Hi everyone! For those unaware, I am a PhD student at the University of Nottingham looking at spiral galaxies in Galaxy Zoo (for an overview, see this blog post). Following the release of my first refereed publication last year, my second refereed publication has now been accepted (woohoo!). As can be seen from my previous post (found here), we found remarkable differences between the spiral galaxies that we observe in the local Universe, simply by comparing galaxies with different numbers of spiral arms. Galaxies with two spiral arms are distinctly redder in colour than many-armed galaxies. However, the exact reasons for these differences was still up for debate. Red galaxies could have very low star-formation rates, or contain a significant amount of dust, blocking the escaping blue light.
With this in mind, we decided to follow-up that paper with panchromatic data from UV and infra-red wavelengths. UV wavelengths bluer than optical probe the very youngest stars, and infra-red wavelengths redder than optical measure dust emission directly. Combining these measurements allowed us to show the following things:
- Star-formation rate does not depend on spiral arm number: all spiral galaxies seem to be forming the same number of stars, regardless of what their spiral arms look like.
- The amount of blue light being absorbed by dust is significantly greater in two-armed spiral galaxies.
These two striking results have now shown us that spiral arms are not simply a visual pattern. They act to change the conditions of star-formation in local galaxies, making them much more sensitive to dust. Interested readers can find the full paper here.
I’m delighted to announce the launch of “Galaxy Zoo: 3D” today – this is a small project from a subset of the Galaxy Zoo team where we ask you to help us identify in detail the locations of internal structures seen in a sample of about 30,000 galaxies.
What’s special about these galaxies is that they have been selected to potentially be observed (or in some cases have already been observed) by the “MaNGA” project.
MaNGA (which stands for “Mapping Nearby Galaxies at Apache Point Observatory” – sorry about that!), is a spectroscopic mapping survey that I have been working with for the last several years. This one of the current surveys which form part of the 4th generation of Sloan Digital Sky Surveys.
SDSS retired its camera in 2012 (its in the basement of the Smithsonian Museum in Washington, D.C!), and is now focusing on measuring spectra of things in space. Instead of taking images of galaxies in just a couple of filters, MaNGA takes spectral images – each of up to hundreds of points in the galaxy has a full spectrum measured, which means we can decode the types of stars and gas found in that part of the galaxy. We can also recover the motions of the stars and gas in the galaxy making use of the Doppler shift (the redshift or blue shift we see in light which comes from moving sources).
MaNGA will ultimately do this for about 10,000 of the total list (this is how many we can manage in 6 years of operations), and since 2015 has already measured these data for a bit more than 3000 galaxies. To help us interpret this vast quantity of data we’re asking you to draw on the galaxies to mark the locations of spiral arms and bars. We also want to double check the galaxy centres are recorded correctly, and that we have found all the foreground stars which might be getting in the way of the galaxy.
Now one thing you know all about as Galaxy Zoo volunteers is the benefit of human eyes on large samples of galaxies. When we first launched Galaxy Zoo we made use of the “Main Galaxy Sample” from the Sloan Digital Sky Survey as the input list of galaxies. This is a sample of 1 million galaxies automatically identified from the SDSS images, and which had their distances (redshifts) measured in SDSS-I/II. However (perhaps ironically) the algorithm which selected this sample wasn’t very good at finding the biggest most nearby galaxies. Specifically it tended to “shred” them into what it thought were multiple galaxies. My favourite demonstration of this is the Pinwheel galaxy (M101), which the first SDSS galaxy detection algorithm interpreted as a cluster of galaxies.
(Don’t worry – ever resourceful, astronomers have made plenty of use of these galaxies which have multiple spectra measured – it turns out to be really useful).
By the time MaNGA came along this problem was well known, and instead of making use of the standard SDSS galaxy catalogues, MaNGA targeted nearby galaxies by making use of the “NASA Sloan Atlas“- a NASA funded project to make a more careful list of nearby bright galaxies in the SDSS images.
