arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2406.03374astro-ph.COgr-qc
收藏

一种用于修正引力的场级模拟器

A field-level emulator for modified gravity

Daniela Saadeh, Kazuya Koyama, Xan Morice-Atkinson

AI总结:

本文提出一种基于map2map神经网络的场级模拟器,通过将修正引力强度作为风格参数,把$Λ$CDM N-body模拟转化为$f(R)$引力下的模拟,在非线性尺度上实现高效且准确的预测,从而利用场级全部信息约束大尺度结构中的修正引力。

摘要:

像LSST和Euclid这样的第四阶段巡天项目,为阐明暗能量的本质提供了独特的机会。然而,除非在所有可观测尺度上都能提供准确的预测,否则它们全部的约束能力将无法被释放。目前,在$Λ$CDM之外的模型中,只有线性区得到了充分理解:在非线性尺度上,必须依赖昂贵的数值模拟,而直接将其用于大型数据集的分析是不切实际的。最近,机器学习技术展现出了打破这一僵局的潜力:通过训练模拟器,我们可以在生成复杂数据场所需时间的一小部分内预测出这些场。在这项工作中,我们提出了一种场级模拟器,能够将$Λ$CDM N-body模拟转化为在$f(R)$引力下演化的模拟。为了实现这一点,我们基于map2map神经网络进行构建,并使用修正引力强度$|f_{R_0}|$作为风格参数。我们发现,我们的模拟器能够正确估计出需要对输入N-body粒子的位置和速度施加的变化,以生成目标模拟。我们通过几种统计量测试了网络的性能,发现在功率谱上直到$k \sim 1$ $h/$Mpc时达到了$1\\%$的一致性,并且与独立的boost模拟器eMantis达到了$1.5\\%$的一致性。尽管该算法是在固定的宇宙学参数下训练的,但我们发现它可以外推到未受过训练的模型中。结合现有的场级模拟器和$Λ$CDM模拟套件,我们的算法可用于在场级利用可用的全部信息,对大尺度结构中的修正引力进行约束。

英文摘要:

Stage IV surveys like LSST and Euclid present a unique opportunity to shed light on the nature of dark energy. However, their full constraining power cannot be unlocked unless accurate predictions are available at all observable scales. Currently, only the linear regime is well understood in models beyond $Λ$CDM: on the nonlinear scales, expensive numerical simulations become necessary, whose direct use is impractical in the analyses of large datasets. Recently, machine learning techniques have shown the potential to break this impasse: by training emulators, we can predict complex data fields in a fraction of the time it takes to produce them. In this work, we present a field-level emulator capable of turning a $Λ$CDM N-body simulation into one evolved under $f(R)$ gravity. To achieve this, we build on the map2map neural network, using the strength of modified gravity $|f_{R_0}|$ as style parameter. We find that our emulator correctly estimates the changes it needs to apply to the positions and velocities of the input N-body particles to produce the target simulation. We test the performance of our network against several summary statistics, finding $1\%$ agreement in the power spectrum up to $k \sim 1$ $h/$Mpc, and $1.5\%$ agreement against the independent boost emulator eMantis. Although the algorithm is trained on fixed cosmological parameters, we find it can extrapolate to models it was not trained on. Coupled with available field-level emulators and simulation suites for $Λ$CDM, our algorithm can be used to constrain modified gravity in the large-scale structure using full information available at the field level.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