Using Velocity Dispersion to Estimate Halo Mass: Is the Local Group in Tension with Λ\LambdaCDM?

Satellite galaxies are commonly used as tracers to measure the line-of-sight velocity dispersion ($\sigma_{\rm LOS}$) of the dark matter halo associated with their central galaxy, and thereby to estimate the halo's mass. Recent observational dispersion estimates of the Local Group, including the Milky Way and M31, suggest $\sigma\sim$50 km/s, which is surprisingly low when compared to the theoretical expectation of $\sigma\sim$100s km/s for systems of their mass. Does this pose a problem for $\Lambda$CDM? We explore this tension using the {\small{SURFS}} suite of $N$-body simulations, containing over 10000 (sub)haloes with well tracked orbits. We test how well a central galaxy's host halo velocity dispersion can be recovered by sampling $\sigma_{\rm LOS}$ of subhaloes and surrounding haloes. Our results demonstrate that $\sigma_{\rm LOS}$ is biased mass proxy. We define an optimal window in $v_{\rm LOS}$ and projected distance ($D_p$) -- $0.5\lesssim D_p/R_{\rm vir}\lesssim1.0$ and $v_{\rm LOS} \lesssim0.5V_{\rm esc}$, where $R_{\rm vir}$ is the virial radius and $V_{\rm esc}$ is the escape velocity -- such that the scatter in LOS to halo dispersion is minimised - $\sigma_{\rm LOS}=(0.5\pm0.1)\sigma_{v,{\rm H}}$. We argue that this window should be used to measure line-of-sight dispersions as a proxy for mass, as it minimises scatter in the $\sigma_{\rm LOS}-M_{\rm vir}$ relation. This bias also naturally explains the results from \cite{mcconnachie2012a}, who used similar cuts when estimating $\sigma_{\rm LOS,LG}$, producing a bias of $\sigma_{\rm LG}=(0.44\pm0.14)\sigma_{v,{\rm H}}$. We conclude that the Local Group's velocity dispersion does not pose a problem for $\Lambda$CDM and has a mass of $\log M_{\rm LG, vir}/M_\odot=12.0^{+0.8}_{-2.0}$.Comment: 8 pages, 7 figures, accepted for publicatio

Tracing HI Beyond the Local Universe

The SKA and its pathfinders will enable studies of HI emission at higher redshifts than ever before. In moving beyond the local Universe, this will require the use of cosmologically appropriate formulae that have traditionally been simplified to their low-redshift approximations. In this paper, we summarise some of the most important relations for tracing HI emission in the SKA era, and present an online calculator to assist in the planning and analysis of observations (hifi.icrar.org).Comment: submitted to PAS

Measuring the growth rate of structure with Type IA Supernovae from LSST

We investigate measuring the peculiar motions of galaxies up to $z=0.5$ using Type Ia supernovae (SNe Ia) from LSST, and predict the subsequent constraints on the growth rate of structure. We consider two cases. Our first is based on measurements of the volumetric SNe Ia rate and assumes we can obtain spectroscopic redshifts and light curves for varying fractions of objects that are detected pre-peak luminosity by LSST (some of which may be obtained by LSST itself and others which would require additional follow-up). We find that these measurements could produce growth rate constraints at $z<0.5$ that significantly outperform those using Redshift Space Distortions (RSD) with DESI or 4MOST, even though there are $\sim4\times$ fewer objects. For our second case, we use semi-analytic simulations and a prescription for the SNe Ia rate as a function of stellar mass and star formation rate to predict the number of LSST SNe IA whose host redshifts may already have been obtained with the Taipan+WALLABY surveys, or with a future multi-object spectroscopic survey. We find $\sim 18,000$ and $\sim 160,000$ SN Ia with host redshifts for these cases respectively. Whilst this is only a fraction of the total LSST-detected SNe Ia, they could be used to significantly augment and improve the growth rate constraints compared to only RSD. Ultimately, we find that combining LSST SNe Ia with large numbers of galaxy redshifts will provide the most powerful probe of large scale gravity in the $z<0.5$ regime over the coming decades.Comment: 12 pages, 1 table, 5 figures. Accepted for publication in ApJ. The Fisher matrix forecast code used in this paper can be found at: https://github.com/CullanHowlett/PV_fisher. Updated to fix error in Eq. 1 (thanks to Eric Linder for pointing this out