Why don't we begin? It is a pleasure today to introduce our speaker, Professor Chris Lynn.
Talk to our own sub Department of Astrophysics.
Chris was a an undergraduate at Cambridge University and did his graduate studies at UCL.
The topic of his PhD thesis was essentially the chemistry of sulphur in the context of star formation.
So when I heard this, I couldn't resist putting to him that between the study of brimstone and the observations of the fires of the first star,
was he not in fact the very incarnation of Satan's astronomer self?
Professor Lintott did not deny the fact that he had moved on from his interest in study for us excuse me,
in his interest in star formation turning into an interest in cosmological star formation.
Led him to his current field of interest, which is galaxies.
And Chris became interested in the problem, which we'll hear about today, which is what is the best way to find peculiar morphology in galaxies?
This is something that a computer by itself is not particularly good at.
For all of its speed, because peculiar is hard to quantify.
Human beings can't look through millions of galaxies by themselves unaided.
So the question is, how do you put these two resources together to try and optimise the search using human brains with the aid of computer technology?
And in that respect, he has succeeded brilliantly, establishing a prototype program for not just galaxies,
but citizen science in general and its use in several different fields.
So he is going to tell us today about citizen science, the story of Zooniverse from Galaxy Zoo to ALS City.
And we get to begin with the penguin. Excellent.
Thank you. Cheers. Well, thank you for that introduction.
Satan's astronomer. Sounds beautifully well-paced, doesn't it?
Not sure. What? Not sure what overheads you get on one soul.
But I'm sure finance will let us know before the end of the lecture.
Thank you. It's a delight to be asked to give the colloquium.
I am assuming that I was selected because I speak loudly enough that I can keep you awake on a Friday afternoon.
So I shall try to do that. And there will indeed be more pictures of penguins later in the talk.
This is actually a peculiar galaxy not found by but adopted by some of the volunteers.
But I thought I'd begin by pointing out how modern astronomy is done.
And I was wrong about this for almost all of my life. I'm one of the few, I think, professional astronomers who grew up as amateur astronomers.
I had a small telescope and from an early age I was thrilled with the idea that I might make a discovery.
Ideally, I wanted to discover a comet because that named after discovers Comet Lintott I think has a particularly fine ring,
but I would have taken anything and equipped with a six inch reflector and observing from the light polluted skies of suburban South Devon,
I found I stood a pretty good chance. The closest I ever got was in viewing this thing.
The Nebulosity.
The gas you see at the bottom of this image is part of the Orion Nebula, a great star forming complex that's visible in the winter sky.
And one evening I nudged my telescope, wasn't equipped with a drive, so the thing just moved.
And this star cluster came into view. I looked it up in my star atlas.
This was before we had the Internet at home, so I had to look at a book for the younger members of audience.
With the audience, the book is like the internet, except that you have to turn the pages manually.
And this cluster wasn't in the atlas,
and I remember getting a pencil and marking across and putting Lyn top one next to now I the thing I like about this idea is
that I think my scientific tendencies are clear in the fact that I used a pencil because this is a provisional result and this.
Lintott One turns out to be better known as NGC 1981, though of course it's still the top one.
To me, it's a perfectly ordinary cluster that was discovered in the 19th century, but looks rather nice at a small telescope.
And this I think, was the start of my growing up as an astronomer.
And I realised that the days in which discoveries were made by ordinary people with small telescopes had long.
And telescopes these days look more like this.
This is the Sloan Digital Sky Survey Telescope that if you, of course, now in New Mexico, a place that like Oxford,
has about 300 clear nights a year and to first order and at Sloan is I think is what happens
when you let particle physicists build your astronomy experiment because it's an experiment.
It was designed not as an observatory,
not as a place that you visit to take control of the telescope and point out your favourite targets, but as a data production machine.
And Sloan allowed the sky to turn over it for eight years, measuring the position of more than 300 million objects.
Of those, 8 million of them were identified as faint, fuzzy things, probably galaxies.
And on the clearest and still is nights, Sloan would return to those and use spectroscopy to measure a distance.
So this thing is making a three dimensional map of our local universe,
and it's doing that so that we can make rather crude comparisons to sophisticated cosmological models
because we want to understand the physics that's driven the large scale evolution of the universe.
So, for example, we take our eight years worth of data on how many galaxies and we reduce them to a mass function.
So this is the density of galaxies in the in the Sloan volume of particular stellar masses.
So the dots here are data black from Sloan Blue for other surveys.
And the green and the red are different flavours of large scale cosmological simulations.
And for those who aren't astronomers, this is a good fit to the data.
We consider ourselves well satisfied with this. But we, of course, have all sorts of interesting properties.
You can see that there's a problem about high mass where we over predict the number of large galaxies.
And you could see there's a problem at low mass as well, where while you can take your picture, you can over, over or under predict.
And if you're a theorist, this is, of course, a problem with the observations.
And if you're an observer, then you go away, you redo the model.
But that seems something slightly reductive about reducing galaxies to point particles that trace the underlying evolution of the universe.
You can do that. You could have particles with particular mass, of course, and density and and perhaps a size.
But when you look at these things and these are all galaxies drawn from Sloan, admittedly local ones with roughly the same mass,
you see that there's more information here and we can call it morphology if you want to sound sophisticated.
