Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

There is tentative evidence for a mismatch between the rest frames of matter and the cosmic microwave background, the “quasar dipole anomaly.” We consider such a dipole in tilted anisotropic models, for a range of scenarios and sources: spatial curvature, cosmic heat flux, large scale electromagnetic fields, and a Khronon field. Crucially, we determine the ancillary effects on other cosmological observables in each of these models, and we show that, apart from the case of the Khronon field, it is unlikely that one can obtain a dipole with the amplitude that is being observed unless one considers additional exotica.

Abstract:

Abstract The galaxy luminosity and stellar mass function (LF, SMF), and halo mass function (HMF), are fundamental quantities in astrophysics and crucial inputs to a range of astrophysical and cosmological analyses. They are typically parametrised by fitting functions that have been chosen ‘by eye’ to match observed or simulated data. We apply symbolic regression—specifically the Exhaustive Symbolic Regression (ESR) algorithm—to automate the search for optimal LF, SMF and HMF functional forms. ESR scores all functions up to a maximum complexity composed of a user-defined basis set of operators using the description length, an approximation to the Bayesian evidence that balances accuracy with complexity. We find many functions that outperform the Schechter and double Schechter functions for the LF and SMF, and that outperform all investigated literature functions (that outperform the Press–Schechter, Warren, Tinker, Sheth–Tormen and Jenkins) for the HMF. By additionally imposing ‘physicality checks’ on functions’ extrapolation and integration properties, we identify the optimal, low-complexity functional forms in terms of accuracy, simplicity and behaviour beyond the data range. As well as providing drop-in replacements for literature LF, SMF and HMF fitting functions, and identifying robust behaviour across well-fitting functions, we present a framework with which symbolic regression may be used to automate the discovery of optimal functions for any astrophysical dataset.

Abstract:

ABSTRACT In the context of maximum-likelihood parametric component separation for next-generation full-sky cosmic microwave background (CMB) polarization experiments, we study the impact of fitting different spectral parameters of Galactic foregrounds in distinct subsets of pixels on the sky, with the goal of optimizing the search for primordial B modes. Using both simulations and analytical arguments, we highlight how the post-component separation uncertainty and systematic foreground residuals in the cleaned CMB power spectrum depend on spatial variations in the spectral parameters. We show that allowing spectral parameters to vary across subsets of the sky pixels is essential to achieve competitive signal-to-noise ratio on the reconstructed CMB after component separation while keeping residual foreground bias under control. Although several strategies exist to define pixel subsets for the spectral parameters, each with its advantages and limitations, we show using current foreground simulations in the context of next-generation space-borne missions that there are satisfactory configurations in which both statistical and systematic residuals become negligible. The exact magnitude of these residuals, however, depends on the mission’s specific characteristics, especially its frequency coverage and sensitivity. We also show that the post-component separation statistical uncertainty is only weakly dependent on the properties of the foregrounds and propose a semi-analytical framework to estimate it. On the contrary, the systematic foreground residuals highly depend on both the properties of the foregrounds and the chosen spatial resolution of the spectral parameters.

Abstract:

We present constraints on the amplitude of matter fluctuations from the clustering of galaxies and their cross-correlation with the gravitational lensing convergence of the cosmic microwave background (CMB), focusing on low redshifts (z ≲ 0.3), where potential deviations from a perfect cosmological constant dominating the growth of structure could be more prominent. Specifically, we make use of data from the 2MASS photometric survey (2MPZ) and the WISE×SuperCOSMOS galaxy survey, in combination with CMB lensing data from Planck. Using a hybrid effective field theory (HEFT) approach to model galaxy bias we obtain constraints on the combination S 8 = σ 8 √(Ω m /0.3), where σ 8 is the amplitude of matter fluctuations, and Ω m is the non-relativistic matter fraction. Using a prior on Ω m based on the baryon acoustic oscillation measurements of DESI, we find S 8 = 0.79 ± 0.06, in reasonable agreement with CMB constraints. We also find that, in the absence of this prior, the data favours a value of Ω m = 0.245 ± 0.024, that is 2.8σ lower than Planck. This result is driven by the broadband shape of the galaxy auto-correlation, and may be affected by theoretical uncertainties in the HEFT power spectrum templates. We further reconstruct the low-redshift growth history, finding it to be compatible with the Planck predictions, as well as existing constraints from lensing tomography. Finally, we study our constraints on the HEFT bias parameters of the galaxy samples studied, finding them to be in reasonable agreement with coevolution predictions.

Abstract:

A primary target of the Euclid space mission is to constrain early-universe physics by searching for deviations from a primordial Gaussian random field. A significant detection of primordial non-Gaussianity would rule out the simplest models of cosmic inflation and transform our understanding of the origin of the Universe. This paper forecasts how well field-level inference of galaxy redshift surveys can constrain the amplitude of local primordial non-Gaussianity, f NL^local, within a Bayesian hierarchical framework, in the upcoming Euclid data. We designed and simulated mock datasets and performed Markov chain Monte Carlo analyses using a full-field forward modelling approach. By including the formation history of the cosmic matter field in the analysis, the method takes into account all available probes of primordial non-Gaussianity, and goes beyond statistical summary estimators of f NL^local. Probes include, for example, two-point and higher-order statistics, peculiar velocity fields, and scale-dependent galaxy biases. Furthermore, the method simultaneously handles systematic survey effects, such as selection effects, survey geometries, and galaxy biases. The forecast shows that, using simulated Euclid data, the method can achieve a precision of σ(f NL^local) = 2.6 (68.3% confidence level), assuming a grid resolution of ΔL = 31.25 h ⁻¹ Mpc and a cut-off scale of k NF = 0.1 h Mpc⁻¹. We also provide data products, including realistic simulations with non-zero values of f NL^local and maps of adiabatic curvature fluctuations. The results underscore the feasibility and advantages of field-level inference to constrain f NL^local in galaxy redshift surveys. Our approach consistently captures all the information available in the large-scale structure to constrain f NL^local, and resolves the degeneracy between early-universe physics and late-time gravitational effects, while mitigating the impact of systematic and observational effects.