发表机构
Cornell University; University of Pennsylvania; Kavli Institute for Cosmology Cambridge; Academia Sinica (ASIAA); Duke University; Lawrence Berkeley National Laboratory; Boston University; Instituto Avanzado de Cosmología A. C.; Universidad Nacional Autónoma de México（康奈尔大学; 宾夕法尼亚大学; 卡弗里宇宙学剑桥研究所; 中央研究院天文及天体物理研究所; 杜克大学; 劳伦斯伯克利国家实验室; 波士顿大学; 先进宇宙学研究所; 墨西哥国立自治大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究结合DESI DR2 LRG与ACT DR6 CMB数据，利用机器学习重建视向速度并测量kSZ效应，以高信噪比（最高14-15）约束暗晕平均光学深度，揭示延伸电离气体，展示了探测弥散重子的新方法。
AI 中文摘要
我们结合运动学Sunyaev Zel'dovich效应（kSZ）测量与利用机器学习重建的非线性视向速度，对由DESI DR2亮红星系（LRG）示踪的大质量暗晕，提出了平均光学深度和气体密度轮廓的新经验约束。配对kSZ信号使用两张ACT DR6宇宙微波背景（CMB）温度图（ILC和150 GHz图）对七个按光度选择的LRG样本进行测量。我们发现两张图的结果一致，且各样本均存在显著探测。我们利用经模拟训练的Transformer机器学习模型，在分别以$z=$0.55和0.8为中心的两个宇宙学时期重建LRG视线方向视向速度，以超越线性近似。通过将kSZ配对相关与从重建速度场测得的配对速度相关进行比较，并分别与使用最佳拟合普朗克宇宙学的理论预测进行比较，推断出平均光学深度，最高显著性分别达到信噪比SNR=14.2和15.1。我们还获得了速度加权的AP滤波kSZ堆叠星系团密度轮廓，这些轮廓显示在巨大LRG星系群周围存在延伸的电离气体，并且在当前测量不确定度内没有强烈的红移演化。这些结果证明了结合光谱星系和CMB数据以及采用机器学习方法来探测弥散重子和相干的大尺度速度场的能力。它们为利用即将到来的Euclid、Roman和Rubin LSST巡天数据，以及CCAT和Simons Observatory的多频CMB/亚毫米数据开辟了新途径。
英文摘要
We present novel empirical constraints on average optical depths and gas density profiles from combining kinematic Sunyaev Zel'dovich effect (kSZ) measurements and nonlinear peculiar velocities, reconstructed using machine learning, for massive halos traced by DESI DR2 luminous red galaxies (LRG). The pairwise kSZ is measured using two ACT DR6 cosmic microwave background (CMB) temperature maps (ILC and 150 GHz maps) for seven luminosity-selected LRG samples. We find consistent results between the two maps and significant detections across the samples. We reconstruct LRG line-of-sight peculiar velocities in two cosmic epochs, centered on $z=$ 0.55 and 0.8, using a simulation-trained Transformer machine learning model to extend beyond the linear approximation. Average optical depths are inferred by comparing the kSZ pairwise correlation to the measured pairwise velocity correlation from the reconstructed velocity field and, separately, from a theoretical prediction using the best fit Planck cosmology, with the highest significance reaching SNR = 14.2 and 15.1 respectively. We also obtain velocity-weighted AP-filtered kSZ-stacked cluster density profiles, which show evidence of extended ionized gas around massive LRG groups and no strong redshift evolution within current measurement uncertainties. These results demonstrate the power of combining spectroscopic galaxy and CMB data, and employing machine learning methods, to probe diffuse baryons and coherent large-scale velocity fields. They open up new avenues to leverage upcoming galaxy survey data from Euclid, Roman and Rubin LSST, in tandem with multi-frequency CMB/sub-mm data from CCAT and Simons Observatory.
Comments18 pages, 6 figures
