Dr Harry Desmond

Abstract:

Abstract The rotation velocities of disc galaxies trace dark matter halo structure, providing direct constraints on the galaxy–halo connection. We construct a Bayesian forward model to connect the dark matter halo population predicted by ΛCDM with an observed sample of disc galaxies (SPARC) through their maximum rotation velocities. Our approach combines a subhalo abundance matching scheme (accounting for assembly bias) with a parameterised halo response to galaxy formation. When assuming no correlation between selection in the SPARC survey and halo properties, reproducing the observed velocities requires strong halo expansion, low abundance matching scatter and a halo proxy that strongly suppresses the stellar masses in satellite haloes. This is in clear tension with independent clustering constraints. Allowing for SPARC-like galaxies to preferentially populate low Vmax haloes at fixed virial mass greatly improves the goodness-of-fit and resolves these tensions: the preferred halo response shifts to mild contraction, the abundance matching scatter constraint relaxes to σSHAM < 0.30 dex at 1σ and the proxy becomes consistent with clustering. However, the inferred selection threshold is extreme, implying that SPARC galaxies occupy the lowest ~15 per cent of the Vmax, halo distribution at fixed Mvir. Moreover, even with selection, the inferred scatter remains in statistical disagreement with the low-mass clustering constraints, which are most representative of the SPARC galaxies in our sample. Our analysis highlights the advantage of augmenting clustering-based constraints on the galaxy–halo connection with kinematics and suggests a possible tension using current data.

Abstract:

We constrain the difference in gravitational potential between our location and sources at (Formula presented.) using datasets at those redshifts. Our motivation is that the Hubble tension might be caused by a local void, as suggested by galaxy number counts. This would increase the redshift through outflow and gravitational redshift (GR). Only the latter is important at high redshift, where a void contributes a fixed additional GR contribution of (Formula presented.) due to our location on a potential hill. This (Formula presented.) model has various subtle effects that were not previously considered, including a hotter CMB and reduced BAO scale (Formula presented.). We test whether (Formula presented.) can have the previously expected value of 0.84%, which was based on the fitting of void parameters to galaxy number counts and local (Formula presented.) measurements. Combining BBN, CMB, BAO, and CC datasets at (Formula presented.), we find that (Formula presented.) = (Formula presented.), which rises to (Formula presented.) when extending our analysis down to (Formula presented.). Although the results prefer the standard value of (Formula presented.), the best-fitting model with (Formula presented.) fits the data almost as well as (Formula presented.) CDM, with (Formula presented.). We find that (Formula presented.) CDM faces a (Formula presented.) BAO anomaly in the standard (Formula presented.) parameter space, where different regions are preferred by BAO and non-BAO datasets from (Formula presented.). Fixing (Formula presented.) reduces this to (Formula presented.). This suggests that a local void large enough to solve the Hubble tension cannot be ruled out by higher-redshift datasets, despite its novel impacts on them.

Abstract:

ABSTRACT The advent of high-precision Gaia parallaxes for Milky Way Cepheids enables percent-level calibration of the local distance ladder and the Hubble constant $H_0 $. We revisit the Milky Way Cepheid calibration from Gaia EDR3 parallaxes using a fully forward-modelled Bayesian framework that simultaneously infers the period–luminosity relation, the Gaia parallax zero-point offset, and individual stellar distances while explicitly incorporating the disc geometry of the Galaxy through the distance prior and the selection functions specified in two HST SH0ES campaigns. We derive an analytic treatment of the detection probability that accounts for magnitude, parallax, period, and extinction cuts and reduces it to a tractable integral over distance and sky position. Posterior predictive checks show that this generative model matches the observed distributions of parallaxes, magnitudes, and periods. Modelling Galactic structure and survey truncation self-consistently in a Bayesian framework yields period–luminosity parameters that agree with the SH0ES maximum-likelihood values at the ${\lt }0.5\, \sigma $ level, a consequence of the small intrinsic scatter of the Cepheid period–luminosity relation. Adopting the uniform-in-volume prior recently advocated by M. Högås & E. Mörtsell, without simultaneously accounting for selection, leads to a ${\sim }\, 0.05~\mathrm{mag} $ bias in the period–luminosity zero-point and posterior predictive distributions incompatible with the observed data; this shift is mostly driven by the omission of the selection model, and produces an apparent and unjustified shift in $H_0 $ that reflects this mismodelling. A consistent Bayesian treatment of Galactic structure and selection effects reinforces the local distance-ladder determination of $H_0 $, and hence the Hubble tension with early-Universe inferences.

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.