基于生成式预训练Transformer的致密双星系统波形生成
Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer
AI总结：
针对天基引力波探测中波形处理的难题，提出可解释预训练大模型CBS-GPT，三类致密双星波形预测精度最高达99%，展现强泛化性，为引力波领域相关研究提供新方向。
摘要：
天基引力波（GW）探测是未来十年最受期待的引力波探测项目之一，有望探测到大量致密双星系统。目前，深度学习方法在引力波波形生成与外推方面尚未得到广泛探索。为解决由探测器响应和第二代时延干涉测量（TDI 2.0）带来的数据处理难题与日益增加的波形复杂度，本文提出了一种名为CBS-GPT（基于生成式预训练Transformer的致密双星系统波形生成）的可解释预训练大模型。针对致密双星系统波形，研究人员训练了三个模型分别预测大质量黑洞并合（MBHB）、极端质量比旋近（EMRI）和银河系双星（GB）的波形，预测精度最高分别达到99%、91%和99%。CBS-GPT模型展现出显著的泛化性与可解释性，即便存在复杂的仪器响应和较宽的参数范围，其隐藏参数也能有效捕捉波形的复杂信息。本研究证明了大模型在引力波领域的潜力，为引力波科学中的复杂波形生成、缺口补全以及深度学习模型设计等未来研究开辟了新机遇并提供了指导。
英文摘要：
Space-based gravitational wave (GW) detection is one of the most anticipated GW detection projects in the next decade, which promises to detect abundant compact binary systems. At present, deep learning methods have not been widely explored for GW waveform generation and extrapolation. To solve the data processing difficulty and the increasing waveform complexity caused by the detector's response and second-generation time-delay interferometry (TDI 2.0), an interpretable pre-trained large model named CBS-GPT (Compact Binary Systems Waveform Generation with Generative Pre-trained Transformer) is proposed. For compact binary system waveforms, three models were trained to predict the waveforms of massive black hole binaries (MBHB), extreme mass-ratio inspirals (EMRIs), and galactic binaries (GB), achieving prediction accuracies of at most 99%, 91%, and 99%, respectively. The CBS-GPT model exhibits notable generalization and interpretability, with its hidden parameters effectively capturing the intricate information of waveforms, even with the complex instrument response and a wide parameter range. Our research demonstrates the potential of large models in the GW realm, opening up new opportunities and guidance for future researches such as complex waveforms generation, gap completion, and deep learning model design for GW science.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
