利用流形学习追踪星系环境的演化

中文摘要 AI 辅助

我们提出了SONDE（排序邻域距离嵌入）：一种可扩展的流形学习方法，用于创建星系局部环境几何的细致、连续、低维表示。SONDE是Isomap的一种变体，利用星系邻域之间的相似性距离来构建嵌入空间，在该空间中，星系根据其邻域几何进行聚类。我们还提出了一种新颖的方法，将不同的数据集投影到同一嵌入空间中，从而能够跨红移比较环境嵌入。我们在$z = 0 - 5$的TNG100模拟上演示了该方法，并表明嵌入值随红移演化，且与星系的物理性质相关。我们训练了一个神经网络，基于环境特征预测物理性质，并表明我们的第一个嵌入维度优于标准的环境量化指标——邻居数量，而前三个维度编码的环境信息与十区间径向轮廓相当。我们的结果与已知的密度相关性一致，包括星系晕质量、中央星系与卫星星系状态以及熄灭。SONDE提供了一种直接的方式来编码星系环境的细微差别，这可能对未来的星系-环境相互作用分析产生重要影响。

英文摘要

We present SONDE (SOrted Neighbor Distance Embedding): a scalable manifold learning approach to create a nuanced, continuous, low-dimensional representation of the geometry of galaxies' local environments. SONDE is a variation on Isomap that uses similarity distances between galaxy neighborhoods to create an embedding space, within which galaxies cluster according to their neighborhood geometries. We additionally present a novel method to project different datasets into the same embedding space, enabling comparisons of environmental embeddings across redshifts. We demonstrate this method on the TNG100 simulations at $z = 0 - 5$ and show that the embedding values evolve with redshift and correlate with galaxies' physical properties. We train a neural network to predict physical properties based on environmental features and show that our first embedding dimension outperforms a standard environmental quantifier, the number of neighbors, while the top three dimensions encode as much environmental information as a ten-bin radial profile. Our results are consistent with known density correlations, including galaxy halo mass, central vs. satellite status, and quenching. SONDE provides a straightforward way to encode subtleties of galaxy environments that could have significant implications for future analysis of galaxy-environment interactions.

发表机构

• The Ohio State University（俄亥俄州立大学）
• John A. Paulson School of Engineering and Applied Sciences, Harvard University（哈佛大学约翰·A·保尔森工程与应用科学学院）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

备注

15 pages, 5 figures