arXiv subjects

Hiroyasu Ando

Publications and source records attributed to Hiroyasu Ando.

At least 19 recordsLinked to original sources

arXiv2026

Non-adiabatic Effect on Convective Mode

The systematic analysis of non-adiabatic effect on convective mode has been conducted using wave energy relation. In the adiabatic analysis, the "propagation diagram" for convective mode is proposed as a useful tool to see its behavior. In the non-adiabatic analysis, it is found that for strongly non-adiabatic case, a monotonically growing convective mode becomes oscillatory. In this phase, the radial displacement and the distribution of wave energy show only one bump, in which the distribution of entropy energy eS almost overlaps with the distribution of gravity energy eg. Entropy energy eS seems to act as potential energy of oscillatory convection. In addition to this, this change occurs not gradually, but abruptly with change of non-adiabatic indicator.

astro-ph.SR↗

arXiv2026

Unraveling Year-Long Radial Velocity Variations in Red Clump Region -- I: Comprehensive analysis of a K0 Giant star, 2 Draconis

Slow-rotating evolved stars frequently exhibit radial velocity (RV) variations on annual timescales, complicated by instrumental systematics and aliasing in the one-year regime. Here we investigate the origin of the near-yearly periodicity in 2 Dra, a star located in the red-clump region, assessing possible causes between stellar activity, instrumental profile (IP) effects, sampling alias, and planetary companions. We applied two independent approaches: (1) constraining diagnostic signals and performing a correlation analysis ($r$) between period-confined signals, and (2) evaluating phase stability by partitioning Keplerian fits. These methods enabled us to examine the physical connections and phase coherence among stellar activity indicators, RV measurements, and IP diagnostics. Our analysis suggests a stellar rotation period of $\simeq270\text{--}320$\,d for 2~Dra. The 340-d RV signal does not appear to originate from stellar activity in this chromospherically quiet star ($|r| \lesssim 0.33$), nor from instrumental systematics near the annual period ($|r| \lesssim 0.1$). This conclusion is supported by contrasting phase behavior: the RV and stellar activity phases remain stable, whereas the IP phases do not. We therefore propose that the 340-d variation likely arises from either small-amplitude intrinsic variability or a tentative gas giant companion with potential weak activity-induced modulation. The case of 2~Dra provides a framework for distinguishing the origins of $\sim$1-yr RV variations in other evolved stars.

astro-ph.SR↗

arXiv2025

Temporal dependence in exposure and hazard-based infectious disease interventions

In randomized controlled trials (RCTs) of infectious disease interventions, it is well recognized that unmeasured individual heterogeneity at baseline can induce selection bias over time, thereby complicating the interpretation of the estimated hazard ratio. The present study examines a simplified setting: RCTs consisting of homogeneous participants, with no individual heterogeneity at baseline. However, even in such an apparently ideal setting, selection bias can emerge over time due to temporal dependence in exposure, a realistic feature of infectious disease transmission. In this study, we mathematically characterize the mechanism underlying this bias and quantitatively evaluate its magnitude. Our results show that this bias should be recognized as an issue in both the design and interpretation of RCTs of infectious disease interventions.

stat.ME↗

arXiv2025

Beyond Gaussian Assumptions: A Nonlinear Generalization of Linear Inverse Modeling

The Linear Inverse Model (LIM) is a class of data-driven methods that construct approximate linear stochastic models to represent complex observational data. The stochastic forcing can be modeled using either Gaussian white noise or Ornstein-Uhlenbeck colored noise; the corresponding models are called White-LIM and Colored-LIM, respectively. Although LIMs are widely applied in climate sciences, they inherently approximate observed distributions as Gaussian, limiting their ability to capture asymmetries. In this study, we extend LIMs to incorporate nonlinear dynamics, introducing White-nLIM and Colored-nLIM which allow for a more flexible and accurate representation of complex dynamics from observations. The proposed methods not only account for the nonlinear nature of the underlying system but also effectively capture the skewness of the observed distribution. Moreover, we apply these methods to a lower-dimensional representation of ENSO and demonstrate that both White-nLIM and Colored-nLIM successfully capture its nonlinear characteristic.