But the shape of the galaxy encodes its integrated dynamical history.
The shape, as James Binney and others will tell you at great length and with great certitude, great understanding.
And the shape of the galaxy depends on the orbits of the stars.
The orbits of the stars are a measure of the dynamical environments through which the galaxies have passed.
And so if you know the shape of the galaxy, you can say something about how it's interacted with its surroundings,
how it's interacted with other galaxies, and even how and where it stars have formed.
And this is not a new idea. Edwin Hubble was the first to sort of systematically look at this in the thirties,
wrote a book called The Realm of the Nebulae, in which he proposed a system for classifying galaxies for Hubble's tuning fork.
And there's some sense that he saw it as an evolutionary scheme with elliptical galaxies like this one collapsing under gravity to form disks,
which then wound up on wound themselves into various types of spiral.
It's not really clear whether even Hubble believed that. But nonetheless, he knew that the shape of these galaxies was important.
And back then, and throughout the fifties and sixties,
we had few enough high resolution images of galaxies that eminent professors would devote themselves to creating, first of all,
new classification schemes, then classifying all the while image galaxies according to those schemes,
and then going to conferences and arguing about whose scheme was best.
And so they multiplied in both a number, the classification schemes, but also in sophistication.
So this is not just the spiral galaxy, this might be it's a spiral galaxy or let's be or it might be an SB three and so on and so forth and so on.
And that kept people happy doing detailed studies of local galaxies for a long time.
But by the 1980s, surveys had improved. And we had things like the Palomar Sky Survey, which I discovered the other day.
To my surprise, this was the first really good survey of the northern sky to go deep,
and it was able to take deep images of the sky because of a new type of photographic plate.
This is in the 1980s. So this is the last pre-digital survey.
Palomar produced thousands of galaxies that were worth classified and simultaneously the astronomical world in the late.
Discovered that you don't need to be a professor to like Galaxy something grad students can do
perfectly good job just as the number of galaxies that needed to be classified reached thousands.
People started that phrase by classifying a thousand galaxies and using that data.
So this worked very well. But people could see that projects like Sloan were coming and that this would it work.
When you have a million galaxies classified, you need to find new approaches.
And there's an excellent paper by my old supervisor with whom I never discussed Galaxy morphology during my Ph.D. offer Laugh.
He's now at UCL when he does two things. First of all,
he gets a panel of experts to classify a set of galaxies and then shows in this in the footnotes that
you can derive who was who student just from their classifications with no other prior knowledge.
But then secondly, he starts the push towards decent machine learning to attack this problem.
And people do. People like Mick Bull, who is at Sussex and then Nottingham do whole PhDs on neural network approaches to galaxy classification.
And almost anyone who attempts this discovers that 70% of the galaxies are easy.
Getting to 80% takes a lot of effort, and beyond that you're really stuck and your accuracy is important.
And we'd like to have reliable cross-sections for the vast majority of these galaxies.
Or at least we'd like a system that will tell us which galaxies are reliably classified.
The machine learning approaches of the time can't even tell you which 30% are wrong.
When I arrived in Oxford, a new solution had been found, which was to to find students who were more committed to the subject.
I think you particularly if a guy for coverage, Lewinsky, who's now 88.
As a senior fellow there and Kevin had looked at 50,000 galaxies and he'd shown, amongst other things, first of all, that that's the limit.
A student will look at 50,000 galaxies before he tells you where to stuff the rest of them.
I'm paraphrasing so we can think of this as the Kevin Limit and other students approach, but do not exceed the Kevin limit.
And then the other thing is that he showed really that it mattered to have a person look at this.
His consultations with different from those you obtain from your networks.
And when you chased the discrepancies down, you found that Kevin was right, at least according to the experts.
I often joke that the obvious solution is to have 20 Ph.D. students work on this, but even then you still only got one classification per galaxy.
And you're you have this uncomfortable truth that any result you produce depends on these classifications.
You have no way of checking that. And so a broader approach was necessary.
And without really thinking about it, we got some friends to knock together a website for Galaxy Z.
This is what it looked like. It gave you an image of the galaxy and it gave you six buttons are not generic.
It's a elliptical galaxy. We if it's a spiral galaxy, if it's a spiral, we want to know which way the arms are going.
You want to hear that story? Ask me about it. In the questions we ask for mergers.
And then a few stars got through and we put the song online.
Pete Wilson and friends in the University Press Office helped us get some attention from the BBC, and within a day we were doing 1.2.
Kevin Weeks and our own Kevin Week is the unit of Galaxy Classifications and 50,000.
We were doing something like 70,000 classifications an hour.
We haven't kept going at that rate, but Galaxy Series received literally hundreds of millions of classifications of galaxies.
And the impressive thing is that taken collectively, those classifications look very good.
So for each of these galaxies, I not only can sort them into their categories, but I can say so.
This one, it's an elliptical. And I have a measure of confidence because ten out of ten people said it was a little sceptical,
whereas this galaxy is a spiral, but only seven out of ten people say it's a spiral.
So we have this not only a classification but an estimate of accuracy.
That number, that vote fraction is proportional to the probability that this really is a spiral.
