The SAGA Survey. II. Building a Statistical Sample of Satellite Systems around Milky Way-like Galaxies

Abstract

We present the Stage II results from the ongoing Satellites Around Galactic Analogs (SAGA) Survey. Upon completion, the SAGA Survey will spectroscopically identify satellite galaxies brighter than M r,o = −12.3 around one hundred Milky Way (MW) analogs at z ∼ 0.01. In Stage II we have more than quadrupled the sample size of Stage I, delivering results from 127 satellites around 36 MW analogs, with an improved target selection strategy and deep photometric imaging catalogs from the Dark Energy Survey and the Legacy Surveys. We have obtained 25,372 galaxy redshifts, peaking around z = 0.2. These data significantly increase spectroscopic coverage for very low-redshift objects in 17 < r o < 20.75 around SAGA hosts, creating a unique data set that places the Local Group in a wider context. The number of confirmed satellites per system ranges from 0 to 9, and correlates with host galaxy luminosity and brightest satellite luminosity. We find that the number and the luminosities of MW satellites are consistent with being drawn from the same underlying distribution as SAGA systems. The majority of confirmed SAGA satellites are star forming, and the quenching fraction increases as satellite stellar mass and projected radius from the host galaxy decrease. Overall, the satellite quenching fraction among SAGA systems is lower than that in the Local Group. We compare the luminosity functions and radial distributions of SAGA satellites with theoretical predictions based on Cold Dark Matter simulations and an empirical galaxy-halo connection model, and find that the results are broadly in agreement.

Satellite mass functions and the faint end of the galaxy mass-halo mass relation in LCDM

“…The number of MW satellites with M sat * ≥ 10 6 M is in reasonable agreement with both models (see left panel in Fig. 10), as well as with data from the SAGA survey, which targeted the bright end of the satellite population within 300 kpc of MW-like primaries (Mao et al 2020). We note that our models refer to satellites within the virial radius of the assumed halo (r200 ∼ 215 kpc for our choice of V200 = 150 km/s) rather than the 300 kpc used in the observational data.…”

“…In the case of MW and M31, thicker lines show results for satellites within 300 kpc, while thinner lines correspond to satellites within r 200 (214 kpc for V 200 = 150 km/s, and 236 kpc for V 200 = 165 km/s, assuming h=0.7). The first panel additionally shows the satellite stellar mass function of MW-mass analogs observed as part of the SAGA survey(Mao et al 2020). For the case of the LMC, we show model results assuming V 200 = 50 (colored, solid) as well as 100 (colored, long-dashed) km/s, compared to likely Magellanic satellites according toSantos-Santos et al (2021).…”

Galaxy Properties at the Faint End of the HI Mass Function

“…As one moves to lower and lower masses, the systems host fewer stars and thus are intrinsically faint in the optical, infrared, and ultraviolet regimes with correspondingly low surface brightnesses. There are numerous, on-going efforts to search for low-mass galaxies including, for example, searches for ultra-faint dwarfs within the Local Group (e.g., the Dark Energy Survey; Drlica-Wagner et al 2020), searches for low surface brightness and satellite galaxies outside the Local Group but within the Local Volume (e.g., Smercina et al 2018;Greco et al 2018;Carlsten et al 2020), and searches for satellites of more massive galaxies outside the Local Volume in a statis-tical sample of galaxies (the SAGA Survey; Geha et al 2017;Mao et al 2020).…”

An Accurate Comprehensive Approach to Substructure: III. Masses and Formation Times of the Host Haloes

“…But things are rapidly changing. The incoming new data gathered by means of the Gaia satellite (Gaia Collaboration et al 2018) will allow to accurately determine the MW's substructure and it has now become feasible to observe satellites in neighbouring MW analogues (Danieli et al 2017;Geha et al 2017;Smercina et al 2018;Bennet et al 2019Bennet et al , 2020Crnojević et al 2019;Carlsten et al 2020;Mao et al 2020;Carlsten et al 2021). In parallel, simulations have also greatly improved.…”