It’s been a while, but we are very pleased to be able to write to you today with the results of the first Space Warps project launched in May 2013. We asked for your help with finding lenses which may have been missed by robotic searches in the Canada France Hawaii Telescope Legacy Survey (CFHTLS) imaging. We have now combined and analysed all your classifications, and carefully sifted through the results. It’s good news: in addition to finding 80 previously published candidate gravitational lenses, you helped discover 29 new candidate gravitational lenses (and another 30 objects that might turn out be lenses). Nice work, people!
We just posted two research papers on the “arxiv” pre-print server (where astronomers put their work for their colleagues to read), showing our results from our first project. You can check out the two papers here and here. The first paper is about how well “the system” (that’s you!) performed, in terms of spotting the sims and rejecting the duds. The second paper is about the new lens candidates that you found – and how they compare with the “known lenses” that two robots had previously found.
We’ll be submitting these papers to an astronomy journal (Monthly Notices of the Royal Astronomical Society) in a couple of weeks, so if you have comments or questions about either paper, you can post them as “issues” on the Space Warps GitHub repository, and we’ll work them into the text in the meantime. Thanks!
So, what do we mean by “candidates” that “might turn out to be lenses”? A few of the objects you found are clearly gravitational lenses – we can tell just by looking at them. For those that are less obvious, making a model that reproduces the image configurations seen can give us more confidence. While we were working on putting the sample together, Rafael Kueng and Prasenjit Saha wrote up a test of their web-based lens modeling software, which some of you took part in – you can read their paper on the arxiv too!
Here is an example of what astronomers can do with the candidate lenses that you discover – Since you found this amazing red ring (called 9io9 based on your popular choice) in the VICS82 project last January, Jim Geach (co-PI of VICS82) and his team have been busy making more observations with a variety of telescopes. They recently finished analyzing the data. Jim, Aprajita and others helped confirm that 9io9 is indeed a lens with spectroscopy, and Anupreeta and Neal Jackson (University of Manchester) made a well-constrained mass model of the system to understand how magnified is the red galaxy in the background. This red lensed galaxy turns out to be pretty interesting – check out the paper to find out more.
We’ll write more on all this good stuff in the coming weeks. If you like, ask us a question in the comments below, and we’ll try and answer them as we go :-)
Thanks for all your classifications, these are very exciting results!
Phil, Aprajita, and Anupreeta
Apologies for the long radio silence – we’ve been hunkered down analyzing your classifications and writing up the results as a number of research publications. We’re currently in the process of posting two papers from the CFHTLS project, but one (that describes the system) is stuck in the works for being too massive. (Insert gravitational lensing joke here.)
So, we’ll post a complete overview of all our recent progress tomorrow, but for now, here’s a trailer: check out the CFHTLS results paper. New lenses!
Having recovered somewhat from the madness that was BBC Stargazing Live, the SpaceWarps team have been continuing to work through the marvelous data supplied by warp hunters since the relaunch of the project. As we’ve said before, there are lots of good candidates, but much of the attention has continued to be on the object we featured on the program.
Observatories have continued to be generous with their time, and the team are particularly excited by this image. It may not look like much, but this is a picture taken with the James Clark Maxwell Telescope’s SCUBA2 camera. This is interesting because it fills a gap in our wavelength coverage of the object, which allows us to continue pinning down exactly what type of galaxy our lens hunters have captured. On a personal note, having spent a lot of my PhD there the JCMT is my favourite telescope, so it’s great to see it getting involved in this follow-up campaign.
Since we were featured on the BBC’s Stargazing Live programme on Tuesday evening the project has been manic. We’ve now had almost 6 million classifications of images from tens of thousands of people. The team have been furiously working to extract your candidates from the data to be able to share them – live on the BBC – tonight.
As part of these efforts we have convinced several telescopes around the world to try and point at one particularly lovely candidate lens to see what we can learn in time for tonight’s show. Last night, Chris Lintott and Robert Simpson (who are at Jodrell for the show) went outside to capture the moment that the gigantic Lovell dish turned to look at your lens candidate.
