The goal of this project is to see whether or not galaxy interactions can enhance their rates of star formation. I will compute the specific star formation rate (sSFR) across the surfaces of the galaxies in our sample. Then I will compare the sSFRs in paired galaxies to a sample of control galaxies to test if galaxy interactions do indeed enhance the sSFR.
This work is based on the data from the SDSS's (Sloan Digital Sky Survey) MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) survey. MaNGA is an integral field spectroscopic (IFS) survey which has observed 10,000 nearby galaxies. Spectroscopy takes the light from an object and splits it by wavelength. Astronomers can tell many things about an object by its spectra. You can tell what king of object you are looking at, the ages of stars, the chemical composition, et cetera.
Traditionally, for large spectroscopic surveys, light is collected through fiber optic cables to prevent spectra from overlapping with other light sources. A single fiber optic cable is placed on a single source, and a single spectrum is collected. IFS takes a whole bundle of fiber optic cables and places them onto a single target. This gives astronomers several spectra for a single target, each of which covers a different part of the object. With MaNGA, IFS allows us to simultaneously study a galaxy's center and disk with a single observation.
The First step of the project is to build a sample of merging galaxies in the MaNGA survey. While the MaNGA survey contains information for its 10,000 target galaxies (~6,400 galaxies at the time of this project), there are many other objects which may fall within the fields-of-views of the survey. Many of these objects will be stars and foreground/background galaxies; however, some of these objects may be galaxies which are gravitationally paired with the MaNGA target galaxy. In a previous project (MaNGAObj), I built a catalog of all ancillary objects within the survey. I will use the galaxies identified in the catalog as a starting point for my sample.
The star formation rate is the number of stars that are created in a galaxy or a region of a galaxy every year. While this sounds like a simple definition, directly measuring this for galaxies is difficult. Individual stars in a galaxy cannot be separately resolved from the billions of other stars so astronomers cannot simply count the number of new stars in a galaxy. Astronomers instead have to rely on tracers of new stars to infer star formation rates.
When stars are born, they are not created one at a time. They are created in groups in what are called stellar nurseries. The new population of stars are not identical and will have varying masses. The most massive stars, O and B type stars, are very luminous but will also have short lifespans (only a few million years). This means that if we detect tracers of OB stars, the galaxy or region must have recently created new stars. One tracer of OB stars with optical spectra is the H-alpha emission line shown in the figure below. This emission line is created by hydrogen gas which has been ionized by nearby OB stars. The luminosity of the line is directly related to the star formation rate. That is, galaxies with stronger H-alpha luminosities are creating more new stars.
Unfortunately, there is a caveat to using the H-alpha line to infer star formation rates. There are other sources that can ionize hydrogen gas. The main one is an active galactic nucleus (AGN). An AGN is where the region around a galaxy's central supermassive black hole emits high energy photons because the black hole is accreting large amounts of material. Another sources is from hot low-mass evolved stars which may also be able to ionize hydrogen gas. Fortunately, these ionization sources can be separated from each other using the galaxy's spectra.
I use the figure below to select galaxies how emission lines are generated by star formation. The left panel shows the color-magnitude diagram for our galaxies. The plot shows the galaxies' color on the y-axis, bluer galaxies are further down on the figure while redder galaxies are further up on the figure. Massive stars with short lifespans will be bluer while small stars with long lifespans will be redder. That means that red galaxies are dominated by older less massive stars while blue galaxies are dominated by young massive stars. Since I am are looking for young stars, I require that our galaxies fall below the lower dashed line (the blue cloud) to be in our sample.
The middle panel shows the BPT diagram (Baldwin et al. 1981) of our galaxies. The panel plots two emission line ratios on its x and y axis which creates this "seagull-like" distribution. Each wing represents a different source of ionization. The right branch represents galaxies whose dominant ionization source is an AGN while the left branch represents galaxies whose dominant ionization source are OB stars. I select galaxies which fall below the dashed line (the left branch) to be in our sample.
The right panel shows the WHAN diagram (Cid Fernandes et al. 2011) of our galaxies. The panel plots an emission line ratio on the x axis against the equivalent width of the H-alpha line on the y axis. Cid Fernandes et al. 2011 has shown that the equivalent width of the H-alpha line is a good tracer for hot low mass evolved stars and that galaxies whose emission lines are dominated by these objects can be separated by imposing an H-alpha equivalent width cut at 3 Angstroms. Using these three methods, I create a sample of star forming galaxies.
With a set of star forming galaxies identified in the survey, I need to determine which galaxies are actually gravitationally paired with the target galaxies. While two galaxies may appear close to each other on the sky, there may be a significant distance between the two objects along our line-of-sight. To verify that they are paired galaxies, the two galaxies need to be near each other on the sky, have a similar redshift (which tells us how far the galaxies are from us), and have a limited relative velocity between the two galaxies. Since I want to study how galaxy mergers influence star formation, I also create a sample of isolated control galaxies which have no nearby companions. In total, I have 169 paired galaxies and 1830 control galaxies to work with.
