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[Paper Review] Forward-modelling the Luminosity, Distance, and Size distributions of the Milky Way Satellites

Viraj Manwadkar, Andrey V. Kravtsov|arXiv (Cornell University)|Dec 8, 2021
Galaxies: Formation, Evolution, PhenomenaPhysics and Astronomy201 references49 citations
TL;DR

This paper uses the GRUMPY forward modeling framework to simulate the luminosity, distance, and size distributions of Milky Way satellite galaxies within the Caterpillar zoom-in simulations. It successfully reproduces observed distributions from DES, PS1, and SDSS without requiring orphan galaxy models, predicts 440+201−147 satellites with MV < 0 and r1/2 > 10 pc within 300 kpc, and shows that reionization imprinting flattens the M★−Mpeak relation at low halo masses, explaining the high fraction of faint galaxies in low-mass haloes.

ABSTRACT

We use exttt{GRUMPY}, a simple regulator-type model for dwarf galaxy formation and evolution, to forward model the dwarf galaxy satellite population of the Milky Way (MW) using the Caterpillar zoom-in simulation suite. We show that luminosity and distance distributions of the model satellites are consistent with the distributions measured in the DES, PS1 and SDSS surveys, even without including a model for the orphan galaxies. We also show that our model for dwarf galaxy sizes can simultaneously reproduce the observed {\it distribution} of stellar half-mass radii, $r_{1/2}$, of the MW satellites and the overall $r_{1/2}-M_\star$ relation exhibited by observed dwarf galaxies. The model predicts that some of the observed faint stellar systems with $r_{1/2}<10$ pc are ultra-faint dwarf galaxies. Scaling of the stellar mass $M_\star$ and peak halo mass $M_{ m peak}$ for the model satellites is not described by a power law, but has a clear flattening of $M_\star-M_{ m peak}$ scaling at $M_{ m peak}<10^8\,M_\odot$ imprinted by reionization. As a result, the fraction of low mass haloes ($M_{ m peak} < 10^8\, M_\odot$) hosting galaxies with $M_V<0$ is predicted to be 50% at $M_{ m peak} \sim 3.6 imes 10^7\,M_\odot$. We find that such high fraction at that halo mass helps to reproduce the number of dwarf galaxies discovered recently in the HSC-SSP survey. Using the model we forecast that there should be the total of $440^{+201}_{-147}$ (68\% confidence interval) MW satellites with $M_V < 0$ and $r_{1/2} > 10$ pc within 300 kpc and make specific predictions for the HSC-SSP, DELVE-WIDE and LSST surveys.

Motivation & Objective

  • To model the luminosity, distance, and size distributions of Milky Way satellite galaxies using a forward modeling approach.
  • To test whether a simple regulator-type model (GRUMPY) can reproduce observed distributions from DES, PS1, and SDSS without including orphan galaxies.
  • To investigate how reionization affects the stellar mass–halo mass relation and the resulting satellite population properties.
  • To forecast the number of detectable satellites in upcoming surveys such as HSC-SSP, DELVE-WIDE, and LSST.
  • To assess the role of feedback processes in shaping the faint-end of the luminosity function and the size–mass relation of dwarf galaxies.

Proposed method

  • The GRUMPY model is used to simulate dwarf galaxy formation and evolution, linking stellar mass (M★) to peak halo mass (Mpeak) with a regulator-type feedback prescription.
  • The model is applied to the Caterpillar zoom-in simulation suite, which provides high-resolution dark matter and baryonic structure for MW-mass hosts.
  • The model predicts luminosity functions, radial distance distributions, and stellar half-mass radii (r1/2) for satellites, which are compared to observational data from DES, PS1, and SDSS.
  • The M★−Mpeak relation is found to flatten at Mpeak < 10^8 M⊙ due to reionization feedback, which suppresses star formation in low-mass haloes.
  • The model forecasts the number of satellites with MV < 0 and r1/2 > 10 pc within 300 kpc, using a 68% confidence interval.
  • Observational constraints are applied using mass estimates from Vasiliev et al. (2021), Deason et al. (2021), and Shen et al. (2022), with results shown to be robust across different MW mass assumptions.

Experimental results

Research questions

  • RQ1Can a simple regulator-type model reproduce the observed luminosity and distance distributions of Milky Way satellites without including orphan galaxies?
  • RQ2How does reionization feedback imprint a flattening in the M★−Mpeak relation at low halo masses, and what is its impact on the faint-end of the luminosity function?
  • RQ3To what extent can the model reproduce the observed distribution of stellar half-mass radii (r1/2) and the r1/2−M★ relation for MW satellites?
  • RQ4What is the predicted number of MW satellites with MV < 0 and r1/2 > 10 pc within 300 kpc, and how does this compare to upcoming survey expectations?
  • RQ5Why is the fraction of low-mass haloes (Mpeak < 10^8 M⊙) hosting galaxies with MV < 0 so high (50% at Mpeak ∼3.6×10^7 M⊙), and how does this affect the observed satellite population?

Key findings

  • The GRUMPY model successfully reproduces the observed luminosity and distance distributions of MW satellites from DES, PS1, and SDSS without requiring a model for orphan galaxies.
  • The model predicts that 50% of haloes with Mpeak ∼3.6×10^7 M⊙ host galaxies with MV < 0, a high fraction driven by reionization feedback suppressing star formation in low-mass haloes.
  • The M★−Mpeak scaling relation flattens at Mpeak < 10^8 M⊙ due to reionization, which is critical for matching the observed number of faint satellites.
  • The model simultaneously reproduces the observed distribution of stellar half-mass radii (r1/2) for MW satellites and the broader r1/2−M★ relation seen in dwarf galaxies.
  • The model forecasts a total of 440+201−147 MW satellites with MV < 0 and r1/2 > 10 pc within 300 kpc, providing specific predictions for HSC-SSP, DELVE-WIDE, and LSST surveys.
  • The model identifies some faint systems with r1/2 < 10 pc as ultra-faint dwarf galaxies, consistent with their observed properties and the theoretical expectation of early, metal-poor star formation.

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This review was created by AI and reviewed by human editors.