math.NA↗

arXiv2025

Statistical Modeling of Networked Evolutionary Public Goods Games

Repeated small dynamic networks are integral to studies in evolutionary game theory, where networked public goods games offer novel insights into human behaviors. Building on these findings, it is necessary to develop a statistical model that effectively captures dependencies across multiple small dynamic networks. While Separable Temporal Exponential-family Random Graph Models (STERGMs) have demonstrated success in modeling a large single dynamic network, their application to multiple small dynamic networks with less than 10 actors, remains unexplored. In this study, we extend the STERGM framework to accommodate multiple small dynamic networks, offering an approach to analyzing such systems. Taking advantage of the small network sizes, our proposed approach improves accuracy in statistical inference through direct computation, unlike conventional approaches that rely on Markov Chain Monte Carlo methods. We demonstrate the validity of this framework through the analysis of a networked public goods experiment into individual decision-making about cooperation and defection. The resulting statistical inference uncovers novel insights into the dynamics of social dilemmas, showcasing the effectiveness and robustness of this modeling and approach.

stat.AP↗

arXiv2025

Making the Peers' Subjective Well-being Visible Impairs Cooperator-centered Experimental Social Networks

Past experiments show that reputation or the knowledge of peers' past cooperation can enhance cooperation in human social networks. On the other hand, the knowledge of peers' wealth undermines cooperativeness, and that of peers' interconnectedness and network structure does not affect it. However, it is unknown if making peers' subjective well-being (SWB) available or visible in social networks may enhance or undermine cooperation. Therefore, we implemented online network experiments (N = 662 in 50 networked groups with 15 rounds of interactions), in which study participants cooperated with or defected against connected peers through Public Goods Game, made and cut social ties with others, and rated their SWB. We manipulated the visibility of connected peers' SWB (25 visible vs. 25 invisible SWB networked groups) while keeping the connected peers' reputation and in-game wealth visible. Results show that making the peers/ SWB visible did not alter overall cooperativeness, wealth, inter-connectedness, or SWB. In contrast, the visible SWB networked groups exhibited a higher number of communities and lower transitivity (the proportion of the cases where a peer of a peer is also a peer) than the invisible SWB networked groups. These phenomena are explained by an altered decision-making pattern in the visible SWB networks: cooperators were less likely to connect with cooperators and more likely to connect with defectors, and consequently, cooperators could not maintain their popularity or stay in the center of the networks.

cs.SI↗

arXiv2024

On the Cyclostationary Linear Inverse Models: A Mathematical Insight and Implication

Cyclostationary linear inverse models (CS-LIMs), generalized versions of the classical (stationary) LIM, are advanced data-driven techniques for extracting the first-order time-dependent dynamics and random forcing relevant information from complex non-linear stochastic processes. Though CS-LIMs lead to a breakthrough in climate sciences, their mathematical background and properties are worth further exploration. This study focuses on the mathematical perspective of CS-LIMs and introduces two variants: e-CS-LIM and l-CS-LIM. The former refines the original CS-LIM using the interval-wise linear Markov approximation, while the latter serves as an analytic inverse model for the linear periodic stochastic systems. Although relying on approximation, e-CS-LIM converges to l-CS-LIM under specific conditions and shows noise-robust performance. Numerical experiments demonstrate that each CS-LIM reveals the temporal structure of the system. The e-CS-LIM optimizes the original model for better dynamics performance, while l-CS-LIM excels in diffusion estimation due to reduced approximation reliance. Moreover, CS-LIMs are applied to real-world ENSO data, yielding a consistent result aligning with observations and current ENSO understanding.

math.NA↗

arXiv2024

Reducing Total Trip Time and Vehicle Emission through Park-and-Ride -- methods and case-study

