All Hail China!
This is China’s Chang’e 3 landing on the Moon over the weekend.
China has now joined the very select club of just 3 nations who have landed on the Moon and this was the first soft landing for 37 years. Just under 3 minutes in the lander rotates to begin its vertical descent onto Sinus Iridum. You can see Moon dust being blown around just before landing.
The 6 wheeled Jade Rabbit (Yutu) lunar rover has now rolled onto the surface and will soon start analysing the lunar regolith.
A big weekend for China!
December 19: Splashdown
After 13 days in space Eugene Cernan, Ronald Evans, and Harrison (Jack) Schmitt aboard the Apollo 17 command module Challenger parachuted to a safe splashdown at 19:20 GMT on 19 December 1972, 648 km southeast of American Samoa. The last humans to have walked on the Moon.
There is still much to learn from the Apollo 17 mission. Moon Zoo needs your help to explore the Apollo 17 landing site. Celebrate the anniversary with us. Go to http://www.moonzoo.org/ and start clicking! Follow “live” mission tweets from @moonzoo
December 12: I was strolling on the Moon one day…
The song “The Fountain in the Park” as featured in Tom and Jerry, Bugs Bunny and Mickey Mouse cartoons and sung by Judy Garland in “Strike up the Band” was given the Schmitt and Cernan treatment while setting up experiments during an EVA. Watch the astronauts enjoying themselves in this video. (placed on You Tube by Jordaxe12.)
Transcript from the mission transcripts:
SCHMITT (Singing) I was strolling on the Moon one day, in the merry merry month of December-
CERNAN May – May’s the month.
SCHMITT – May – that’ s right.
CERNAN May is the (garble) month.
SCHMITT – when much to my surprise, a pair of funny eyes (singing)
CAPCOM (Capsule Communicator back in mission control) Sorry about that, guys, but today may be December.
Computers counting craters
By Anthony Milbourne (Birkbeck College)
In this blog I would like to talk about automated crater counting, which as the name suggests, is the use of computers to identify and count craters. Computers are very good (and getting better) at many things, but they are still not as good as humans at many important classes of problems. The human brain is amazingly powerful (you knew that) and the latest generation of super-computers have only just reached the same order of magnitude of computing power. In other words, there are currently only a handful of machines in the world that can truly rival the human brain in terms of raw processing-power. Humans are particularly good at pattern recognition and computer scientists have been trying to create computer programs to do this for a long time, but the results generally look rather pathetic compared to humans. One application of pattern recognition is crater identification, which I will talk about below.
In general there are two approaches to pattern recognition: designed algorithms and machine learning.
A designed algorithm is a set of very specific instructions that allow the computer to solve a specific problem. It would be like programming a car to drive from point A to point B by defining exactly how much to accelerate or brake at what points and exactly how much to turn the wheel when. If you know the car will always be running on the same road then this approach is reliable and predictable, but it’s not very flexible. If a pedestrian steps into the road they are in trouble because the car will take no account of the changed environment, and if the car has to go on a different road it will be close to useless.
In machine learning the computer is still given detailed instructions, not on how to solve a specific task, but on how to learn what works well to solve the task. This would be like giving the car-driving program instructions on what the brake, accelerator and steering wheel did and then letting it experiment until it found a route from A to B. Obviously, this requires a training phase, where the algorithm crashes a lot (let’s hope the car wasn’t expensive), but eventually it figures out some general principles about driving and is able to deal with a certain amount of change in its environment.
Designed algorithms are safe and predictable; they don’t need training and are often easier to implement and faster to run, but they are inflexible. Machine learning may be better at the job and will certainly be more flexible, but it is tricky to train and you may end up training it to take short-cuts that you didn’t want: http://neil.fraser.name/writing/tank/
An example of a commonly used designed algorithm is the Circular Hough (pronounced like rough) Transform (CHT). We assume that the image is taken from above and that the vast majority of craters will be roughly circular. The program then uses a CHT to look for circular patterns of a set radius (perhaps repeating many times for different radii). A CHT essentially takes each image pixel that represents an edge and uses it to generate a vote for all possible circles that the edge could be on. The centres of all these possible image circles form a circle of votes around the edge point. If you do this for every edge point then the votes tend to build up at points that really are the centres of circles (it’s easier to see in the diagram).
