Paper Title: COSMOS2020: Ubiquitous AGN Activity of Massive Quiescent Galaxies at 0 < z < 5 Revealed by X-ray and Radio Stacking
Authors: Kei Ito, Masayuki Tanaka, Takamitsu Miyaji, Olivier Ilbert, Olivier B. Kauffmann, Anton M. Koekemoer, Stefano Marchesi, Marko Shuntov, Sune Toft, Francesco Valentino, and John R. Weaver
First Author’s Institution: Department of Astronomical Science, The Graduate University for Advanced Studies, SOKENDAI, Tokyo, Japan and National Astronomical Observatory of Japan, Tokyo, Japan
Status: Accepted by ApJ [open access]
While most passive or “dead” galaxies we see today have had fairly passive lives, distant passive galaxies in the early Universe may have had a more active path to passivity. Detailed studies of these nearby quiescent galaxies (QGs) have revealed they follow a simple evolutionary track: a burst of star formation early on in their life followed by a quiet existence with low rates of star formation. In contrast to this picture of a big burst followed by a gradual decline of star formation in nearby QGs, recent discoveries have uncovered a new population of QGs that get quenched faster and earlier on than should be possible with this simple evolutionary track (for example the distant QGs covered in this Astrobite and that Astrobite). The existence of so many QGs so early on in the Universe is a problem for galaxy evolution models, and the intense starburst phase and rapid suppression of star formation has been difficult to reproduce with cosmological simulations.
A big unresolved question related to this problem is how the burst of star formation gets suddenly shut off or quenched in these galaxies. Are the streams of gas from the cosmic web that fuel star formation getting cut off? Or is the gas flowing in and being expelled by some feedback mechanism? One such possible feedback mechanism is triggered by the galaxy’s central supermassive black hole (SMBH), as it funnels in material and creates a disk of hot, luminous gas and dust around it (an active galactic nucleus, or AGN). The AGN devours some of the gas and throws out the rest as radiation, wind, and jets from the AGN eject the gas inside and around the galaxy.
In today’s paper, the authors leverage the extensive multiwavelength COSMOS2020 catalog to explore the AGN activity in QGs across cosmic time, through two primary AGN signatures: X-ray and radio detections. However, many of these galaxies and the possible AGN within them, especially those farthest away, are faint enough that they are not individually detected in X-ray and radio surveys. To both overcome this faintness and to focus on typical (rather than extreme bright) sources, the authors use a technique called stacking to characterize the average properties of a QG sample and a comparison star-forming galaxy (SFG) sample. Beyond comparing the stacks of QGs and SFGs, the authors create a grid of stacks spanning stellar mass (basically, how big) and redshift (how far away and therefore early on in the Universe) to investigate trends along these axes.
Galaxy Pancakes
To better understand the stacking technique and the grid of stacks, imagine each galaxy is a pancake. Some pancakes are regular (QG) and the ones that have a little more going on are buttermilk (SFG). Now let’s say all of the pancakes have berries in them, but eating a single pancake won’t get you a full serving of fruit. So, to portion out a daily fruit intake you make stacks of pancakes on each plate, separating out regular and buttermilk.
Besides the regular and buttermilk types of pancakes, let’s say they also come in different sizes, from silver dollar to the size of the plate – this represents the stellar mass axis. And of course, the pancakes weren’t made simultaneously: the stacks of pancakes made earlier are farther down the table from where you’re sitting, and the newer ones are right in front of you (like redshift).
To build their grid of galaxy pancake stacks, the authors used optical and infrared data as well as redshifts from the COSMOS2020 catalog (at wavelengths the galaxies were detected at) to decide which galaxies were star-forming versus quiescent, then how massive each was. Now with their comparable QG and SFG samples to stack in a grid of stellar mass and redshift at wavelengths they were undetected in, they had constructed the largest sample of typical QGs out to the highest redshift so far.
Taking an X-ray
The first stacking analysis they conducted was with X-ray data, with some representative stacks shown in Figure 1.
Beyond some general trends, physically interpreting these stacks takes another step: what is causing the X-ray emission? X-ray emission comes from two main sources in galaxies: X-ray binaries, a dense stellar remnant energetically drawing material from a star in its orbit, and AGN. Returning to our analogy, the fruit content in the pancakes could mainly come from whole berries scattered around the pancake (X-ray binaries) or from a berry jam filling in the center (AGN).
But if you only know the average amount of berries in each pancake stack, how do you decide if it’s from whole berries or a jam filling? Based on some known relations between the star formation rate and stellar mass in a galaxy and the amount of X-ray binaries expected, the authors determined the relative contribution from X-ray binaries and AGN. With these models, they found that X-ray binaries could explain most of the X-ray emission for the SFG stacks. On the other hand, for QGs, the average X-ray emission in each stack was 5-50 times higher than expected from just X-ray binaries, implying that much of the X-ray emission came from AGN. Additionally, they found the biggest difference between the SFG and QG samples at the highest redshift bin, providing hints that AGN may have a role in quenching star formation at early times.
Tuning into the radio
To further verify their findings, the authors then stacked data from the other major signature of AGN: radio data. Similarly to X-ray, radio emission comes from two main sources in galaxies: one related to ongoing star formation and one related to AGN. Taking an empirically known correlation between star formation rate and radio luminosity, the authors determined the QG stacks to have 3-10 times higher radio emission than expected from just star formation, while the SFG samples could be explained primarily by star formation based radio emission. Consistent with the X-ray result, this suggests that faint AGN are ubiquitous in QGs.
How to quench a pancake
How does this AGN feedback mechanism work to quench galaxies? In nearby galaxies, we know that quenching tends to occur with more active AGN. This happens in two processes: quasar-mode feedback and radio-mode feedback. In quasar-mode, wind from a bright AGN expels gas from the galaxy and suppresses star formation. In radio-mode, a typically fainter AGN heats the gas in and around the galaxy with radio jets, which prevents gas from cooling and forming stars. In this way, radio maintains quiescence rather than just reducing the possible star formation by tossing out fuel. The authors note their faint, typical sample is probably mostly undergoing radio-mode feedback, with some non-AGN environmental quenching coming into play at lower redshifts.
So what do these stacks tell us about galaxy evolution? The ubiquitous AGN signatures in both X-ray and radio gives us an interesting clue about quenching: everyday quiescent galaxy pancakes are often filled with AGN berry jam, and feedback from faint AGN within them are likely the culprit for shutting off star-forming buttermilk berry galaxy pancakes so suddenly and early in the Universe.
Astrobite edited by Alice Curtin
Featured image credit: adapted from PeopleImages (X-ray) & Pixabay (pancakes)
