arXiv subjects

Jinsu Kim

Publications and source records attributed to Jinsu Kim.

At least 37 records · Page 2Linked to original sources

arXiv2024

Design Optimization of Nuclear Fusion Reactor through Deep Reinforcement Learning

This research explores the application of Deep Reinforcement Learning (DRL) to optimize the design of a nuclear fusion reactor. DRL can efficiently address the challenging issues attributed to multiple physics and engineering constraints for steady-state operation. The fusion reactor design computation and the optimization code applicable to parallelization with DRL are developed. The proposed framework enables finding the optimal reactor design that satisfies the operational requirements while reducing building costs. Multi-objective design optimization for a fusion reactor is now simplified by DRL, indicating the high potential of the proposed framework for advancing the efficient and sustainable design of future reactors.

physics.plasm-ph↗

arXiv2024

A reaction network model of microscale liquid-liquid phase separation reveals effects of spatial dimension

Proteins can form droplets via liquid-liquid phase separation (LLPS) in cells. Recent experiments demonstrate that LLPS is qualitatively different on two-dimensional (2d) surfaces compared to three-dimensional (3d) solutions. In this paper, we use mathematical modeling to investigate the causes of the discrepancies between LLPS in 2d versus 3d. We model the number of proteins and droplets inducing LLPS by continuous-time Markov chains and use chemical reaction network theory to analyze the model. To reflect the influence of space dimension, droplet formation and dissociation rates are determined using the first hitting times of diffusing proteins. We first show that our stochastic model reproduces the appropriate phase diagram and is consistent with the relevant thermodynamic constraints. After further analyzing the model, we find that it predicts that the space dimension induces qualitatively different features of LLPS which are consistent with recent experiments. While it has been claimed that the differences between 2d and 3d LLPS stems mainly from different diffusion coefficients, our analysis is independent of the diffusion coefficients of the proteins since we use the stationary model behavior. Therefore, our results give new hypotheses about how space dimension affects LLPS.

q-bio.QM↗

arXiv2024

Boundary-induced slow mixing for Markov chains and its application to stochastic reaction networks

Markov chains on the non-negative quadrant of dimension $d$ are often used to model the stochastic dynamics of the number of $d$ entities, such as $d$ chemical species in stochastic reaction networks. The infinite state space poses technical challenges, and the boundary of the quadrant can have a dramatic effect on the long term behavior of these Markov chains. For instance, the boundary can slow down the convergence speed of an ergodic Markov chain towards its stationary distribution due to the extinction or the lack of an entity. In this paper, we quantify this slow-down for a class of stochastic reaction networks and for more general Markov chains on the non-negative quadrant. We establish general criteria for such a Markov chain to exhibit a power-law lower bound for its mixing time. The lower bound is of order $|x|^θ$ for all initial state $x$ on a boundary face of the quadrant, where $θ$ is characterized by the local behavior of the Markov chain near the boundary of the quadrant. A better understanding of how these lower bounds arise leads to insights into how the structure of chemical reaction networks contributes to slow-mixing.

math.PR↗

arXiv2024

Supervised low-rank semi-nonnegative matrix factorization with frequency regularization for forecasting spatio-temporal data

We propose a novel methodology for forecasting spatio-temporal data using supervised semi-nonnegative matrix factorization (SSNMF) with frequency regularization. Matrix factorization is employed to decompose spatio-temporal data into spatial and temporal components. To improve clarity in the temporal patterns, we introduce a nonnegativity constraint on the time domain along with regularization in the frequency domain. Specifically, regularization in the frequency domain involves selecting features in the frequency space, making an interpretation in the frequency domain more convenient. We propose two methods in the frequency domain: soft and hard regularizations, and provide convergence guarantees to first-order stationary points of the corresponding constrained optimization problem. While our primary motivation stems from geophysical data analysis based on GRACE (Gravity Recovery and Climate Experiment) data, our methodology has the potential for wider application. Consequently, when applying our methodology to GRACE data, we find that the results with the proposed methodology are comparable to previous research in the field of geophysical sciences but offer clearer interpretability.

stat.ML↗

arXiv2024

Josephson Parametric Amplifier based Quantum Noise Limited Amplifier Development for Axion Search Experiments in CAPP

