Sub-Gyr variability around the SFMS and its contribution to the scatter

TL;DR

This study quantifies how short-term variability around the SFMS contributes to its observed scatter by deriving galaxy SFHs from full spectral fitting for 8,960 MaNGA galaxies and tracking their SFMS positions over the last $1\ \mathrm{Gyr}$. By counting SFMS crossings and measuring deviations and intervals, the authors identify a mixed variability picture: predominantly stochastic sub-100 Myr fluctuations with a statistically significant preference for time-scales near $\sim$135–150 Myr, indicative of self-regulated star formation and bursts/quenching episodes. However, short-term variability cannot fully explain the SFMS scatter, which also grows from longer, halo-scale differences and gas-accretion histories, as evidenced by correlations with current SFMS/MZR positions and by the fraction of time galaxies spend above the SFMS. The results align with theoretical predictions and demonstrate the feasibility of using SFH-derived variability analyses for large spectroscopic surveys to disentangle short- and long-term drivers of galaxy evolution.

Abstract

We aim to measure the evolution of individual galaxies around the Star Formation Main Sequence (SFMS) during the last Gyr as a function of their stellar mass to quantify how much of its scatter is due to short-term variability.We derived star formation histories using full spectral fitting for a sample of 8,960 galaxies from the MaNGA survey to track the position of the galaxies in the SFMS during the last Gyr.The variability correlates with both the stellar mass of the galaxies and their current position in both the SFMS and the mass-metallicity relation (MZR), with the position in the latter strongly affecting variability in SFR. While most of the fluctuations are compatible with stochasticity, there is a very weak but statistically significant preference for $\sim135-150$ Myr time-scales. These results support a strong self-regulation of SFR within galaxies, establishing characteristic intensities and time-scales for bursts of star formation and quenching episodes. We also find that short-term variability cannot account for the entirety of the scatter in the SFMS. It appears to originate to a similar degree in short-term variability and long-term (halo-level) differentiation and fits predictions from models.

Figures (15)

• Figure 1: Example SFHs obtained from the full spectral fitting performed on the MaNGA sample. We show one galaxy within the main sequence of the SFMS (MS) and one located over 1$\sigma$ below it (RET). The corresponding PLATEIFU identifiers of the data cubes are 12514-12701 for the MS galaxy and 10001-1902 for the RET galaxy.
• Figure 2: Comparison between the SFR measured using emission lines (X-axis) and the full spectral fitting we employ selecting the last 30 Myr (Y-Axis) for all 8960 galaxies in our sample. The contours are set to encompass 95%, 65% and 35% of the sample. The top panel shows the distribution of data-points.
• Figure 3: SFMS and MZR measured from the most recent values of the SFHs and the [Z/H] we employ ($\sim$20 Myr) obtained by full spectral fitting. The full distribution (ALL) is shown as black points, with MS (blue) and RET (brown) shown in colored points in the left and right columns, respectively. The contours follow the star-forming sub-sample as defined in Sec. \ref{['sec:cross_det']} which is used to measure the SFMS and MZR (red lines). In the top two panels we show the SFMS at z$\sim0$ from Speagle2014 (left) and Popesso2023 (right) for comparison with ours.
• Figure 4: Example of how crossings of the SFMS are detected. We show the position relative to the SFMS in dex of two galaxies with similar mass but very different number of crossings, obtained by subtracting the value of the SFMS at each age from the SFR at each age. The age bins where a crossing is detected (the previous age bin has opposite sign of $\Delta$log SFR) are indicated with a dot.
• Figure 5: Distribution of the parameters for ALL galaxies for three M$_\star$ bins. The top panel shows the average number of crossings per galaxy $\mathcal{C}_{SFMS}$ and in the bottom panel the average deviation from the SFMS $\Delta\mathrm{SFR}$ is shown. Each distribution is shown as both a histogram and the kernel density estimator (KDE) smoothed curve for each M$_\star$ bin. The kernel bandwidth is estimated using the default method from SciPy.