MaNGA provides a 2D distribution of galaxy spectra for each of its observations. To prep the data for comparison, I will reduce this 2D distribution to a 1D profile as a function of the galaxy's radius. Typical galaxies are circular; however, if they are at an angle with the observer they will appear to be an ellipse on the sky. The geometry of the galaxies needs to be deprojected to account for this effect. I show this process in the plot below. The left panel shows one of the survey's galaxies with elliptical profiles from various previous surveys overlaid. I calculate the inclination angle for the galaxies using the major-to-minor axis ratio of the shown ellipses. The inclination angle is then used to calculate each pixel's radius as shown in the middle panel. The radius is given in "effective radii," which is the radius that contains half of the galaxy's light. In the right panel I now show the star formation rate as a function of galaxy radius. The black squares represent individual pixels while the red line shows the average star formation rate within discrete radius bins. I create these star formation profiles for each galaxy in our pair and control samples.
Now that I have radial profiles of star formation for every galaxies, I can compare the star formation rates between the paired galaxies and the control galaxies. It is known that a galaxy's star formation rate is dependent on its stellar mass, so I will compare paired galaxies to similar mass control galaxies. I show this in the below figure. In the left hand panel and middle panel, I show the median profiles of star formation for the control galaxies and paired galaxies respectively. Each colored profile in the panels represents a 0.5 dex range in stellar mass. The dash-dot line marks a radius of 1.5 effective radii, beyond which the survey's coverage is limited. The right panel shows the difference (in log space) between the paired and control galaxies. A positive value in the panel means that paired galaxy sample has a higher star formation rate than the control sample while a negative value means that the paired galaxy sample has a lower star formation rate. The black dashed line represents the average of the colored profiles.
The star formation is centrally enhanced in the centers of the paired galaxies while there is a weak suppression of star formation at wider radii. This trend is independent of the galaxies' stellar mass. This tells us that galaxy mergers induce new star formation in the centers of the interacting galaxies and suppresses star formation at wider radii.
While the results have no correlation with galaxy mass, there may be other parameters that do influence the star formation rate in mergers. One possible thing to consider is where the galaxy pair is at in the merger event. Are the two galaxies actively colliding, have they just collided, or are they still on approach to the first collision? The timescale of a merger event is on the order of 100 million to a billion years, so the galaxy mergers that we see on the sky is just a small snapshot in time which makes it difficult to tell exactly what part of the merger event the two galaxies are in. A decent proxy for merger stage is the separation between the two galaxies. If the two galaxies are closely separated, they are either actively interacting or have just passed their first or second pericenter. If the two galaxies have a wide separation, they may at the apocenter after their first collision or they may still be on the approach to their first collision.
If the two galaxies have not collided yet, it is unlikely that they will experience any merger induced effects and the strongest merger induced effects are likely to occur just after the galaxies collide. This means that we would expect that the centrally enhanced star formation in our paired galaxies to occur at short separations and we would expect that widely separated pair would show little to no enhancement to their star formation rates.
To study how the star formation is effected by galaxy separation, I can split the sample into separation bins; however, since we know that the star formation rate is dependent on the mass, the samples will also need to be separated by stellar mass. The sample of paired galaxies is not large enough to split up into too many groups, so I need to come up with a different way to handle the sample. My solution is to match every paired galaxy with 20 similar control galaxies. The 20 controls are selected to have similar stellar masses, redshifts, and effective radii. I set a tolerance for how similar the controls can be and select all galaxies within the given range. If a paired galaxy finds more than 20 similar controls, I randomly select 20 galaxies out of the sample. If a paired galaxy fails to find at least 20 similar controls, I iteratively expand the tolerance until 20 similar control galaxy are found. Once this is done, I take the difference between the star formation rate profile of the paired galaxy and the median star formation rate profile of the 20 control galaxies. This leaves us with a "difference" profile which tells us where star formation is enhanced or suppressed in that given paired galaxy.
With these difference profiles constructed for each paired galaxy, we are free to bin the sample along parameters other than stellar mass. Below I split our pair sample by galaxy separation. In the left panel I show the difference profiles of the star formation rate as a function of galaxy radius following the same format as the previous figure and in the left panel I show the difference in the star formation rate extracted from the central 0.5 effective radii as a function of galaxy separation. Again, positive values indicates where the star formation is enhanced and negative values indicates where the star formation is suppressed. It is clear that there is a relationship between the star formation rate and galaxy separation, where closely separated paired galaxies feature increased star formations rates.
Next we want to see if we observe any trends with the paired galaxies' mass ratio. The mass ratio is the ratio between the stellar masses of the two galaxies. The stellar mass is in logarithmic space, so I define the mass ratio as the difference between the mass of the MaNGA target and the identified companion. This means that a mass ratio of 0 would be a pair where both galaxy same mass while a mass ratio of 1 is a pair where the MaNGA target galaxy is ten times more massive than the other galaxy. A value of -1 would be where the identified companion is ten times more massive than the MaNGA target. I expect to see greater changes to the star formation rate in galaxies with similar masses than in galaxies with significantly different masses.
In the below figure I repeat what I did in the above figure. The first row is for the absolute value of the mass ratio and the second row is the regular mass ratio. I do this since I expect that similar mass galaxies will experience greater tidal effect from the merger, but I also expect that in pairs with different masses that the more massive galaxy may respond to the merger differently from the less massive galaxy. In the top row we see that similar mass galaxies experience the largest enhancement to their star formation rate. In the bottom row, we see something interesting. In pairs with different stellar masses, we see that the less massive galaxy experiences a central enhancement to their star formation rate while the more massive galaxy experiences little to no change in the star formation rate. At wider radii we also see that the more massive galaxy has enhanced star formation while the less massive galaxy has suppressed star formation.
This work has been published in Steffen et al. 2021.