Of course, this is much harder in practice as the images have a lot of noise and other features that generally confuse the algorithm, so various people have come up with various ways of improving it and making it faster. There are of course many other types of designed algorithm, but I won’t bore you with all of them.
An example of a machine learning approach is to use a neural network. This is a program that tries to simulate a simplified model of how the brain works. It consists of a large number of ‘nodes’ which are connected to each other by links of varying strength. The nodes are normally arranged in layers, and each node combines the inputs from nodes in the preceding layer and sends the result out to the nodes in the next layer. The nodes in the first layer act as input points and the nodes in the last layer as the output. The strength of the links can be varied in order to change the behaviour of the network, and this is done during training when the error from each training run is used to adjust the link strengths.
Neural networks are deceptively simple in concept but are very powerful and can end up spotting trends that are not clear to humans, or that are too complex or nuanced to implement easily as a designed algorithm. However the number of nodes needed to achieve anything useful is normally large, so figuring out what is actually happening inside the network is not practical. For this reason you can never be quite sure that the network, given new or unexpected data, won’t do something crazy!
Again, this is not the only way of implementing machine learning, but it gives an idea of the way this sort of system works – trained rather than designed.
In general, machine learning approaches take more processing power than designed algorithms, so in most cases a pipeline is used. First, a quicker, more predictable, designed algorithm is used to select areas of interest (potential craters), and then a machine learning approach is used to sort the real craters from the noise.
The images at left are an example of the kind of output that can be achieved by automated crater recognition (this one is based on Hough Transforms), and the problems with it. This is far from the best algorithm available, and other researchers have developed much more accurate programs, but they all suffer to a greater or lesser degree from image noise. The first image shows how accurate an algorithm can be in a clear image with little noise. It misses many smaller craters and there are a few false positives (which are somewhat surprising to a human eye), but in general it finds the rims of the most obvious craters very accurately. The second image features degraded crater rims, a lumpy surface and sub-optimal illumination. The result is that the same algorithm does very badly at spotting craters. This is not surprising; even a human would have to look harder at the second image, but the algorithm performs so badly that it is arguably not worth using, and this is the sort of image where humans really are the only (reliable) show in town at the moment.You might think that automated crater counting would be a direct competitor to crowd sourcing efforts like Moon Zoo, and in some cases you would be correct, but it can also be used as a complementary technique. This could be done by using moon Zoo crater identifications as the areas of interest and then running an algorithm to find the exact location of the crater rim, or using an algorithm to spot Moon Zoo data which has been entered by mistake, or by users who didn’t understand the task.
Most interestingly, in my view, is the idea that algorithms are just another type of user. Some algorithms are not great at spotting craters, but some human users are a bit variable too. Admittedly, the best humans are far better than the best algorithms, but the best algorithms are probably better than the worst humans, so they fall within the quality spectrum that Moon Zoo (and the rest of the Zooniverse) already deals with. They probably won’t be much good at spotting unusual objects and they certainly won’t be much fun on the forums, but perhaps we might one day be working with algorithms as our (less able) peers.
By Anthony Milbourne (Birkbeck College)
Meteorites on Ice
Although not exactly Moon focused, I hope that this story is of interest to Moon Zoo Forum members as this is the way many lunar meteorites are found here on Earth (see previous Moon Zoo blog ).This past winter (2011-2012) I was lucky enough to join the Antarctic Search for Meteorites (ANSMET) team to hunt for meteorites in Antarctica. The ANSMET programme, funded by NASA and the US National Science Foundation and run between Case Western Reserve University, NASA JSC and the Smithsonian, has been running since 1976 exploring the ice of Antarctica for meteorites. So far about 20,000 meteorites have been collected and made available to the scientific community to study to understand planetary processes. Most of these meteorites originate from the asteroid belt, but some very rare ones have come from Mars and the Moon.Why Antarctica? The team collects meteorites in Antarctica because they are well preserved in the cold dry icy environment. They are also easy to spot as dark rocks on the white ice. Most meteorites are found in icefields close to the Transantarctic Mountain range – you can see a map where all the yellow labels mark places that meteorites have been collected. These localities are really great for concentrating lots meteorites that have travelled from the South Pole ice plateau, and are brought up to the ice sheet surface near mountain ranges
How do we get to Antarctica and what is life like on the ice? Our team of six guys and two girls flew to Christchurch, New Zealand, and then were flown down to the US McMurdo base in Antarctica. We spent a week or so in McMurdo preparing for our expedition. We packed up our gear and selected food supplies to last us for six weeks camping on the ice. When we were trained and prepared we flewout to the Transantarctic Mountains on a military Hercules plane with skis and then a smaller Twin Otter plane. We set up camp in the Miller Range – a stunning area with mountains and glaciers. Our camp consisted of four tents, where we lived two people to a tent, and a tent with a toilet (a glamorous bucket!) and one that we used to all gather in the evenings (the party tent). Temperatures varied from about -10°C down to -30°C outside, but when the wind was blowing from the South Pole plateau, it felt a lot colder! You have to wrap up in many layers to stay warm – I typically wore four layers on my legs and between five and seven on top, including my big orange down jacket. I also wore a full face mask to protect my face and eyes from the cold and glare from the sun.