Eventually the telescope did move and here is an animated GIF of it slewing toward a Space Warps source. We will report with more detail when we have it, but it’s going to be right up to the wire, so we hope you’ll be able to watch the show tonight and see the results.
Happy New Year everyone! We’re starting 2014 off with a bang, with a brand new dataset, and hopefully a whole new army of spotters who’ll have heard about Space Warps from the BBC Stargazing Live programmes. Welcome to Space Warps, you guys :-)
So how about this new data then? Here’s an example gravitational lens from the VICS82 infrared survey – and here’s PI Jim Geach of the University of Hertfordshire to explain the survey.
Jim says: VICS82 stands for “VISTA-CFHT Stripe 82”, and is the largest near-infrared imaging survey of its kind, mapping nearly 200 square degrees of the Sloan Digital Sky Survey ‘Stripe 82’ – a narrow strip of sky that is the deepest part of the SDSS. VICS82 is using two 4-m class telescopes fitted with large-format near-infrared cameras: the Canada-France-Hawaii Telescope (atop Mauna Kea in Hawaii) and the VISTA survey telescope in the Chilean Atacama.
Over the last few years, Stripe 82 has received much attention from a wide range of different telescopes, covering the millimetre and radio bands, through the optical and infrared and (soon) high-energy x-rays. Its location along the celestial equator makes the Stripe a great target for facilities in both the northern and southern hemispheres – in fact, it’s shaping up into the first of a new generation of very large and deep extragalactic survey fields. Previously there has been a compromise between survey depth and sky area that has limited the size of the fields we can observe if we want to study the distant Universe (you can go very deep and therefore see very far, but only over very small patches of sky – like the Hubble Deep Field). But with ever-improving sensitivity and mapping capabilities in instrumentation right across the electromagnetic spectrum we’re now able to map much larger areas to much deeper depths than every before. While certainly not as deep as the HDF, VICS82 is the deepest near-infrared survey that exists for the size of the sky it has imaged, and can see normal galaxies out to a redshift of about 1 or so, when the Universe was roughly half its present age. It can see quasars out to much higher redshifts – these objects shine like beacons across the Cosmos.
So, one of the main goals of VICS82 is to survey a huge volume of the Universe to detect a mixture of massive and passive (ie, not star-forming) galaxies, and also dusty and actively star-forming galaxies and quasars. While VICS82 uses wavebands that complement the existing SDSS imaging (improving photometric redshifts for example), some of the objects detected by VICS82 are expected to be very faint or even invisible in the current optical imaging of the Stripe, showing up only at the longer wavelengths (1-2 microns) probed by VICS82. We call these systems ‘red’, and they are very important to consider in our census of galaxies if we are to properly piece-together the story of galaxy evolution.
With SpaceWarps we hope to identify examples of rare ‘red arcs’ which might be very distant, highly reddened galaxies lensed by a foreground mass like a group or cluster. If these galaxies contain lots of dust, their visible light might be completely extinguished internally, and so would not be detectable at shorter wavelengths, but the infrared photons can more easily escape. If we can find even a few of these systems, then the possibilities for detailed follow-up work are tremendous, since – when armed with a model of the lensing mass – we can really dissect the galaxy, exploring its inner workings in a way that is simply impossible without the benefit of lensing.
What we’ve done is select about 40,000 images from the survey, that each contain either a possible lens (ie a massive galaxy or group of galaxies), or a possible quasar source. The images are a little fuzzy, because the night sky is so bright in the infrared – this makes it quite difficult for computers to detect the faint lensed features. Sounds like a job for Space Warps! Good hunting, and thanks for all your contributions – see you on Talk!
Well, Space Warps Refine has been running for just over a week, and it’s had a fantastic response from you all. THANK YOU! With over 140,000 classifications of the 3679 images, we have very good data on almost all of them – and some exciting new lens candidates are popping out of the pipeline!