And so that data for the first time gives us a really powerful, reliable, consistent set of morphologies.
And we went on Galaxy Zoo originally was designed to do the simplest thing that was useful.
Is it elliptical or is it a spiral these days? Yeah.
The Galaxy Zoo. You will find a complicated decision tree that still requires no prior knowledge,
but which takes you through the detailed is there are bulge, there is what shape of a spiral arms?
If so, how many are there? And so on and so forth. Each piece of information encoding a different part of the galaxies dynamical past.
And you also find that we're no longer restricted to Sloan. We quickly finished classifying all of the Sloan galaxies,
and we've now branched out to most of the large surveys that the Hubble Space Telescope has done.
So we're able to compare local morphology, the population of galaxies we see around us with those that existed, perhaps at a redshift of one.
A few billion years ago also.
And this is I don't have time to talk about this in detail, but for example, if you're interested in whether galaxies have a bar at the centre,
this linear feature you can see in this plot, the grey is slightly poorly conceived simulation work based on a small number of simulations.
But look at the data. This is the fraction of spiral galaxies that have a bar as a function of redshift and in
pink from Tom Melvin in Portsmouth and then in black for Brook Simmons here in Oxford,
we can show this this decline in the fraction of barred galaxies over time.
And this is interesting because various people have predicted there should be no barred galaxies out here,
the disks of galaxies out beyond the redshift of what dynamically hot particularly
in the gas we know that from work done with came on another Oxford type projects.
And so those disks, it's perhaps surprising, maybe it's sustainable.
But you also see this this change in morphology. We're beginning to be able to do serious comparison across a very large redshift,
large enabled by the fact that our classification scheme is the same in both cases, because we have to take into account bias,
we have to deal with the fact that these are distant galaxies and that faint, fuzzy,
distant things tend to look featureless, but we can measure all of that correct for it and get these robust results.
I just want to take a few minutes to I can't give you all the science highlights of galaxies.
There are far too many, but I wanted to highlight one particular story that we've been following here in recent years.
This is a nearby galaxy rejoices in the name NGC 4395 and it's a disk galaxy.
You probably call it a spiral that what it is really is a flatulent galaxy, my favourite kind of galaxy, because it's fun to say the word flatulent.
I recommend you all try it later. We can do it together now if you like to try.
Try it in the privacy of your own homes later. But what you'll notice is that this thing is missing a classic component of a typical spiral galaxy.
Patrick Moore is used to give public talks in which he described the Milky Way as true.
Fried eggs clapped back to back an experiment I don't recommend.
But the point being that you've got the disk and then you have this bulge of older stars at the centre and this is a truly bulge less galaxy.
It's one of two things that are strange about it. One thing that's all there is that it's bulges.
The second thing that's strange is that the black hole at its centre,
it's going to call it a supermassive black hole, but it barely qualifies for that title.
It's only about 300,000 solar masses, about a factor of ten lower than you'd expect for a galaxy of this mass.
So could these things will be connected? Well, yes, there's a nice story here.
So the fact that it's bold to us tells you that this galaxy is guaranteed merger free.
So the simulators and the dynamics tell us that if you have any even minor merger,
maybe something with a something a 10th of the mass of this galaxy comes in and collides with it,
then that inevitably kicks stars out of the disk and into a bulge.
And so a bulge is galaxy is a laboratory for what happens if you let a galaxy evolve in isolation instead of in the presence of multiple mergers.
Now, one can speculate people have made good arguments that it's the merging of different galaxies that
drives both the growth of the galaxy themselves and the growth of the black holes at the centre.
And that this explains the tight relation that we see between the mass of a galaxy and the mass of it's black hole.
So the fact that this is pulseless and has a low black hole fits that theory.
Low mass black hole fits that theory rather well. But it's one galaxy amongst the galaxy do we could do better than that.
We ask the question, how prominent is the central bulge?
We could pick those four where people say there's no bulge or there's just noticeable.
We actually then for this particular study,
restrict ourselves only to those where we're utterly convinced there's no bulge and which have currently growing black holes.
ADRIANNE So these are bulges, galaxies, merger free, which are currently active.
And you can see that because they have point sources at the centre. So these aren't small bulges.
If you do the modelling, these things are just the gas around the accreting black hole.
Now we'll get rid of two of them because those are mergers about to start and then we can look at the properties of these galaxies.
So here they are. This is the black hole mass and this is the bulge stellar mass.
And we can directly measure the black hole mass for two of these systems.
The rest we can put limits on by assuming that they're accreting at the Eddington luminosity, which is hard but not always about limit.
That's a little unfair to these guys.
And if you measure the accretion rate or the luminosity rather from these two and you assume that that's typical of the whole population,
well, these things move and you get a picture that looks like this.
And this is, I think, the least surprising graph I could possibly show.
We've selected galaxies because they're bulge bulges and we find that for a given black hole mass.
Sorry, this black stripe is normal galaxies. From Harring and Rex.
We selected these galaxies because they're faultless. We find that for a given black hole mass, they have smaller bulges than they should do.
This is not an exciting result. But if I plotted instead of the black hole mass.
So it's the bulge mass, the total mass of the galaxy.
These things look normal. So if you if the bulge is taken out, plan, all you care about is the evolution of the galaxy.
