Now that we’ve been initiated into the cool waters of Tools (Part 1), we’ve compared our *own* galaxies to the rest of the post-quenched sample (Part 2), and we’ve put your classifications to use, looking for what makes post-quench galaxies special compared to the rest of the riff-raff (Part 3), we’re ready for Part 4 of the Quench ‘How-To-Guide’.
This segment is inspired by a post on Quench Talk in response to Part 3 of this guide. One of our esteemed zoo-ite mods noted:
There are more Quench Sample mergers (505) than Control mergers (245)… It seems to suggest mergers have a role to play in quenching star formation as well.
Whoa! That’s a statistically significant difference and will be a really cool result if it holds up under further investigation!
I’ve been thinking about this potential result in the context of the Kaviraj article, summarized by Michael Zevin at http://postquench.blogspot.com/. The articles finds evidence that massive post-quenched galaxies appear to require different quenching mechanisms than lower-mass post-quenched galaxies. I wondered — can our data speak to their result?
Let’s find out!
Step 1: Copy this Dashboard to your Quench Tools environment, as you did in Part 3 of this guide.
- This starter Dashboard provides a series of tables that have filtered the Control sample data into sources showing merger signatures and those that do not, as well as sources in low, mid, and high mass bins.
- Mass, in this case, refers to the total stellar mass of each galaxy. You can see what limits I set for each mass bin by looking at the filter statements under the ‘Prompt’ in each Table.
Step 2: Compare the mass histogram for the Control galaxies with merger signatures with the mass histogram for the total sample of Control galaxies.
- Click ‘Tools’ and choose ‘Histogram’ in the pop-up options.
- Choose ‘Control’ as the ‘Data Source’.
- Choose ‘log_mass’ as the x-axis, and limit the range from 6 to 12.
- Repeat the above, but choose ‘Control – Merging’ as the ‘Data Source’.
The result will look similar to the figure below. Can you tell by eye if there’s a trend with mass in terms of the fraction of Control galaxies with merger signatures?
It’s subtle to see it in this visualization. Instead, let’s look at the fractions themselves.
Step 3: Letting the numbers guide us… Is there a higher fraction of Control galaxies with merger signatures at the low-mass end? At the high-mass end? Neither?
To answer this question, we need to know, for each mass bin, the fraction of Control galaxies that show merger signatures. I.e.,
Luckily, Tools can give us this information.
- Click on the ‘Control – Low Mass’ Table and scroll to its lower right.
- You’ll see the words ‘1527 Total Items’.
- There are 1527 Control galaxies in the low mass bin.
- Similarly, if you look in the lower right of the ‘Control – Merging – Low Mass’ Table, you’ll see that there are 131 galaxies in this category.
- This means that the merger fraction for the low mass bin is 131/1527 or 8.6%.
- Find the fraction for the middle and high mass bins.
Does the fraction increase or decrease with mass?
Step 4: Repeat the above steps but for the post-quenched galaxy sample.
You may want to open a new Dashboard to keep your window from getting too cluttered.
Step 5: How do the results compare for our post-quenched galaxies versus our Control galaxies? How can we best visualize these results?
- In thinking about the answer to this question, you might want to make a plot of mass (on the x-axis) versus merger fraction (on the y-axis) for the Control galaxies.
- On that same graph, you’d also show the results for the post-quenched galaxies.
- To determine what mass value to use, consider taking the median mass value for each mass bin.
- Determine this by clicking on ‘Tools’, choosing ‘Statistics’ in the pop-up options, selecting ‘Control – Low Mass’ as your ‘Data Source’, and selecting ‘Log Mass’ as the ‘Field’.
- This ‘Statistics’ Tool gives you the mean, median, mode, and other values.
- You could plot the results with pen on paper, use Google spreadsheets, or whatever plotting software you prefer. Unfortunately Tools, at this point, doesn’t provide this functionality.
It’d be awesome if you posted an image of your results here or at Quench Talk. We can then compare results, identify the best way to visualize this for the article, and build on what we’ve found.
You might also consider repeating the above but testing for the effect of choosing different, wider, or narrower mass bins. Does that change the results? It’d be really useful to know if it does.