Here’s one: a nice example of a small lensing cluster, with a longish gravitational arc. We think this probably wasn’t picked up by the robotic ArcFinder because it has such low surface brightness, and the field is so crowded.
Here’s another good one: a binary lens? The blue arc looks like its composed of three merging images of a small blue background galaxy, strongly lensed by the lower red galaxy. But what’s that yellow object? It’s a little bigger than a star would be, so it’s probably another massive galaxy – and its colour suggests that it’s at a lower redshift than the lens galaxy. If it is in the foreground, then it is lensing both the blue arc, and the red lens! So-called “compound lenses” like these are very interesting: we might be able to learn about the mass of the yellow galaxy as well as the red one. With enough examples of systems like this we might even be able to say something about how fast the Universe is expanding… Tom’s written a paper on this that you might find interesting.
New lenses are not the only things turning up from the Refinement analysis: there are a very small number of false positives sneaking through, but as you might expect, they are pretty convincing imposters! Follow the links in the images’ comments feeds to see the problems with this apparent Einstein Ring, and this nice looking bright arc!
We’ll leave the images on Space Warps Refine up over the holiday period to give you the chance to classify as many of them as you want, and in the New Year we’ll do the final analysis of their probabilities taking all your votes into account. Just as we had hoped for, it looks very much like the outcome will be a short list of very good lens candidates, ranked by probability. An excellent publishable result! When we have the final list, we’ll be taking it to Talk, and starting the process of capturing, with your help, all your investigations of them, including the zoomed in views that show the lens configurations best, and the models that you have been making.
So, it’s been a wonderful first year for Space Warps: a more or less completed first project, and some exciting new lens candidates. Next year, we’ll be back with some new survey data – a new challenge for you.
Thanks very much for all your contributions – we hope you all have a very good holiday season!
Phil, Aprajita and Anupreeta
After a huge effort by all the Space Warps volunteers, who have together contributed over 10 million classifications, we have very nearly finished working through the 431,550 images of the CFHT Legacy Survey. A remarkable achievement!
It looks as though the result of this search will be a sample of just over 3300 gravitational lens candidates. Some of them are lenses that we already know about, from various automated searches, while some of them will be new discoveries. However, most will be “false positives” – objects that look like lenses, but actually are not. How do we go about sorting the wheat from the chaff?
The answer is: take a second look! We are setting up the SW website to enable a new round of classifications, one where we ask you to take a really good look at each image, and use all your lens-spotting experience to assess it – and, crucially, only mark it if you really think you see a gravitational lens. We are trying to refine the sample, to leave us with us a sample of candidates that have a very high probability of being lenses. We’ll always have the larger, complete sample from the first round; what we want now is a pure sample of lens candidates to present to the rest of the astronomical community.
To help you in this task, we’re making a few changes around the site. The first is that we are replacing the old “dud” training images (the ones where know there is no lens) with some more difficult images, that contain example false positives that we have identified. The challenge is not to be fooled by them, and only mark the objects you really think are lenses! Likewise, we’re selecting only the hardest sims to include in Space Warps Refine, to keep you on your toes… Secondly, we’re updating the Spotter’s Guide to include some new types of false positive that you’ve pointed out to us over the last few months: red stars are a good example, that we didn’t include in the original guide. We’re also adding to the Spotter’s Guide a gallery of known lenses for you to browse, to see the kinds of features we’re after. Some of the differences in appearance between gravitational lenses and spiral galaxies, mergers, and so can be quite subtle, so we think the Spotter’s Guide will be even more important in this refinement phase than ever. Likewise, it’s likely that you’ll want to call the Quick Dashboard into action more often than before, as you inspect the candidates. Finally, to make it obvious that the site is set up for the refinement, where more discernment is required, we’re painting it bright orange :-)
It won’t take long for us all to look through the candidates, even when taking more time to make a considered judgement: but it should be fun, since every single image will contain something worth looking at. And we should have the final results from Space Warps very soon after! We’ll send an email out to the community when the reconfigured website is ready to go and the first classification phase is complete, which should be very soon now. In fact, you can help speed us along by making one last classification push :-) Thanks again for all your contributions!