How does meteorite hunting work? We had a surprisingly large amount of snow during our field season – which is rather unusual for Antarctica as it is supposed to be a cold dry desert. The snow caused us lots of problems trying to find the meteorites, so we spent a lot of time stuck in our tents rather than looking for meteorites. When the weather was good enough to allow us to look we would get on our snowmobiles (skidoos) and drive out to a new area of ice. We lined up and drove up and down in straight line formation with about ten metres between each team member. When someone spotted a black rock on the ice they would jump off their skidoo, check it was a meteorite, and then wave their arms madly in the air to call the other people over to come and help collect it. We photographed the meteorite, logged its location and carefully put it into a special collection bag ready to send back to NASA. Sometimes we would walk across the ice to search, and other times we would look in rocky areas called moraines to see if we could spot a meteorite. It was a frustrating process when you didn’t find a meteorite, but great fun and satisfying when you did. Our team found 302 stones this year, which considering the bad weather, wasn’t a bad total at all. In fact we were lucky enough to collect the 20,000th sample ANSMET have collected, which was cause for a big celebration.
The samples are all now back at NASA Johnson Space Center ready to be classified and studied by scientists all over the world. Initial identification of the meteorites was recently announced at http://curator.jsc.nasa.gov/antmet/amn/amn.cfm#352 and the meteorites our team collected have been given the name Miller Range 11XXX as they were collected in the 2011 ANSMET season. So far (the curation staff are still working hard to classify all the samples!) it doesn’t look like we found any lunars or martian meteorites this year, but did find a large Howardite and several Diogenite meteorites, which may have originated from the asteroid Vesta.
ANSMET will be taking place this year, the team sets off to the ice at the beginning of December 2012, and you can follow the blog charting the expedition via http://geology.cwru.edu/~ansmet/
More information about the ANSMET programme can be found at http://curator.jsc.nasa.gov/antmet/program.cfm and http://geology.cwru.edu/~ansmet/
This is modified from a blog that first appeared on http://earthandsolarsystem.wordpress.com/2012/07/30/meteorites-on-ice/
The White Rings of Marius
Forum member kodemunkey’s recent excursion around the Marius region threw up some interesting questions. Over to kodemunkey to tell us why…
The Marius Hills are one of my favourite spots on the moon (as anyone who keeps an eye on the “Black Stuff” thread will attest to )
Besides being host to a sinkhole, twenty rilles and two hundred and fifty volcanoes (count ’em if you want ) and a the bulk of my ‘Black Stuff’ finds, what else is there to see?
Click the first two images to view in full size.
Marius Hills (North is up.)
Well, there’s these:
They cover the entire region and are quite noticeable in the NAC strips even at the lowest zoom levels, most of them have a high albedo,and others look very old. Having said that, I went looking for these rings elsewhere on the near side, and have found them as far down as the area incorporating Gassendi, Dopplemayer, Pulseux and Leibig craters.
I then turned my attention to the far side, particularly Tsiolkovsky Crater and the Apollo Basin. Perhaps a little disappointingly I didn’t find any there.
1: Why such a high albedo, especially since the area is so old?
2:Why so many in comparison to somewhere like Mare Tranquilatis?
3: Is this only a near side phenomenon?
New Look for Moon Zoo
Moon Zoo launched more than 18 months ago and we’ve been meaning to make some changes to the site. Later today our refreshed site will go live! You’ll notice that we’ve had the decorators in – the website looks a little different and a new tutorial (see below). On the back end, we’ve added new images and retired some old ones.