So what we discovered when putting together the sample for Galaxy Zoo: 3D is that not all MaNGA galaxies have Galaxy Zoo classifications. In fact about 10% are missing, and we also found some more galaxies we missed first time round. It turns out that by relying on automatic galaxy finding there were a quite a few galaxies which had been missed before.
So these are back in the main site right now.
In Galaxy Zoo: 3D we will only ask you to draw spiral arms on galaxies you have previously said have spiral arms, so we’ll be making use of the new classifications to sort out the last 10% of MaNGA galaxies. We’ll also create a complete Galaxy Zoo classification list for the MaNGA sample, which will be really useful for people working with that sample.
To tempt you to give it a go, here are some interesting and beautiful MaNGA galaxies being discussed in Talk by our beta testers (the purple hexagon indicates the part of the galaxy where MaNGA can measure spectra). More than half of all the galaxies in MaNGA them are nearby galaxies with lots of structure. I think you’re really going to enjoy exploring them, and at the same time really help us learn a lot about galaxies.
Hi everyone, it’s Mel and Hugh from Minnesota, and we (especially Mel) would like to give a big THANK YOU for all of your help classifying these last couple of months! When we originally launched the second Ferengi set , it was estimated that it would take four months for the data to be complete, based on the current classification rates. Thanks to your help, that time was cut in half, and Mel’s thesis is officially saved! (Stay tuned this Spring for updates on how Mel is using these classifications to study morphological transformations of Hubble galaxies from 6 billion years ago to today.)
Now that those are complete, we have another announcement…
Illustris is back!
This week Galaxy Zoo volunteers may notice the appearance of simulated galaxy images produced by the Illustris project.
Illustris is one of several large-scale cosmological simulations that play a key role in helping us to understand how galaxies formed and how the Universe and its contents have evolved throughout cosmic history.
Hello Zooniverse citizen scientists! We’re extremely excited to announce the release of a new dataset on Galaxy Zoo. For the past several months we’ve been working with scientific collaborators from the Galaxy And Mass Assembly Survey and the VST Kilo-Degree Survey. This blog post will give you a few details about these surveys, the new data set, and what we hope to achieve with Galaxy Zoo classifications.
The Galaxy And Mass Assembly (GAMA) Survey is an international project to exploit the latest generation of ground and space-based survey facilities. Its aim is to study cosmology and galaxy formation and evolution from scales of thousands up to millions of light years across. The science goals include furthering our understanding of how the mass of stars within galaxies builds up over time, how and when do galaxies form their stars, how are those previous questions related to a galaxy’s environment, and at what epoch did star-formation and mass-build-up dominate? Visual morphologies from Galaxy Zoo will allow us to explore if, how, when, and where galaxies transition from one type into another, what impact this has on the formation of stars, and to look for new types of unique and interesting galaxies.
The observations are from the Kilo-Degree Survey (KiDS) on the 2.6m VLT Survey Telescope (VST) located at the ESO Paranal Observatory in Chile. KiDS is a large optical imaging survey in the Southern sky designed to tackle some of the most fundamental questions of cosmology and galaxy formation of today. At the heart of KiDS lies the 300 million pixel camera OmegaCAM. Its instantaneous field of view is a full square degree and it was designed to provide extremely accurate measurements of the intrinsic shapes of faint, small galaxies.
The 2.6m VLT Survey Telescope (VST), located at the ESO Paranal Observatory in Chile, is carrying out observations for the Kilo-Degree Survey (KiDS).
The scientific teams behind GAMA and KiDS have been working closely to put together this new set of images. Galaxies have been selected from a catalogue produced by the GAMA Survey and images have been constructed based on observations from KiDS. While some of these galaxies have already been looked at by Galaxy Zoo citizen scientists before using their Sloan Digital Sky Survey (SDSS) images, the improvement in the resolution and depth of KiDS images over SDSS imaging is remarkable. With this new GAMA-KiDS data set we hope to be able to study the very faintest structures within galaxies, as well as more accurately classify features which may have been missed before. Take a look at the image below to see how much clearer the new images are!
