arXiv subjects

Tomohiro Harada

Publications and source records attributed to Tomohiro Harada.

At least 19 recordsLinked to original sources

arXiv2026

Discrete self-similarity imprints on primordial-black-hole mass functions

We study how discrete self-similarity (DSS) in the critical behavior of scalar-field collapse is imprinted on primordial-black-hole (PBH) mass functions. Using the DSS-modulated critical scaling law found in cosmological simulations, we propagate the near-threshold mass map into normalized PBH mass functions during a {kination} era. We compare Gaussian window function, $k$-space top-hat window function, and real-space top-hat window function, together with a real-space top-hat window function multiplied by a kination transfer function. We find that critical behavior in gravitational collapse produces an irreducible minimum width even for an infinitesimally narrow primordial spectrum. DSS then modulates this critical-scaling profile, generating approximately log-periodic features in mass. At fixed horizon mass, successive equal-phase points of the DSS-modulated critical mass map satisfy $\Delta\ln M_{\rm PBH}=\gamma P_{\rm ln}$. Consequently, in the narrow-spectrum limit, the corresponding structures in the final mass function are expected to satisfy $\Delta\ln m\simeq\gamma P_{\rm ln}$, or $m_{n+1}/m_n\simeq5.6$, for the fiducial Choptuik DSS parameters $\gamma$ and $P_{\rm ln}$. For broad primordial spectra, the convolution over horizon masses dephases the DSS pattern and progressively washes out the critical substructure. We normalize the mass functions so that we may primarily study the profile shape and the survival of DSS substructure, rather than the absolute PBH abundance. These results provide a bridge between cosmological DSS collapse simulations and PBH phenomenology including population observables.

gr-qc↗

arXiv2026

Profiling Lightweight Large Language Models

Lightweight large language models (LLMs) are increasingly being deployed locally on personal computers and are expected to play a growing role in resource-constrained edge and mobile environments. In such settings, energy consumption, execution time, and memory usage directly affect practical usability, yet existing evaluations of LLM efficiency largely rely on proxy descriptors such as parameter count or FLOPs, often decoupled from task precision. This paper introduces a PTME-based experimental framework for the precision-aware profiling of lightweight LLM inference, jointly measuring Precision, execution Time, peak Memory usage, and Energy consumption through direct hardware-level measurements. The methodology is applied to a representative set of lightweight LLMs executed locally under edge-class resource envelopes on a controlled desktop platform, using benchmarks spanning code generation, mathematical reasoning, and multi-task understanding. We find that static proxy descriptors approximate inference cost well but fail to predict precision. Tightening the resource envelope increases cost without affecting precision, amplifying execution time more strongly than energy and penalizing larger models the most. Moreover, no single model dominates across all PTME dimensions, and a Pareto analysis reveals non-dominated configurations that would be hidden by accuracy-only or efficiency-only assessments, providing practical guidance for selecting models under different resource envelopes. These results show that selecting lightweight LLMs by size, FLOPs, latency, or accuracy alone can select the wrong deployment candidate; PTME profiling exposes configurations that preserve useful accuracy at lower physical cost.

cs.AI↗

arXiv2026

Determining Kerr black hole spin and inclination from a segment of the critical curve in black hole images

We present a method for determining the spin parameter $a/M$ and inclination angle $i$ of a non-extremal Kerr black hole from segments of the critical curve identified in black hole images. Although the critical curve itself is not directly observable, higher-order photon rings accumulate near it, and in realistic observations localized portions of the resulting brightness enhancement may be available for identifying segments of the critical curve. We introduce standardized segments of the critical curve and define three observables that characterize their geometry. We show that these observables uniquely determine $(a/M,i)$, together with an auxiliary parameter $r_{nl}\in[0,1]$ specifying the location of the identified segment along the critical curve, within the domain considered. Thus, even a segment of the critical curve contains sufficient geometric information to constrain the black hole spin and inclination without reconstructing the full critical curve. The framework is naturally suited to realistic observations and may be extended to more general rotating black hole spacetimes.