I’m very happy to be posting again to the How-To-Guide. We’ve made a number of updates to Quench data and Quench Tools. Before I launch into Part 3 of the Guide, here are the recent updates:
- The classification results for the 57 control galaxies that needed replacements have been uploaded into Quench Tools.
- We’ve applied two sets of corrections to the galaxies magnitudes: the magnitudes are now corrected for both the effect of extinction by dust and the redshifting of light (specifically, the k-correction).
- We’ve uploaded the emission line characteristics for all the control galaxies.
- We’ve uploaded a few additional properties for all the galaxies (e.g., luminosity distances and star formation rates).
- We corrected a bug in the code that mistakenly skipped galaxies identified as ‘smooth with off-center bright clumps’.
In Part 3 we’re going to use the results from the classifications that you all provided to see if there’s anything different about the post-quenched galaxies that have merged or are in the process of merging with a neighbor, and those that show no merger signatures.
The figure below is of one of my favorite post-quenched galaxies with merger signatures. Gotta love those swooping tidal tails!
Let’s get started!
Step 1: Because of the updates to Tools, first clear your Internet browser’s cache, so it uploads the latest Quench Tools data.
Step 2: Copy my starter dashboard with emission line ratios ready for play.
- Open my Dashboard and click ‘Copy Dashboard’ in the upper right. This way you can make changes to it.
- In this Dashboard, I’ve uploaded the post-quenched galaxy data.
- I also opened a Table, just as you did in Part 2 of this How-To-Guide. I called the Table ‘All Quench Table’.
- In the Table, notice how I’ve applied a few filters, by using the syntax:
filter .’Halpha Flux’ > 0
- This reduces the table to only include sources that fulfill those criteria.
- Also notice that I’ve created a few new columns of data, just as you did in Part 2, by using the syntax:
field ‘o3hb’, .’Oiii Flux’/.’Hbeta Flux’
- That particular syntax means that I took the flux for the doubly ionized oxygen emission line ([0III]) and divided it by the flux in one of the Hydrogen emission lines (Hbeta).
- This ratio and the ratio of [NII]/Halpha are quite useful for identifying Active Galactic Nuclei (AGN).
- It’d be really interesting if we find that AGN play a role in shutting off the star formation in our post-quenched galaxies. A major question in galaxy evolution is whether there’s any clear interplay between merging, AGN activity, and shutting off star formation.
Step 3: Create the BPT diagram using the ratios of [OIII]/Hb and [NII]/Ha.
- BPT stands for Baldwin, Phillips, and Terlevich (1981), among the first articles to use these emission line ratios to identify AGN. Check out the GZ Green Peas project’s use of the BPT diagram.
- Click on ‘Tools’. Choose ‘Scatter plot’ in the pop-up options.
- In the new Scatterplot window, choose ‘All Quench Table’ as your ‘Data Source’.
- For the x-axis, choose ‘logn2ha’. For the y-axis, choose ‘logo3hb’.
- Adjust the min/max values so the data fits nicely within the window, as shown in the figure below.
- Remember that you can click on the comb icon in the upper-left of the plot to make the menu overlay disappear.
- Do you notice the two wings of the seagull in your plot? The left-hand wing is where star forming galaxies reside (potentially star-bursting galaxies) while the right-hand wing is where AGN reside. Our post-quenched sample of galaxies covers both wings.
Step 4: Compare the BPT diagram for post-quenched galaxies with and without signatures of having experienced a merger.
- To do this, you’ll need to first create two new tables, one that filters out merging galaxies and the other that filters out non-merging galaxies.
- Click on ‘Tools’. Choose ‘Table’ in the pop-up options.
- In the new Table window, choose ‘All Quench Table’ as the ‘Data Source’. Notice how this new table already has all the new columns that were created in the ‘All Quench Table’. That makes our life easier!
- Look through the column names and find the one that says ‘Merging’. Possible responses are ‘Neither’, ‘Merging’, ‘Tidal Debris’, or ‘Both’.
- Let’s pick out just the galaxies with no merger signatures.
- Under ‘Prompt’ type:
filter .Merging = ‘Neither’
- If you scroll to the bottom of the Table, you’ll notice that you now have only 2191 rows, rather than the original 3002.
- Call this Table ‘Non-Mergers Table’ by double clicking on the ‘Table-4’ in the upper-left of the Table and typing in the new name.