This study addresses important issues of traffic congestion and vehicle emissions in urban areas by developing a comprehensive mathematical framework to evaluate Park-and-Ride (PnR) systems. The proposed approach integrates queueing theory and emissions modeling to simultaneously assess waiting times, travel times, and vehicle emissions under various PnR usage scenarios. The methodology employs a novel combination of Monte Carlo simulation and matrix geometric analytic methods to analyze a queueing network representing PnR facilities and road traffic. A case study of Tsukuba, Japan demonstrates the model's applicability, revealing potential reductions in social costs related to total trip time and emissions through optimized PnR policies. Specifically, the study found that implementing optimal bus frequency and capacity policies could reduce total social costs by up to 30\% compared to current conditions. This research contributes to the literature by providing a unified framework for evaluating PnR systems that considers both time and environmental costs, offering valuable insights for urban planners and policymakers seeking to improve transportation sustainability. The proposed model utilizes a single server queue with a deterministic service time and multiple arrival streams to represent traffic flow, incorporating both private cars and public buses. Emissions are calculated using the Methodologies for Estimating Air Pollutant Emissions from Transport (MEET) framework. The social cost of emissions and total trip time (SCETT) is introduced as a comprehensive metric for evaluating PnR system performance.

stat.CO↗

arXiv2024

Colored-LIM: A Data-Driven Method for Studying Dynamical Systems with Temporally Correlated Stochasticity

In real-world problems, environmental noise is often idealized as Gaussian white noise, despite potential temporal dependencies. The Linear Inverse Model (LIM) is a class of data-driven methods that extract dynamic and stochastic information from finite time-series data of complex systems. In this study, we introduce a new variant of LIM, called Colored-LIM, which models stochasticity using Ornstein-Uhlenbeck colored noise. Despite the non-trivial correlation between observable and colored noise, we show that Colored-LIM unveils the desired information merely from the correlation function of the observable. Therefore, this approach not only accounts for the memory effects of environmental noise, traditionally represented by time-uncorrelated white noise in the Classical LIM framework, but does so using the same observation dataset without requiring additional data. Furthermore, we show that Colored-LIM does not reduce to Classical LIM in the white noise limit, underscoring the importance of temporal dependencies in stochastic systems. In this paper, we rigorously develop the Colored-LIM, explore its connections with the Classical LIM and Dynamic Mode Decomposition, and validate its effectiveness on both ideal linear and nonlinear systems. In addition, we illustrate the potential applications and implications of Colored-LIM for real-world problems, including the El Ni\~no-Southern Oscillation and the electricity network of Tohoku University.

math.NA↗

arXiv2023

Two long-period giant planets around two giant stars: HD 112570 and HD 154391

We present the discoveries of two giant planets orbiting the red giant branch (RGB) star HD 112570 and the red clump (RC) star HD 154391, based on the radial velocity (RV) measurements from Xinglong station and Okayama Astrophysical Observatory (OAO). Spectroscopic and asteroseismic analyses suggest that HD 112570 has a mass of $1.15\pm0.12\,M_{\odot}$, a radius of $9.85\pm0.23\,R_{\odot}$, a metallicity [Fe/H] of $-0.46\pm0.1$ and a ${\rm log}\,g$ of $2.47\pm0.1$. With the joint analysis of RV and Hipparcos-Gaia astrometry, we obtain a dynamical mass of $M_{\rm p}={3.42}_{-0.84}^{+1.4}\ M_{\rm Jup}$, a period of $P={2615}_{-77}^{+85}$ days and a moderate eccentricity of $e={0.20}_{-0.14}^{+0.16}$ for the Jovian planet HD 112570 b. For HD 154391, it has a mass of $2.07\pm0.03\,M_{\odot}$, a radius of $8.56\pm0.05\,R_{\odot}$, a metallicity [Fe/H] of $0.07\pm0.1$ and a ${\rm log}\,g$ of $2.86\pm0.1$. The super-Jupiter HD 154391 b has a mass of $M_{\rm p}={9.1}_{-1.9}^{+2.8}\ M_{\rm Jup}$, a period of $P={5163}_{-57}^{+60}$ days and an eccentricity of $e={0.20}_{-0.04}^{+0.04}$. We found HD 154391 b has one of the longest orbital period among those ever discovered orbiting evolved stars, which may provide a valuable case in our understanding of planetary formation at wider orbits. Moreover, while a mass gap at $4\,M_{\rm Jup}$ seems to be present in the population of giant stars, there appears to be no significant differences in the distribution of metallicity among giant planets with masses above or below this threshold. Finally, The origin of the abnormal accumulation near 2 au for planets around large evolved stars ($R_{\star}>21\,R_{\odot}$), remains unclear.