This image compares SDSS images (on the left) with those from GAMA-KiDS (right) for three example galaxies: G107214, G298570 and G551505. Our new images reveal a lot more detail!
We’re really excited about getting classifications for these new images, and we hope you are too! We’re more than happy to talk about any interesting galaxies you may come across and to answer any questions you may have. Until then, enjoy, and thank you for your help!
– by Dr Lee Kelvin, on behalf of the GAMA and KiDS teams
Hi everyone! I’m James and I’ve joined the RGZ team as a Communication/Engagement intern. I’m a PhD Candidate at the Australian National Centre for the Public Awareness of Science (CPAS) which is part of the Australian National University (ANU). I’m also a Sessional Academic (read: Tutor and marker) for a couple undergraduate courses covering things from ‘the Public Awareness of Science’ to ‘Science, Risk and Ethics’. And to pay the bills I work for the ANU in an administration role at (essentially) the Business School as well as a few other odd jobs.
But I am at heart an errant astronomer – having double majored in Astronomy/Astrophysics and Science Communications at the ANU for my B.Sci, graduating with Honours in 2015. I grew up in Alice Springs in the middle of Australia and had a purely spectacular night sky to look at. Something I only appreciated when I lived Brazil after graduating high school.
As part of my undergraduate studies I did dabbled a bit in some astronomy research. Firstly I did a project with Dr Charley Lineweaver (if you don’t know Charley, you should!) looking at the (surprisingly fuzzy) distinctions we make between objects in space e.g. planet, dwarf-planet, asteroid, moon. Let’s just say the project didn’t go where I thought it would.
Secondly, as part of an Astronomy Winter School I did research looking for ‘intergalactic stellar bridges’. Essentially chains of stars going from one galaxy to another which may have played a role in stellar formation in galaxies. I think. It was several years ago and the weather was against us when we went to do observations, so it didn’t go anywhere and my memory is pretty fuzzy on the details.
Outside of academia, I was involved in the ANU Black Hole Society (the Astronomy Club), the ANU Physics Society and the Science Communication Society. Also I absolutely love the TV series Cosmos, both the Carl Sagan original which I saw as a teenager and then the Neil deGrasse Tyson remake from a few years ago.
Since my astronomy research didn’t turn out particularly well, I ended up going down the science communication route. I’ve since done research looking into the effects of fictional doctors on young people’s perceptions of healthcare, factors affecting the uptake of vaccinations in Australia and the relationship between people’s perceptions of ‘Superfoods’ and their health behaviours. But I do miss the Astronomy and Astrophysics side of things so I’m super excited to be able to combine my two interests as part of the Radio Galaxy Zoo team.
(Also for some random fun facts about me – I used to host a music program on a Canberra community radio station, I founded the Canberra pop-culture festival ‘GAMMA.CON’ which is basically our local Comic-Con and I fly Hot Air Balloons with the ACT branch of the Scout Association.)
I’ll be hanging around in the forums under the name ‘JRAnsell’ and am keen to hear from you – if you’ve got questions about RGZ specifically or astronomy more broadly let me know! You can also hit me up on Twitter @radiogalaxyzoo or at firstname.lastname@example.org.
This end-of-year post is written by Jean Tate, an RGZ citizen scientist and associate science team member who is providing us with the 2016 update on her team’s hunt for more Spiral Double Radio-lobe AGNs — SDRAGNs. My warmest congratulations again to the SDRAGN Team! I will be sure to look out for more SDRAGN news in 2017. More information can be found at the SDRAGN team’s RadioTalk Discussion thread.
