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arXiv 2107.00657astro-ph.COastro-ph.GAastro-ph.IM
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未来宇宙学数据的最佳机器驱动获取

Optimal machine-driven acquisition of future cosmological data

Andrija Kostić, Jens Jasche, Doogesh Kodi Ramanah, Guilhem Lavaux

摘要:

我们提出的地图分类区域的天空,根据其信息增益潜力量化的Fisher信息。这些地图可以指导有针对性的宇宙学搜索的相关物理信息的最佳检索。具体来说,我们计算所观察到的宇宙结构的宇宙学模型中的微扰变化的响应,并绘制各自的贡献的Fisher信息。我们的物理正演建模机制超越了基于统计摘要的当代分析的局限性,以产生个体3D结构的详细表征。我们证明了这一点使用星系计数数据和展示我们的方法通过研究后发座星系团的信息增益的潜力。我们发现,暗条和星系团核心附近的区域(重力下降导致质量吸积)是我们关于宇宙结构形成的物理模型的信息最丰富的区域。因此,在这些地区收集数据将是测试我们的模型预测的最佳选择。在这项工作中提出的结果是他们的第一个,并阐明了宇宙信息在宇宙中的不均匀分布。这项研究为对宇宙的基本物理进行有效的有针对性的搜索铺平了新的道路,其中搜索策略在主动学习框架内通过新的宇宙学数据集逐步完善。

英文摘要:

We present maps classifying regions of the sky according to their information gain potential as quantified by the Fisher information. These maps can guide the optimal retrieval of relevant physical information with targeted cosmological searches. Specifically, we calculate the response of observed cosmic structures to perturbative changes in the cosmological model and chart their respective contributions to the Fisher information. Our physical forward modeling machinery transcends the limitations of contemporary analyses based on statistical summaries to yield detailed characterizations of individual 3D structures. We demonstrate this using galaxy counts data and showcase the potential of our approach by studying the information gain of the Coma cluster. We find that regions in the vicinity of the filaments and cluster core, where mass accretion ensues from gravitational infall, are the most informative about our physical model of structure formation in the Universe. Hence, collecting data in those regions would be most optimal for testing our model predictions. The results presented in this work are the first of their kind and elucidate the inhomogeneous distribution of cosmological information in the Universe. This study paves a new way forward to perform efficient targeted searches for the fundamental physics of the Universe, where search strategies are progressively refined with new cosmological data sets within an active learning framework.

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