宇宙学中经遗传算法优化的神经网络
Neural Networks Optimized by Genetic Algorithms in Cosmology
AI总结：
本文提出用遗传算法自动优化宇宙学中人工神经网络的超参数，并在距离模数重建、quintessence状态方程推断和测光红移预测三个案例中验证其优于传统网格搜索方法。
摘要：
人工神经网络在宇宙学领域的应用在过去十年中取得了显著成功，这归因于它们对大量数据集和复杂非线性函数强大的建模能力。然而，在某些情况下，它们的使用仍然存在争议，因为当超参数未被仔细选择时，它们容易产生不准确的结果。在本文中，为了找到人工神经网络超参数的最优组合，我们提出利用遗传算法。作为概念验证，我们分析了三个不同的宇宙学案例，以测试通过遗传算法获得的架构的性能，并将其与由包含所有可能配置的网格组成的标准过程进行比较。首先，我们利用Ia型超新星汇编对距离模数进行了不依赖模型的重建。其次，神经网络学习推断 quintessence 模型的状态方程；最后，利用来自组合红移目录的数据，神经网络在给定六个测光波段（urgizy）的情况下预测测光红移。我们发现，遗传算法显著改善了神经网络架构的生成，这可以确保其物理结果具有更高的可信度，因为与网格方法相比，其在度量指标上具有更好的性能。
英文摘要：
The applications of artificial neural networks in the cosmological field have shone successfully during the past decade, this is due to their great ability of modeling large amounts of datasets and complex nonlinear functions. However, in some cases, their use still remains controversial because their ease of producing inaccurate results when the hyperparameters are not carefully selected. In this paper, to find the optimal combination of hyperparameters to artificial neural networks, we propose to take advantage of the genetic algorithms. As a proof of the concept, we analyze three different cosmological cases to test the performance of the architectures achieved with the genetic algorithms and compare them with the standard process, consisting of a grid with all possible configurations. First, we carry out a model-independent reconstruction of the distance modulus using a type Ia supernovae compilation. Second, the neural networks learn to infer the equation of state for the quintessence model, and finally with the data from a combined redshift catalog the neural networks predict the photometric redshift given six photometric bands (urgizy). We found that the genetic algorithms improve considerably the generation of the neural network architectures, which can ensure more confidence in their physical results because of the better performance in the metrics with respect to the grid method.
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