A small band of intrepid scientists – citizen and regular – have been hunting SDRAGNs for quite some time now. These strange beasts were mythical, until 1998 when one was spotted above the Antipodes (it goes by the highly memorable name of 0313-192 … not). Since then a dozen or so other Spiral galaxies which host Double Radio lobes (and which have Active Galactic Nuclei; SDRAGN, get it?) have been bagged. With thousands of sharp-eyed citizen scientists, RGZ is an ideal place to look for more.
It has been relatively easy to find SDRAGN candidates – two known ones were flagged by RGZooites, who were quite unaware of their status – but rather more challenging to turn candidates into certainties; for example, chance alignments can appear very convincing. Anyway, from ~a thousand “possibles”, the SDRAGN team picked ten really promising ones, and is now writing up a paper on them (actually, while doing some final checks, two of the ten turned out to be imposters; never mind, there are dozens more good candidates for a second paper). Curiously, one of the most difficult questions was (and still is) “is this really a spiral?”
The figure above shows J1649+26, an SDRAGN published by Minnie M. in 2015 (URL Link to her paper). The red contours represent the double radio lobes emanating from the supermassive black hole of this galaxy.
You can see some of the SDRAGN candidates in RGZ Talk, by searching for the hashtag #SDRAGN (some will also have the hashtag #spiral; many candidates do not have either hashtag). If you find an SDRAGN candidate, please include the #SDRAGN hashtag in your comment.
Hey volunteers! This is Mel G from the Minnesota science team, and I’m excited to announce the launch of the second set of FERENGI images on Galaxy Zoo today!
Some of you may remember classifying the first batch of FERENGI images back in 2013. For new volunteers, or experienced volunteers who need a refresher, FERENGI is a code that takes an image of a nearby galaxy and produces a new, simulated image of what that galaxy would look like if it was actually much farther away. 288 galaxies that were already classified by Galaxy Zoo volunteers were selected to be “ferengified” in that first sample; from these, 6,624 images were created of these galaxies at different distances and brightnesses. With your help, all images were classified and used to measure how distance affects classifications, which enabled us to debias and finally release the Galaxy Zoo: Hubble catalog just last month!
Recently, I found another use for these FERENGI classifications as I worked on my project on red disk galaxies, which will go into my PhD thesis to be completed this summer (coming up soon, yikes!). For this project I’ve been using data from Galaxy Zoo: Hubble to study the transformation of star-forming disk galaxies into non star-forming (aka “dead”) ellipticals between now and 6 billion years ago. Part of this research involves tracking the galaxy colors over time, which are indicators of whether the galaxies are still forming stars or not. A common way to distinguish star-forming galaxies from dead galaxies is to use a color-color diagram (this blog post goes into the details quite well, for the interested!). The short version is that galaxies in the upper-left of this plot, the “red sequence”, are no longer forming stars, and the lower-right portion, the “blue cloud”, are still producing lots of new stars. Typically the blue cloud is full of disk galaxies and the red sequence is full of ellipticals, but that statement is not 100% true; there are actually quite a few disk galaxies mixed in with ellipticals up in the red sequence. We think these might represent a “transition” stage between blue/active disks and red/passive ellipticals, and studying how this population evolves with time will tell us more about how the shutting down of star formation is related to the morphological transformation.
What does this have to do with FERENGI? Well, detecting disk galaxies at high redshift is pretty hard – as we learned during the data reduction of the GZH catalog. Using raw Galaxy Zoo classifications, disks tend to be classified very similarly to ellipticals if they are very far away, so the number of disks we count is probably smaller than the true value. Using the FERENGI data, however, we can predict how many disks we should be detecting as a function of distance, and use that information to adjust the numbers of disks we count in the real Hubble data! The catch is that since galaxies with different colors tend to look a little different on average, it’s important to measure this incompleteness for both the red sequence and the blue cloud galaxies. Here comes the problem: in the original FERENGI sample,only 44 of the 300 galaxies have color data, leaving only 9 red sequence and 36 blue cloud galaxies to study. Unfortunately those numbers are too small to get a good measurement!