gr-qc↗

arXiv2026

COSMOS: A numerical relativity code specialized for PBH formation

Primordial black holes (PBHs) are black holes generated in the early universe without having gone through stellar evolution. In the standard formation process, PBHs are formed from super-horizon primordial fluctuations with non-linearly large initial amplitude. In order to simulate the non-linear gravitational dynamics of PBH formation, one has to rely on numerical relativity solvers to approximate the solution of the Einstein equations. COSMOS is a C++ package for solving the Einstein equations in 3+1 dimensions, providing simple tools for the simulation of PBH formation. In order to resolve the collapsing region, non-Cartesian scale-up coordinates and a fixed mesh-refinement procedure are implemented. In COSMOS, a massless scalar field and a perfect fluid with a linear equation of state are implemented as matter fields. To achieve a practically acceptable computational speed, OpenMP is used for the parallelization. COSMOS has no other dependencies, which makes for an easier installation.

gr-qc↗

arXiv2026

Supermassive black hole seeds from direct collapse of CDM-curvature peaks

We study black hole (BH) formation from the nonlinear growth and collapse of primordial perturbations during the matter-dominated era. Modelling cold dark matter (CDM) as pressureless dust, we describe the collapse in a fully nonlinear relativistic framework using the Lema\^{i}tre-Tolman-Bondi (LTB) and quasi-spherical Szekeres solutions as exact perturbations of a spatially-flat Friedmann-Lema\^{i}tre-Robertson-Walker (FLRW) $\Lambda$CDM background. At first order in relativistic scalar perturbation theory, the growing mode of any relevant quantity can be expressed in terms of the conserved gauge-invariant curvature perturbation $\mathcal{R}_c$, which acts as a potential for the 3-curvature of hypersurfaces orthogonal to the matter 4-velocity. We use this result to express the active gravitational mass and curvature functions of the LTB and Szekeres models in terms of the initial values of $\mathcal{R}_c$ and its spatial derivatives. From these initial curvature data we derive: (i) the turn-around, collapse, and apparent-horizon formation times, and (ii) the regularity conditions required for BH formation. We show that sinusoidal and Gaussian profiles do not provide viable BH-forming channels, whereas broad compensated curvature peaks, naturally predicted by peak theory, do. We then estimate the formation times of $10^{3}-10^{6}~\mathrm{M}_\odot$ massive BH seeds produced by the direct collapse of primordial CDM curvature peaks, finding full BH formation at redshifts $z>5$, with core collapse beginning at $10 \lesssim z \lesssim 16$. Finally, we characterize the local dynamics and singularity type of the collapse (point-like, cigar-like, or pancake-like) directly from the initial comoving curvature data, clarifying the role of the initial shear in selecting the collapse end-state.

gr-qc↗

arXiv2026

Gravitational wave emission from nonspherical collapse in an early matter-dominated era using N-body simulations

We study the dynamics of the collapse of a nonspherical overdense patch during an early matter-dominated era and the associated production of gravitational waves (GWs) using a semirelativistic N-body framework that we develop. The collapsing patch is initialized through a Zel'dovich deformation of a homogeneous sphere and evolved in an Einstein--de Sitter background, while the emitted signal is computed directly from the numerical quadrupole evolution. We show that a reliable prediction of the signal requires a fully numerical treatment of the nonlinear collapse dynamics. In particular, fitting-based procedures and Zel'dovich-based estimates fail to capture the post-shell-crossing evolution and can over/under-estimate the emitted power of the GWs. After averaging over realizations weighted by the Doroshkevich and BBKS (peak theory) distributions, we find that the two spectra have similar shapes and remain within the same overall order of magnitude at the peak amplitude, although the BBKS result is systematically smaller. The dominant contribution arises from peaks of relatively modest height, around $\nu \simeq 3$, while a larger variance significantly enhances the signal. Finally, by varying the horizon mass and reheating temperature, we map the present-day GW spectra to the sensitivity bands of different classes of detectors. In this way, the signal can populate a broad range of frequencies, from pulsar timing arrays to very high-frequency experiments, showing that GWs from nonspherical collapse can provide a probe of the pre-BBN thermal history.

