MaNGA – An exciting survey taking ‘Spectrographic Photos’ of 10,000 galaxies


A couple of weeks ago, while yet again trying to find the query page on the main SDSS website (turns out what I was looking for was on SkyServer), I ran across a “How to find…” page containing a list of the surveys covered in the SDSS. There were a couple of names in there that I didn’t recognize, so I figured I should take a moment to familiarize myself with them. The one that stuck out to me the most was MaNGA:

Listed as a survey containing spectra, the name rang a bell but I’d never bothered to learn about it before. Further investigation revealed this survey to be a brilliant undertaking, indeed!

What is MaNGA?

MaNGA (standing for “Mapping Nearby Galaxies at Apache point observatory”) aims to create 2-D maps of the faces of nearby galaxies using Integral Field Spectroscopy. What that means is that instead of getting one spectrum for each object, typically a glimpse into only a tiny part of the core of a galaxy, this survey will take 19 to 127 spectra at once across the face of each galaxy. Now each galaxy will have a 2-D ‘map’ of spectra, so that you can see the individual spectra of not only the core, but also locations in the arms, halo, nebulous regions, bar, etc. Those individual spectra can then be combined into a false-color representation, showing various physical properties of the galaxy.

It’s like taking a regular image composed of pixels, only instead of each pixel having a “red”, “green”, and “blue” value, each pixel now has 6,731 different color values available. You can pick a pixel to look at individually, by spreading out all the color values side-by-side to make a spectrum, or you can make a false-color image of the galaxy showing, say, all pixels with a bright peak at 6860Å as red, and all pixels with a bright peak at 6790Å as blue, and all pixels with values in between colored accordingly. This means that you can make maps showing cool things like the motion of the stars inside the galaxy, the temperature and motion of the gas inside the galaxy, and the locations of different kinds of stars within the galaxy. To quote the MaNGA page:

“MaNGA will provide two-dimensional maps of stellar velocity and velocity dispersion, mean stellar age and star formation history, stellar metallicity, element abundance ratio, stellar mass surface density, ionized gas velocity, ionized gas metallicity, star formation rate and dust extinction for a statistically powerful sample.”

To achieve the impressive feat of taking an average of 73 spectra for each object, MaNGA will use bundles of spectroscopic fibers called “Integral Field Units” or “IFU”s. Each plate has 17 IFUs, meaning that exposures of 17 objects can be taken simultaneously. Since each IFU is made of a bundle of fibers, each of which is taking its own spectrum, one plate will be able to take 1,247 spectra at the same time. Including the calibration fibers, a total of 1423 spectra will be taken at once. WOW!

What does the data look like?

Here are some examples of the kind of data maps being produced by this survey:

The SDSS image of a galaxy observed by MaNGA; the pink hexagon shows the size of the MaNGA IFU

Example of the same galaxy observed with MaNGA, showing from left to right: stellar velocity field, Hα emission line map, galactic gas velocity field. Credit: Francesco Belfiore, Univ. of St Andrews Print & Design.

From the Kavli Institude for Cosmology, Cambridge website.
Credit: Dana Berry / SkyWorks Digital, Inc., David Law, SDSS Collaboration

How can I use MaNGA?

You can download MaNGA data to use yourself! All you need to view it is a program that can read .FITS files, such as QFitsView or NASA FITS Liberator, both of which are free. While NASA FITS Liberator is a great tool, QFitsView is much better suited to viewing spectra and spectral cubes, so that’s what I’ll base the following tutorial on.

If you just want to check out what MaNGA data looks like and don’t have a specific query you want to run, there are a couple of ways to access the data. For one, galaxies in the SkyServer Explore tool now show whether they have MaNGA observations, so if you’re checking out your favorite galaxy and see a note that says “This object was also observed in MaNGA”, you’ve won the jackpot! Otherwise, if you just want to check out what MaNGA data looks like and would rather not click around on the Explore tool until you find a galaxy that’s been observed by MaNGA (I don’t blame you!) then a good way to quickly access the data is through the Science Archive Server:
(SAS also provides access to basically all the data ever produced by the SDSS. Imaging data is located under /sas/dr14/eboss/photoObj/frames –because I was wondering too! )

In SAS, click on the directory for the latest data release (as of the time of this writing, dr14), and then navigate to /manga/spectro/redux/v2_1_2 and pick a folder. Then click on “images/” and start looking at the .png images. (I’d recommend using the full size images rather than the ‘_thumb’ thumbnails.) Each image shows a galaxy or star that has data in this folder. Find one that you like, and copy the number at the beginning of the file name. Go back to the parent directory of “/sas/dr14/manga/spectro/redux/v2_1_2/[your chosen folder]/stack”, and search for the number you copied down. (On many browsers, you can hit ctrl+F to open a search bar for the page at hand.) Once you find your number, pick one of the files ending in “LINCUBE.fits.gz” or “LOGCUBE.fits.gz”. (LINCUBE uses linear wavelength units and LOGCUBE uses logarithmic wavelength units. If you’re just going to be viewing the data in FITS Liberator, it doesn’t really matter which one you pick.)

If you want to make your own analyses, with maps of stellar velocity, dispersion, etc; you’ll need a different program than FITS Liberator. There aren’t many programs for Windows that work with astronomical data, but thankfully we do have one for this task: QFitsView. If you’re running Linux, you’ll have a plethora of really cool software to pick from! Check out the SDSS page on MaNGA tutorials for more:

No comments yet.

Leave a Reply