in pp collisions at a center-of-mass energy $\sqrt{s}=8$ TeV. The analysis is based on data collected at the LHC and corresponds to an integrated luminosity of 1.28 pb$^{-1}$. Quarkonia are reconstructed in the dimuon-decay channel. The differential production cross sections are measured as a function of the transverse momentum $p_{\rm T}$ and rapidity $y$, over the $p_{\rm T}$ ranges $0

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

Abstract: The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure $CP$-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2-5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to $CP$-violating effects.

Recoiling from a kick in the head-on collision of spinning black holes

Abstract: Recoil ``kicks'' induced by gravitational radiation are expected in the inspiral and merger of black holes. Recently the numerical relativity community has begun to measure the significant kicks found when both unequal masses and spins are considered. Because understanding the cause and magnitude of each component of this kick may be complicated in inspiral simulations, we consider these effects in the context of a simple test problem. We study recoils from collisions of binaries with initially head-on trajectories, starting with the simplest case of equal masses with no spin and then adding spin and varying the mass ratio, both separately and jointly. We find spin-induced recoils to be significant relative to unequal-mass recoils even in head-on configurations. Additionally, it appears that the scaling of transverse kicks with spins is consistent with post-Newtonian theory, even though the kick is generated in the nonlinear merger interaction, where post-Newtonian theory should not apply. This suggests that a simple heuristic description might be effective in the estimation of spin kicks.

Identifying Key Hospital Service Quality Factors in Online Health Communities

Abstract: Background: The volume of health-related user-created content, especially hospital-related questions and answers in online health communities, has rapidly increased. Patients and caregivers participate in online community activities to share their experiences, exchange information, and ask about recommended or discredited hospitals. However, there is little research on how to identify hospital service quality automatically from the online communities. In the past, in-depth analysis of hospitals has used random sampling surveys. However, such surveys are becoming impractical owing to the rapidly increasing volume of online data and the diverse analysis requirements of related stakeholders. Objective: As a solution for utilizing large-scale health-related information, we propose a novel approach to identify hospital service quality factors and overtime trends automatically from online health communities, especially hospital-related questions and answers. Methods: We defined social media–based key quality factors for hospitals. In addition, we developed text mining techniques to detect such factors that frequently occur in online health communities. After detecting these factors that represent qualitative aspects of hospitals, we applied a sentiment analysis to recognize the types of recommendations in messages posted within online health communities. Korea’s two biggest online portals were used to test the effectiveness of detection of social media–based key quality factors for hospitals. Results: To evaluate the proposed text mining techniques, we performed manual evaluations on the extraction and classification results, such as hospital name, service quality factors, and recommendation types using a random sample of messages (ie, 5.44% (9450/173,748) of the total messages). Service quality factor detection and hospital name extraction achieved average F1 scores of 91% and 78%, respectively. In terms of recommendation classification, performance (ie, precision) is 78% on average. Extraction and classification performance still has room for improvement, but the extraction results are applicable to more detailed analysis. Further analysis of the extracted information reveals that there are differences in the details of social media–based key quality factors for hospitals according to the regions in Korea, and the patterns of change seem to accurately reflect social events (eg, influenza epidemics). Conclusions: These findings could be used to provide timely information to caregivers, hospital officials, and medical officials for health care policies. [J Med Internet Res 2015;17(4):e90]