论文标题
限制最近的星系的恒星形成历史：近似贝叶斯计算方法
Constraining the recent star formation history of galaxies : an Approximate Bayesian Computation approach
论文作者
论文摘要
[删节]尽管发现星系遵循其恒星形成率和恒星质量之间的紧密关系，但预计它们会显示出复杂的恒星形成历史（SFH），并具有短期波动。这项试验研究的目的是提出一种方法，该方法将确定在最后数十至数百MYR中经历恒星形成活性发生较大变化的星系。换句话说，所提出的方法将确定是否需要对SFH的最后几百种MYR的变化来正确对SED进行建模而不是平滑的正常SFH。为此，我们使用高信噪比宽带光度法分析了宇宙星系样本。我们应用近似贝叶斯计算，这是一种最先进的统计方法来执行模型选择，与机器学习算法相关联，以提供基于星系星系的观察到的通量密度比优选灵活SFH的概率。我们介绍该方法并在模拟SED样本上进行测试。馈送到该算法的输入信息是每个星系的一组宽带紫外线到NIR（REST-FRAME）磁通率。该方法在恢复正确的SFH时具有21％的错误率，并且对大于1个DEX的SFR变量敏感。根据贝叶斯信息标准的拟合比较，测试了一种使用CIGALE的更传统的SED拟合方法，以实现相同的目标，但获得的最佳错误率较高，为28％。我们将新方法应用于宇宙星系样本。与平滑的延迟 - $τ$ SFH的星系相比，在较低m*的平滑延迟延迟的星系中，具有强大至决定性证据的星系的恒星质量分布。我们讨论了一个事实，即由于我们的培训，该结果并不来自任何偏见。最后，我们认为需要灵活的SFH可以覆盖可能的最大的SFR-M*参数空间。
[Abridged] Although galaxies are found to follow a tight relation between their star formation rate and stellar mass, they are expected to exhibit complex star formation histories (SFH), with short-term fluctuations. The goal of this pilot study is to present a method that will identify galaxies that are undergoing a strong variation of star formation activity in the last tens to hundreds Myr. In other words, the proposed method will determine whether a variation in the last few hundreds of Myr of the SFH is needed to properly model the SED rather than a smooth normal SFH. To do so, we analyze a sample of COSMOS galaxies using high signal-to-noise ratio broad band photometry. We apply Approximate Bayesian Computation, a state-of-the-art statistical method to perform model choice, associated to machine learning algorithms to provide the probability that a flexible SFH is preferred based on the observed flux density ratios of galaxies. We present the method and test it on a sample of simulated SEDs. The input information fed to the algorithm is a set of broadband UV to NIR (rest-frame) flux ratios for each galaxy. The method has an error rate of 21% in recovering the right SFH and is sensitive to SFR variations larger than 1 dex. A more traditional SED fitting method using CIGALE is tested to achieve the same goal, based on fits comparisons through Bayesian Information Criterion but the best error rate obtained is higher, 28%. We apply our new method to the COSMOS galaxies sample. The stellar mass distribution of galaxies with a strong to decisive evidence against the smooth delayed-$τ$ SFH peaks at lower M* compared to galaxies where the smooth delayed-$τ$ SFH is preferred. We discuss the fact that this result does not come from any bias due to our training. Finally, we argue that flexible SFHs are needed to be able to cover that largest SFR-M* parameter space possible.