So, as a sequel to the original FERENGI project, and as motivation to take proper measurements of red disks over time for my thesis, I’ve created FERENGI-2: a new set of FERENGI images from 936 galaxies. Each has been ferengified to 8 different distances, producing a total of 7,488 images that I need your help classifying. As you can see in the color-color plots here, these classifications will allow me to measure incompleteness for 388 galaxies in the red sequence (previously only 9) and 548 galaxies in the blue cloud (previously 36). This increase in data is huge, and will help not only the completion of my thesis, but many future projects that benefit from debiasing of Hubble data. Thanks again for your help!
We submitted the Galaxy Zoo CANDELS paper in May. Now, after some discussion with a very helpful referee, the paper is accepted! I hope our volunteers are as thrilled as I was to get the news. It happened within days of the Galaxy Zoo: Hubble paper acceptance. Hurray!
If you’d like to read the paper, it’s publicly available as a pre-print now and will be published at some point soon in the Monthly Notices of the Royal Astronomical Society. The pre-print version is the accepted version, so it should only differ from the eventual published paper by a tiny bit (I’m sure the proof editor will catch some typos and so on).
The paper may be a little long for a casual read, so here’s an overview:
- We collected 2,149,206 classifications of 52,073 subjects, from 41,552 registered volunteers and 53,714 web browser sessions where the classifier didn’t log in. In the analysis we assumed each of those unique browser sessions was a separate volunteer.
- The raw consensus classifications are definitely useful, but we also weighted the classifications using a combination of “gold standard” data and consensus-based weighting. That is, classifiers were up- or down-weighted according to whether they could tell a galaxy apart from a star most of the time, and then the rest of the weighting proceeded in the same way it has for every other GZ dataset. No surprise: the majority of volunteers are excellent classifiers.
- 6% of the raw classifications were from 86 classifiers who both classified a lot and gave the same answer (usually “star or artifact”) at least 98% of the time, no matter what images they saw. We have some bots, but they’re quite easy to spot.
- Even with a pretty generous definition of what counts as “featured”, less than 15% of galaxies in the relatively young Universe that this data examines have clear signs of features. Most galaxies in the data set are relatively smooth and featureless.
- Galaxy Zoo compares well with visual classifications of the same galaxies done by members of the CANDELS team, despite the fact that the comparison is sometimes hard because the questions they asked weren’t the same as what we did. This is, of course, a classic problem when comparing data sets of any kind: to some extent it’s always apples-vs-oranges, and the devil is in the details.
- By combining Galaxy Zoo classifications with multi-wavelength light profile fitting — where we fit a 2D equation to the distribution of light in a galaxy, the properties of which correlate pretty well with whether a galaxy has a strong disk component — we’ve identified a population of likely disk-dominated galaxies that also completely lack the features that are common in disk galaxies in the nearby, more evolved Universe. These disks don’t have spiral arms, they don’t have bars, they don’t have clumps. They’re smooth, but they are disks, not ellipticals. They tend to be a bit more compact than disk galaxies that do have features, even though they’re at the same luminosities. They’re also hard to identify using color alone (which echoes what we’ve seen in past Galaxy Zoo studies of various different kinds of galaxies). You really need both kinds of morphological information to reliably find these.
- The data is available for download for those who would like to study it: data.galaxyzoo.org.
With the data releases of Galaxy Zoo: Hubble and Galaxy Zoo CANDELS added to the existing Galaxy Zoo releases, your combined classifications of over a million galaxies near and far are now public. We’ve already done some science together with these classifications, but there’s so much more to do. Thanks again for enabling us to learn about the Universe. This wouldn’t have been possible without you.
I’m incredibly happy to report that the main paper for the Galaxy Zoo: Hubble project has just been accepted to the Monthly Notices of the Royal Astronomical Society! It’s been a long road for the project, but we’ve finally reached a major milestone. It’s due to the efforts of many, including the scientists who designed the interface and processed the initial images, the web developers who managed our technology and databases, more than 80,000 volunteers who spent time classifying galaxies and discussing them on the message boards, and the distributed GZ science team who have been steadily working on analyzing images, calibrating data, and writing the paper.