It's the first galaxy that I showed that's the anomaly and that we can grow perfectly normal galaxies with normal sized black holes without mergers,
at least in these cases. We have a huge programme of work to try and understand whether these are freaks and unusual in some way
or whether this is a mode of galaxy and black hole growth that we need to care about more generally.
But it starts with morphology. But let's return to the fundamental thing again.
Galaxies. Those are details. You know, we talked about bars and bulges,
but the fundamental morphological difference of the population of galaxies is are they spiral or elliptical?
And all sorts of properties correlate with these two spirals tend to but all that exclusively be star forming which are lots of red spirals.
Ellipticals tend to be old, red and dead, devoid of both the gas and of the new stars that might form from that gas.
Although we have a whole load of blue ellipticals that I could tell you about as well.
But splitting the population into these two systems helps you understand it.
So in the top left here, this is the I like to think of.
This is the Hertz from Russell Diagram of Galaxy Evolution.
It's how we understand these things. So this is mass versus colour.
So blue galaxies are down here, red up here, small galaxies, big galaxies, or if you prefer, less luminous and more luminous galaxies.
And then in the controls you see the two features of this landscape.
We see the blue cloud where normal galaxies spiral galaxies, and the red sequence composed primarily of ellipticals.
It's kind of interesting that the Milky Way is in a strange place on this diagram where green and the green,
but you can see and then what I've plotted in colour here in this slightly making green sorry about
that is the fraction of galaxies at that point in the diagram but have actively growing black holes.
And so you can see if you look to all galaxies and eventually you conclude it was the intermediate mass galaxies where the action was happening.
But if we split into up in the top right elliptical galaxies,
early types you see it's the least massive the in the present day have 18 and if you look at light type spirals you see it's the most massive spirals.
But the picture you use you talk about is different depending on whether you're looking at ellipticals or spirals.
And we could see this in other places as well.
Kevin Quincey of the The Kevin Week wrote a paper last year with the Galaxy Z team and Data which rejoices in the title.
The Green Valley is a red herring. Now there's literature that shows that silly titles don't get more citations,
but this thing has been cited more than 30 times in its first six months, so it must be a very good paper.
Um, and what you can see is a sort of equivalent of the diagram I just been showing instead of mass.
This is colour, we've got colour here blue, red and this is now an ultraviolet colour.
So this is brightest in the ultraviolet fights in the ultraviolet, brightest in the ultraviolet.
And you see once again that we have this nice bimodal distribution.
And the question that Kevin is trying to answer derives from the previous plot.
He's trying to work out how galaxies cross the Green Valley,
this region with relatively few galaxies that lives between the blue cloud of the red sequence.
And in particular, he's interested in the question of how quickly galaxies could cross.
So what he does is he sets up a toy model. He says, okay, these galaxies have constant star formation, right?
And then something quenches star formation by some exponential process and the speed with which quenching can happen is left as a free parameter.
And then then he thinks, okay, let's think about what that would do.
If all your galaxies start blue and then you quench them, the big blue stars start dying off.
You're not replacing them with star formation because of the quenching, and you find they follow these tracks and in a sensible amount of time.
In the 4 billion years since he locates this quenching, you find that if you have a very rapid quench,
they get all the way up here and they're red in optical colour and that red in the ultraviolet as well.
Whereas if you have slow processing, they stay blue. Sometimes we can do good science with very simple models.
And then he says, okay, let's look at where green spirals and green electricals are.
You see that here? These are the green spirals. There are bands fit with this rapid quenching model.
The rapid quenching model leaves you with galaxies that are too red rather than to the green.
And so it's a poor fit for the spirals. So spirals must quench only slowly.
The ellipticals in orange up here can be fit by those fast quenching models, but not by the slow ones.
So ellipticals quench faster.
So to keep in the Green Valley is a red herring because it's just the tails of these two distributions slow quenching spirals,
rapidly quenching ellipticals. Another difference in morphology.
It's a good result. It's an interesting result. It's inspired a lot of work.
But slightly embarrassingly in this paper, we do this by, we look at this diagram, but we say,
Yeah, that's a good fit and that's not really good enough anymore, so do it properly.
You ask a PhD student to do it. Becky Smethurst, who works with me,
has just fitted a full Bayesian MCM model of galaxy quenching to the entire galaxies population using incidentally that fuzzy logic.
The fact that we know that this is a spiral galaxy, but only seven out of ten people said it was a spiral.
So using that to quantify how strong each galaxies evidence is and this is a plot of the time at
which we expect quenching from the present day back to the beginning or near to the Big Bang.
We have a strike. We have strategy and physical models.
But don't let that distract you and then the degree of quenching from slow.
Patrick The first question and the point here is just to point out that for green galaxies, blue galaxies,
red galaxies, for disk galaxies and for smooth galaxies, a wide variety of quenching rates exist.
So both ellipticals and spirals have fast and rapid quenching.
Which brings me to Brian May. Red Star, Resolute, Young quenched.
Even after 30 years. I've only just thought of that. So glad. I thought it would get a laugh, but it hasn't.
So the only thing I could do now is make this sort of a matter joke between me and you as the audience.
So thank you for going with that. I'm Brian.
