arXiv subjects

Ryotaro Sato

Publications and source records attributed to Ryotaro Sato.

3 recordsLinked to original sources

arXiv2025

Learning to detect continuous gravitational waves: an open data-analysis competition

We report results of a public data-analysis challenge, hosted on the open data-science platform Kaggle, to detect simulated continuous gravitational-wave signals (CWs). These are weak signals from rapidly spinning neutron stars that remain undetected despite extensive searches. The competition dataset consisted of a population of CW signals using both simulated and real LIGO detector data matching the conditions of actual CW searches. The competition attracted more than 1,000 participants to develop realistic CW search algorithms. We describe the top 10 approaches and discuss their applicability as a pre-processing step compared to standard CW-search approaches. For the competition's dataset, we find that top approaches can reduce the computing cost by 1 to 3 orders of magnitude at a false-dismissal probability comparable to standard CW searches. Additionally, the competition drove the development of new GPU-accelerated detection pipelines, which facilitated their adoption in other areas of gravitational-wave data analysis. We release the associated dataset, which constitutes the first open standardized benchmark for CW detection, to enable reproducible method comparisons and to encourage further developments toward the first detection of these elusive signals.

gr-qc↗

arXiv2025

On the Invariance of Cross-Correlation Peak Positions Under Monotonic Signal Transformations, with Application to Fast Time Difference Estimation

We present a theorem concerning the invariance of cross-correlation peak positions. This theoretical result provides the foundation for a new method for time difference estimation that is potentially faster than the conventional fast Fourier transform (FFT) approach for real/complex sequences. Specifically, it shows that the peak position of the cross-correlation function between two shifted discrete-time signals remains unchanged under arbitrary monotonic transformations of the input signals. By exploiting this property, we design an efficient estimation algorithm based on the cross-correlation function between signals quantized into low-bit integers. The proposed method requires only integer arithmetic instead of real-valued operations, and further computational efficiency can be achieved through number-theoretic algorithms. Numerical experiments demonstrate that the proposed method achieves a shorter processing time than conventional FFT-based approaches within a specific range of signal lengths.

eess.SP↗

arXiv2025

Exact Matching in Matrix Multiplication Time

Let $A_0,A_1\in\mathbf{F}^{n\times n}$ be square matrices over a finite field $\mathbf{F}$ and consider the matrix pencil $A_0+yA_1$ with indeterminate $y$. We observe that, once $A_0+\lambda A_1$ is nonsingular for some $\lambda\in\mathbf{F}$, the polynomial $\det(A_0+yA_1)$ can be reconstructed by computing one determinant, one inverse matrix, and the characteristic polynomial of a single matrix. Consequently, this determinant polynomial can be computed in $\mathrm{O}(n^\omega)$ field operations, avoiding the polylogarithmic overhead of a general polynomial-matrix determinant algorithm in this special setting. Applying this observation to random evaluation of the Tutte matrix of a graph, we obtain a matrix-multiplication-time randomized algorithm for the so-called exact matching problem. Specifically, one can decide, simultaneously for all $k$, whether a given $0/1$-weighted graph has a perfect matching of weight exactly $k$ in $\mathrm{O}(n^\omega)$ field operations, where $n$ denotes the number of vertices in the graph. We also discuss the analogous extension to the exact linear matroid parity problem and its consequences for a perfect packing of Mader's $\mathcal{S}$-paths of minimum total length and for a shortest cycle through three specified vertices.

cs.DS↗