astro-ph.EP↗

arXiv2023

Revisiting Planetary Systems in Okayama Planet Search Program: A new long-period planet, RV astrometry joint analysis, and multiplicity-metallicity trend around evolved stars

In this study, we revisit 32 planetary systems around evolved stars observed within the framework of the Okayama Planet Search Program and its collaborative framework of the EAPS-Net to search for additional companions and investigate the properties of stars and giant planets in multiple-planet systems. With our latest radial velocities obtained from Okayama Astrophysical Observatory (OAO), we confirm an additional giant planet in the wide orbit of 75 Cet system ($P_{\rm{c}} = 2051.62_{-40.47}^{+45.98}\ \rm{d}$, $M_{\rm{c}}\sin i=0.912_{-0.090}^{+0.088}\ M_{\rm{J}}$, and $a_{\rm{c}}=3.929_{-0.058}^{+0.052}\ \rm{au}$), along with five stars exhibiting long-term radial velocity accelerations, which indicates massive companions in the wide orbits. We have also found that the radial velocity variations of several planet-harboring stars may indicate additional planet candidates, stellar activities, or other understudied sources. These stars include $\epsilon$ Tau, 11 Com, 24 Boo, 41 Lyn, 14 And, HD 32518, and $\omega$ Ser. We further constrain the orbital configuration of the HD 5608, HD 14067, HD 120084, and HD 175679 systems by combining radial velocities with astrometry, as their host central stars exhibit significant astrometric accelerations. For other systems, we simply refine their orbital parameters. Moreover, our study indicates that the OPSP planet-harboring stars are more metal-poor compared to the currently known planet-harboring stars, and this is likely due to the $B-V$ color upper limit at 1.0 for star selection in the beginning of the survey. Finally, by investigating the less-massive giant planets ($< 5 M_{\rm{J}}$) around currently known planet-harboring evolved stars, we have found that metallicity positively correlates with the multiplicity and total planet mass of the system, which can be evidence for the core-accretion planet formation model.

astro-ph.EP↗

arXiv2023

Recovering Arrhythmic EEG Transients from Their Stochastic Interference

Traditionally, the neuronal dynamics underlying electroencephalograms (EEG) have been understood as arising from \textit{rhythmic oscillators with varying degrees of synchronization}. This dominant metaphor employs frequency domain EEG analysis to identify the most prominent populations of neuronal current sources in terms of their frequency and spectral power. However, emerging perspectives on EEG highlight its arrhythmic nature, which is primarily inferred from broadband EEG properties like the ubiquitous $1/f$ spectrum. In the present study, we use an \textit{arrhythmic superposition of pulses} as a metaphor to explain the origin of EEG. This conceptualization has a fundamental problem because the interference produced by the superpositions of pulses generates colored Gaussian noise, masking the temporal profile of the generating pulse. We solved this problem by developing a mathematical method involving the derivative of the autocovariance function to recover excellent approximations of the underlying pulses, significantly extending the analysis of this type of stochastic processes. When the method is applied to spontaneous mouse EEG sampled at $5$ kHz during the sleep-wake cycle, specific patterns -- called $\Psi$-patterns -- characterizing NREM sleep, REM sleep, and wakefulness are revealed. $\Psi$-patterns can be understood theoretically as \textit{power density in the time domain} and correspond to combinations of generating pulses at different time scales. Remarkably, we report the first EEG wakefulness-specific feature, which corresponds to an ultra-fast ($\sim 1$ ms) transient component of the observed patterns. By shifting the paradigm of EEG genesis from oscillators to random pulse generators, our theoretical framework pushes the boundaries of traditional Fourier-based EEG analysis, paving the way for new insights into the arrhythmic components of neural dynamics.

