使用SquiRels寻找LiMRs：一种对宇宙微波背景中轻而大质量遗迹进行模型无关研究的方法
Using SquiRels to Find LiMRs: A Model-Insensitive Approach to Cosmic Microwave Background Studies of Light but Massive Relics
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中文总结 AI 辅助
研究针对宇宙微波背景中轻而大质量遗迹，确定三个独立物理量，引入SquiRels方法，证明普朗克观测对LiMRs相空间分布函数形状不敏感并给出限制，还表明未来地面观测台能区分LiMRs与无质量暗辐射并提供分布形状信息。
中文摘要 AI 辅助
轻而大质量遗迹（LiMRs）是标准模型之外许多物理理论的普遍预测，是当前和未来宇宙学调查的重要目标。由于产生LiMRs的情景多样，其相空间分布广泛，因此对LiMRs特征进行模型无关的量化很有价值。为此，我们确定了三个独立物理量，引入了“Squished Relics”（SquiRels）现象学族来高效灵活地搜索不同相空间分布的LiMRs印记。我们主要关注在宇宙微波背景时期从辐射转变为物质的LiMRs。利用该框架，我们证明当前普朗克观测对LiMRs相空间分布函数形状不敏感，能给出模型无关且可重新解释的限制。还表明未来地面观测台能区分LiMRs与无质量暗辐射种类，并提供其分布形状信息。
英文摘要
Light but Massive Relics (LiMRs), cosmological relics that are relativistic at some point in the observable early universe but are non-relativistic today, are a generic prediction of many theories of physics beyond the Standard Model, and are a target of high interest for current and upcoming cosmological surveys. The enormous variety of scenarios that can give rise to LiMRs also gives rise to a wide range of possible phase space distributions for these relics, making it valuable to have a model-agnostic quantification of LiMR signatures. Toward that end we identify three independent physical quantities that govern LiMRs' dominant effects on cosmological observables, and introduce a phenomenological family of "Squished Relics" (SquiRels) that allow us to efficiently and flexibly search for the imprint of LiMRs with different phase space distributions in data. Our primary focus here is on LiMRs that transition from radiation to matter during the Cosmic Microwave Background epoch. Using this framework we explicitly demonstrate that current Planck observations are not sensitive to the shape of the LiMR's phase space distribution function, which allows us to provide a model-insensitive and readily reinterpretable limit on LiMRs that become non-relativistic at redshifts z_{NR}<10^5. We further demonstrate that upcoming and future ground-based observatories will be able to distinguish LiMRs from massless dark radiation species, and could begin to provide information about the shape of its distribution.