As many of you know, was a Ph.D. student at Imperial College working on the zodiacal light on dust in the universe, dust in the solar system.
Rather, when his rock career took off, he took a 30 year break, and at that point,
Imperial offered him an honorary degree, which he turned down on the grounds that he wanted to finish his thesis.
And I've been known to say at this point this has been recorded.
So I am not saying that it helps if you want to take a 30 year break, if you work on something so boring,
but it's still there when you come back 30 years later, actually it's some fad, new observational techniques.
And come on, Brian was actually able to go back and do more observing and do a longitudinal study over 30 years of the behaviour,
the darker light which no one else could have done because no one else can fund themselves for 30 years.
And he's got more nature papers than me. But as he was getting back into this idea, he has look it up, I've got more citations for.
That's fine. Anyway, it's true.
I, I at the time he was getting back into this, I was writing a popular book with pride,
and I taught him how to use Astro from the archive as the modern because he set off for the library on his first day back in, back in research.
And so in return he promoted galaxies a creating a permanent bias in the musical taste of our classifiers.
This is a Dutch schoolteacher called Honey Barnacle, who likes bronze music.
She's got the same guitar who discovered this thing. She's become a poster child for the other real advantage of having humans look at your data.
So we've dealt with the fact that sometimes you need to scale to millions of people to cope with the size of modern data sets.
But humans are also distractible, wonderfully distractible.
We have this almost unique ability to be doing a routine task and then be brought up short by the unusual and unexpected.
And this is surprisingly hard to program. You could build anomaly detection routines that get distracted mostly by common anomalies and Galaxy Z.
You find an awful lot of satellite trails,
but to build a classifier capable of doing a good job of classifying galaxies and then stopping and saying not just that this is a spiral galaxy,
but that it's a blue blob here is rather hard. This blue blob, a quite the name of the that we thought it was a technical term.
It turns out to be Dutch for object or thingy, but monthly notices let us print it.
So this is now the official name of this object.
And this was a return to what I thought astronomers did when I was playing around with my small telescope because we found.
And a weird thing. And then we pointed lots of telescopes at it.
And when we did that, we discovered, first of all, that it's at the same redshift as this galaxy.
So it's distant and it's large. We discovered that it's hot.
It's about 50,000 Kelvin. The gas in the vulva.
And it contains no stars, or at least no obi stars.
No bright stars. And so we suspected that this was being heated by a jet, it coming from activity at the nucleus of this galaxy.
So you do see activity in spiral galaxies and we do sometimes see spectacular jets.
Here's a discovery from the end of last year made by another Galaxies user on a version of the project that's looking at radio data.
So the idea was that there would be jets of material that comes out here this cold see either side of the border.
We know that from radio and that this is excited by the impact of the jets on the spectrum of the ball.
VEP is consistent with it being shock heated, which would fit.
The only problem is that when you look at this galaxy with an X-ray telescope, you discover that there's almost no source at all.
So there isn't a luminous enough source, even with ridiculous obscuration by dust,
to have an active and powerful enough heating of this amount of gas right now.
And so the conclusion that we came to was that this the vulva was a light echo of this.
It was telling us about the state of the galaxy 50,000 years ago, because there's a dogleg in here of about 50,000 light years.
And so, in other words, 50,000 light years, 50,000 years ago, if we looked at the system,
we would see a bright again would be magnitude eight in the visible.
You'd be able to see it in binoculars, and it would, in fact be the nearest quasar to the Milky Way.
But in the last 50,000 years, it's shut down and it's gone from active to quiet.
Something that we knew happened, but which was very difficult to catch in the act.
And so this is great fun. It's an interesting story.
We've got a new way of studying the shut down of an AGM because we can read off the history of about 50,000
years worth of the radiation from this black hole by looking at different distances along the vulvar.
But the explanation smelled funny because why would the nearest quasar just shut down?
Unless this happens all the time. And so these are the four values, which is the diminutive of a vulva.
It's amazing what you learn when doing this stuff.
And these are all the blue things you can see in these images are 18 ionised clouds in a variety of slowing galaxies, some a powerful AGM.
But a third of them are not. And so we think a third of these systems have shut down within the last 20 to 100000 years.
And we have a Hubble Space Telescope program to look at them and to try and work out
the geometry of what is actually quite a strange and fascinating set of objects.
The complexity of these images is both astounding and deeply annoying,
because if you've got a really simple geometry, you can read off the history much more easily.
But in step, we've got this mess now. So hopefully by this point, I've convinced you that I think galaxies are interesting.
That's the subtext. But also that getting people involved in the sort of crowdsourcing and what we call citizen science has benefits,
not just because of scale, but because of the potential for serendipity as well.
So the next thing you might wonder is how else you can apply this. This is obviously an interesting method.
It was also free and it also has huge outreach potential.
So what else can we do? First question, Oh, are we going to run out of people?
Is that a serious concern that we should worry about? And then my argument is that it isn't.
Every time you talk about an Internet project,
you have to mention a guy called Clay Shirky who writes annoyingly bestselling books about how the Internet is changing everything.
This is my Clay Shirky slide. The big box shows the 200000000000 hours American adults spent watching television in 2009.
And the small box is 100000000000 hours it took to create Wikipedia.