q-bio.NC↗

arXiv2022

A Close-in Planet Orbiting Giant Star HD 167768

We report the detection of a giant planet orbiting a G-type giant star HD 167768 from radial velocity measurements using HIgh Dispersion Echelle Spectrograph (HIDES) at Okayama Astrophysical Observatory (OAO). HD 167768 has a mass of $1.08_{-0.12}^{+0.14} M_{\odot}$, a radius of $9.70_{-0.25}^{+0.25} R_{\odot}$, a metallicity of $\rm{[Fe/H]}=-0.67_{-0.08}^{+0.09}$, and a surface gravity of $\log g = 2.50_{-0.06}^{+0.06}$. The planet orbiting the star is a warm Jupiter, having a period of $20.6532_{-0.0032}^{+0.0032}\ \rm{d}$, a minimum mass of $0.85_{-0.11}^{+0.12}\ M_{\rm{J}}$, and an orbital semimajor axis of $0.1512_{-0.0063}^{+0.0058}\ \rm{au}$. The planet has one of the shortest orbital periods among those ever found around deeply evolved stars ($\log g < 3.5$) using radial velocity methods. The equilibrium temperature of the planet is $1874\ \rm{K}$, as high as a hot Jupiter. The radial velocities show two additional regular variations at $41\ \rm{d}$ and $95\ \rm{d}$, suggesting the possibility of outer companions in the system. Follow-up monitoring will enable validation of the periodicity. We also calculated the orbital evolution of HD 167768 b and found that the planet will be engulfed within 0.15\,Gyr.

astro-ph.EP↗

arXiv2022

A Trio of Giant Planets Orbiting Evolved Star HD 184010

We report the discovery of a triple-giant-planet system around an evolved star HD 184010 (HR 7421, HIP 96016). This discovery is based on observations from Okayama Planet Search Program, a precise radial velocity survey, undertaken at Okayama Astrophysical Observatory between 2004 April and 2021 June. The star is K0 type and located at beginning of the red-giant branch. It has a mass of $1.35_{-0.21}^{+0.19} M_{\odot}$, a radius of $4.86_{-0.49}^{+0.55} R_{\odot}$, and a surface gravity $\log g$ of $3.18_{-0.07}^{+0.08}$. The planetary system is composed of three giant planets in a compact configuration: The planets have minimum masses of $M_{\rm{b}}\sin i = 0.31_{-0.04}^{+0.03} M_{\rm{J}}$, $M_{\rm{c}}\sin i = 0.30_{-0.05}^{+0.04} M_{\rm{J}}$, and $M_{\rm{d}}\sin i = 0.45_{-0.06}^{+0.04} M_{\rm{J}}$, and orbital periods of $P_{\rm{b}}=286.6_{-0.7}^{+2.4}\ \rm{d}$, $P_{\rm{c}}=484.3_{-3.5}^{+5.5}\ \rm{d}$, and $P_{\rm{d}}=836.4_{-8.4}^{+8.4}\ \rm{d}$, respectively, which are derived from a triple Keplerian orbital fit to three sets of radial velocity data. The ratio of orbital periods are close to $P_{\rm{d}}:P_{\rm{c}}:P_{\rm{b}} \sim 21:12:7$, which means the period ratios between neighboring planets are both lower than $2:1$. The dynamical stability analysis reveals that the planets should have near-circular orbits. The system could remain stable over 1 Gyr, initialized from co-planar orbits, low eccentricities ($e=0.05$), and planet masses equal to the minimum mass derived from the best-fit circular orbit fitting. Besides, the planets are not likely in mean motion resonance. HD 184010 system is unique: it is the first system discovered to have a highly evolved star ($\log g < 3.5$ cgs) and more than two giant planets all with intermediate orbital periods ($10^2\ \rm{d} < P < 10^3\ \rm{d}$).

