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Upcoming galaxy surveys will allow us to probe the growth of the cosmic large-scale structure with improved sensitivity compared to current missions, and will also map larger areas of the sky. This means that in addition to the increased precision in observations, future surveys will also access the ultralarge-scale regime, where commonly neglected effects such as lensing, redshift-space distortions, and relativistic corrections become important for calculating correlation functions of galaxy positions. At the same time, several approximations usually made in these calculations such as the Limber approximation break down at those scales. The need to abandon these approximations and simplifying assumptions at large scales creates severe issues for parameter estimation methods. On the one hand, exact calculations of theoretical angular power spectra become computationally expensive, and the need to perform them thousands of times to reconstruct posterior probability distributions for cosmological parameters makes the approach unfeasible. On the other hand, neglecting relativistic effects and relying on approximations may significantly bias the estimates of cosmological parameters. In this work, we quantify this bias and investigate how an incomplete modelling of various effects on ultralarge scales could lead to false detections of new physics beyond the standard ΛCDM model. Furthermore, we propose a simple debiasing method that allows us to recover true cosmologies without running the full parameter estimation pipeline with exact theoretical calculations. This method can therefore provide a fast way of obtaining accurate values of cosmological parameters and estimates of exact posterior probability distributions from ultralarge-scale observations.

 

ABSTRACT Stream stars removed by tides from their progenitor satellite galaxy or globular cluster act as a group of test particles on neighbouring orbits, probing the gravitational field of the Milky Way. While constraints from individual streams have been shown to be susceptible to biases, combining several streams from orbits with various distances reduces these biases. We fit a common gravitational potential to multiple stellar streams simultaneously by maximizing the clustering of the stream stars in action space. We apply this technique to members of the GD-1, Palomar 5 (Pal 5), Orphan, and Helmi streams, exploiting both the individual and combined data sets. We describe the Galactic potential with a Stäckel model, and vary up to five parameters simultaneously. We find that we can only constrain the enclosed mass, and that the strongest constraints come from the GD-1, Pal 5, and Orphan streams whose combined data set yields $M(\lt 20\, \mathrm{kpc}) = 2.96^{+0.25}_{-0.26} \times 10^{11} \, \mathrm{ M}_{\odot}$. When including the Helmi stream in the data set, the mass uncertainty increases to $M(\lt 20\, \mathrm{kpc}) = 3.12^{+3.21}_{-0.46} \times 10^{11} \, \mathrm{M}_{\odot}$.