This paper provides a comprehensive overview of the development of flux-driven Josephson Parametric Amplifiers (JPAs) as Quantum Noise Limited Amplifier for axion search experiments conducted at the Center for Axion and Precision Physics Research (CAPP) of the Institute for Basic Science. It focuses on the characterization, and optimization of JPAs, which are crucial for achieving the highest sensitivity in axion particle detection. We discuss various characterization techniques, methods for improving bandwidth, and the attainment of ultra-low noise temperatures. JPAs have emerged as indispensable tools in CAPPs axion search endeavors, playing a significant role in advancing our understanding of fundamental physics and unraveling the mysteries of the universe.

hep-ex↗

arXiv2024

Interplay between Higgs inflation and dark matter models with dark $U(1)$ gauge symmetry

We investigate dark matter phenomenology and Higgs inflation in a dark $U(1)_D$-extended model. The model features two dark matter candidates, a dark fermion and a dark vector boson. When the fermion dark matter $ψ$ is heavier than the vector dark matter $W_D$, there is an ample parameter space where $ψ$ is dominant over $W_D$. The model can then easily evade the stringent bounds from direct detection experiments, since $ψ$ has no direct coupling to the Standard Model particles. Furthermore, the model can accommodate inflation in three different ways, one along the Standard Model Higgs direction, one along the dark Higgs direction, and one along the combination of the two. Considering the running of the parameters and various observational constraints, we perform a detailed numerical analysis and identify allowed parameter spaces that explain both dark matter and Higgs inflation in a unified manner. We discuss in detail how the imposition of Higgs inflation severely constrains the dark matter parameter space. The existence of the dark Higgs field is found to play a crucial role both in dark matter phenomenology and in generalised Higgs inflation.

hep-ph↗

arXiv2024

Extensive search for axion dark matter over 1\,GHz with CAPP's Main Axion eXperiment

We report an extensive high-sensitivity search for axion dark matter above 1\,GHz at the Center for Axion and Precision Physics Research (CAPP). The cavity resonant search, exploiting the coupling between axions and photons, explored the frequency (mass) range of 1.025\,GHz (4.24\,$μ$eV) to 1.185\,GHz (4.91\,$μ$eV). We have introduced a number of innovations in this field, demonstrating the practical approach of optimizing all the relevant parameters of axion haloscopes, extending presently available technology. The CAPP 12\,T magnet with an aperture of 320\,mm made of Nb$_3$Sn and NbTi superconductors surrounding a 37-liter ultralight-weight copper cavity is expected to convert DFSZ axions into approximately $10^2$ microwave photons per second. A powerful dilution refrigerator, capable of keeping the core system below 40\,mK, combined with quantum-noise limited readout electronics, achieved a total system noise of about 200\,mK or below, which corresponds to a background of roughly $4\times 10^3$ photons per second within the axion bandwidth. The combination of all those improvements provides unprecedented search performance, imposing the most stringent exclusion limits on axion--photon coupling in this frequency range to date. These results also suggest an experimental capability suitable for highly-sensitive searches for axion dark matter above 1\,GHz.

hep-ex↗

arXiv2024

A path method for non-exponential ergodicity of Markov chains and its application for chemical reaction systems

In this paper, we present criteria for non-exponential ergodicity of continuous-time Markov chains on a countable state space. These criteria can be verified by examining the ratio of transition rates over certain paths. We applied this path method to explore the non-exponential convergence of microscopic biochemical interacting systems. Using reaction network descriptions, we identified special architectures of biochemical systems for non-exponential ergodicity. In essence, we found that reactions forming a cycle in the reaction network can induce non-exponential ergodicity when they significantly dominate other reactions across infinitely many regions of the state space. Interestingly, the special architectures allowed us to construct many detailed balanced and complex balanced biochemical systems that are non-exponentially ergodic. Some of these models are low-dimensional bimolecular systems with few reactions. Thus this work suggests the possibility of discovering or synthesizing stochastic systems arising in biochemistry that possess either detailed balancing or complex balancing and slowly converge to their stationary distribution.

