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The impact of disc disruption on Milky Way satellite counts

Mark R. Lovell, Alexander H. Riley, Isabel Santos-Santos

Abstract

Estimates for the total number of Milky Way (MW) satellites are often generated from a combination of the observed number of satellites in surveys, adjustments for the completeness of those surveys, and theoretical expectations from halo assembly modelling. One of the features of this modelling is disruption by the MW stellar disc. We examine the effect of degrees of disc disruption on inferred satellite counts, by means of an N-body simulation of a MW-mass halo plus a toy model for this disruption. We use a fictional all-sky survey to show that high resilience to disc disruption predicts small populations of satellites that are radially very concentrated around the central galaxy and are hosted by massive subhaloes, while low resilience predicts many more satellites with a less concentrated radial distribution and hosted within less massive subhaloes. We show that the most massive subhaloes are particularly susceptible to disruption due to their radial orbits, and in their putative absence galaxy formation must occur in lower mass haloes that have a shallower radial number density profile. We then demonstrate this phenomenon for a combination of the Pan-STARRS and DES surveys. It is therefore necessary to account for uncertainty in the disc disruption radius when making predictions for MW satellite distributions.

The impact of disc disruption on Milky Way satellite counts

Abstract

Estimates for the total number of Milky Way (MW) satellites are often generated from a combination of the observed number of satellites in surveys, adjustments for the completeness of those surveys, and theoretical expectations from halo assembly modelling. One of the features of this modelling is disruption by the MW stellar disc. We examine the effect of degrees of disc disruption on inferred satellite counts, by means of an N-body simulation of a MW-mass halo plus a toy model for this disruption. We use a fictional all-sky survey to show that high resilience to disc disruption predicts small populations of satellites that are radially very concentrated around the central galaxy and are hosted by massive subhaloes, while low resilience predicts many more satellites with a less concentrated radial distribution and hosted within less massive subhaloes. We show that the most massive subhaloes are particularly susceptible to disruption due to their radial orbits, and in their putative absence galaxy formation must occur in lower mass haloes that have a shallower radial number density profile. We then demonstrate this phenomenon for a combination of the Pan-STARRS and DES surveys. It is therefore necessary to account for uncertainty in the disc disruption radius when making predictions for MW satellite distributions.
Paper Structure (10 sections, 1 equation, 7 figures)

This paper contains 10 sections, 1 equation, 7 figures.

Figures (7)

  • Figure 1: Images of the halo used in this study at $z=0$. In the left-hand panel we show the original halo within the EAGLE DMO-100Mpc box, and in the right-hand panel we present our zoomed resimulation. Each image slice is 4 $h^{-1}\rmn{Mpc}$ wide and 2 $h^{-1}\rmn{Mpc}$ thick. The image intensity indicates the density and hue the velocity dispersion, with purple for velocity dispersion $\hbox{$\sim$} \hbox{$<$} 5$$\,{\rm km}\,{\rm s}^{-1}$ and yellow for velocity dispersion $\hbox{$\sim$} \hbox{$>$} 200$$\,{\rm km}\,{\rm s}^{-1}$.
  • Figure 2: The impact of halo mass on orbital parameters. Left-hand panel: the median first pericentre, last apocentre, and present-day distance-to-host for subhaloes as a function of peak mass shown as blue, red, and yellow solid lines respectively. The 68 per cent scatter regions for the first pericentre and the last apocentre are shown as shaded regions, while the 68 per cent scatter in the present day distance is instead indicated with a pair of dashed lines. Right-hand panel: the median infall angle as a function of peak mass, plus the 68 per cent scatter.
  • Figure 3: The ratio of the subhalo counts computed for subhalo populations outside and inside 50 kpc when using $R_\rmn{DD}=10$ kpc (purple) and $R_\rmn{DD}=20$ kpc (magenta) as a function of $M_\rmn{tr}$. The shaded regions indicate the $1\sigma$ Poisson uncertainties.
  • Figure 4: Peak mass functions computed for subhaloes within $50$ kpc (dashed lines) and $300$ kpc (solid lines) assuming different values of $R_\rmn{DD}$. Bluer lines show lower values of $R_\rmn{DD}$ and red lines higher values as indicated in the figure legend. Left-hand panel: all subhaloes are included. Right-hand panel: only subhaloes that have a collapse redshift $z_{8}>5.5$. In both panels the notional survey 50 kpc satellite count of 20 is shown with a horizontal black dotted line. The vertical coloured dotted lines then indicate the threshold mass $M_\rmn{tr}$ for each $R_\rmn{DD}$ value while the corresponding horizontal dotted lines indicate the estimated satellite counts, $n_\rmn{est}$. Both quantities are quoted in the figure legend when there are at least 20 subhaloes within 20 kpc, where we give the threshold mass in units of $10^{8}$${\,\rm M_\odot}$, i.e. $M_\rmn{tr,8}\equiv M_\rmn{tr}/(10^{8}$${\,\rm M_\odot})$. In the bottom panels we show the ratio of the mass functions with $R_\rmn{DD}\ge 5$ kpc to their $R_\rmn{DD}=2$ kpc counterparts.
  • Figure 5: Radial distributions for the survey-selected subhaloes while varying $R_\rmn{DD}$ and the adoption or otherwise of the $z_{8}>5.5$ cut. $R_\rmn{DD}$ is indicated by colour as shown in the legend. The application of $z_{8}>5.5$ as dashed lines compared to solid where it is not applied; $n_\rmn{est}$ in these models is indicated to the right and left of the slash in the legend respectively. The grey line indicates the spatial distribution of the observed MW satellites.
  • ...and 2 more figures