arXiv subjects

Kyungmin Kim

Publications and source records attributed to Kyungmin Kim.

48 records · Page 3Linked to original sources

arXiv2021

Identification of Lensed Gravitational Waves with Deep Learning

Similar to light, gravitational waves (GWs) can be lensed. Such lensing phenomena can magnify the waves, create multiple images observable as repeated events, and superpose several waveforms together, inducing potentially discernible patterns on the waves. In particular, when the lens is small, $\lesssim 10^5 M_\odot$, it can produce lensed images with time delays shorter than the typical gravitational-wave signal length that conspire together to form ``beating patterns''. We present a proof-of-principle study utilizing deep learning for identification of such a lensing signature. We bring the excellence of state-of-the-art deep learning models at recognizing foreground objects from background noises to identifying lensed GWs from noise present spectrograms. We assume the lens mass is around $10^3 M_\odot$ -- $10^5 M_\odot$, which can produce the order of millisecond time delays between two images of lensed GWs. We discuss the feasibility of distinguishing lensed GWs from unlensed ones and estimating physical and lensing parameters. Suggested method may be of interest to the study of more complicated lensing configurations for which we do not have accurate waveform templates.

gr-qc↗

arXiv2021

Neutron Star Structure in Hořava-Lifshitz Gravity

We present interesting aspects of neutron stars (NSs) from the standpoint of a modified theory of gravity called Hořava-Lifshitz (HL) gravity. A deviation from general relativity (GR) in HL gravity can change typical features of the NS structure. In this study, we investigate the NS structure by deriving the Tolman-Oppenheimer-Volkoff equation in HL gravity. We find that a NS in HL gravity with a larger radius and heavier mass than a NS in GR remains stable without collapsing into a black hole.

gr-qc↗

arXiv2020

Odds-Ratio Thompson Sampling to Control for Time-Varying Effect

Multi-armed bandit methods have been used for dynamic experiments particularly in online services. Among the methods, thompson sampling is widely used because it is simple but shows desirable performance. Many thompson sampling methods for binary rewards use logistic model that is written in a specific parameterization. In this study, we reparameterize logistic model with odds ratio parameters. This shows that thompson sampling can be used with subset of parameters. Based on this finding, we propose a novel method, "Odds-ratio thompson sampling", which is expected to work robust to time-varying effect. Use of the proposed method in continuous experiment is described with discussing a desirable property of the method. In simulation studies, the novel method works robust to temporal background effect, while the loss of performance was only marginal in case with no such effect. Finally, using dataset from real service, we showed that the novel method would gain greater rewards in practical environment.

cs.LG↗

arXiv2019

Ranking Candidate Signals with Machine Learning in Low-Latency Search for Gravitational-Waves from Compact Binary Mergers

In the multi-messenger astronomy era, accurate sky localization and low latency time of gravitational-wave (GW) searches are keys in triggering successful follow-up observations on the electromagnetic counterpart of GW signals. We, in this work, focus on the latency time and study the feasibility of adopting supervised machine learning (ML) method for ranking candidate GW events. We consider two popular ML methods, random forest and neural networks. We observe that the evaluation time of both methods takes tens of milliseconds for $\sim$ 45,000 evaluation samples. We compare the classification efficiency between the two ML methods and a conventional low-latency search method with respect to the true positive rate at given false positive rate. The comparison shows that about 10\% improved efficiency can be achieved at lower false positive rate $\sim 2 \times 10^{-5}$ with both ML methods. We also present that the search sensitivity can be enhanced by about 18\% at $\sim 10^{-11}$Hz false alarm rate. We conclude that adopting ML methods for ranking candidate GW events is a prospective approach to yield low latency and high efficiency in searches for GW signals from compact binary mergers.

astro-ph.IM↗

arXiv2018

Classifying Lensed Gravitational Waves in the Geometrical Optics Limit with Machine Learning

Gravitational waves are theorized to be gravitationally lensed when they propagate near massive objects. Such lensing effects cause potentially detectable repeated gravitational wave patterns in ground- and space-based gravitational wave detectors. These effects are difficult to discriminate when the lens is small and the repeated patterns superpose. Traditionally, matched filtering techniques are used to identify gravitational-wave signals, but we instead aim to utilize machine learning techniques to achieve this. In this work, we implement supervised machine learning classifiers (support vector machine, random forest, multi-layer perceptron) to discriminate such lensing patterns in gravitational wave data. We train classifiers with spectrograms of both lensed and unlensed waves using both point-mass and singular isothermal sphere lens models. As the result, classifiers return F1 scores ranging from 0.852 to 0.996, with precisions from 0.917 to 0.992 and recalls ranging from 0.796 to 1.000 depending on the type of classifier and lensing model used. This supports the idea that machine learning classifiers are able to correctly determine lensed gravitational wave signals. This also suggests that in the future, machine learning classifiers may be used as a possible alternative to identify lensed gravitational wave events and to allow us to study gravitational wave sources and massive astronomical objects through further analysis.