math.PR↗

arXiv2023

Enhancing Disruption Prediction through Bayesian Neural Network in KSTAR

Disruption in tokamak plasmas, stemming from various instabilities, poses a critical challenge, resulting in detrimental effects on the associated devices. Consequently, the proactive prediction of disruptions to maintain stability emerges as a paramount concern for future fusion reactors. While data-driven methodologies have exhibited notable success in disruption prediction, conventional neural networks within a frequentist approach cannot adequately quantify the uncertainty associated with their predictions, leading to overconfidence. To address this limit, we utilize Bayesian deep probabilistic learning to encompass uncertainty and mitigate false alarms, thereby enhancing the precision of disruption prediction. Leveraging 0D plasma parameters from EFIT and diagnostic data, a Temporal Convolutional Network adept at handling multi-time scale data was utilized. The proposed framework demonstrates proficiency in predicting disruptions, substantiating its effectiveness through successful applications to KSTAR experimental data.

physics.plasm-ph↗

arXiv2023

Probing inflationary moduli space with gravitational waves

We investigate the spectrum of gravitational waves arising from primordial inflation in the presence of a string-theoretical higher curvature correction, specifically, the Gauss-Bonnet coupling term for the inflaton (modulus) field. We show that if the modulus field exhibits a wall-crossing like behavior in the moduli space, there can be a period of Gauss-Bonnet coupling term domination during the usual slow-roll. This phenomenon is potentially detectable as the gravitational wave spectrum exhibits a characteristic peak caused by the brief domination of the Gauss-Bonnet coupling term. We explore the possibility of measuring such gravitational waves with pulsar timing array experiments such as NANOGrav, and future space-borne interferometers such as LISA, DECIGO, and Taiji.

astro-ph.CO↗

arXiv2023

A new path method for exponential ergodicity of Markov processes on $\mathbb Z^d$, with applications to stochastic reaction networks

This paper provides a new path method that can be used to determine when an ergodic continuous-time Markov chain on $\mathbb Z^d$ converges exponentially fast to its stationary distribution in $L^2$. Specifically, we provide general conditions that guarantee the positivity of the spectral gap. Importantly, our results do not require the assumption of time-reversibility of the Markov model. We then apply our new method to the well-studied class of stochastically modeled reaction networks. Notably, we show that each complex-balanced model that is also ``open'' has a positive spectral gap, and is therefore exponentially ergodic. We further illustrate how our results can be applied for models that are not necessarily complex-balanced. Moreover, we provide an example of a detailed-balanced (in the sense of reaction network theory), and hence complex-balanced, stochastic reaction network that is not exponentially ergodic. We believe this to be the first such example in the literature.

math.PR↗

arXiv2023

Search for the Sagittarius Tidal Stream of Axion Dark Matter around 4.55 $μ$eV

We report the first search for the Sagittarius tidal stream of axion dark matter around 4.55 $μ$eV using CAPP-12TB haloscope data acquired in March of 2022. Our result excluded the Sagittarius tidal stream of Dine-Fischler-Srednicki-Zhitnitskii and Kim-Shifman-Vainshtein-Zakharov axion dark matter densities of $ρ_a\gtrsim0.184$ and $\gtrsim0.025$ GeV/cm$^{3}$, respectively, over a mass range from 4.51 to 4.59 $μ$eV at a 90% confidence level.

hep-ex↗

arXiv2023

Nonminimally Assisted Inflation: A General Analysis

The effects of a scalar field, known as the "assistant field," which nonminimally couples to gravity, on single-field inflationary models are studied. The analysis provides analytical expressions for inflationary observables such as the spectral index ($n_s$), the tensor-to-scalar ratio ($r$), and the local-type nonlinearity parameter ($f_{\rm NL}^{(\rm local)}$). The presence of the assistant field leads to a lowering of $n_s$ and $r$ in most of the parameter space, compared to the original predictions. In some cases, $n_s$ may increase due to the assistant field. This revives compatibility between ruled-out single-field models and the latest observations by Planck-BICEP/Keck. The results are demonstrated using three example models: loop inflation, power-law inflation, and hybrid inflation.

astro-ph.CO↗

arXiv2023

Axion Dark Matter Search around 4.55 $μ$eV with Dine-Fischler-Srednicki-Zhitnitskii Sensitivity