And that, if you want to be facetious about it, Angry Birds use 16 years of human attention every hour.
It would be much better for science if Angry Birds was the study of avian behaviour than just a game.
So we only need a small, tiny fraction of the attention that's out there to be used for good, to get huge processing power so we can scale this.
We can, if we want to, without harming the progress of astrophysics, have people look at penguins.
So one of the things that happened to me once we started doing this was the other researchers would sidle up to me or call or email and say,
You know, your people do like looking at galaxies. Do you think they'd look at my data as well?
And in most cases, the answer is yes, because the people who looked at galaxies were motivated by the desire to contribute to science.
And so this is an image from a project run out of the Department of Zoology here in Oxford.
They used to visit remote parts of the Antarctic by yacht, and I'm told the yacht is not as luxurious as that makes it sound.
In the same way that when I say I'm going to Hawaii, it doesn't mean I'm going to the beach.
Sometimes I go to the beach. They used to visit Count penguins.
Penguin colonies are sensitive to climate change, so this is interesting.
And they revisit the same colony every year or two.
Now they can buy a cheap camera for 100 quid, leave it out, and it will take pictures every 5 minutes or 5 hours.
And these cameras survive the Antarctic winter.
So now they still got one Ph.D. student, but instead of a notebook filled with penguin counts, they come back with hard drives full of images.
So the task here is to count the number of Penguins game machine learning.
Can't do a very good job of this. Anyone want to tell me what the answer is?
Three three. It depends whether you count integer penguins or not.
I agree. I agree that three is an upper limit, but you'll be amazed how often people check out decimals.
All right, so you're warmed up. So the next one is a speed test.
So I want the first person to count the penguins in the next image. Just call out the answer.
I'm just going to the pub while you work that.
So the point is, this is one of the interesting things about this task is that there's a wide variety of difficulties.
This is a more normal image. And here's the solution. We built a project called Penguin Watch, which lets you count penguins in your spare time.
Surprisingly enough, this has proved rather popular. More than a million penguins have been counted,
and I feel the need to point out that we considered but rejected the name Penguin Hunters for this project,
although I think that would have been still more popular. And we can go from penguins to particle physics.
Not. And obviously it's not funny. Penguins are funny. Particle physics?
No. Yeah. Maybe I need to. Subterranean physics jokes.
This is a project called Higgs. Or this is data from Atlas. Right.
But this is data from Atlas. From a very special source.
So you're seeing a cross-section through the LHC here, of course, a collision and a particle cascade coming from a collision.
Most of the LHC data doesn't hit a hard drive, as I'm sure lots of you know it's safe if it satisfies a series of triggers.
And in the the debt detritus in the waste that doesn't hit those triggers is mostly a lot of routine stuff, some nonsense.
I'm just possibly a few Nobel Prize winning discoveries because anything really unexpected would end up missing the triggers as well.
I think this is a project this is almost the first project that Joe proposed we build.
When I came to talk to him about Galaxy Z, so we finally did it, which is good.
And the point about this is this is here for two reasons. One is it's kind of interesting.
It really gets the sense of what the Atlas data is like. But the other thing is it's a test of how well we understand our community.
The many also volunteers we have on our projects have told us that they do these projects because they want to help science.
This is an incredibly long shot. It's scientifically useful, but this project will almost certainly fact it succeeds.
Then there will be Nobel Prizes. And on the first day that we launched, I got this email from Alan.
Look, if Alan's here. I saw him early. I think he's escaped. And so this was about this image that you just say, Who's messing with us?
We've got a jet of new ones, which is a feature predicted in sum beyond the standard model theories.
But it's never been seen in the wild. I don't remember us put together things like this into the simulation.
Is this real? So I thought in an hour we've.
We've solved this problem. It turns out this is actually just a breakthrough.
These are normal particles that have escaped. It's not a jet of neurones.
We're still looking for Nobel Prizes, but the project is quite popular and I recommend you give it a go.
And you've got the idea by now that we can try this on all sorts of things.
Probably the furthest distance we've travelled from galaxies.
There is a project led by Brooks Simmons. It will also let the pulses galaxy work I talked about.
But this is the Planetary Response Network.
We have a partnership with providers of satellite imagery to react to a disaster like a typhoon hitting a developed area
to provide rapid assessment of the damage and destruction to teams on the ground who need this kind of information.
So we're testing that now if you want to join in. There's a go around.
If you go and test, we're testing it with the historical data because clearly in this circumstance,
you really need to demonstrate that you've got your data analysis right before you deploy the system.
All of these things live in an umbrella site called the Zooniverse.
Word I hope you've heard. I was reminded by Rob Simpson the other day that we have 42 Zooniverse projects have launched.
They all lots of them have dice with the logos can see there's a sort of acceleration and then a fairly steady pace.
This is felt very rapid and you can also detect in this graph and times where we've made changes.
So for example, this cluster of projects were built using software or built in-house that we called Juggernaut.
And Juggernaut was a piece of software that ran one project. So you built a Galaxy Zoo juggernaut and then you use much of the same code,
but you built a lunar juggernaut and then so on and so upon it, hunting juggernaut.
And it turns out that by the time you get to this many projects, you spend all of your time maintaining those pieces of code.
