脉冲星计时中超轻标量暗物质的相关信号
Correlated signals of ultralight scalar dark matter in pulsar timing
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中文总结 AI 辅助
研究脉冲星计时中超轻标量暗物质，开发自洽分析方法，将ULDM场视为高斯随机场，使其振幅先验在相关与不相关极限间连续插值，应用于线性和二次耦合标量ULDM，用归一化流代理表示分布，并在模拟数据集验证。
中文摘要 AI 辅助
脉冲星计时阵列（PTA）对质量范围在\(10^{-24}\) - \(10^{-20}\,\mathrm{eV}\)的超轻暗物质（ULDM）敏感，现有数据集已在探索开放参数空间，未来PTA有望大幅提升探测范围。然而，目前PTA对ULDM的搜索通常采用极限描述。分析在完全相关极限（阵列中局部ULDM振幅共享）或完全不相关极限（每个脉冲星有独立局部振幅）下进行。由于两种状态之间的转变发生在PTA敏感质量范围内，预测灵敏度和数据约束可能取决于所采用的极限描述。我们首次开发了一种自洽分析方法，将ULDM场视为具有有限空间相关性的高斯随机场，使PTA信号模型中使用的振幅先验能在完全相关和完全不相关极限之间连续插值。我们将该框架应用于线性和二次耦合标量ULDM，后者包括振荡ULDM压力产生的普遍引力信号。通过增强的潜在场先验纳入脉冲星距离不确定性，并用归一化流代理表示距离边缘化的潜在振幅分布。我们在模拟PTA数据集上验证了该方法，包括盲信号注入测试。
英文摘要
Pulsar timing arrays (PTAs) are sensitive to ultralight dark matter (ULDM) in the $10^{-24}$-$10^{-20}\,\mathrm{eV}$ mass range, with existing datasets already probing otherwise open parameter space and future PTAs promising substantial improvements in reach. Thus far, however, PTA searches for ULDM have typically been formulated using limiting descriptions. Analyses are performed in either the fully correlated limit, in which the local ULDM amplitude is shared across the array, or the fully uncorrelated limit, in which each pulsar has an independent local amplitude. Because the transition between these regimes occurs within the PTA-sensitive mass range, projected sensitivities and data-derived constraints can depend on which limiting description is assumed. For the first time, we develop a self-consistent analysis that treats the ULDM field as a Gaussian random field with finite spatial correlations, allowing the amplitude prior used in PTA signal models to interpolate continuously between the fully correlated and fully uncorrelated limits. We apply the framework to both linearly and quadratically coupled scalar ULDM, the latter including the universal gravitational signal sourced by the oscillating ULDM pressure. Pulsar-distance uncertainties are incorporated through an augmented latent-field prior, and the resulting distance-marginalized latent-amplitude distribution is represented with a normalizing-flow surrogate. We validate the method on mock PTA datasets, including blinded signal injection tests.
