arXiv subjects

Qiyuan Yang

Publications and source records attributed to Qiyuan Yang.

5 recordsLinked to original sources

arXiv2026

Efficient Evaluation of Gravitational Lensing Amplification Factors: A Deep Learning Framework

Wave optics is essential for analyzing lensed gravitational waves (GWs), yet evaluating the diffraction integral $F(\omega, y)$ is computationally expensive. We present a Sinusoidal Representation Networks (SIRENs) framework for the dimensionless amplification factor, demonstrating its efficacy and generalization through Point Mass Lens (PML) and Singular Isothermal Sphere (SIS) test cases. Unlike standard architectures that suffer from spectral bias, the network's periodic activation functions structurally align with the integral's oscillatory kernel, effectively resolving high-frequency spectral features. The resulting estimator achieves $\mathcal{O}(10^{-3})$ relative accuracy and a $\sim 100\times$ speedup compared to direct numerical integration. By shifting the computational burden to offline training, our framework yields a stable $\mathcal{O}(1)$ inference complexity. This guarantees constant, sub-millisecond evaluation times even in the weak-lensing diffraction tail where traditional methods stagnate. Additionally, the dimensionless formulation ensures intrinsic scale invariance, enabling direct application across astrophysical regimes from stellar-mass lenses in the ground-based LVK band to supermassive black holes in the space-based LISA band.

astro-ph.IM↗

arXiv2026

Time-Domain Deep Learning for Pairwise Identification of Strongly Lensed Gravitational-Wave Candidates

As gravitational wave (GW) catalogs continue to expand, exhaustive Bayesian comparisons of candidate event pairs become increasingly computationally expensive, which motivates the development of fast prescreening methods for strongly lensed GW searches. We formulate lensed-pair identification as a binary verification problem using two preprocessed strain segments. To address this task, we propose Physics-Inspired ResNet (PI-ResNet), a Siamese one-dimensional residual network for pairwise GW candidate classification. Unlike spectrogram-based prescreening approaches, PI-ResNet operates directly on whitened time-domain strain data and avoids an intermediate time--frequency image representation. A shared residual backbone with Squeeze-and-Excitation (SE) modules encodes the two input segments, and the paired embeddings are compared through absolute feature differences and Hadamard-product interactions. We train and evaluate the model using simulated GW signals from binary black hole mergers lensed by point-mass (PM) and singular isothermal sphere (SIS) lenses, injected into simulated LIGO and Einstein Telescope (ET) detector noise. Under ET design noise, PI-ResNet achieves accuracies of $95.60\%$ for SIS lenses and $93.80\%$ for PM lenses, while maintaining $84.03\%$ and $78.25\%$ accuracy under simulated LIGO H1--L1 Gaussian noise. These results suggest that direct learning from 1D strain data provides an efficient and physically motivated preselection statistic for candidate lensed GW pairs, while also indicating the need for detector-domain adaptation.

astro-ph.HE↗

arXiv2025

Considering lensing effect on gravitational wave signals from black holes in mass gap

The pair-instability supernova (PISN) mechanism predicts a mass gap in the black hole population, where no stellar-origin black holes are expected to form. However, several binary black hole (BBH) merger events exhibit component masses that appear to lie within the PISN mass gap. If a gravitational-wave (GW) source is lensed, neglecting lensing effects leads to an underestimation of the luminosity distance and hence the redshift, resulting in an overestimation of the source-frame masses and potentially placing them within the mass-gap region. In this work, we analyze two representative events, GW190521 and GW231123. We establish a direct mapping between the lensing magnification and the fraction of posterior samples with source-frame masses below the mass-gap boundary. Adopting a lower bound of $65\,M_{\odot}$, we find that the magnifications required for $90\%$ of the posterior samples to fall below this boundary are $\mu=444$ for GW231123 and $\mu=39$ for GW190521. At these magnifications, the corresponding source-frame masses of the primary black hole are $52^{+10}_{-10}\,M_{\odot}$ and $42^{+19}_{-14}\,M_{\odot}$, with lensed source redshifts of $2.3^{+0.8}_{-0.5}$ and $2.7^{+1.5}_{-1.2}$, respectively. These results provide a quantitative framework for assessing the lensing hypothesis as a possible explanation for BBH mergers observed within the PISN mass gap, and highlight that the extreme magnifications required for GW231123 may challenge the astrophysical plausibility of simple strong-lensing interpretations.

astro-ph.HE↗

arXiv2024

MiniFed : Integrating LLM-based Agentic-Workflow for Simulating FOMC Meeting

The Federal Funds rate in the United States plays a significant role in both domestic and international financial markets. However, research has predominantly focused on the effects of adjustments to the Federal Funds rate rather than on the decision-making process itself. Recent advancements in large language models(LLMs) offer a potential method for reconstructing the original FOMC meetings, which are responsible for setting the Federal Funds rate. In this paper, we propose a five-stage FOMC meeting simulation framework, MiniFed, which employs LLM agents to simulate real-world FOMC meeting members and optimize the FOMC structure. This framework effectively revitalizes the FOMC meeting process and facilitates projections of the Federal Funds rate. Experimental results demonstrate that our proposed MiniFed framework achieves both high accuracy in Federal Funds rate projections and behavioral alignment with the agents' real-world counterparts. Given that few studies have focused on employing LLM agents to simulate large-scale real-world conferences, our work can serve as a benchmark for future developments.

cs.SI↗

arXiv2024

From Babbling to Fluency: Evaluating the Evolution of Language Models in Terms of Human Language Acquisition

We examine the language capabilities of language models (LMs) from the critical perspective of human language acquisition. Building on classical language development theories, we propose a three-stage framework to assess the abilities of LMs, ranging from preliminary word understanding to complex grammar and complex logical reasoning. Using this framework, we evaluate the generative capacities of LMs using methods from linguistic research. Results indicate that although recent LMs outperform earlier models in overall performance, their developmental trajectory does not strictly follow the path of human language acquisition. Notably, in generation tasks, LMs are more similar to human performance in areas where information is easier to extract from the corpus, such as average word length, clauses, and auxiliary verbs. Newer LMs did not exhibit significant progress in terms of specific dimensions, such as clauses and auxiliary verbs, where the variation across corpora is relatively limited. Register theory offers a plausible explanation for these observations, suggesting that the linguistic features of the training data have a substantial impact on the models' abilities.

cs.CL↗