The total satellite population of the Milky Way (1708.04247v2)

Abstract: The total number and luminosity function of the population of dwarf galaxies of the Milky Way (MW) provide important constraints on the nature of the dark matter and on the astrophysics of galaxy formation at low masses. However, only a partial census of this population exists because of the flux limits and restricted sky coverage of existing Galactic surveys. We combine the sample of satellites recently discovered by the Dark Energy Survey (DES) with the satellites found in Sloan Digital Sky Survey (SDSS) Data Release 9 (together these surveys cover nearly half the sky) to estimate the total luminosity function of satellites down to $M_{\rm V}=0$. We apply a new Bayesian inference method in which we assume that the radial distribution of satellites independently of absolute magnitude follows that of subhaloes selected according to their peak maximum circular velocity. We find that there should be at least $124^{+40}_{-27}$ (68 per cent CL, statistical error) satellites brighter than $M_{\rm V}=0$ within $300$ kpc of the Sun. As a result of our use of new data and better simulations, and a more robust statistical method, we infer a much smaller population of satellites than reported in previous studies using earlier SDSS data only; we also address an underestimation of the uncertainties in earlier work by accounting for stochastic effects. We find that the inferred number of faint satellites depends only weakly on the assumed mass of the MW halo and we provide scaling relations to extend our results to different assumed halo masses and outer radii. We predict that half of our estimated total satellite population of the MW should be detected by the Large Synoptic Survey Telescope. The code implementing our estimation method is available online.

An Analysis of the Satellite Galaxies of the Milky Way

This paper offers a detailed examination and estimation of the total satellite galaxy population orbiting the Milky Way (MW). Analyzing satellite galaxies can provide vital insights into the characteristics of dark matter and the processes influencing galaxy formation. However, current estimates rely heavily on a partial census of this population due to limitations in flux detection and sky coverage. The authors aim to overcome these limitations by combining data from the Dark Energy Survey (DES) and the Sloan Digital Sky Survey (SDSS) to enhance the understanding of the MW's satellite luminosity function.

The paper embarks on a thorough exploration of Bayesian inference methodologies, applying new algorithms to estimate the total luminosity function of satellites around the MW. A standout aspect of this paper is the employment of subhalo radial distributions derived from high-resolution simulations. These simulations utilize subhaloes selected based on their peak maximum circular velocity, offering a reliable representation of the spatial distribution of MW satellites. This approach substantiates the luminosity-independent nature of satellite distributions, creating robust priors for extrapolation.

Results of the paper indicate a significantly smaller number of satellites compared to estimates from earlier studies. The authors predict at least 124 satellites brighter than $M_{=0$ within a radial distance of 300kpc. This estimate, while lower than previous predictions, challenges assumptions primarily due to enhanced data and improved statistical methods that better account for stochastic effects.

Several implications arise from these findings. The total inferred satellite count provides constraints for theoretical models concerning the hierarchy of galaxy formation and attributes of dark matter. Additionally, the paper contemplates practical implications, notably the detectability of faint satellite galaxies in future surveys like the Large Synoptic Survey Telescope. Such upcoming observational endeavors could verify the weaker dependence of predicted satellite counts on assumed MW dark matter halo mass, refining models further.

This research also explores influences such as MW halo mass assumptions and outer radius cut-offs, offering methods to adapt the satellite counts to varying MW masses and radial distances. The comprehensive methodology and subsequent forecast underscore the complexities of cosmic structure and emphasize the need for continuous advancements in survey depth and coverage for more accurate galactic census-taking.

In contemplating trajectories for further research, there is potential for deeper surveys that could uncover unidentified, low surface brightness dwarf galaxies, and a careful reassessment of DE satellites’ associations with the LMC could yield slight modifications in the population count. These avenues promise new insights into galaxy formation theories and dark matter characteristics, facilitating a richer understanding of cosmic evolution.

The authors contribute their code publicly, allowing for reproducibility and potential enhancements in research applications. This openness in scientific inquiry aligns with broader aspirations in computational cosmology and encourages a collaborative approach to refining cosmic structure models.

In conclusion, this paper constitutes a pivotal contribution to satellite galaxy research by enhancing methodological precision and providing substantial data-driven insight into the MW's satellite population, underscoring the dynamic interaction between observational data, computational models, and theoretical advancements in cosmology.