利用ASKAP RACS估算局域恒星形成率密度
Estimating the local star formation rate density from ASKAP RACS
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中文总结 AI 辅助
本研究提出一种基于监督机器学习的方法，利用ASKAP RACS数据识别恒星形成星系，估算出局域z<0.1的恒星形成率密度，为未来深巡天研究宇宙恒星形成率演化提供了可行方案。
中文摘要 AI 辅助
理解宇宙恒星形成率密度（SFRD）的演化是揭示宇宙如何演化为当前状态的关键。本文提出一种新颖高效的局域SFRD估算方法，该方法首先利用监督机器学习识别恒星形成星系（SFGs）群体，再通过澳大利亚平方公里阵列探路者（ASKAP）探测到的1.4 GHz射电连续谱发射确定恒星形成率（SFRs）。具体而言，研究采用梯度提升决策树模型，利用RACS-mid和WISE测光数据，将Beck等人（2022，MNRAS，515，4711）星表中的河外源分类为星系或类星体。包含389392个源的完整样本按70%-15%-15%的比例划分为训练集、验证集和测试集。优化后的模型在测试数据集上达到加权F1分数0.93、准确率0.94，最终将336674个源分类为星系，52718个源分类为类星体。利用得到的z<0.1深度匹配星系样本及Beck等人（2022，MNRAS，515，4711）的测光红移预测，确定了修正后的1.4 GHz SFR校准关系，基于11293个源得到局域、完备性校正的z<0.1 SFRD为(1.4±0.5)×10⁻² M⊙ yr⁻¹ Mpc⁻³，该值与此前结果一致。因此，本研究证明利用监督学习识别大量SFGs群体以研究SFRD演化的可行性，这为未来更深入的巡天项目（如EMU）提供了令人兴奋的前景，将使人类能够探测更高红移处的宇宙SFRD。
英文摘要
Understanding the evolution of the cosmic star formation rate density (SFRD) is key to uncovering how the Universe arrived at its present state. This paper presents a novel and efficient method to estimate the local SFRD, which uses supervised machine learning to first identify a population of star-forming galaxies (SFGs). Next, star-formation rates (SFRs) are determined using the 1.4-GHz radio-continuum emission detected by the Australian Square Kilometre Array Pathfinder (ASKAP). Specifically, a gradient-boosted decision tree model was implemented to classify extragalactic sources from the Beck et al. (2022, MNRAS, 515, 4711) catalogue as either galaxies or quasars using RACS-mid and WISE photometry. The full sample, consisting of 389,392 sources, was partitioned into a 70%-15%-15% split for training, validating, and testing. The optimised model achieved a weighted F1 score of 0.93 and an accuracy of 0.94 on the test dataset, ultimately classifying 336,674 sources as galaxies and 52,718 sources as quasars. Using the resulting $z<0.1$ depth-matched galaxy sample and the photometric redshift predictions from Beck et al. (2022, MNRAS, 515, 4711), a modified 1.4-GHz SFR calibration was determined, yielding a local, completeness-corrected, $z<0.1$ SFRD of $(1.4 \pm 0.5) \times 10^{-2} \; \rm M_{\odot}\, yr^{-1}\,Mpc^{-3}$ using 11,293 sources. This value is consistent with previous results. Thus, this study demonstrates the feasibility of using supervised learning to identify large populations of SFGs in order to investigate the SFRD evolution. This presents an exciting prospect for future, deeper surveys such as EMU, which will enable the cosmic SFRD to be probed out to higher redshifts.
