Persistent and occasional: Searching for the variable population of the ZTF/4MOST sky using ZTF Data Release 11
Persistent and occasional: Searching for the variable population of the ZTF/4MOST sky using ZTF Data Release 11
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持续和偶然：使用 ZTF 数据版本 11 搜索 ZTF/4MOST 天空的可变群体
DOI：
10.1051/0004-6361/202346077
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发表时间：
2023
影响因子：
6.5
通讯作者：
Förster, F.
中科院分区：
文献类型：
--
作者：
Sánchez-Sáez, P.;Arredondo, J.;Bayo, A.;Arévalo, P.;Bauer, F. E.;Cabrera-Vives, G.;Catelan, M.;Coppi, P.;Estévez, P. A.;Förster, F.
AimsWe提出了一种基于变异性、颜色和形态学的分类器，旨在从Zwicky瞬态设施（ZTF）数据发布11（DR 11）扩展和点源的光变曲线中识别多类瞬态和持续可变和不可变源。开发这个模型的主要动机是为了确定活动星系核（AGN）在不同的红移范围内被观察到的4 MOST智利AGN/星系演化巡天（ChANGES）。话虽如此，它也作为一个更一般的时域astronomy study.MethodsThe模型使用9种颜色计算从CatWISE和泛星1（PS1），从PS1的形态分数，和61单波段的变化功能计算从ZTF DR 11 gandrlight曲线。我们训练了两个版本的模型，每个ZTF波段一个，因为ZTF DR 11独立地处理在场、滤波器和电荷耦合器件（CCD）象限的特定组合中观察到的光变曲线。我们使用了一个分层的本地分类器每个父节点的方法，其中每个节点是由一个平衡的随机森林模型。我们采用了17个类的分类法：不变的恒星，不变的星系，三个瞬变（SNIa、SN-其他和CV/Nova），五类随机变量（lowz-AGN，midz-AGN，highz-AGN，Blazar和YSO）和七类周期变量结果g-带模型的宏观平均精确度、召回率和F1得分分别为0.61、0.75和0.62，g-带模型的宏观平均精确度、召回率和F1得分分别为0.60、0.75和0.62，0.74和0.61。当把4个活动星系核分类（lowz-AGN，midz-AGN，highz-AGN，和Blazar）归为一个类时，其查全率和F1得分分别为1.00，0.95和0.97。这证明了该模型在分类活动星系核候选者方面的良好性能。我们将该模型应用于ZTF/4 MOST重叠天空中的所有源（-28 ≤ Dec ≤ 8.5），避免覆盖银河系凸起的ZTF场（|半乳糖B| ≤ 9和gal_l≤ 50）。该区域包括该带的86 576 577条光变曲线和该带的140 409 824条光变曲线，有20个或更多观测结果，相应带的平均震级低于20.5。只有0.73%的g-带光变曲线和2.62%的t-带光变曲线被归类为随机的、周期的或高概率的瞬态的（Pinit≥ 0.9）。即使这两个模型得到的指标是相似的，我们发现，在一般情况下，更可靠的结果时，使用theg-band模型。利用它，我们确定了384242个活动星系核候选者（包括低、中、高红移活动星系核和Blazar），其中287156个的Pinit ≥ 0.9。
AimsWe present a variability-, color-, and morphology-based classifier designed to identify multiple classes of transients and persistently variable and non-variable sources from the Zwicky Transient Facility (ZTF) Data Release 11 (DR11) light curves of extended and point sources. The main motivation to develop this model was to identify active galactic nuclei (AGN) at different redshift ranges to be observed by the 4MOST Chilean AGN/Galaxy Evolution Survey (ChANGES). That being said, it also serves as a more general time-domain astronomy study.MethodsThe model uses nine colors computed from CatWISE and Pan-STARRS1 (PS1), a morphology score from PS1, and 61 single-band variability features computed from the ZTF DR11gandrlight curves. We trained two versions of the model, one for each ZTF band, since ZTF DR11 treats the light curves observed in a particular combination of field, filter, and charge-coupled device (CCD) quadrant independently. We used a hierarchical local classifier per parent node approach-where each node is composed of a balanced random forest model. We adopted a taxonomy with 17 classes: non-variable stars, non-variable galaxies, three transients (SNIa, SN-other, and CV/Nova), five classes of stochastic variables (lowz-AGN, midz-AGN, highz-AGN, Blazar, and YSO), and seven classes of periodic variables (LPV, EA, EB/EW, DSCT, RRL, CEP, and Periodic-other).ResultsThe macro-averaged precision, recall, and F1-score are 0.61, 0.75, and 0.62 for theg-band model, and 0.60, 0.74, and 0.61, for ther-band model. When grouping the four AGN classes (lowz-AGN, midz-AGN, highz-AGN, and Blazar) into one single class, its precision-recall, and F1-score are 1.00, 0.95, and 0.97, respectively, for both thegandrbands. This demonstrates the good performance of the model in classifying AGN candidates. We applied the model to all the sources in the ZTF/4MOST overlapping sky (−28 ≤ Dec ≤ 8.5), avoiding ZTF fields that cover the Galactic bulge (|gal_b| ≤ 9 andgal_l≤ 50). This area includes 86 576 577 light curves in thegband and 140 409 824 in therband with 20 or more observations and with an average magnitude in the corresponding band lower than 20.5. Only 0.73% of theg-band light curves and 2.62% of ther-band light curves were classified as stochastic, periodic, or transient with high probability (Pinit≥ 0.9). Even though the metrics obtained for the two models are similar, we find that, in general, more reliable results are obtained when using theg-band model. With it, we identified 384 242 AGN candidates (including low-, mid-, and high-redshift AGN and Blazars), 287 156 of which havePinit≥ 0.9.
影响因子：
2.4
作者：
Azad Naik;H. Rangwala
通讯作者：
H. Rangwala
DOI：
10.31645/2013.11.2.3
发表时间：
2013
期刊：
Journal of Independent Studies and Research Computing
影响因子：
--
作者：
Adarsh Khalique;Rahim Hasnani
通讯作者：
Rahim Hasnani