astro-ph.CO↗

arXiv2026

Cosmological discrete self-similarity in primordial black hole formation

We demonstrate that discrete self-similarity (DSS), originally discovered in the collapse of a massless scalar field in an asymptotically flat system, survives in primordial black hole (PBH) formation within an expanding cosmological background. Using fully relativistic numerical simulations of massless scalar-field collapse in an Friedmann-Lema\^{i}tre-Robertson-Walker universe, we resolve the critical regime down to $|p-p_c|\sim 10^{-8}$, where $p$ and $p_c$ respectively are a parameter of the family of initial data and its threshold value, and find clear log-periodic oscillations in the PBH mass scaling relation. The detailed structure of these oscillations differs from that previously reported in the asymptotically flat case, exhibiting a more pronounced asymmetry between peaks and troughs. Analyzing two distinct families of initial data (Gaussian and piecewise rational curvature profiles), we find critical exponents and DSS periods that differ slightly but are broadly consistent within uncertainties. The presence of DSS implies characteristic log-periodic modulations in the PBH mass spectrum, with potential consequences for PBH abundances and the spectrum of induced gravitational waves.

astro-ph.CO↗

arXiv2026

A Methodology for Effective Surrogate Learning in Complex Optimization

Solving complex problems requires continuous effort in developing theory and practice to cope with larger, more difficult scenarios. Working with surrogates is normal for creating a proxy that realistically models the problem into the computer. Thus, the question of how to best define and characterize such a surrogate model is of the utmost importance. In this paper, we introduce the PTME methodology to study deep learning surrogates by analyzing their Precision, Time, Memory, and Energy consumption. We argue that only a combination of numerical and physical performance can lead to a surrogate that is both a trusted scientific substitute for the real problem and an efficient experimental artifact for scalable studies. Here, we propose different surrogates for a real problem in optimally organizing the network of traffic lights in European cities and perform a PTME study on the surrogates' sampling methods, dataset sizes, and resource consumption. We further use the built surrogates in new optimization metaheuristics for decision-making in real cities. We offer better techniques and conclude that the PTME methodology can be used as a guideline for other applications and solvers.

cs.NE↗

arXiv2026

Green Optimization: Energy-aware Design of Metaheuristics by Using Machine Learning Surrogates to Cope with Real Problems

Addressing real-world optimization challenges requires not only advanced metaheuristics but also continuous refinement of their internal mechanisms. This paper explores the integration of machine learning in the form of neural surrogate models into metaheuristics through a recent lens: energy consumption. While surrogates are widely used to reduce the computational cost of expensive objective functions, their combined impact on energy efficiency, algorithmic performance, and solution accuracy remains largely unquantified. We provide a critical investigation into this intersection, aiming to advance the design of energy-aware, surrogate-assisted search algorithms. Our experiments reveal substantial benefits: employing a state-of-the-art pre-trained surrogate can reduce energy consumption by up to 98\%, execution time by approximately 98%, and memory usage by around 99\%. Moreover, increasing the training dataset size further enhances these gains by lowering the per-use computational cost, while static pre-training versus continuous (iterative) retraining have relatively different advantages depending on whether we aim at time/energy or accuracy and general cost across problems, respectively. Surrogates also have a negative impact on costs and accuracy at times, and then they cannot be blindly adopted. These findings support a more holistic approach to surrogate-assisted optimization, integrating energy with time and predictive accuracy into performance assessments.

cs.NE↗

arXiv2026

Energy-Aware Metaheuristics

This paper presents a principled framework for designing energy-aware metaheuristics that operate under fixed energy budgets. We introduce a unified operator-level model that quantifies both numerical gain and energy usage, and define a robust Expected Improvement per Joule (EI/J) score that guides adaptive selection among operator variants during the search. The resulting energy-aware solvers dynamically choose between operators to self-control exploration and exploitation, aiming to maximize fitness gain under limited energy. We instantiate this framework with three representative metaheuristics - steady-state GA, PSO, and ILS - each equipped with both lightweight and heavy operator variants. Experiments on three heterogeneous combinatorial problems (Knapsack, NK-landscapes, and Error-Correcting Codes) show that the energy-aware variants consistently reach comparable fitness while requiring substantially less energy than their non-energy-aware baselines. EI/J values stabilize early and yield clear operator-selection patterns, with each solver reliably self-identifying the most improvement-per-Joule - efficient operator across problems.