- Now follow the instructions from Step 3 to create a BPT scatter plot for your post-quenched galaxies with no merger signatures. Be sure to choose ‘Non-Mergers Table’ as the ‘Data Source’.
- You might notice that this plot looks pretty similar to the plot for the full post-quenched galaxy sample, just with fewer galaxies.
What about post-quenched galaxies that show signatures of merger activity? Do they also show a similar mix of star forming galaxies and AGN?
- To find out, create a new Table, but this time under ‘Prompt’ type:
filter .Merging != ‘Neither’
- The ‘!=’ syntax stands for ‘Not’, which means this filter picks out galaxies that had any other response under the ‘Merging’ column (i.e, tidal tails, merger, both). Notice how there are 505 sources in this Table.
- Now create a BPT scatter plot for your ‘Mergers Table’.
- Make sure this plot has a similar xmin,xmax,ymin,ymax as your other plots to ensure a fair comparison.
- You might also compare histograms of log(NII/Ha) for the different subsamples.
What do you find? Do you notice the difference? What could this be telling us about our post-quenched galaxies?!
Before you get too carried away in the excitement, it’s a good idea to compare the post-quenched galaxy sample BPT results against the control galaxy sample.
This comparison with the control sample will tell you whether this truly is an interesting and unique result for post-quenched galaxies, or something typical for galaxies in general. You might consider doing this in a new Dashboard, as I have, to keep things from getting too cluttered. In that new Dashboard, click ‘Data’, choose ‘Quench’ in the pop-up options, and choose ‘Quench Control’ as your data to upload. Now repeat Steps 1-4.
Do you notice any differences between your control galaxy and post-quenched galaxy sample results? What do you think this tells us about our post-quenched galaxies?
Stay tuned for Part 4 of this How-To-Guide. I’d love to build from your results from this stage, so definitely post the URLs for your Dashboards here or within Quench Talk and your questions and comments.
Since finishing the classifications for the GZ: Quench project, many of our volunteers have been analyzing that consensus data using the tools at tools.galaxyzoo.org. We made a few changes to the site earlier this week, and I’d like to describe them and talk about how it might affect your work on the project.
First, a quick reminder of how the data is presented. As most of you probably remember, the classification process on GZ: Quench (and all GZ projects since GZ2) is what we call a “decision tree”. We begin with a broad question on morphology (ie, “Is this galaxy smooth, or does it have features or a disk?”) for the volunteer to answer. We then ask more specific follow-up questions that depend on the previous answers. For example – if you said the galaxy doesn’t have any spiral arms, it doesn’t make sense for us to then ask you how many arms there are – it doesn’t apply to this galaxy! So, out of 11 potential questions covering galaxy morphology, a single classifier will only answer a subset (between 4 and 9) of them. Here’s a flowchart of the decision tree for GZ: Quench — it’s an interesting exercise to look at it and work out how many unique morphologies you could sort galaxies into by going through the tree.
So, why this discussion? When we added the data to the Tools website, we added a label in each category that gave the most common response to that question. For example, under “Arm tightness”, you could see that all galaxies were either “Tight”, “Medium”, or “Loose”. However, this is problematic when you’re trying to analyze data and compare different sets of galaxies. For smooth (or elliptical) galaxies, though, this arm classification is the result of very few votes (or even none) — they don’t represent the majority of classifications, and thus we really shouldn’t be including them when trying to compare what makes a medium-wound vs. a loosely-wound spiral.
The solution we’ve adopted has been to edit the data on Tools — questions whose answers don’t apply to the consensus morphology (eg, spiral arms in a smooth galaxy, or the roundness of a spiral) are now blank. This means that if you look at the average color or size of any of these morphology properties, you’re now truly comparing similar groups of objects (apples to apples). Including other galaxies in earlier samples likely introduced a significant amount of bias – the science team thinks that this will largely help to address that.
What does this mean for your analysis? Most of your old Dashboards and results should still work and remain valid results. For any work where you were analyzing morphological details (especially for spiral structure), though, we encourage you to revisit these and run them again on the new, filtered dataset. Please keep posting any questions you have on Talk, and we’ll answer them as soon as we can. Good luck!