arXiv subjects

Shitong Guo

Publications and source records attributed to Shitong Guo.

3 recordsLinked to original sources

arXiv2026

A pre-merger-informed spectral-level ringdown inference framework for black-hole spectroscopy

Black-hole spectroscopy aims to infer properties of the remnant spacetime from the quasinormal-mode (QNM) spectrum of the gravitational-wave ringdown signal. In most implementations, however, this inference is performed with waveform models that already incorporate Kerr or other theory-specific QNM spectral relations, thereby entangling spectral measurement with remnant or beyond-Kerr parameter inference. At the same time, conventional ringdown analyses commonly excise the pre-merger data, which in principle contain information about the excitation amplitudes and phases of the QNMs. We introduce \texttt{SPRING} (\textit{Spectral-level Pre-merger-informed RINGdown inference}), a framework designed to separate ringdown spectral inference from theory-side interpretation while propagating pre-merger information through amplitude-scale estimation. As a demonstration, we apply \texttt{SPRING} to GW250114 using an agnostic two-component damped-sinusoid (2DS) model for Kerr remnant inference. \texttt{SPRING} improves the Bayesian support for the agnostic 2DS signal model relative to analyses that do not use pre-merger information, with an increase of $\Delta\ln B\sim 5$--$10$. The resulting remnant posterior remains closely consistent with the inspiral-merger-ringdown estimate, despite the extra freedom introduced by the second DS component. This work bridges pre-merger information and ringdown inference, establishing a fully spectral-level route for future black-hole spectroscopy.

gr-qc↗

arXiv2025

GTAC: A Generative Transformer for Approximate Circuits

Targeting error-tolerant applications, approximate computing relaxes rigid functional equivalence to significantly improve power, performance, and area. Traditional approximate logic synthesis (ALS) relies on incremental rewriting, limiting design space exploration. Meanwhile, the inherently probabilistic nature of Transformer-based generative AI makes it a natural fit for generating approximate circuits. Exploiting this, we propose GTAC, an end-to-end framework for arbitrary-scale generative ALS. To overcome the memory bottleneck of generative AI, GTAC partitions a large circuit into tractable subcircuits, applies a generative core to produce approximate candidates for each subcircuit, and finally selects proper candidates to form the final design. Its core generative Transformer utilizes a novel irredundant encoding to compactly encode a circuit, alongside a masking mechanism to exclude designs violating the given error bound. Empowered by a self-evolutionary training strategy, GTAC establishes a new paradigm that demonstrates superior performance: It reduces delay by 30.9% and gate count by 50.5% over exact generative baselines and saves 6.5% area with a 4.3x speedup against traditional ALS methods. Furthermore, its irredundant encoding achieves a 33.3x reduction in sequence length and a 61.6x reduction in peak memory compared to conventional memoryless traversal.

cs.AR↗

arXiv2025

A theory-agnostic hierarchical Bayesian framework for black-hole spectroscopy: a case study on GW250114 in Einstein-dilaton-Gauss-Bonnet gravity

Black-hole spectroscopy has emerged as a powerful probe of strong-field gravity in the era of gravitational-wave astronomy. In this context, many current tests of modified or extended gravity are implemented by searching for predicted signatures modeled as perturbative corrections to general-relativistic waveforms; however, this approach may introduce model-dependent systematics and limit applicability to broader classes of theories. To complement such methods, we develop a theory-agnostic hierarchical Bayesian framework that connects ringdown observations -- modeled as damped sinusoids -- directly with theoretical quasinormal mode spectra, performing the comparison at the spectral level rather than through theory-specific waveform matching. The framework incorporates a soft-truncation module to account for the finite domain of validity in the theory's parameter space and is equipped with quantitative diagnostics that identify stable analysis time windows. As an illustrative application, we implement the framework within Einstein-dilaton-Gauss-Bonnet gravity and apply it to the gravitational-wave event GW250114, finding that the resulting posterior for the dimensionless coupling $\zeta$ is robust against prior assumptions yet remains only weakly informative over the range considered in this work. We further perform controlled ringdown injection studies across different values of $\zeta$, confirming that nonzero couplings can be recovered while also indicating a potential systematic effect: Kerr-based priors in the $\zeta$ inference may partially absorb spectral deviations arising in alternative theories of gravity. This work establishes a transparent and extensible foundation for future strong-field gravity tests, naturally compatible with the growing precision and modal resolution of next-generation gravitational-wave detectors.

gr-qc↗