astro-ph.EP↗

arXiv2021

Regular Radial Velocity Variations in Nine G- and K-type Giant Stars: Eight Planets and One Planet Candidate

We report the detection of radial velocity variations in nine evolved G- and K-type giant stars. The observations were conducted at Okayama Astrophysical Observatory. Planets or planet candidates can best explain these regular variations. However, a coincidence of near 280-day variability among five of them prevents us from fully ruling out stellar origins for some of the variations, since all nine stars behave similarly in stellar properties. In the planet hypotheses to the RV variations, the planets (including one candidate) may survive close to the boundary of the so-called "planet desert" around evolved stars, having orbital periods between 255 and 555 days. Besides, they are the least-massive giant planets detected around G- and K-type giant stars, with minimum masses between 0.45$M_{\rm{J}}$ and 1.34$M_{\rm{J}}$. We further investigated other hypotheses for our detection, yet none of them can better explain regular RV variation. With our detection, it is convinced that year-long regular variation with amplitude down to 15 $\rm{m\ s^{-1}}$ for G- and K-type giant stars is detectable. Moreover, we performed simulations to further confirm the detectability of planets around these stars. Finally, we explored giant planets around intermediate-mass stars, and likewise found a 4 Jupiter mass gap (e.g. Santo et al. 2017), which is probably a boundary of the giant planet population.

astro-ph.EP↗

arXiv2020

Computation harvesting in road traffic dynamics

Owing to recent advances in artificial intelligence and internet of things (IoT) technologies, collected big data facilitates high computational performance, while its computational resources and energy cost are large. Moreover, data are often collected but not used. To solve these problems, we propose a framework for a computational model that follows a natural computational system, such as the human brain, and does not rely heavily on electronic computers. In particular, we propose a methodology based on the concept of `computation harvesting', which uses IoT data collected from rich sensors and leaves most of the computational processes to real-world phenomena as collected data. This aspect assumes that large-scale computations can be fast and resilient. Herein, we perform prediction tasks using real-world road traffic data to show the feasibility of computation harvesting. First, we show that the substantial computation in traffic flow is resilient against sensor failure and real-time traffic changes due to several combinations of harvesting from spatiotemporal dynamics to synthesize specific patterns. Next, we show the practicality of this method as a real-time prediction because of its low computational cost. Finally, we show that, compared to conventional methods, our method requires lower resources while providing a comparable performance.

cs.LG↗

arXiv2020

Decentralized Algorithms for Consensus-Based Power Packet Distribution

Power packets are proposed as a transmission unit that can deliver power and information simultaneously. They are transferred using the store-and-forward method of power routers. A system that achieves power supply/demand in this manner is called a power packet network (PPN). A PPN is expected to enhance structural robustness and operational reliability in an energy storage system (ESS) with recent diverse distributed sources. However, this technology is still in its early stage, and faces numerous challenges, such as high cost of implementation and complicated energy management. In this paper, we propose a novel power control based on decentralized algorithms for a PPN. Specifically, the power supply is triggered and managed by communications between power routers. We also discuss the mechanism of the decentralized algorithm for the operation of power packets and reveal the feasibility of the given control method and application by forming biased power flows on the consensus-based distribution.

eess.SY↗

arXiv2019

Road traffic reservoir computing

Reservoir computing derived from recurrent neural networks is more applicable to real world systems than deep learning because of its low computational cost and potential for physical implementation. Specifically, physical reservoir computing, which replaces the dynamics of reservoir units with physical phenomena, has recently received considerable attention. In this study, we propose a method of exploiting the dynamics of road traffic as a reservoir, and numerically confirm its feasibility by applying several prediction tasks based on a simple mathematical model of the traffic flow.

cs.ET↗