VizieR Online Data Catalog: Catalogue of outflows in E+A galaxies (Baron+, 2022)

Although some studies presented indirect evidence for AGN feedback taking place in E+A galaxies (e.g. Kaviraj et al. 2007MNRAS.382..960K; French et al. 2018ApJ...862....2F, Cat. J/ApJ/862/2), little is known about this process, in particular about AGN-driven winds, in this short-lived phase. Tremonti et al. (2007ApJ...663L..77T) found high-velocity ionized outflows, traced by Mg II absorption, in z ~ 0.6 post-starburst galaxies. Using an unsupervised Machine Learning algorithm that searches for rare phenomena in a dataset, Baron & Poznanski (2017MNRAS.465.4530B, Cat. J/MNRAS/465/4530) found a post-starburst E+A galaxy with massive AGN-driven winds traced by ionized emission lines. In a follow-up study in Baron et al. (2017MNRAS.470.1687B), we used newly obtained ESI/Keck 1D spectroscopy to model the star formation history (SFH) and the ionized outflows in this system. In Baron et al. (2018MNRAS.480.3993B, 2020MNRAS.494.5396B), we used the integral field units (IFUs) KCWI/Keck and MUSE/VLT to study the spatial distribution of the stars, gas, and outflows, in two post-starburst galaxies hosting AGNs and winds. This might suggest that AGN feedback, in the form of galactic-scale outflows, is significant in the E+A galaxy phase.

The goal of this paper is to construct the first well-defined sample of post starburst E+A galaxies with both AGNs and ionized outflows. The main difference between our sample and those presented in past studies is that the outflows in our sample are traced by strong optical emission lines, allowing us to constrain the mass and energetics of the winds (see e.g. Baron & Netzer 2019MNRAS.486.4290B, Cat. J/MNRAS/486/4290). Using this sample, we aim to check whether AGN feedback, in the form of galactic outflows, can have a significant effect on their hosts evolution, (i.e see section Introduction).

First, we select post-starburst galaxies by an optical spectrum with strong Balmer absorption lines, this evolutionary stage must be very short, due to the short lifetime of A-type stars, making such systems rare and comprising only ~3 per cent of the general galaxy population. Due to the AGN duty cycle, post-starburst galaxies hosting AGNs are expected to be even rarer, and post-starburst galaxies hosting AGNs and outflows are expected to be extremely rare. We describe our method to select such sources from the Sloan Digital Sky Survey (SDSS; York et al. 2000AJ....120.1579Y) using their integrated (1D) optical spectra of more than 2 million galaxies, (i.e refer to the section Sample selection). Using model-fitting unsupervised ML approaches, we found a total of 520 post starburst E+A galaxies with narrow emission lines that are consistent with pure AGN photoionization. Out of these, 215 show evidence for an ionized outflow in one emission line, and 144 in multiple lines. Deriving outflow properties, such as mass outflow rate and kinetic power, requires the detection of the outflow in multiple emission lines (see e.g. Baron & Netzer 2019MNRAS.486.4290B, Cat. J/MNRAS/486/4290). We therefore restrict the analysis to the 144 sources for which we detected multiple broad emission lines.

Next, from IRAS FIR 12, 15, 60, and 100 μm data and SDSS optical spectra, we extract metadata properties on our 144 E+A galaxies and AGN such as component luminosities (i.e sections 3.1 FIR data from IRAS, 3.2.1 Stellar properties, 3.2.2 Ionized and neutral gas properties and 3.2.3 AGN properties). We present optical and FIR properties in two metadata tables mdat1fg.dat and mdat2fg.dat. Finally, using these metadata and optical emission line spectra (i.e see section 3.2.2 and 3.2.4 Outflow energetics), we estimated the ionized and neutral outflow properties are based on various earlier methods. Our results are presented in two groups, firstly ionized gas results are divided in 4 tables according to parameters variations r = (0.3,1,3) kpc and n_e = 10³ cm^-3 as ionrmin.dat, ionravg.dat, ionrmax.dat and ioncstn.dat. Secondly, neutral gas results are divided in 3 tables according to parameter variations r = (0.3,1,3) kpc as ntralmin.dat, ntralavg.dat and ntralmax.dat.

(9 data files).