cs.NE↗

arXiv2026

An Evolutionary Framework for Automatic Optimization Benchmark Generation via Large Language Models

Optimization benchmarks play a fundamental role in assessing algorithm performance; however, existing artificial benchmarks often fail to capture the diversity and irregularity of real-world problem structures, while benchmarks derived from real-world problems are costly and difficult to construct. To address these challenges, we propose an evolutionary automatic benchmark generation framework that leverages a large language model (LLM) as a generative operator, termed the LLM-driven evolutionary benchmark generator (LLM-EBG). In this framework, the LLM serves as an evolutionary operator that generates and evolves benchmark problems within a flexible, expressive representation space. As a case study, we generate unconstrained single-objective continuous minimization problems represented as mathematical expressions designed to induce significant performance differences between a genetic algorithm (GA) and differential evolution (DE). Experimental results show that LLM-EBG successfully produces benchmark problems in which the designated target algorithm consistently outperforms the comparative algorithm in more than 80\% of trials. Furthermore, exploratory landscape analysis reveals that benchmarks favoring GA are highly sensitive to variable scaling, demonstrating that the proposed framework can generate problems with distinct geometric characteristics that reflect the intrinsic search behaviors of different optimization algorithms.

cs.NE↗

arXiv2025

Cosmological long-wavelength solutions in non-adiabatic multi-fluid systems

We develop a formulation of nonlinear cosmological perturbations on superhorizon scales in multi-fluid systems. It is based on the Arnowitt-Deser-Misner formalism combined with a spatial gradient expansion characterized by a small expansion parameter defined as the ratio of the comoving wavenumber to the Hubble scale. The background spacetime is assumed to be a flat Friedmann-Lemaitre-Robertson-Walker universe. Within this framework, we explicitly construct nonlinear long-wavelength solutions for cosmological perturbations. Since multi-fluid systems are inherently non-adiabatic, these solutions admit both adiabatic and entropy modes already at leading nonlinear order. We define adiabatic and entropy perturbations and discuss the non-uniqueness in defining pure entropy perturbations. Using different choices of pure entropy initial conditions, we analyze the time evolution of physical quantities such as the curvature perturbation and density perturbations in the geodesic slicing for two-fluid systems.

gr-qc↗

arXiv2025

Primordial Black Hole Formation and Spin in Matter Domination Revisited

In this article, we calculate the mass distribution of primordial black holes (PBHs) formed in the matter-dominated (MD) era by the peak theory. We apply the Zel'dovich approximation to track the nonlinear evolution of overdensities and compute the PBH abundance and mass function by incorporating a PBH formation criterion based on the hoop conjecture. We find that the PBH abundance $\beta$ follows the scaling law $\beta \simeq A_\gamma \sigma_h^{*5}$ for $\sigma_h^*\ll 1$. Here, $\sigma_h^*$ is the quantity that characterizes the variance of the density fluctuation at the horizon entry. We also find that, in contrast to the previous estimates, the PBH spin is very small for $\sigma_h^*\ll 1$ but could be larger for larger $\sigma_h^*$ and broader power spectra. Finally, specializing to a monochromatic power spectrum, we prove analytically that the PBH mass distribution becomes effectively monochromatic and reveal that the resultant PBH abundance is approximately 19 times the previous prediction.

gr-qc↗

arXiv2025

Energy and Quality of Surrogate-Assisted Search Algorithms: a First Analysis

Solving complex real problems often demands advanced algorithms, and then continuous improvements in the internal operations of a search technique are needed. Hybrid algorithms, parallel techniques, theoretical advances, and much more are needed to transform a general search algorithm into an efficient, useful one in practice. In this paper, we study how surrogates are helping metaheuristics from an important and understudied point of view: their energy profile. Even if surrogates are a great idea for substituting a time-demanding complex fitness function, the energy profile, general efficiency, and accuracy of the resulting surrogate-assisted metaheuristic still need considerable research. In this work, we make a first step in analyzing particle swarm optimization in different versions (including pre-trained and retrained neural networks as surrogates) for its energy profile (for both processor and memory), plus a further study on the surrogate accuracy to properly drive the search towards an acceptable solution. Our conclusions shed new light on this topic and could be understood as the first step towards a methodology for assessing surrogate-assisted algorithms not only accounting for time or numerical efficiency but also for energy and surrogate accuracy for a better, more holistic characterization of optimization and learning techniques.

