arXiv subjects

Young-Min Kim

Publications and source records attributed to Young-Min Kim.

45 records · Page 3Linked to original sources

arXiv2018

Ferroelectric polarization rotation in order-disorder-type LiNbO3 thin films

The direction of ferroelectric polarization is prescribed by the symmetry of the crystal structure. Therefore, rotation of the polarization direction is largely limited, despite the opportunity it offers in understanding important dielectric phenomena such as piezoelectric response near the morphotropic phase boundaries and practical applications such as ferroelectric memory. In this study, we report the observation of continuous rotation of ferroelectric polarization in order-disorder type LiNbO3 thin films. The spontaneous polarization could be tilted from an out-of-plane to an in-plane direction in the thin film by controlling the Li vacancy concentration within the hexagonal lattice framework. Partial inclusion of monoclinic-like phase is attributed to the breaking of macroscopic inversion symmetry along different directions and the emergence of ferroelectric polarization along the in-plane direction.

physics.app-ph↗

arXiv2018

Generating Event Triggers Based on Hilbert-Huang Transform and Its Application to Gravitational-Wave Data

We present a new event trigger generator based on the Hilbert-Huang transform, named EtaGen ($η$Gen). It decomposes a time-series data into several adaptive modes without imposing a priori bases on the data. The adaptive modes are used to find transients (excesses) in the background noises. A clustering algorithm is used to gather excesses corresponding to a single event and to reconstruct its waveform. The performance of EtaGen is evaluated by how many injections in the LIGO simulated data are found. EtaGen is viable as an event trigger generator when compared directly with the performance of Omicron, which is currently the best event trigger generator used in the LIGO Scientific Collaboration and Virgo Collaboration.

astro-ph.IM↗

arXiv2018

Sensing and vetoing loud transient noises for the gravitational-wave detection

Since the first detection of gravitational-wave (GW), GW150914, September 14th 2015, the multi-messenger astronomy added a new way of observing the Universe together with electromagnetic (EM) waves and neutrinos. After two years, GW together with its EM counterpart from binary neutron stars, GW170817 and GRB170817A, has been observed. The detection of GWs opened a new window of astronomy/astrophysics and will be an important messenger to understand the Universe. In this article, we briefly review the gravitational-wave and the astrophysical sources and introduce the basic principle of the laser interferometer as a gravitational-wave detector and its noise sources to understand how the gravitational-waves are detected in the laser interferometer. Finally, we summarize the search algorithms currently used in the gravitational-wave observatories and the detector characterization algorithms used to suppress noises and to monitor data quality in order to improve the reach of the astrophysical searches.

gr-qc↗

arXiv2018

Tidal Deformability of Neutron Stars with Realistic Nuclear Energy Density Functionals

We investigate the constraints on the mass and radius of neutron stars by considering the tidal deformability in the merge of neutron star binaries. In order to extract the most reliable range of uncertainty from theory, we employ models based upon the Skyrme force and density functional theory and select models that are consistent with empirical data of finite nuclei, measured properties of nuclear matter around the saturation density, and observation of the maximum mass of neutron stars. From the selected models, we calculate the Love number $k_2$, dimensionless tidal deformability $Λ$, and mass-weighted deformability $\tildeΛ$ in the binary system. We find that all the models considered in this work give $\tildeΛ$ less than 800 which is the upper limit obtained from the measurement of GW170817. However, the model dependence of tidal deformability is manifest such that our results on the tidal deformability exhibit critical sensitivity to the size of neutron stars.

nucl-th↗

arXiv2017

Direct Photon Elliptic Flow at BNL Relativistic Heavy Ion Collider (RHIC) and CERN Large Hadron Collider (LHC)

We use an event-by-event hydrodynamical description of the heavy-ion collision process with Glauber initial conditions to calculate the thermal emission of photons. The photon rates in the hadronic phase follow from a spectral function approach and a density expansion, while in the partonic phase they follow from the AMY perturbative rates. The calculated photon elliptic flows are lower than those reported recently by both the ALICE and PHENIX collaborations.

