标题： 使用LSST超新星的特殊速度进行增长率测量的预测 显示英文标题

标题： Forecast for growth-rate measurement using peculiar velocities from LSST supernovae

摘要： 在本工作中，我们研究了利用来自薇拉·C·鲁宾天文台的时空遗产调查（LSST）中的Ia型超新星（SNe Ia）的特殊速度（PVs）测量宇宙增长率参数$f\sigma_8$的可行性。 我们使用一种现实的LSST观测策略，对不同类型的SN进行模拟，包括噪声、差分图像分析（DIA）管道的光度检测以及从Uchuu UniverseMachine模拟中建模的PV场。 我们测试了三种观测情景，从理想条件下的光谱宿主星系红移和光谱SN分类，到更现实的设置，涉及光度分类和非Ia型超新星的污染。 使用最大似然技术，我们表明在最现实的情况下，LSST可以在红移范围$ 0.02 < z < 0.14 $内以$10\%$的精度测量$f\sigma_8$。 使用三个分层箱，LSST可以在高达$z = 0.14$的范围内将增长率参数的误差控制在$18\%$以下。 我们还测试了污染对最大似然方法的影响，并发现当污染比例低于$\sim 2\%$时，测量结果保持无偏。 这些结果突显了LSST Ia型超新星样本在高红移处补充红移空间畸变测量的潜力，为检验广义相对论和暗能量模型提供了一种新途径。 显示英文摘要

摘要： In this work, we investigate the feasibility of measuring the cosmic growth-rate parameter, $f\sigma_8$, using peculiar velocities (PVs) derived from Type Ia supernovae (SNe Ia) in the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We produce simulations of different SN types using a realistic LSST observing strategy, incorporating noise, photometric detection from the Difference Image Analysis (DIA) pipeline, and a PV field modeled from the Uchuu UniverseMachine simulations. We test three observational scenarios, ranging from ideal conditions with spectroscopic host-galaxy redshifts and spectroscopic SN classification, to more realistic settings involving photometric classification and contamination from non-Ia supernovae. Using a maximum-likelihood technique, we show that LSST can measure $f\sigma_8$ with a precision of $10\%$ in the redshift range $ 0.02 < z < 0.14 $ in the most realistic case. Using three tomographic bins, LSST can constrain the growth-rate parameter with errors below $18\%$ up to $z = 0.14$. We also test the impact of contamination on the maximum likelihood method and find that for contamination fractions below $\sim 2\%$, the measurement remains unbiased. These results highlight the potential of the LSST SN Ia sample to complement redshift-space distortion measurements at high redshift, providing a novel avenue for testing general relativity and dark energy models.