While checking out your lens candidates in Talk, I often found myself wanting to take a closer look at the images – usually to see if I can see a counter-image to the main lensed arc that you’ve flagged. This is not easy, because these counter-images are often fainter, and more central – closer to the lensing object – and this is where the light from the lens galaxy is brightest. In almost all cases though, the lens galaxy (or galaxies) are yellow-red in colour, which means they are bright in the CFHT r, i and z bands, but not so bright in the g-band. Meanwhile, the lensed features are usually blue – so brightest in the g-band. Sometimes it’s useful to be able to look at the different bands’ images individually, while sometimes you just want to be able to change the color contrast and brightness in the composite image. The Space Warps development team have given us a tool for doing exactly this – so in this blog post I thought I’d show you a couple of examples of where I’ve found it useful, and how you can use it yourself.
Here’s a good example: some of the science team have teamed up with a few other spotters to try modeling one of our best candidates, ASW0004dv8 – you might have seen them discussing their progress here. I wondered if we could see a faint counter-image to the big arc on the opposite side of the yellow galaxy causing it – so opened the image in the dashboard, using the button marked “Open In Tools” in the top righthand corner of the object page:
After zooming in (by scrolling) and re-centering (by dragging the image), and then playing around a bit with the “nonlinearity parameters” (which are like brightness and contrast), and the colour scales (to bring out the blues at the expense of the reds), I got the following image:
I reckon I can see a very faint blue counter image, buried in the left-hand bright red lens galaxy, at about 4 o’clock! See what you think – you too can see my dashboard here. Isn’t that cool? You can show someone your dashboard any time, just by posting the link back into Talk, like this:
For me, this is the best thing about this new tool: it allows us first to focus on a particular object in an image, and then show each other what we can see.
Have fun, spotters!
One of the objectives of the Space Warps project was to find gravitational lens candidates that have been missed by previous computer algorithm searches of the CFHT-LS. We are very excited to be able to show you a few of the new objects that have been found by the Space Warps community, and are currently under discussion in Talk. They are what we would consider to be highly probable lens candidates, and have not been found by the automated searches in the CFHT-LS. These are examples of exactly what we were hoping the Space Warps project would find with citizen scientists – so well done everyone!!!
The first of the candidates is ASW0004dv8.
Here, the collective mass of the foreground group of yellow galaxies (primarily the two bright galaxies) is large enough to bend the light of a blue galaxy lying behind forming a beautiful blue arc. There are some fainter and smaller yellow galaxies spread across the field that are also likely to be members of the galaxy group. Such galaxy groups (and the more massive clusters of galaxies) mark the most massive and dense regions of dark matter in the Universe. In contrast, the background blue galaxy that is being lensed into an arc is of much lower mass. It’s blue because it has many younger stars, and only just forming the bulk of its stars.
In contrast to the large blue arc in ASW0004dv8, there are also easy-to-miss, compact arcs like ASW0003wsu. On a quick glance, the nearby bright star might have grabbed your attention sooner than the potential lensed arc. Just to the left of the star in the image below, there’s a nice clear arc next to the yellow lensing galaxy. Other similarly coloured yellow galaxies in this image that may form a galaxy group.
Another interesting system is at the bottom of ASW00047ae.
On initial inspection, this looks to be like the examples above, a blue arc around a yellow galaxy. However, if you look closely at the arc, it’s not as smooth as the blue arcs in the examples above. The lensed blue feature actually comprises three blue emission peaks that are aligned in a “cusp”-like configuration around the primary lensing galaxy. Also there is a neighbouring small galaxy to the right of the primary lens that may also contribute to the gravitational lens. To verify this we would need to make a lens model to try and explain the image features we see. Using custom software, we program in the position, extent and mass of a lens (a single galaxy or group of galaxies), then place a virtual distant blue galaxy behind it, and then try to reproduce the shape and light of the lensed blue arc or features. (We’ll be blogging more about lens modelling in the coming weeks – it’s an important part of the discovery process!)
