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arXiv 2005.00155astro-ph.HEastro-ph.IM
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Pan-STARRS $3 π$巡天中星系的恒星形成和形态学特性- I。星系和超新星分类的机器学习方法

Star formation and morphological properties of galaxies in the Pan-STARRS $3 π$ survey- I. A machine learning approach to galaxy and supernova classification

A. Baldeschi, A. Miller, M. Stroh, R. Margutti, D. L. Coppejans

摘要:

我们提出了一个分类的星系在泛星1(PS1)3 $π$调查的基础上,他们最近的恒星形成历史和形态。具体来说,我们训练和测试两个随机森林(RF)分类器使用光度特征(颜色和时刻)从PS1数据发布2。形态分类的标签来自Huertas-Company+2011,而恒星形成分数(SFF)的标签来自Blanton+2005目录。我们发现,颜色比光度矩提供更高的预测精度。我们从形态学上将星系分为早期和晚期两类,我们的RF模型达到了78%的分类准确率。我们的第二个模型将星系分类为具有低到中等或高SFF。该模型达到了89%的分类准确率。我们将两种RF分类器应用于整个PS1 $3π$数据集,允许我们为每个PS1源分配两个分数:$P_\mathrm{HSFF}$,量化具有高SFF的概率,以及$P_\mathrm{spiral}$,量化具有晚型形态的概率。最后,作为概念的证明,我们将我们的分类框架应用于来自Zwicky Transient Factory和Lick Observatory超新星搜索样本的超新星(SN)宿主星系。我们表明,通过选择$P_\mathrm{HSFF}$或$P_\mathrm{spiral}$,它是可能的,以显着提高或抑制分数的核心崩溃SNe(或热核SNe)在样品中相对于随机猜测。这一结果表明,如何上下文信息可以帮助在第一次检测时的瞬态分类。在当前光谱学匮乏的时域天文学时代,迅速的自动分类至关重要。

英文摘要:

We present a classification of galaxies in the Pan-STARRS1 (PS1) 3$π$ survey based on their recent star formation history and morphology. Specifically, we train and test two Random Forest (RF) classifiers using photometric features (colors and moments) from the PS1 data release 2. Labels for the morphological classification are taken from Huertas-Company+2011, while labels for the star formation fraction (SFF) are from the Blanton+2005 catalog. We find that colors provide more predictive accuracy than photometric moments. We morphologically classify galaxies as either early- or late-type, and our RF model achieves a 78\% classification accuracy. Our second model classifies galaxies as having either a low-to-moderate or high SFF. This model achieves an 89\% classification accuracy. We apply both RF classifiers to the entire PS1 $3π$ dataset, allowing us to assign two scores to each PS1 source: $P_\mathrm{HSFF}$, which quantifies the probability of having a high SFF, and $P_\mathrm{spiral}$, which quantifies the probability of having a late-type morphology. Finally, as a proof of concept, we apply our classification framework to supernova (SN) host-galaxies from the Zwicky Transient Factory and the Lick Observatory Supernova Search samples. We show that by selecting on $P_\mathrm{HSFF}$ or $P_\mathrm{spiral}$ it is possible to significantly enhance or suppress the fraction of core-collapse SNe (or thermonuclear SNe) in the sample with respect to random guessing. This result demonstrates how contextual information can aid transient classifications at the time of first detection. In the current era of spectroscopically-starved time-domain astronomy, prompt automated classification is paramount.

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