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arXiv 2409.03333astro-ph.CO
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YOLO-CL在Rubin/LSST DC2模拟中的星系团检测

YOLO-CL cluster detection in the Rubin/LSST DC2 simulation

Kirill Grishin, Simona Mei, Stephane Ilic, Michel Aguena, Dominique Boutigny, Marie Paturel, the LSST Dark Energy Science Collaboration

备注：
Submitted to A&A, 14 pages, 6 figures
发表信息：
A&A 695, A246 (2025)
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AI总结：

YOLO-CL利用深度卷积网络直接处理LSST DC2模拟图像检测星系团，无需测光目录，对高质量晕完备性达100%，选择函数平坦，性能优于传统方法。

摘要：

LSST将提供红移z~1的星系团目录，一旦其选择函数被充分理解，这些目录可用于约束宇宙学模型。我们将用于星系团检测的深度卷积网络YOLO（YOLO-CL）应用于暗能量科学合作组织数据挑战2（DC2）的LSST模拟，并刻画了LSST YOLO-CL星系团选择函数。我们在混合样本的图像上训练并验证了该网络，该样本包括：（1）在斯隆数字巡天中观测到并通过红序列匹配滤波概率渗流方法检测到的星系团；（2）质量M_{200c} > 10^{14} M_{\odot}的模拟DC2暗物质晕。我们量化了YOLO-CL星系团目录相对于质量M_{200c} > 10^{14} M_{\odot}的DC2暗物质晕的完备性和纯度。对于0.2<z<0.8范围内质量M_{200c} > 10^{14.6} M_{\odot}的暗物质晕，YOLO-CL星系团目录的完备性为100%；对于质量M_{200c} > 10^{14} M_{\odot}且红移z \lesssim 1的暗物质晕，完备性为94%，且仅有6%的假阳性检测。所有假阳性检测均为质量在10^{13.4} M_{\odot} \lesssim M_{200c} \lesssim 10^{14} M_{\odot}范围内的暗物质晕。YOLO-CL的选择函数在0.2 \lesssim z \lesssim 0.9范围内相对于暗物质晕质量几乎是平坦的。YOLO-CL的整体性能与当前和未来光学及红外巡天中使用的其他星系团检测方法相当或更优。与使用苏尼亚耶夫-泽尔多维奇效应进行当前巡天检测相比，YOLO-CL对低质量星系团表现出更好的完备性，并且能检测到比基于X射线的目录更高红移的星系团。YOLO-CL相对于传统星系团检测技术的强大优势在于它直接处理图像，不需要测光和测光红移目录，也无需掩蔽恒星源和伪影。

英文摘要：

LSST will provide galaxy cluster catalogs up to z$\sim$1 that can be used to constrain cosmological models once their selection function is well-understood. We have applied the deep convolutional network YOLO for CLuster detection (YOLO-CL) to LSST simulations from the Dark Energy Science Collaboration Data Challenge 2 (DC2), and characterized the LSST YOLO-CL cluster selection function. We have trained and validated the network on images from a hybrid sample of (1) clusters observed in the Sloan Digital Sky Survey and detected with the red-sequence Matched-filter Probabilistic Percolation, and (2) simulated DC2 dark matter haloes with masses $M_{200c} > 10^{14} M_{\odot}$. We quantify the completeness and purity of the YOLO-CL cluster catalog with respect to DC2 haloes with $M_{200c} > 10^{14} M_{\odot}$. The YOLO-CL cluster catalog is 100% and 94% complete for halo mass $M_{200c} > 10^{14.6} M_{\odot}$ at $0.2<z<0.8$, and $M_{200c} > 10^{14} M_{\odot}$ and redshift $z \lesssim 1$, respectively, with only 6% false positive detections. All the false positive detections are dark matter haloes with $ 10^{13.4} M_{\odot} \lesssim M_{200c} \lesssim 10^{14} M_{\odot}$. The YOLO-CL selection function is almost flat with respect to the halo mass at $0.2 \lesssim z \lesssim 0.9$. The overall performance of YOLO-CL is comparable or better than other cluster detection methods used for current and future optical and infrared surveys. YOLO-CL shows better completeness for low mass clusters when compared to current detections in surveys using the Sunyaev Zel'dovich effect, and detects clusters at higher redshifts than X-ray-based catalogs. The strong advantage of YOLO-CL over traditional galaxy cluster detection techniques is that it works directly on images and does not require photometric and photometric redshift catalogs, nor does it need to mask stellar sources and artifacts.

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