Like ASW00047ae, the Space Warps Community have found several more multiply imaged examples including very compact ones that are tricky-to-spot . Some good examples of these are
These are very different lenses to the galaxy group lenses above with distinct blue arcs. Rather, in these examples a blue, compact background galaxy or quasar is being lensed by a single foreground galaxy into multiple discrete images. In these cases, the lensing galaxy is the red galaxy at the centre of the zoomed in images above. There are actually 2 red galaxies seen in ASW0004q9e but the galaxy with the blue images around it is the primary lens. As with ASW00047ae, we would need to model the system to see if the second red galaxy had an impact on the lensed images. There are two images of the background blue quasars or compact galaxies in ASW0005mp6 and ASW0004q9e. In ASW0001yqb there are three distinct images of the blue background galaxy, arranged in a circular fashion around the lens. In ASW0001yqb and ASW0004q9e, the blue images of the lensed background galaxy are so bright that they almost overshadow the red light of the lens.
Note the colour of this lensing galaxy is much redder than the yellow colour of the lensing galaxies in the examples of blue arcs shown above. These red lenses must lie at a higher redshift than the yellow galaxy groups, i.e. the strongest emission from this type of galaxy, seen in yellow in ASW0004dv8, moves into the redder bands at higher redshift making the galaxy appear red rather than yellow.
Among all of the candidates shown here, ASW0001yqb is probably the hardest to identify. It is a very compact system and it is not easy to differentiate the three blue images of the background galaxy from the faint, red, central galaxy. In fact, ASW0001yqb is very similar to a lens found by the RingFinder computer algorithm of Raphael Gavazzi et al. seen in ASW0000x1l. ASW0000x1l has been confirmed to be a gravitational lens by the SL2S team but just hasn’t been published yet! The similar ASW0001yqb was not found by RingFinder, this demonstrates how Citizen Scientists have a vital role to play in identifying potential lenses missed by the algorithms and therefore providing key information on how to optimise lens finding algorithms. Raphael is going to help us do a complete cross-check between the RingFinder and Space Warps samples, when we have them.
How did the Space Warps Community collectively classify these lens candidates?
The lens candidates shown here were selected from good lens candidates that are being discussed in Talk.In our last blog post, we described what happens after you have made your clicks, the way we analyse your marks to remove images that don’t contain lenses and select potential lens candidates from the stream.
The following image is the same trajectory plot explained in the “What happens to your markers” blog post. The probability of being a lens is shown increasing from left to right, and the number of classifications increases from top to bottom. Lens candidates, as voted by you when you mark images, appear on the right hand side of the plot as with each mark they aggregate a higher probability of being a lens.
The two magenta lines shown are the tracks for ASW0004dv8 and ASW00047ae that we discussed above. You see both track move to the right hand edge of the plot meaning that you collectively voted that these systems are very good lens candidates!
ASW0004dv8 was classified as a highly probable lens candidates after 9 classifications (the upper magenta track) and ASW00047ae, after 8 views (lower magenta track). That’s excellent, it means that our collective classifications are producing very probable lens candidates after only ~10 views.
We’ll be blogging about more highly probable candidates from the early dataset releases as they come up – and we are putting together a sample of candidates from the collective classification for us all to analyse further.
These first examples demonstrate how well the Space Warps community is doing. Thanks for all your hard work and we’re looking forward to some exciting times ahead!
Once you hit the big “Next” button, you’re moving on to a new image of the deep night sky – but what happens to the marker you just placed? And you may have noticed us in Talk commenting that each image is seen by about ten people – so what happens to all of those markers? In this post we take you inside the Space Warps Analysis Pipeline, where your markers, get interpreted and translated into image classifications and eventually, lens discoveries.
