发表机构
University of California, Berkeley; Lawrence Berkeley National Laboratory（加州大学伯克利分校; 劳伦斯伯克利国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对原初非高斯性测量受宇宙方差限制的问题，该研究提出基于暗物质环境的多示踪剂分析方法，通过模拟验证其理论框架，可将局域f_NL的约束精度提升2-3倍，为未来星系巡天实现高精度测量提供可行方案。
AI 中文摘要
原初非高斯性（PNG）为检验超出最简单单一场情景的暴胀物理提供了有力手段。在大尺度结构中，偏置示踪剂对局域型PNG的响应具有独特的尺度依赖特征，该特征在大尺度上最为显著，但单示踪剂测量根本上受限于宇宙方差。多示踪剂方法可缓解这一限制；然而，许多现有方案需要利用大范围的晕质量来划分示踪剂，而这些质量往往无法获取，或需要使用无法直接观测的晕次级属性。在此，我们开发并验证了一种替代策略，即通过大尺度暗物质环境划分示踪剂样本。我们推导了经环境选择的示踪剂的$f_{\rm NL}$响应的理论框架，并在$N$体模拟中对其进行了测试，证实了分离宇宙预测与直接功率谱推断之间的一致性。为便于观测实施，我们表明，从晕出发对暗物质环境进行简单的线性理论重构，可在$\mathtt{Quijote}$模拟中给出无偏的$f_{\rm NL}$约束。对DESI LRG样本的预测表明，与传统单示踪剂分析相比，环境多示踪技术可将高密度示踪剂的局域PNG约束精度提升2-3倍，这与假设晕形成时间完全已知时获得的提升相当。我们建立了一个解析模型，解释了为何采用晕环境划分时提升幅度较小，以及为何分离宇宙预测在这种情况下失效。与需要组装偏置建模或推断晕次级属性的方法不同，我们的方法仅依赖线性理论。这些结果确立了环境多示踪技术是利用即将开展的星系巡天实现$\sigma(f_{\rm NL})\sim 1$的一条有前景的途径。
英文摘要
Primordial non-Gaussianity (PNG) offers a powerful test of inflationary physics beyond the simplest single-field scenarios. In large-scale structure, biased tracers respond to local-type PNG with a distinctive scale-dependent signature that is strongest on large scales, but single-tracer measurements are fundamentally limited by cosmic variance. Multitracer methods can mitigate this limitation; however, many existing proposals require splitting tracers using a wide range of halo masses which are often not available, or using secondary halo properties that are not directly observable. Here we develop and validate an alternative strategy based on splitting a tracer sample by its large-scale dark matter environment. We derive a theoretical framework for the $f_{\rm NL}$ response of environmentally selected tracers and test it in $N$-body simulations, confirming consistency between separate-universe predictions and direct power-spectrum inference. To enable observational implementation, we show that a simple linear-theory reconstruction of the dark matter environment from halos yields unbiased $f_{\rm NL}$ constraints in $\mathtt{Quijote}$ simulations. Forecasts for the DESI LRG sample indicate that environmental multitracing can improve constraints on local PNG by a factor of 2-3 relative to conventional single-tracer analyses for high density tracers, comparable to gains achieved when halo formation time is assumed to be perfectly known. We develop an analytic model explaining why the gains are smaller for a halo environment split and why separate-universe predictions fail in this case. Unlike approaches that require modeling assembly bias or inferring secondary halo properties, our method relies only on linear theory. These results establish environmental multitracer techniques as a promising route toward $σ(f_{\rm NL})\sim 1$ with upcoming galaxy surveys.
Comments16 + 14 pages, 5 + 3 figures, comments welcome
