Pengfei Li, Federico Lelli, Stacy McGaugh, James Schombert, Kyu-Hyun Chae

The application of Bayesian techniques to astronomical data is generally non-trivial because the fitting parameters can be strongly degenerated and the formal uncertainties are themselves uncertain. An example is provided by the contradictory claims over the presence or absence of a universal acceleration scale (g$_\dagger$) in galaxies based on Bayesian fits to rotation curves. To illustrate the situation, we present an analysis in which the Newtonian gravitational constant $G_N$ is allowed to vary from galaxy to galaxy when fitting rotation curves from the SPARC database, in analogy to $g_{\dagger}$ in the recently debated Bayesian analyses. When imposing flat priors on $G_N$, we obtain a wide distribution of $G_N$ which, taken at face value, would rule out $G_N$ as a universal constant with high statistical confidence. However, imposing an empirically motivated log-normal prior returns a virtually constant $G_N$ with no sacrifice in fit quality. This implies that the inference of a variable $G_N$ (or g$_{\dagger}$) is the result of the combined effect of parameter degeneracies and unavoidable uncertainties in the error model. When these effects are taken into account, the SPARC data are consistent with a constant $G_{\rm N}$ (and constant $g_\dagger$).

Matteo Zennaro, Raul E. Angulo, Marcos Pellejero-Ibáñez, Jens Stücker, Sergio Contreras, Giovanni Aricò

We present an emulator for the two-point clustering of biased tracers in real space. We construct this emulator using neural networks calibrated with more than $400$ cosmological models in a 8-dimensional cosmological parameter space that includes massive neutrinos an dynamical dark energy. The properties of biased tracers are described via a Lagrangian perturbative bias expansion which is advected to Eulerian space using the displacement field of numerical simulations. The cosmology-dependence is captured thanks to a cosmology-rescaling algorithm. We show that our emulator is capable of describing the power spectrum of galaxy formation simulations for a sample mimicking that of an typical Emission-Line survey at $z \sim 1$ with an accuracy of $1-2\%$ up to nonlinear scales $k \sim 0.7 h \mathrm{Mpc}^{-1}$.

Nickolas Kokron, Joseph DeRose, Shi-Fan Chen, Martin White, Risa H. Wechsler, (2) UCSC, (3) UC Berkeley, (4) LBL)

We implement a model for the two-point statistics of biased tracers that combines dark matter dynamics from $N$-body simulations with an analytic Lagrangian bias expansion. Using Aemulus, a suite of $N$-body simulations built for emulation of cosmological observables, we emulate the cosmology dependence of these nonlinear spectra from redshifts $z = 0$ to $z=2$. We quantify the accuracy of our emulation procedure, which is sub-per cent at $k=1\, h {\rm Mpc}^{-1}$ for the redshifts probed by upcoming surveys and improves at higher redshifts. We demonstrate its ability to describe the statistics of complex tracer samples, including those with assembly bias and baryonic effects, reliably fitting the clustering and lensing statistics of such samples at redshift $z\simeq 0.4$ to scales of $k_{\rm max} \approx 0.6\, h\mathrm{Mpc}^{-1}$. We show that the emulator can be used for unbiased cosmological parameter inference in simulated joint clustering and galaxy--galaxy lensing analyses with data drawn from an independent $N$-body simulation. These results indicate that our emulator is a promising tool that can be readily applied to the analysis of current and upcoming datasets from galaxy surveys.