The marker positions are automatically stored in a database which is then copied and sent to the Science Team every morning for analysis. The first problem we have to face at Space Warps is the same one we run into in life every day – namely, that we are only human, and we make mistakes. Lots of them! If we were all perfect, then the Space Warps analysis would be easy, and the CFHTLS project would be done by now. Instead though, we have to allow for mistakes – mistakes that we make when we’ve done hundreds of images tonight already and we’re tired, or mistakes we make because we didn’t realise what we were supposed to be looking for, or mistakes we make – well, you know how it goes. We all make mistakes! And it means that there’s a lot of uncertainty encoded in the Space Warps database.
What we can do to cope with this uncertainty is simply to allow for it. It’s OK to make mistakes at Space Warps! Other people will see the same images and make up for them. What we do is try and understand what each volunteer is good at: Spotting lenses? Or rejecting images that don’t contain lenses? We do this by using the information that we have about how often each volunteer gets images “right”, so that when a new image comes along, we can estimate the probability that they got it “right” that time. This information has to come from the few images where we do actually know “the right answer” – the training images. Each time you classify a training image, the database records whether you spotted the sim or caught the empty image, and the analysis software uses this information to estimate how likely you are to be right about a new, unseen survey image. But this estimation process also introduces uncertainty, which we also have to cope with!
We wrote the analysis software that we use ourselves, specially for Space Warps. It’s called “SWAP”, and is written in a language called python (which hopefully makes it easy for you to read!) Here’s how it works. Every volunteer, when they do their first classification, is assigned a software “agent” whose job it is to interpret its volunteer’s marker placements, and estimate the probability of the image at hand containing a gravitational lens. These “agents” are very simple-minded: in order to make sense of the markers, we’ve programmed them to make a basic assumption: that they can interpret their volunteer’s classification behavior using just two numbers, the probabilities of being right when a lens is present, and of being right when a lens is not present, which they estimate using your results for the training images. The advantage of working with such simple agents is that SWAP runs quickly (easily in time for the next day’s database dump!), and can be easily checked: it’s robust. The whole collaboration of volunteers and their SWAP agents makes up a giant “supervised learning” system: you guys train on the sims, and the agents then try and learn how likely you are to have spotted, or missed, something. And thanks to some mathematical wizardry from Surhud, we also track how likely the agents are to be wrong about their volunteers.
What we find is that the agents have a reasonably wide spread of probabilities: the Space Warps collaboration is fairly diverse! Even so, *everyone* is contributing. To see this we can plot, for each image, its probability of containing a lens, and follow how this probability changes over time as more and more people classify it. You can see one of these “trajectory” plots above: images start out at the top, assigned a “prior” probability of 1 in 5000 (about how often we expect lenses to occur). As they are classified more and more times they drift down the plot, and either to the left (the low probability side) if no markers are placed on them, and to the right (the high probability side) if they get marked. You can see that we do pretty well at rejecting images for not containing lenses! And you can also see that at each step, no image falls straight down: every time you classify an image, its probability is changed in response.
Notice how nearly all of the red “dud” images (where we know there is no lens) end up on the left hand side, along with more than 99% of the survey images. All survey images that end up to the left of the red dashed line get “retired” – withdrawn from the interface and not shown any more. The sims, meanwhile, end up mostly on the right, as they are correctly classified as lenses: at the moment we are only missing about 8% of the sims, and when we look at those images, it does turn out they are the ones containing the sims that are the most difficult to spot. This gives us a lot of confidence that we will end up with a fairly complete sample of real lenses.
Indeed, what do we expect to find based on the classifications you have made so far? We have already been able to retire almost 200,000 images with SWAP, and have identified over 1500 images that you and your agents think contain lenses with over 95% probability. That means that we are almost halfway through, and that we can expect a final sample of a bit more than 3000 lens candidates. Many of these images will turn out not to contain lenses (a substantial fraction will be systems that look very much like lensed systems, but are not actually lenses) – but it’s looking as though we are doing pretty well at filtering out empty images while spotting almost all the visible lenses. Through your classifications, we achieving both of these goals well ahead of our expectations. Please keep classifying and there will be some exciting times ahead!