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:

The pristine underdense patches of the Universe, cosmic voids, are powerful cosmological laboratories, uniquely sensitive to dark energy, modified gravity, and neutrino masses, yet their baryonic content remains uncharacterized. We present the first constraint on baryon underdensity in voids, exploiting the dispersion measures (DMs) of fast radio bursts (FRBs) as tracers of the electron column. By stacking 3455 sight lines from CHIME/FRB with ∼15′ localizations on 1228 Sloan Digital Sky Survey (SDSS) BOSS voids over redshifts 0.2 < z < 0.7, we measure a DM deficit toward void centers at 3.2σ significance, indicating that diffuse baryons inhabit the emptiest corners of the cosmic web at a suppressed level. The measured signal amplitude is consistent with an effective Universe model built directly from the observed galaxy underdensity in these voids, and a baryonic model calibrated to the FRB DM–redshift relation (αv = 1.80 ± 0.87). A uniform-density void model yields an electron density contrast of δe,v = −0.58 ± 0.30, implying a tentative ∼60% ± 30% underdensity of baryons in void interiors relative to the cosmic mean. Jointly interpreting our FRB measurement with existing stacks of the thermal Sunyaev–Zel’dovich effect on voids further constrains the mean gas temperature to Te ≲ (1.1 ± 0.7) × 10⁶ K, pointing to a warm-hot diffuse phase, consistent with hydrodynamical simulations. With forthcoming FRB and galaxy surveys, this approach opens a new window onto baryon mapping, with direct implications for feedback models governing gas expulsion into low-density environments, and for the use of cosmic voids to extract cosmological constraints.

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.