arXiv cs.AI· Nadav Bojan Sellam, Kevin Park, Alex Bronstein, Zolt\'an Haiman·· 1 天前AI 评分18
Nullasar：用生成模型学习类星体光变，替代 DRW 零假设
Moving Beyond the DRW Model: Learning Quasar Variability with Generative Models
AI 导读
研究团队提出生成模型 Nullasar，用 flow matching 直接从时序数据学习类星体光变，无需显式假设噪声模型。在合成光变曲线的周期信号检测中，Nullasar 作为零假设的表现与对每条曲线拟合正确 DRW 模型相当，且在光变遵循更灵活的 AR(2) 模型时优于参数拟合。作者同时指出训练集被非零假设信号污染会带来敏感性限制，将模型条件向量投影到主成分上可大幅缓解。
正文 · 原文
Abstract:The optical continuum variability of quasars is often described by a damped random walk (DRW) model, with characteristic damping timescales $\tau_{\rm DRW}$ of hundreds of days and amplitudes $\sigma_{\rm DRW}$ of $\mathcal{O}(0.1)$ magnitudes. This model has been widely used as a null hypothesis to assess the significance of periodic signals in quasar light curves in the search for supermassive black hole binaries, the significance of extreme quasar variability like tidal disruption events (TDEs) and changing-look (CL) quasars, or to model time lags in reverberation mapping. However, the DRW model is known to be incomplete, and an incorrect null hypothesis can lead to biased significance estimates. We investigate the impact of such null-hypothesis mis-specification on the detection of periodic signals in synthetic quasar light curves and introduce the generative model Nullasar, capable of learning quasar variability directly from time-series data. We demonstrate that Nullasar, trained with flow matching, can learn delicate statistical properties of stochastic light-curve variability without explicitly assuming any noise model. We further demonstrate that Nullasar can learn correlated distributions of noise parameters and generate new light-curve realizations from a range of deviations from the Gaussian DRW noise model, approximating parametric descriptions of stochastic processes. When used as a null hypothesis for detecting periodic signals in synthetic light curves, Nullasar performs comparably to fitting the correct DRW model to each light curve, and outperforms parametric fitting when the variability follows a more flexible AR(2) model. Finally, we identify practical limitations, including sensitivity to contamination of the training set by non-null signals, and show that projecting the conditioning vector of the model onto its leading principal components largely mitigates it.
| Subjects: | Astrophysics of Galaxies (astro-ph.GA); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.15040 [astro-ph.GA] |
| (or arXiv:2611.15040v1 [astro-ph.GA] for this version) | |
| https://doi.org/10.48550/arXiv.2611.15040 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Nadav Bojan Sellam [view email]
[v1]
Wed, 11 Nov 2026 04:59:36 UTC (6,704 KB)
来源：arXiv cs.AI · arxiv.org