发表机构
National Centre for Nuclear Research; SRON Netherlands Institute for Space Research; Instituto de Radioastronomía y Astrofísica, Universidad Nacional Autónoma de México（国家核研究センター; 荷兰空间研究所; 墨西哥国立自治大学无线电天文学与天体物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究旨在比较星系合并的形态分类器与机器学习模型性能并给出更新标准。通过计算模拟图像的G、$M_{20}$和C值，用MCMC方法分类星系，得出精度与ML方法一致且对新数据稳健的形态分类器，还指出需新统计识别合并后星系特征。
AI 中文摘要
目的：非参数形态统计可用于星系合并的有效分类。本工作旨在比较形态合并分类器与当前先进的机器学习（ML）模型的性能。另一个目的是基于非参数形态统计得出合并的更新标准。方法：基于IllustrisTNG和Horizon - AGN模拟以及HSC - SSP的观测数据，计算模拟的Hyper Suprime - Cam Subaru战略计划（HSC - SSP）图像的基尼系数（G）、$M_{20}$统计量和集中度（C）。利用IllustrisTNG图像通过马尔可夫链蒙特卡罗（MCMC）方法在二维形态空间中找到最佳区分合并星系和非合并星系的线。结果：基于MCMC结果，我们将满足$G>( - 0.267\pm0.081)M_{20}+(0.143\pm0.012)$或$G>(0.162\pm0.048)C-(0.149\pm0.12)$的星系分类为合并星系，应用于之前未见过的IllustrisTNG模拟HSC - SSP图像时，这些标准的精度分别为69.5%和72.3%。形态分类的精度与当前先进的ML方法一致。形态分类器在仅选择合并前星系方面有效；合并后星系在G、$M_{20}$和C值方面与非合并星系难以区分。形态分类器在红移约0.52之前对新数据显示出与ML方法相似的稳健性，在红移范围$0.52<z<1$中比基于卷积神经网络的ML方法保持更好的稳健性。结论：本工作提出了更新的形态分类器，其精度与基于ML的合并分类器相似，对新数据具有高稳健性。需要新的形态统计来识别合并后星系的特征。
英文摘要
Aims. Non-parametric morphological statistics can be used for efficient classification of galaxy mergers. This work aims to compare the performance of morphological merger classifiers to state-of-the-art machine learning (ML) models. A secondary aim is to produce updated criteria for mergers based on non-parametric morphological statistics. Methods. The Gini coefficient (G), $M_{20}$ statistic, and concentration ($C$) were calculated for mock Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) images based on the IllustrisTNG and Horizon-AGN simulations, and observations from HSC-SSP. The IllustrisTNG images were used to find the line which best separates mergers and non-mergers in 2D morphological space with a Markov Chain Monte-Carlo (MCMC) method. Results. Based on the MCMC results, we classified galaxies with $G>(-0.267\pm0.081)M_{20}+(0.143\pm0.012)$ or $G>(0.162\pm0.048)C-(0.149\pm0.12)$ as mergers, these criteria had precisions of 69.5\% and 72.3\% respectively when applied to previously unseen IllustrisTNG mock HSC-SSP images. The precisions of the morphological classifications are consistent with state-of-the-art ML methods. The morphological classifiers were found to be effective at selecting only pre-mergers; post-merger galaxies are indistinguishable from non-mergers in terms of their $G$, $M_{20}$, and $C$ values. Morphological classifiers displayed a similar robustness to new data to ML methods up to a redshift of $\sim0.52$ and maintained robustness better than ML methods based on convolutional neural networks in the redshift range $0.52<z<1$. Conclusions. This work presents updated morphological classifiers which achieve similar precisions to ML based merger classifiers with a high robustness to new data. New morphological statistics are needed to identify the features of post-merger galaxies.
Comments20 pages, 25 figures, 3 tables, 3 appendices, published in Astronomy & Astrophysics