We report an axion dark matter search at Dine-Fischler-Srednicki-Zhitnitskii sensitivity with the CAPP-12TB haloscope, assuming axions contribute 100\% of the local dark matter density. The search excluded the axion--photon coupling $g_{aγγ}$ down to about $6.2\times10^{-16}$ GeV$^{-1}$ over the axion mass range between 4.51 and 4.59 $μ$eV at a 90\% confidence level. The achieved experimental sensitivity can also exclude Kim-Shifman-Vainshtein-Zakharov axion dark matter that makes up just 13\% of the local dark matter density. The CAPP-12TB haloscope will continue the search over a wide range of axion masses.

hep-ex↗

arXiv2023

Primordial black holes and gravitational waves from nonminimally coupled supergravity inflation

We study the formation of primordial black holes and the generation of gravitational waves in a class of cosmological models that are direct supersymmetric analogs of the observationally favored nonminimally coupled Higgs inflation model. It is known that this type of model naturally includes multiple scalar fields which may be regarded as the inflaton. For the sake of simplicity we focus on the case where the inflaton field space is two dimensional. We analyze the multifield dynamics and find the region of parameters that gives copious production of primordial black holes that may comprise a significant part of the present dark matter abundance. We also compute the spectrum of the gravitational waves and discuss their detectability by means of future ground-based and space-borne gravitational wave observatories.

astro-ph.CO↗

arXiv2022

A two-component vector WIMP -- fermion FIMP dark matter model with an extended seesaw mechanism

We consider an extension of the Standard Model that explains the neutrino masses and has a rich dark matter phenomenology. The model has two dark matter candidates, a vector WIMP and a fermion FIMP, and the sum of their relic densities matches the total dark matter abundance. We extensively study the dark matter production mechanisms and its connection with the neutrino sector, together with various bounds from present and future experiments. The extra scalar field in the model may induce a first-order phase transition in the early Universe. We study the production of stochastic gravitational waves associated with the first-order phase transition. We show that the phase transition can be strong, and thus the model may satisfy one of the necessary conditions for a successful electroweak baryogenesis. Detectability of the phase transition-associated gravitational waves is also discussed.

hep-ph↗

arXiv2022

Mixing times for two classes of stochastically modeled reaction networks

The past few decades have seen robust research on questions regarding the existence, form, and properties of stationary distributions of stochastically modeled reaction networks. When a stochastic model admits a stationary distribution an important practical question is: what is the rate of convergence of the distribution of the process to the stationary distribution? With the exception of \cite{XuHansenWiuf2022} pertaining to models whose state space is restricted to the non-negative integers, there has been a notable lack of results related to this rate of convergence in the reaction network literature. This paper begins the process of filling that hole in our understanding. In this paper, we characterize this rate of convergence, via the mixing times of the processes, for two classes of stochastically modeled reaction networks. Specifically, by applying a Foster-Lyapunov criteria we establish exponential ergodicity for two classes of reaction networks introduced in \cite{anderson2018some}. Moreover, we show that for one of the classes the convergence is uniform over the initial state.

math.PR↗

arXiv2022

Computational translation framework identifies biochemical reaction networks with special topologies and their long-term dynamics

Long-term behaviors of biochemical systems are described by steady states in deterministic models and stationary distributions in stochastic models. Obtaining their analytic solutions can be done for limited cases, such as linear or finite-state systems, as it generally requires solving many coupled equations. Interestingly, analytic solutions can be easily obtained when underlying networks have special topologies, called weak reversibility (WR) and zero deficiency (ZD), and the kinetic law follows a generalized form of mass-action kinetics. However, such desired topological conditions do not hold for the majority of cases. Thus, translating networks to have WR and ZD while preserving the original dynamics was proposed. Yet, this approach is limited because manually obtaining the desired network translation among the large number of candidates is challenging. Here, we prove necessary conditions for having WR and ZD after translation, and based on these conditions, we develop a user-friendly computational package, TOWARDZ, that automatically and efficiently identifies translated networks with WR and ZD. This allows us to quantitatively examine how likely it is to obtain WR and ZD after translation depending on the number of species and reactions. Importantly, we also describe how our package can be used to analytically derive steady states of deterministic models and stationary distributions of stochastic models. TOWARDZ provides an effective tool to analyze biochemical systems.

q-bio.MN↗