The preprint for the Galaxy Zoo: Hubble paper is available here. The release of GZH also syncs up with the publication of the Galaxy Zoo: CANDELS catalog, led by Brooke Simmons; she’ll have a blog post up later today, and the GZC paper is also available as a preprint.
Galaxy Zoo: Hubble began in 2010; it was the first work of GZ to move beyond the images taken with the Sloan Digital Sky Survey (SDSS). We were motivated by the need to study the evolution and formation of galaxies billions of years ago, in the early days of the Universe. While SDSS is an amazing telescope, it doesn’t have the sensitivity or resolution to make a quality image of a typical galaxy beyond a redshift of about z=0.4 (distances of a few billion parsecs). Instead, we used images from the Hubble Space Telescope, the flagship and workhorse telescope of NASA for the past two decades, and asked volunteers to help us classify the shapes of galaxies in several of Hubble’s largest and deepest surveys. After more than two years of work, the initial set of GZH classifications were finished in 2012 and the site moved on to other datasets, including CANDELS, UKIDSS, and Illustris.
So why has it taken several years to finish the analysis and publication of the data? The reduction of the GZH data ended up being more complicated and difficult than we’d originally anticipated. One key difference lies in our approach to a technique we call debiasing; these refer to sets of corrections made to the raw data supplied by the volunteers. There’s a known effect where galaxies that are less bright and/or further away will appear dimmer and/or smaller in the images which are being classified. This skews the data, making it appear that there are more elliptical/smooth galaxies than truly exist in the Universe. With SDSS images, we dealt with this by assuming that the nearest galaxies were reliably measured, and then deriving corrections which we applied to the rest of the sample.
In Galaxy Zoo: Hubble, we didn’t have that option available. The problem is that there are two separate effects in the data that affect morphological classification. The first is the debiasing issue just mentioned above; however, there’s also a genuine change in the populations of galaxies between, say, 6 billion years ago and the present day. Galaxies in the earlier epochs of the Universe were more likely to have clumpy substructures and less likely to have very well-settled spiral disks with features like bars. So if we just tried to correct for the debiasing effect based on local galaxies, we would have explicitly removed any of the real changes in the population over cosmic time. Since those trends are exactly what we want to study, we needed another approach.
Our solution ended up bringing in another set of data to serve as the calibration. Volunteers who have classified on the current version of the site may remember classifying the “FERENGI” sample. These were images of real galaxies that we processed with computer codes to make them look like they were at a variety of distances. The classifications for these images, which were completed in late 2013, gave us the solution to the first effect; we were able to model the relationship between distance to the galaxy and the likelihood of detecting features, and then applied a correction based on that relationship to the real GZH data.
The new GZH data is similar in format and structure to the data release from GZ2. The main product is a very large data table (113,705 rows by 172 columns) that researchers can slice and dice to study specific groups of galaxies with morphological measurements. We’re also releasing data from several related image sets, including experiments on fading and swapping colors in images, the effect of bright active galactic nuclei (AGN), different exposure depths, and even a low-redshift set of SDSS Stripe 82 galaxies classified with the new decision tree. All of the data will be published in electronic tables along with the paper, and are also downloadable from data.galaxyzoo.org. Our reduction and analysis code is available as a public Github repository.
The science team has already published two papers based on preliminary Galaxy Zoo: Hubble data. This included a paper led by Edmond Cheung (UCSC/Kavli IPMU) that concluded that there is no evidence connecting galactic bars and AGN over a range of redshifts out to z = 1.0. Tom Melvin (U. Portsmouth) carefully examined the overall bar fraction in disks using COSMOS data, measuring a strong decrease in bar fraction going back to galaxies 7.8 billion years ago. We’re now excited to continue new research areas, including a project led by Melanie Galloway (U. Minnesota) on the evolution of red disk galaxies over cosmic time. We hope GZH will enable a lot more science very soon from both our team and external researchers, now that the data are publicly released.
