The universal rotation curve (URC) of disk galaxies was originally proposed to predict the shape and amplitude of any rotation curve (RC) based solely on photometric data. Here, the URC is investigated with an extensive set of spatially resolved RCs drawn from the PROBES-I, PROBES-II, and MaNGA databases with matching multiband surface brightness profiles from the DESI-LIS and Wide-Field Infrared Survey Explorer surveys for 3846 disk galaxies. Common URC formulations fail to achieve an adequate level of accuracy to qualify as truly universal over fully sampled RCs. We develop neural network (NN) equivalents for the proposed URCs that predict RCs with higher accuracy, showing that URC inaccuracies are not due to insufficient data but rather nonoptimal formulations or sampling effects. This conclusion remains even if the total RC sample is pruned for symmetry. The latest URC prescriptions and their NN equivalents trained on our subsample of 579 disk galaxies with symmetric RCs perform similarly to the URC/NN trained on the complete data sample. We conclude that a URC with an acceptable level of accuracy (ΔV _circ ≲ 15%) at all radii would require a detailed modeling of a galaxy’s central regions and outskirts (e.g., for baryonic effects leading to contraction or expansion of any dark-matter-only halo).

中文翻译：

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几乎通用的圆盘星系旋转曲线


盘状星系的通用旋转曲线（URC）最初被提出是为了仅基于光度数据来预测任何旋转曲线（RC）的形状和幅度。在这里，使用从 PROBES-I、PROBES-II 和 MaNGA 数据库中提取的大量空间分辨 RC 来研究 URC，并使用来自 DESI-LIS 和广域红外测量浏览器对 3846 圆盘进行的匹配多波段表面亮度剖面进行研究。星系。常见的 URC 公式无法达到足够的准确度，无法在完全采样的 RC 上真正通用。我们为所提出的 URC 开发了神经网络（NN）等价物，可以以更高的准确度预测 RC，表明 URC 的不准确不是由于数据不足，而是由于非最佳公式或采样效应。即使为了对称性而修剪总 RC 样本，这个结论仍然成立。在具有对称 RC 的 579 个盘状星系子样本上训练的最新 URC 处方及其 NN 等效项与在完整数据样本上训练的 URC/NN 表现类似。我们的结论是，在所有半径上具有可接受的精度水平（ΔV _circ ≲ 15%）的 URC 需要对星系的中心区域和外围进行详细建模（例如，对于导致任何暗物质收缩或膨胀的重子效应）只晕）。