The Star Formation Main Sequence in the Hubble Space Telescope Frontier Fields

Abstract

We investigate the relation between star formation rate (SFR) and stellar mass (M), i.e. the Main Sequence (MS) relation of star-forming galaxies, at 1.3 ≤ z < 6 in the first four HST Frontier Fields, based on rest-frame UV observations. Gravitational lensing combined with deep HST observations allows us to extend the analysis of the MS down to log M/M ⊙ ∼ 7.5 at z 4 and log M/M ⊙ ∼ 8 at higher redshifts, a factor of ∼10 below most previous results. We perform an accurate simulation to take into account the effect of observational uncertainties and correct for the Eddington bias. This step allows us to reliably measure the MS and in particular its slope. While the normalization increases with redshift, we fit an unevolving and approximately linear slope. We nicely extend to lower masses the results of brighter surveys. Thanks to the large dynamic range in mass and by making use of the simulation, we analyzed any possible mass dependence of the dispersion around the MS. We find tentative evidence that the scatter decreases with increasing mass, suggesting larger variety of star formation histories in low mass galaxies. This trend agrees with theoretical predictions, and is explained as either a consequence of the smaller number of progenitors of low mass galaxies in a hierarchical scenario and/or of the efficient but intermittent stellar feedback processes in low mass halos. Finally, we observe an increase in the SFR per unit stellar mass with redshift milder than predicted by theoretical models, implying a still incomplete understanding of the processes responsible for galaxy growth.

Evolution of dwarf galaxy observable parameters

“…Qualitatively, the evolution of the M * − SFR relation that we find in our models agrees with the observed evolution (Tasca et al 2015;Santini et al 2017), although the latter is derived for more massive galaxies. In particular, the specific SFR (SFR/M * ) increases with redshift, and galaxies have similar specific SFRs across the mass range at a given redshift.…”

“…In particular, the specific SFR (SFR/M * ) increases with redshift, and galaxies have similar specific SFRs across the mass range at a given redshift. Quantitatively, the specific SFR in the most massive galaxies in our models is lower by a factor of a few than the average observational result, both at z = 0 (Peng et al 2010) and at higher redshift (Tasca et al 2015;Santini et al 2017). The SFRs observed in low-mass galaxies agree very closely with our model results, however (figure 2, panel c).…”

The MUSEHubbleUltra Deep Field Survey

“…If we interpret our point at z ≈ 5.5 as a statistical fluctuation 1σ below a true value ≈ 0.35 as found by De Barros et al (2017), we confirm this shallower increase of the LAE fraction towards z ≈ 5 and z ≈ 6. This could indicate the stop of the evolution of the Lyα escape fraction, possibly related to the plateau evolution of the star formation main sequence at z ≈ 5-6 suggested by Speagle et al (2014) and Salmon et al (2015), and implying a constant stellar mass at a given M 1500 (see however, Santini et al 2017).…”

The high-redshift SFR–M* relation is sensitive to the employed star formation rate and stellar mass indicators: towards addressing the tension between observations and simulations

“…We can see that the observations of Heinis et al (2014) and Bouwens et al (2012) differ from the EAGLE reference model by ≃ 0.5 − 1 dex. However, the Salmon et al (2015) and Santini et al (2017) observations are within ≃ 0 − 0.3 dex from the predictions. This behavior is found at all redshifts with the reference EAGLE model and observations having offset star formation rates from −0.2 to 1.0 dex depending on masses and redshifts.…”

“…We note that these assumptions are motivated by observational studies but not necessarily stand neither for the real/observed nor the EAGLE+SKIRT simulated galaxies (in table 1 we sumarize the SED fitting assumptions used by different authors). We employ numerous wavelengths filters like GALEXF U V , GALEXNUV , SDSSu, SDSSg, SDSSr, SDSSi, SDSSz, TwoMassJ , TwoMassH , TwoMassKs, UKIDDSZ, UKIDDSY , UKIDDSJ , UKIDDSH, UKIDDSK, JohnsonU , JohnsonB , JohnsonV , JohnsonR, JohnsonI , JohnsonJ , JohnsonM , WISEW 1, WISEW 2, WISEW 3, WISEW 4, IRAS12, IRAS25, IRAS60, IRAS100, IRACI1, IRACI2, IRACI3, IRACI4, MIPS24, MIPS70, MIPS160, PACS70, PACS100, PACS160, SPIRE250, SPIRE350 and SPIRE500 in order to limit parameter degeneracies to the SED fitting procedure (Katsianis et al 2016;Santini et al 2017).…”