So you throw that out and we build code called a robot us.
Don't worry about that.
And The Robots is one app that runs most of our projects that work well, but we were getting stuck at the limit of about 20 projects.
And so the difference between penguins and planet and penguins and particle physics is small enough that you can further generalise the problem.
So we have a new code called Panopto is Panopto is is a whole set of services that will allow people to build and run their own projects.
So instead of coming to us and having us develop a new project, what we'll do instead is for projects where the interface is simple.
You'll just come along as a scientist, upload your data, choose from some options about what questions you want to ask,
what tasks you want to sign, people to click a button to.
You have your project live on the web where a month or two or three I'm sorry to see which developers are in the room.
They're not here. So we're only about a month away from being able to put this life.
If you have lots of data in the form of images or video or perhaps even sound that it would benefit you to have people look at Please,
please come and talk to us. We need test cases for this stuff and we hope that the Zooniverse will go to hundreds or
thousands of projects because we don't have to worry about yet about running out of people,
because the more projects we do, we could treat each one as a natural experiment.
And we've done easy projects of difficult projects.
We've done projects where it takes 2 seconds to look at an image and projects where it takes 10 minutes.
We've done projects where the images are beautiful and we've done projects where people look at graphs for fun.
Fascinated me. The beauty of the images doesn't seem to matter, it just kind of reassuring,
but backs up this idea that people are doing this because they want to help science.
We also see general patterns emerging. So this is a graph showing the contributors to one particular project.
I think this is one of the incarnations of galaxies in which box is a person.
The colours are meaningless other for the purposes of making it look nice.
And then you can see we get a huge number of classifications from these people over here on the left, each of whom does a huge amount of work.
But we also get a lot of classifications for people who only breeze by is common to almost all of our projects.
And we deliberately design so that both of these sets of people are useful.
If you rely just on the experts, you never create enough people to become experts.
You rely on these people. You miss out on a lot of the stuff.
And so we now have this very harmonious situation in which machines collect the data and
we have enough humans with the help of our citizen scientists to work through them.
Machine and human always worked together really well.
Sorry. Wrong picture. This is my version of that nightmare, because this harmonious situation isn't going to last.
This is the large synoptic survey telescope. LCT, of which Oxford is an important member, is as big as the biggest telescopes today or less.
But it's a survey telescope. So the scientists we were arguing about how it will move, but it'll scan the whole sky roughly every three nights.
It will produce something like three terabytes of data a night.
And another way of understanding that is to subscribe each of you to the closest, the alert service, which will send you a message.
Whenever anything in the closest field changes, you'll get about two and a half text message, two and a half million text messages a night.
There's an awful lot of stuff there and it's real. This is the top of a mountain being flattened.
Flattened for Allah says to turn up a mirror exists.
This project will start commissioning in 2018. Now if you want to study the common and the predictable, this isn't too much of a problem.
So if you want to study, for example, conventional type want a supernovae somebody, some grad students and postdocs,
somewhere some team of people will build a service that looks through those
alerts and provides you with more type one supernovae than you can possibly use.
We'll throw some out too, but that's okay.
But if you want to understand the unusual, you want to be able to react in real time to truly, truly unpredicted things.
We're going to need to keep people in that loop of classification left to use computers too.
We have to get better, more efficient at using the people.
So that's what we've been working on and that's what I want to talk about in my last few minutes.
As an example, I'm going to use a project called Space Warps.
PR is the three science prize apogee to Verma one of them who who's here in this department?
Phil Marshall, who many of you will know spacewalks,
is a search for gravitational lenses for these distant galaxies whose light has been bent by passage in front of a nearby galaxy.
And the nice thing about this is it's really easy to calculate what these things should look like.
And so we could put fake, but let's call them simulated galaxies into the system.
And if you see one of these and you catch it, then we say, yes, well done.
You call a simulated lens and you get some feedback and you gain confidence in your classifications.
And then to really test the system, we found a use for particle physicist.
So sorry. This is Brian Cox standing in front of the level telescope at Jodrell.
We partnered with his program Stargazing Live to promote this project.
And this is what happened when Brian Cox told people to go and look at space walks.
This is a million constellations arriving within an hour, so it's a million images that have been sorted through.
We can really do this at scale. And for each of these people,
we get a sense of confidence about them because we see how they behave with these simulated images as on the x axis here,
you've got the probability that somebody says there's a lens,
that if there is a lens and on the Y axis you've got the probability that there isn't a lens.
They say there isn't a lens when there is a one.
So you can see most people up here on the top right do this is 50 randomly selected people in the top.
Right. People do quite well. Our astute users are here in the bottom left.
We've got some people doing less well, although note that if you know that somebody is wrong all the time,
they're precisely as useful as somebody who's right all the time.
A principle, I'm told, is understood by senior members of this department, although they won't give me any examples as to how they apply this.
But you can also play different games. Everyone on the right of this diagram is an optimist, right?
They tend to say that that's something that everyone on the left is a pessimist.
And so you can start to think about how you might pass tasks around.
If I've got an image that's been seen by a few people and I've begun to be confident that there's something there,
I might pass that deliberately to a pessimist. And if they say that's something bad, but my confidence that it's real goes up dramatically.
