arXiv 2411.040582024-11-17astro-ph.CO

利用神经网络区分耦合暗能量模型

Distinguishing Coupled Dark Energy Models with Neural Networks

L. W. K. Goh, I. Ocampo, S. Nesseris, V. Pettorino

备注：: Accepted for publication in A&A
发表信息：: A&A 692, A101 (2024)
DOI：: 10.1051/0004-6361/202451099

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AI总结：

本文构建经Optuna优化的神经网络分类器，基于模拟Stage IV巡天的$fσ_8(z)$数据区分ΛCDM与层析耦合暗能量模型，在二分类和多分类任务中取得高准确率。

摘要：

我们研究神经网络（NN）能否准确区分通过两种模型模拟的宇宙大尺度结构（LSS）增长率数据：宇宙学常数加Λ冷暗物质（CDM）模型，以及层析耦合暗能量（CDE）模型。我们构建了一个NN分类器，并测试其区分宇宙学模型的准确性。对于数据集，我们针对ΛCDM和层析CDE模型在不同模型参数取值下生成了$fσ_8(z)$增长率观测量，以模拟现实的第四阶段（Stage IV）星系巡天类设置。随后，我们使用Optuna对NN进行优化和训练，旨在避免过拟合并最大化训练模型的准确性。我们分别在二分类和多分类情形下开展分析：二分类比较ΛCDM与仅激活一个层析耦合区间的CDE模型；多分类则将所有模型组合起来。在二分类情形下，我们发现NN能够可靠地（准确率超过86%）检测到非零层析耦合值，且不受耦合被激活的红移范围影响，并能以100%的置信水平检测到ΛCDM模型。在多分类任务中，NN在区分ΛCDM、低红移耦合CDE模型和高红移耦合模型方面表现足够好，准确率分别为99%、79%和84%。通过利用机器学习的能力，我们的流程可成为分析增长率数据并最大化现有巡天探测广义相对论偏差潜力的有用工具。

英文摘要：

We investigate whether neural networks (NNs) can accurately differentiate between growth-rate data of the large-scale structure (LSS) of the Universe simulated via two models: a cosmological constant and $Λ$ cold dark matter (CDM) model and a tomographic coupled dark energy (CDE) model. We built an NN classifier and tested its accuracy in distinguishing between cosmological models. For our dataset, we generated $fσ_8(z)$ growth-rate observables that simulate a realistic Stage IV galaxy survey-like setup for both $Λ$CDM and a tomographic CDE model for various values of the model parameters. We then optimised and trained our NN with \texttt{Optuna}, aiming to avoid overfitting and to maximise the accuracy of the trained model. We conducted our analysis for both a binary classification, comparing between $Λ$CDM and a CDE model where only one tomographic coupling bin is activated, and a multi-class classification scenario where all the models are combined. For the case of binary classification, we find that our NN can confidently (with $>86\%$ accuracy) detect non-zero values of the tomographic coupling regardless of the redshift range at which coupling is activated and, at a $100\%$ confidence level, detect the $Λ$CDM model. For the multi-class classification task, we find that the NN performs adequately well at distinguishing $Λ$CDM, a CDE model with low-redshift coupling, and a model with high-redshift coupling, with 99\%, 79\%, and 84\% accuracy, respectively. By leveraging the power of machine learning, our pipeline can be a useful tool for analysing growth-rate data and maximising the potential of current surveys to probe for deviations from general relativity.

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