nucl-th↗

arXiv2017

Enhanced electrocatalytic activity via phase transitions in strongly correlated SrRuO3 thin films

Transition metal oxides have been extensively studied and utilized as efficient catalysts. However, the strongly correlated behavior which often results in intriguing emergent phenomena in these materials has been mostly overlooked in understanding the electrochemical activities. Here, we demonstrate a close correlation between the phase transitions and oxygen evolution reaction (OER) in a strongly correlated SrRuO3. By systematically introducing Ru-O vacancies into the single-crystalline SrRuO3 epitaxial thin films, we induced phase transition in crystalline symmetry which resulted in corresponding modification in the electronic structure. The modified electronic structure significantly affect the electrochemical activities, so a 30% decrease in the overpotential for the OER activity was achieved. Our study suggests that a substantial enhancement in the OER activity can be realized even within single material systems, by rational design and engineering of their crystal and electronic structures.

cond-mat.str-el↗

arXiv2016

Temporal Multinomial Mixture for Instance-Oriented Evolutionary Clustering

Evolutionary clustering aims at capturing the temporal evolution of clusters. This issue is particularly important in the context of social media data that are naturally temporally driven. In this paper, we propose a new probabilistic model-based evolutionary clustering technique. The Temporal Multinomial Mixture (TMM) is an extension of classical mixture model that optimizes feature co-occurrences in the trade-off with temporal smoothness. Our model is evaluated for two recent case studies on opinion aggregation over time. We compare four different probabilistic clustering models and we show the superiority of our proposal in the task of instance-oriented clustering.

cs.IR↗

arXiv2015

Application of Artificial Neural Network to Search for Gravitational-Wave Signals Associated with Short Gamma-Ray Bursts

We apply a machine learning algorithm, the artificial neural network, to the search for gravitational-wave signals associated with short gamma-ray bursts. The multi-dimensional samples consisting of data corresponding to the statistical and physical quantities from the coherent search pipeline are fed into the artificial neural network to distinguish simulated gravitational-wave signals from background noise artifacts. Our result shows that the data classification efficiency at a fixed false alarm probability is improved by the artificial neural network in comparison to the conventional detection statistic. Therefore, this algorithm increases the distance at which a gravitational-wave signal could be observed in coincidence with a gamma-ray burst. In order to demonstrate the performance, we also evaluate a few seconds of gravitational-wave data segment using the trained networks and obtain the false alarm probability. We suggest that the artificial neural network can be a complementary method to the conventional detection statistic for identifying gravitational-wave signals related to the short gamma-ray bursts.

astro-ph.IM↗

arXiv2013

Application of machine learning algorithms to the study of noise artifacts in gravitational-wave data

The sensitivity of searches for astrophysical transients in data from the LIGO is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contributed from stationary noise. These "glitches" can easily be confused for transient gravitational-wave signals, and their robust identification and removal will help any search for astrophysical gravitational-waves. We apply Machine Learning Algorithms (MLAs) to the problem, using data from auxiliary channels within the LIGO detectors that monitor degrees of freedom unaffected by astrophysical signals. The number of auxiliary-channel parameters describing these disturbances may also be extremely large; an area where MLAs are particularly well-suited. We demonstrate the feasibility and applicability of three very different MLAs: Artificial Neural Networks, Support Vector Machines, and Random Forests. These classifiers identify and remove a substantial fraction of the glitches present in two very different data sets: four weeks of LIGO's fourth science run and one week of LIGO's sixth science run. We observe that all three algorithms agree on which events are glitches to within 10% for the sixth science run data, and support this by showing that the different optimization criteria used by each classifier generate the same decision surface, based on a likelihood-ratio statistic. Furthermore, we find that all classifiers obtain similar limiting performance, suggesting that most of the useful information currently contained in the auxiliary channel parameters we extract is already being used.

astro-ph.IM↗