And one can quantify this and one can start simulating different modes of task distribution to optimise the number of classifications that you need.
And there's definitely latency there. This is a complicated diagram from the space walks paper, but let's zoom in on the x axis.
Just we're on this box. Sorry I didn't have time to make a new on the axis.
This is the amount of effort it looks like. So these are people who do ten galaxies.
100,000, 10,000, 100,000. And this is the skill.
And the skill is the integrated information contributed.
So we can actually calculate the channel information for each classification.
And then we say, okay, the scale is the integrated amount of information provided by each classifier.
And the first thing you notice is that we've done a good job in building this.
Project. There's no one in the bottom, right?
So no one is doing tens of thousands of notifications badly.
So our feedback and our training works.
But also note that there are all these people on the top left who only do a few classifications but who are very good at the minute.
They see a random image, but if we could detect them, we could spend the little time we have with them on the things where we really need expertise.
And so this is a sign that the project is not yet operating at full efficiency and we could take advantage of that.
What's nice is that once you start doing that, once I start deciding who should see what,
there is no reason that I've just built a system that's perfect for combining
human and machine classifiers because I can take the behaviour of a machine.
What fraction does the machine get right is a function of brightness,
of image or something and decide whether to pass a particular image to a machine or to a human, the two on an equal footing.
And we can cope with wild fluctuations in the number of people who are around.
And so this is the system I think we need to build. Fred Astaire And this is what we're working through.
But there are problems with both halves of this equation.
There has been an enormous advance in machine learning in the last ten years.
You know this because voice recognition basically works on your iPhone, right?
Or systems that recognise your scribbled handwriting onto a tablet work pretty well.
This is a schematic diagram that I don't want you to read really, but is that, you know,
most of this work has been done through developments based on old style neural nets that involve what's called deep learning.
So it's sort of a nested neural network procedure that does sophisticated things in understanding what it does and doesn't know.
The universal problem with these methods is that they rely on extremely large training sets.
If you don't have a large training set, you pass, say, 100,000 examples across five galaxies to one of these routines.
What it does is it learns the individual characteristics of those 100,000 and then fails on anything that isn't exactly like those things.
We ran a competition with 10,000 quid or dollars in prize money for machine learning, people to enter galaxies, to sort through galaxies.
They would try and improve automatic classification of galaxies. And we heard back in a great uniform how the 200,000 example galaxies was not enough,
and the person that won the competition did so by citing new data.
So he realised that the problem with Galaxy classification is rotation, the invariant and rotated all of the images four times.
So you got four different images and then chop them up in different ways.
So from a single image he got 16 expert classifications.
It's very clever actually and was able to try this data on that.
So machines I think have this limit and astronomical surveys, particularly for rare objects,
are not going to produce large enough training sets to allow us to take advantage of modern machine learning.
So the idea that all of these problems are going to go away because the computer scientists have our backs is,
I think, false for common stuff, maybe for simple stuff, maybe.
But for the kind of stuff we're interested in, I don't think we can rely on them to solve the problem entirely.
They're going to do the easy stuff and the rest of it will have to be sorted by people.
But there's a problem with this optimisation for people as well.
This is the performance of a classifier. I just look out on a supernova project, look at the blue light.
Good performance puts this blue line near this top vertex here.
So close to this dot is to the top and this is over time.
And so this is a person who's been well trained and then they change their mind about something and they suddenly become a much worse classifier.
And all my knowledge about this person is now, oh, only go back as well.
So who knows what happened. Maybe they had a drink, maybe they handed their log in to somebody else.
Maybe they were confused about the astrophysics.
So there's very little understanding of how to cope with this because machines don't do this by train.
A machine learning classifier does not suddenly change its mind.
And so how do we detect that? How much effort do we put into detecting that sort of behaviour?
The other problem with humans is that they have opinions about what they like to see.
So the project called the Milky Way Project, which Rob Simpson led,
looking for bubbles around newly formed stars in infrared surveys of our galaxy from Spitzer.
These four images are the Milky Way Project images which most encouraged people to keep classifying.
So we looked at the average number of tasks completed by somebody after seeing an image.
So these things people found interesting and exciting and they're happy to classify and these are the worst behaviours.
They're are a mixture of the confusing at the top and the boring of the bottom.
So I have to be careful if. These are the most difficult pictures in the Milky Way project and I assign them to my best classifiers.
Who are that?
Partly because they even if they tell me that because they want to do science, but they're there because they want to see the pretty pictures.
I've built a system that will systematically drive away my best classifiers and understanding how to automatically balance the requirements
of people who want to be entertained and to feel confident versus just blindly optimising for efficiency is a huge and unexplored problem.
And it's made worse because you can't ask people what they want to do.
We have a problem with our community and it's a problem shared by anyone who tries to do science outreach.
We think that this is a good way of getting people involved in science and thinking about science.
But the people and we know that our volunteers take part because they want to help.
But science people, you're actually doing science is a terrifying and scary thing.
And so somehow we have to build projects.
Does it feel real to convince people that they're doing something that will help astronomers without saying to them,
the weight of the universe is on your shoulders? It's an incredibly difficult problem and it's taught me that galaxies are simply the people.
Thank you very much.