The Evolution of the Interstellar Medium in Post-Starburst Galaxies (1906.01890v2)
Abstract: We derive dust masses ($M_{\rm dust}$) from the spectral energy distributions of 58 post-starburst galaxies (PSBs). There is an anticorrelation between specific dust mass ($M_{\rm dust}$/$M_{\star}$) and the time elapsed since the starburst ended, indicating that dust was either destroyed, expelled, or rendered undetectable over the $\sim$1 Gyr after the burst. The $M_{\rm dust}$/$M_{\star}$ depletion timescale, 205${+58}_{-37}$ Myr, is consistent with that of the CO-traced $M_{\rm H_2}/M_{\star}$, suggesting that dust and gas are altered via the same process. Extrapolating these trends leads to the $M_{\rm dust}/M_{\star}$ and $M_{\rm H_2}/M_{\star}$ values of early-type galaxies (ETGs) within 1-2 Gyr, a timescale consistent with the evolution of other PSB properties into ETGs. Comparing $M_{\rm dust}$ and $M_{\rm H_2}$ for PSBs yields a calibration, log $M_{\rm H_2}$ = 0.45 log $M_{\rm dust}$ + 6.02, that allows us to place 33 PSBs on the Kennicutt-Schmidt (KS) plane, $\Sigma \rm SFR-\Sigma M_{\rm H_2}$. Over the first $\sim$200-300 Myr, the PSBs evolve down and off of the KS relation, as their star formation rate (SFR) decreases more rapidly than $M_{\rm H_2}$. Afterwards, $M_{\rm H_2}$ continues to decline whereas the SFR levels off. These trends suggest that the star-formation efficiency bottoms out at 10${-11}\ \rm yr{-1}$ and will rise to ETG levels within 0.5-1.1 Gyr afterwards. The SFR decline after the burst is likely due to the absence of gas denser than the CO-traced H$2$. The mechanism of the $M{\rm dust}/M_{\star}$ and$M_{\rm H_2}/M_{\star}$ decline, whose timescale suggests active galactic nucleus (AGN) or low-ionization nuclear emission-line region (LINER) feedback, may also be preventing the large CO-traced molecular gas reservoirs from collapsing and forming denser star forming clouds.
Collections
Sign up for free to add this paper to one or more collections.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.