A massive “thank you” again to everyone who’s helped with this project. Galaxy Zoo has made some amazing discoveries with your help in the past eight years, and now that two new unique sets of data are openly available, we’re looking forward to many more.
The Universe is pretty huge, and to understand it we need to collect vast amounts of data. The Hubble Telescope is just one of many telescopes collecting data from the Universe. Hubble alone produces 17.5 GB of raw science data each week. That means since its launch to low earth orbit in April 1990, it’s collected roughly a block of data equivalent in size to 6 million mp3 songs! With the launch of NASA’s James Webb Telescope just around the corner – (a tennis court sized space telescope!), the amount of raw data we can collect from the Universe is going to escalate dramatically. In order to decipher what this data is telling us about the Universe we need to use sophisticated statistical techniques. In this post I want to talk a bit about a particular technique I’ve been using called a Markov-Chain-Monte-Carlo (MCMC) simulation to learn about galaxy evolution.
Before we dive in into the statistics let me try and explain what I’m trying to figure out. We can model galaxy evolution by looking at a galaxy’s star formation rate (SFR) over time. Basically we want know to how fast a particular galaxy is making stars at any given time. Typically, a galaxy has an initial constant high SFR then at a time called t quench (tq) it’s SFR decreases exponentially which is characterised by a number called tau. Small tau means the galaxy stops forming stars, or is quenched, more rapidly. So overall for each galaxy we need to determine two numbers tq and tau to figure out how it evolved. Figure 1 shows what this model looks like.
Figure 1: Model of a single galaxy’s SFR over time. Showing an initial high constant SFR, follow by a exponential quench at tq.
To calculate these two numbers, tq and tau, we look at the colour of the galaxy, specifically the UVJ colour I mentioned in my last post. We then compare this to a predicted colour of a galaxy for a specific value of tq and tau. The problem is that there are many different combinations of tq and tau, how to we find the best match for a galaxy? We use a MCMC simulation to do this.
The first MC – Markov-Chain – just means an efficient random walk. We send “walkers” to have a look around for a good tq and tau, but the direction we send them to walk at each step depends on how good the tq and tau they are currently at is. The upshot of this is we quickly home in on a good value of tq and tau. The second MC – Monte Carlo – just picks out random values of tq and tau and tests how good they are by comparing the UVJ colours and our SFR model. Figure 2 shows a gif of a MCMC simulation of a single galaxy. The histograms shows the positions of the walkers searching the tq and tau space, and the blue crosshair shows the best fit value of tq and tau at every step. You can see the walkers homing in and settling down on the best value of tq and tau. I ran this simulation by running a modified version of the starpy code.
Figure 2: MCMC simulation for a single galaxy, pictured in the top right corner. Main plot shows density of walkers. Marginal histograms show 1D projections of walker densities. Blue crosshair shows best fit values of tq and tau at each step.
The maths that underpins this simulation is called Bayesian Statistics, and it’s quite a novel way of thinking about parameters and data. The main difference is that instead of treating unknown parameters as fixed quantities with associated error, they are treated as random variables described by probability distributions. It’s quite a powerful way of looking at the Universe! I’ve left all of the gory maths detail about MCMC out but if you’re interested an article by a DPhil student here at Oxford does are really good job of explaining it here.
So how does this all relate to galaxy morphology, and Galaxy Zoo classifications? I’m currently running the MCMC simulation showing in Figure 2 over the all the galaxies in the COSMOS survey. This is really cool because apart from getting to play with the University of Oxford’s super computer (544 cores!), I can use galaxy zoo morphology to see if the SFR of a galaxy over time is dependent on the galaxy’s shape, and overall learn what the vast amount of data I have says about galaxy evolution.