astro-ph.IM↗

arXiv2016

Compact Star Matter: EoS with New Scaling Law

In this paper we present a simple discussion on the properties of compact stars using an EoS obtained in effective field theory anchored on scale and hidden-local symmetric Lagrangian endowed with topology change and a unequivocal prediction on the deformation of the compact star, that could be measured in gravitational waves. The objective is not to offer a superior or improved EoS for compact stars but to confront with a forthcoming astrophysical observable the given model formulated in what is considered to be consistent with the premise of QCD. The model so obtained is found to satisfactorily describe the observation of a 2-solar mass neutron star with a minimum number of parameters. Specifically the observable we are considering in this paper is the tidal deformability parameter (equivalently the Love number k_2), which affects gravitational wave forms at the late period of inspiral stage. The forthcoming aLIGO and aVirgo observations of gravitational waves from binary neutron star system will provide a valuable guidance for arriving at a better understanding of highly compressed baryonic matter.

nucl-th↗

arXiv2015

Stiffer EoS for Compact Star with New Scaling Law

We present in this paper a simple and unequivocal prediction on the deformation of the compact star, that will be measured in gravity waves, with an EoS obtained in renormailzation-group implemented effective field theory anchored on scale and hidden-local symmetric Lagrangian endowed with topology change . The objective is not to offer a superior or improved EoS for compact stars but to confront with a forthcoming astrophysical observable the given model formulated in what is considered to be consistent with the premise of QCD. The model so obtained is found to satisfactorily describe the observation of a 2-solar mass neutron star with a minimum number of parameters. Specifically the observable we are considering in this paper is the tidal deformability parameter $λ$ (equivalently the Love number, $k_2$) in gravity waves. The merit of our prediction is that the prediction can be readily confirmed or falsified by forth-coming aLIGO and aVirgo gravity-wave observations and provide a valuable guidance for arriving at a better understanding of highly compressed baryonic matter.

nucl-th↗

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↗

arXiv2012

Triple layered compact star with strange quark matter

We explore the possibility of three phases in the core of neutron star in a form of triple layers. From the center, strange quark matter, kaon condensed nuclear matter and nuclear matter form a triple layer. We discuss how the phase of strange quark matter is smoothly connected to kaon condensed nuclear matter phase. We also demonstrate that the compact star with triple layered structure can be a model compatible with the 1.97-solar-mass object PSR J1614-2230 recently observed.

astro-ph.HE↗

arXiv2011

Dense Stellar Matter with Strange Quark Matter Driven by Kaon Condensation

The core of neutron-star matter is supposed to be at a much higher density than the normal nuclear matter density for which various possibilities have been suggested such as, for example, meson or hyperon condensation and/or deconfined quark or color-superconducting matter. In this work, we explore the implication on hadron physics of a dense compact object that has three "phases", nuclear matter at the outer layer, kaon condensed nuclear matter in the middle and strange quark matter at the core. Using a drastically simplified but not unreasonable model, we develop the scenario where the different phases are smoothly connected with the kaon condensed matter playing a role of "doorway" to a quark core, the equation of state (EoS) of which with parameters restricted within the range allowed by nature could be made compatible with the mass vs. radius constraint given by the 1.97-solar mass object PSR J1614-2230 recently observed.

astro-ph.HE↗

arXiv2009

Dependence of relative abundances of constituents in dense stellar matter on nuclear symmetry energy

For a dense stellar matter, which is electrically neutral and in beta equilibrium, the electron chemical potential, mu_e, will depend nontrivially on baryonic matter density. It is generally expected that as density increases, the electron chemical potential will increase and new degrees of freedom will emerge as mu_e becomes comparable to their energy scales. Assuming the electrical neutrality and beta equilibrium for the stellar matter, we have studied how the density dependence of lepton chemical potentials varies for different models of nuclear interactions that are constrained by experiments up to nuclear matter density, n_0, but extrapolate differently(unconstrained) beyond n_0 and calculated the relative abundances of nucleons(neutron and proton) and leptons(electron and muon) and their density dependencies. We find that the density dependence of the electron chemical potential is strongly dependent on the structure of the nuclear symmetry energy relevant to softness/stfness of the nuclear matter EOS that measures the energy relevant to the neutron-proton asymmetry. As a consequence, the relative abundances of neutrons, protons, electrons, and muons as well as the kaon condensation are strongly dependent on the nuclear symmetry energy. An intriguing result in our finding is that contrary to the accepted lore, kaon condensation in neutron star matter, which is considered to be the first phase transitions beyond n_0 and plays a crucial role in certain scenarios of compact-star formation, is not directly tied to the softness or stiffness of the EOS beyond n_0. This point is illustrated with a "super-soft" EOS that is fit to the pi^-/pi^+ ratio data of GSI which excludes kaon condensation at any density.

astro-ph.HE↗