We have created a new interactive tutorial for Moon Zoo. This tutorial guides you through the basic interface of Moon Zoo and teaches you how to avoid some of the common pitfalls that we’ve seen since the project began. Even if you’ve classified on Moon Zoo before you might need to take the tutorial just once to get back on track – you should find it’s no problem. The tutorial has lists of known craters against which your markings are compared. If you’re too far off the mark with your crater drawing, we’ll ask you to try again. There’s nothing to worry about, just our way of ensuring maximum results from the site by bringing everybody up to speed.
We are retiring the Moon images we used at launch in 2010. But we have some great, new ones that allow us to study secondary craters and volcanic regions of the Moon. These will help us study interesting features and crater types, building on the work that has already been done by Moon Zoo volunteers.
So Moon Zoo: Phase 2 has begun – and will go live today – take a look!
2 Million images!!
Moon Zoo isn’t yet I year old but last week the 2 millionth classification was made. This represents an amazing number of craters measured, boulders examined and interesting features marked. To get an idea of the size of the area this represents just look at some of the equivalent measurements:
- 46,336 square miles (that’s 120,010 square km!)
- 2,000 Manhattans
- 2.91 Switzerlands
- 272,748 Vaticans
- 13,187,802 Taj Mahals
- 352,979 Disneylands
So how much of the Moon have we done? Half? A quarter? Not even anywhere near! This represents just 0.3% of the Moon so there’s plenty of Moon left yet. So thanks a million (or two) to everybody and keep on clicking! Don’t forget to check the Moonometer(TM) from time to time to see how we’re doing.
New Moon Zoo Science Lead Coming Aboard
Hello, Moon Zooites!
Moon Zoo will be a year old in two months, and with time can come a changing of things. Katie Joy, who worked with the original Moon Zoo team to get this project and website off the ground, has a postdoctoral position that has placed large demands on her time and she has been unable to continue to guide Moon Zoo. I have been asked to fill the gap she has left and over the past few months I have been working with the people involved to bring you a revised experience with Moon Zoo in the next few months. More on that when things launch.
I also want to take this opportunity to briefly talk about who I am and what I do so that you know a bit about my background and, just in general, who will soon be at the helm of this project in its science goals. I have a BS degree in astronomy along with double-minors in physics and geology, awarded in 2005. Since then, I have been working towards my Ph.D. in astrophysics with a concentration in geophysics (MS in 2008) here in sunny Boulder, Colorado (USA). My research since 2007 has fairly exclusively focused on Mars and Mars craters in the creation and application of a new giant database of craters and their properties towards questions of Mars geology and the physics of the impact cratering process. So I have a bit of experience in dealing with giant datasets of craters. I think that the potential power of tens of thousands of volunteers identifying small lunar impact craters is huge and can be applied to many different fundamental questions about the moon and cratering in general, as well as the other tasks that we ask you to do, such as with boulders.
One application that I hope to use Moon Zoo data for is the question of ages: The basic idea is that craters form randomly over a given surface with time, but that the longer a surface has been around, the more craters it has. So by counting craters over a given area and doing some fancy statistics, you can come up with ages. This is actually the ONLY way to age-date places OTHER than the moon (since we have sample returns from the moon), but the moon acts as a baseline for ALL crater-age dates. And craters on the moon can still be used for age dating small features that we don’t have samples for, like lava tubes — so we can figure out things like when the moon last had volcanic activity.
If you have any questions/problems/suggestions, don’t hesitate to message me on the Moon Zoo forums; I may not be able to help you directly, but I’ll at least try to find someone who can! I look forward to working with many of you.
Thank you for all your time volunteering, and keep on clickin’!
Moon Zoo Image of the Year
The Moon Zoo forum has elected this curious portion of the Moon’s surface as the 2010 Moon Zoo Image of the Year. Named Ina, this puzzling lunar feature may be the result of fairly recent volcanic activity (millions rather than billions of years old) with two distinct types of terrain: rough jagged rubble-like brighter areas and smoother, darker mounds. No-one knows for certain what caused these two different types of terrain but one view is that it is the result of a “recent” gaseous outburst which has removed part of the top layer of regolith.
The Moon is full of odd and peculiar features and it is fitting that Ina has been elected the favourite so far. The Moon Zoo forum and this blog have become places to discuss just such odd features. So congratulations to the Moon Zoo community on an amazing first year – and may we find many more ‘Ina’s in the future.
If you want to learn more about Ina, you can read an excellent NASA article explaining more about it.