AI 中文总结
研究天文时间序列中脉冲数量和特征的跨维推理问题，提出跨维流匹配后验估计方法t-FMPE，经测试在模拟和实际数据中与MCMC参考后验定性一致，推理速度比传统方法快几个数量级，有大规模分析潜力。
AI 中文摘要
时间序列分析在研究快速瞬变事件中起着重要作用，常见方法是将时间序列分解为脉冲并研究其特征，但估计脉冲数量和特征存在跨维推理问题，传统采样方法难以有效解决。基于模拟的推理方法提供了替代途径。本文介绍了在变压器架构上实现的跨维流匹配后验估计（t-FMPE），可对均匀采样的单变量时间序列数据进行高效、摊销的跨维推理。在三个测试案例中应用该方法，结果表明t-FMPE与MCMC参考后验达成定性一致，训练后的网络推理速度比MCMC和嵌套采样快几个数量级，展示了其在传统采样方法不可行时对时间序列数据集进行大规模分析的潜力。
英文摘要
The analysis of time series plays an important part in the study of (fast) transient events, including gamma-ray bursts, magnetar bursts, fast radio bursts, and solar flares. A common approach is to decompose the time series into pulses and study the pulse characteristics, such as location and amplitude, in order to constrain physical models of the source and its environment. However, estimating both the number and characteristics of these pulses presents a trans-dimensional inference problem that traditional sampling methods such as Markov Chain Monte Carlo (MCMC) and Nested Sampling struggle to solve efficiently. Simulation-based inference methods, often incorporating machine learning techniques, provide an alternative approach when traditional approaches are insufficient. Here, we introduce trans-dimensional Flow Matching Posterior Estimation (t-FMPE) implemented on a transformer architecture capable of efficient, amortized trans-dimensional inference on uniformly sampled univariate time series data. In this initial study, we apply the method to three test cases: simulated time series with known ground-truth parameters, observational data of Fast Radio Bursts and observations of X-ray bursts from magnetars. We show that t-FMPE achieves qualitative agreement with MCMC reference posteriors, successfully reproducing parameter correlations, as quantified through classifier two-sample tests. The trained network performs inference several orders of magnitude faster than MCMC and nested sampling, reaching sampling rates of 100 posterior samples per second for an 80-dimensional parameter space. The results demonstrate potential of t-FMPE for large-scale analysis of time series datasets when traditional sampling methods become infeasible, and also enable inferring unbiased posteriors in the presence of observational biases such as dead time.
Comments21 pages, 18 figures
