Title: Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning Show Chinese title

Title: 基于机器学习的极端质量比旋进快速参数估计

Abstract: Extreme-mass-ratio inspiral (EMRI) signals pose significant challenges in gravitational wave (GW) astronomy owing to their low-frequency nature and highly complex waveforms, which occupy a high-dimensional parameter space with numerous variables. Given their extended inspiral timescales and low signal-to-noise ratios, EMRI signals warrant prolonged observation periods. Parameter estimation becomes particularly challenging due to non-local parameter degeneracies, arising from multiple local maxima, as well as flat regions and ridges inherent in the likelihood function. These factors lead to exceptionally high time complexity for parameter analysis while employing traditional matched filtering and random sampling methods. To address these challenges, the present study applies machine learning to Bayesian posterior estimation of EMRI signals, leveraging the recently developed flow matching technique based on ODE neural networks. Our approach demonstrates computational efficiency several orders of magnitude faster than the traditional Markov Chain Monte Carlo (MCMC) methods, while preserving the unbiasedness of parameter estimation. We show that machine learning technology has the potential to efficiently handle the vast parameter space, involving up to seventeen parameters, associated with EMRI signals. Furthermore, to our knowledge, this is the first instance of applying machine learning, specifically the Continuous Normalizing Flows (CNFs), to EMRI signal analysis. Our findings highlight the promising potential of machine learning in EMRI waveform analysis, offering new perspectives for the advancement of space-based GW detection and GW astronomy. Show Chinese abstract

Abstract: 极端质量比旋进（EMRI）信号由于其低频特性以及高度复杂的波形，在引力波（GW）天文学中带来了重大挑战，这些信号占据高维参数空间，包含大量变量。 由于它们的旋进时间尺度较长且信噪比较低，EMRI信号需要更长的观测时间。 由于非局部参数退化，源于多个局部最大值，以及似然函数中的平坦区域和脊线，参数估计变得尤其具有挑战性。 这些因素导致在使用传统匹配滤波和随机采样方法进行参数分析时，计算时间复杂度异常高。 为解决这些挑战，本研究将机器学习应用于EMRI信号的贝叶斯后验估计，利用基于常微分方程神经网络的最近开发的流匹配技术。 我们的方法在保持参数估计无偏性的前提下，计算效率比传统马尔可夫链蒙特卡洛（MCMC）方法快几个数量级。 我们表明，机器学习技术有潜力高效处理与EMRI信号相关的多达十七个参数的庞大参数空间。 此外，据我们所知，这是首次将机器学习，特别是连续归一化流（CNFs），应用于EMRI信号分析。 我们的研究结果突显了机器学习在EMRI波形分析中的前景，为基于空间的引力波探测和引力波天文学的发展提供了新的视角。