cs.NE↗

arXiv2025

Periapsis shift in the Zipoy-Voorhees spacetime

We study the periapsis shift of timelike bound orbits in the equatorial plane in the Zipoy-Voorhees spacetime, which is an exact, static, axisymmetric, and vacuum solution characterized by the deformation parameter $\gamma$, including the Schwarzschild spacetime as $\gamma = 1$. We newly derive the formula for the periapsis shift by the post-Newtonian (PN) expansion and show that the quadrupole moment contributes to the periapsis shift apparently as in the 2PN order. Applying this formula to observational data on S2, a star orbiting closely to Sagittarius A* (Sgr A*), we show that the parameter $\gamma$ is constrained to $\gamma \gtrsim 1.9 \times 10^{-2}$ for the gravitational field of Sgr A*. This is equivalent to $\tilde{M}_2 \lesssim 9.7 \times 10^2$ in terms of the dimensionless quadrupole moment $\tilde{M}_2$, that is, the supermassive compact object at Sgr A* cannot be very prolate, whereas $\tilde{M}_2 \geq -1/3$ is required from the solution. Finally, we derive a new series representation in this spacetime using a recently proposed prescription, which shows fast convergence not only in the weak-field regime with not necessarily small eccentricity but also in the strong-field regime with small eccentricity.

gr-qc↗

arXiv2025

Primordial black hole formation from a type II perturbation in the absence and presence of pressure

We investigate primordial black holes (PBHs) formed from extremely large amplitudes of primordial curvature fluctuations, classified as type II. Type II fluctuations differ from type I by the presence of a stationary point on the initial time slice, when we see the areal radius as a function of the radial coordinate. Starting from these type II perturbations to form black holes, the nonlinear evolution governed by the Einstein equations generally results in two distinct types, A and B, of horizon configurations, respectively characterized by the absence and presence of a bifurcating trapping horizon where past and future trapping horizons meet. In this paper, we use the Lemaitre-Tolman-Bondi solution to show that type I/II and type A/B classifications are equivalent for a spherically symmetric dust fluid system, regardless of the fluctuation profile. However, this equivalence does not generally hold in the presence of pressure.

gr-qc↗

arXiv2025

General relativistic effects on photon spectrum emitted from dark matter halos around primordial black holes

We investigate general relativistic effects on the photon spectrum emitted from decaying (or annihilating) particle dark matter in the halo surrounding a primordial black hole. The spectrum undergoes significant modification due to gravitational redshifts, which induces broadening as a result of the intense gravitational field near the black hole. This characteristic alteration in the photon spectrum presents a unique observational signature. Future observations of such spectral features may provide critical evidence for a mixed dark matter scenario, involving both primordial black holes and particle dark matter.

hep-ph↗

arXiv2025

Impact of Surrogate Model Accuracy on Performance and Model Management Strategy in Surrogate-Assisted Evolutionary Algorithms

Surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve expensive optimization problems. Although SAEAs use surrogate models that approximate the evaluations of solutions using machine learning techniques, prior research has not adequately investigated the impact of surrogate model accuracy on search performance and model management strategy in SAEAs. This study analyzes how surrogate model accuracy affects search performance and model management strategies. For this purpose, we construct a pseudo-surrogate model with adjustable prediction accuracy to ensure fair comparisons across different model management strategies. We compared three model management strategies: (1) pre-selection (PS), (2) individual-based (IB), and (3) generation-based (GB) on standard benchmark problems with a baseline model that does not use surrogates. The experimental results reveal that a higher surrogate model accuracy improves the search performance. However, the impact varies according to the strategy used. Specifically, PS demonstrates a clear trend of improved performance as the estimation accuracy increases, whereas IB and GB exhibit robust performance when the accuracy surpasses a certain threshold. In model strategy comparisons, GB exhibits superior performance across a broad range of prediction accuracies, IB outperforms it at lower accuracies, and PS outperforms it at higher accuracies. The findings of this study clarify guidelines for selecting appropriate model management strategies based on the surrogate model accuracy.

cs.NE↗