通过场级推断解开修正引力与星系偏差
Disentangling modified gravity and galaxy bias with field-level inference
- Institute of Cosmology and Gravitation, University of Portsmouth（朴茨茅斯大学宇宙学与引力研究所）
- Institute for Computational Cosmology, Department of Physics, Durham University（杜伦大学物理学院计算宇宙学研究所）
- Kavli IPMU (WPI), UTIAS, The University of Tokyo（东京大学 Kavli 宇宙粒子物理数学联合研究院（WPI））
- Yukawa Institute for Theoretical Physics, Kyoto University（京都大学汤川理论物理研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出场级推断框架，利用星系分布完整信息检验引力。克服传统功率谱分析局限，直接对三维星系数计数场进行贝叶斯似然分析，联合约束修正引力和偏差参数，分析模拟数据验证优势。
AI中文摘要：
我们提出了一个用于通过大规模结构测试引力的场级推断框架，该框架利用了星系分布的全部信息内容。基于功率谱的传统分析丢弃了非高斯和傅里叶相位信息，导致修正引力（MG）和星系偏差之间存在强烈的简并性。我们的方法通过直接对三维星系数计数场进行贝叶斯似然分析来克服这一限制，使用幅度和相位联合约束MG和偏差参数。作为一个说明性应用，我们在$f(R)$引力理论和非线性星系偏差模型的背景下分析了模拟数据。在不同的引力强度下，使用共动拉格朗日加速度（COLA）方法对非线性结构形成进行建模，由$f_{R0}$参数化。然后通过非线性偏差处方将由此产生的暗物质场映射到模拟星系目录中。我们证明，在固定和已知初始相位的情况下，相对于仅功率谱分析，包括非高斯和相位信息对$f_{R0}$和主要偏差参数$\beta$产生更严格的约束。值得注意的是，场级方法打破了两点统计固有的MG和星系偏差之间的简并性。通过将宇宙网分类为空洞、墙壁、细丝和星团，我们发现低密度区域是在场级区分引力模型的主要驱动因素。最后，我们建立了我们的管道对初始条件、泊松噪声和星系场阈值变化的鲁棒性，为下一代调查的引力场级测试提供了一条强大的前进道路。
英文摘要：
We present a field-level inference framework for testing gravity with large-scale structure, exploiting the full information of the galaxy distribution. Traditional analyses based on the power spectrum discard non-Gaussian and Fourier phase information, resulting in strong degeneracies between modified gravity (MG) and galaxy bias. Our approach overcomes this limitation by performing a Bayesian analysis directly on the three-dimensional galaxy number counts, jointly constraining MG and bias parameters using both amplitudes and phases. As an illustrative application, we analyse mock data in real space in the context of $f(R)$ gravity and a non-linear galaxy bias model designed to mimic the real-data 2M++ BORG analysis of Jasche&Lavaux (2019). Non-linear structure formation is modelled using COmoving Lagrangian Acceleration (COLA) under different gravity strengths, parameterised by $f_{R0}$. The resulting dark-matter fields are then mapped to mock galaxy catalogues via a non-linear bias prescription. We demonstrate that, with the initial phases assumed known, including non-Gaussian and phase information yields tighter constraints on both $f_{R0}$ and the primary bias parameter $β$ (corresponding to the linear galaxy bias on large scales), relative to power-spectrum-only analyses. Notably, the field-level approach breaks the degeneracies between MG and galaxy bias inherent to two-point statistics. Through a cosmic-web classification into voids, walls, filaments and clusters, we find that under-dense regions are the primary drivers in distinguishing gravity at the field level. Finally, we establish the robustness of our pipeline against variations in initial conditions, Poisson noise, and galaxy-field thresholding, providing a powerful path forward for field-level tests of gravity.
