In a previous post I described the concept of space weather. Whilst monitoring current conditions around Earth, space weather forecasters will produce forecasts of the likelihood of solar eruptions occurring over the next few days. Solar flare forecasts are just one part of these daily guidance documents. To create a forecast, a few simple guidelines are generally followed, which I will outline in this post.
1. Setting the scene.
For any forecast, be it Earth weather or space weather, the forecaster needs to begin by describing what is happening now. They start by examining solar imagery, such as the MDI magnetograms used in Sunspotter. They will identify any features in these magnetograms which are already having, or are likely to have, an impact on space weather conditions in the coming days. See Figure 1 for an example of identifying active regions using magnetograms. In this particular example, regions have been numbered according to the NOAA Space Weather Prediction Centre sunspot numbering scheme – when a new region emerges onto the solar disk it is given a number in order. The Solar Monitor Active Region Tracker method outlined in a previous post is another way to identify regions of interest, which automatically produced the data sets you see in Sunspotter.
The forecaster will examine the history of these identified regions of interest– if something significant has happened recently this might get described in the guidance document, perhaps with images to help the viewer understand what happened and why.
2. What’s likely to happen (the forecast)?
Once the current situation has been described, it is time to move on to what is likely to happen over the period of the forecast. This can be separated into two parts:
2.1 The next 24 hours.
The forecaster will describe in the guidance document how any identified regions are likely to move or develop in the immediate future. For example is a sunspot region growing? Is it complex? Is it likely to produce flares? This is where your classifications in Sunspotter will help define how complex a region is! Once forecasters have decided on a classification, they will calculate the probability of a flare occurring in this region over the next 24 hours.
There are many ways to do this, including Bayesian methods (e.g., Wheatland et al), machine learning (e.g, Qahwaji et al), discriminant analysis (e.g., Barnes et al), and many more. A relatively simple statistical method is often used in operational flare forecasting, which starts with a large database of flare information from previous solar cycles. This database shows how many particular classes of flares each classification of active region produced in that time period. From this an average flare rate can be calculated, and using Poisson statistics a percentage probability of flare occurrence for the next 24 hours will be obtained for each region. See Bloomfield et al, 2012 for a more in-depth description of this method. A little human intervention is also involved here – if a forecaster thinks the value is too small or too big they can change it based on their experience! The percentages for each region can then be added to obtain the probability of a flare occurring across the entire solar disk over the next 24 hours.
2.2 The rest of the forecast period.
The forecaster will then take a briefer look at the whole sun over the next few days. If the next 24 hours look fairly quiet, but something interesting is expected to return to the solar disk in a couple of days time, then the probability of a flare occurring might be increased later on in the forecast period. Similarly if a particularly complex region is due to leave the disk in a few days, the probability might be decreased for that day. An example of a typical flare forecast is shown in Figure 2.
3. Potential Earth impacts.
Once the forecaster has described what we expect to happen on the Sun, next it’s time to explain how this may affect life on (or in the vicinity of) Earth. For example can we expect radio blackouts? Do astronauts onboard the International Space Station need to postpone any space walks? This will be summarised as part of more general guidance documents, which include forecasts of other space weather phenomena such as coronal mass ejections.
These are the typical steps taken to create a flare forecast. However every day is different, and forecasters can diverge from this method if necessary! Check out current space weather conditions on, e.g., the SWPC and Met Office webpages.
Thanks to Senior Operational Meteorologist, Mark Sidaway, for his guidance when creating this blog post. All statements in this post are my own and not those of the Met Office.
I suppose Friday the 13th is as good a day as any to launch a Citizen Science project.
For those of you who helped us classify the previous data set: Welcome back!
And hello to all of our new Sunspotters!
If you haven’t a notion of what Sunspotter is all about, check out our science section. In short, our goal is to determine the complexity of sunspot groups. It is well known (to solar physicists) that more complicated looking sunspot groups produce more solar flares than simple looking ones. But so far, scientists have not found a good way to quantify sunspot group complexity. This is not a task easily accomplished by a computer. Humans, on the other hand, can easily point to the more complex in a pair of objects, ideas, images, and so on.
I’m pretty sure you have an idea of which is the more complex: a graduate text on quantum mechanics, or an Italian cookbook?
On the other hand, it would not be straight-forward for a computer to make that choice. The same is true with sunspot groups.
In round one (lasting only a month!), ~1,600 volunteers helped us to rank ~13,000 images of sunspot groups by choosing the more complex one in ~300,000 pairs of images. This has allowed us to quantify ‘true’ sunspot group complexity for the first time! Now that we have a handle on how to give complexity a number, we want to determine how the complexity of a sunspot group changes over time.
To do this we have automatically detected thousands of sunspot groups and tracked them over time. Each sunspot group has been detected about 15 times per day. Some of these sunspot groups were included in the previous dataset, but now they are being detected in a different way. That means that you will see a number of similar-looking images- but don’t worry if you can’t tell which sunspot group is more complex, just do your best! When graphing the complexity of a sunspot group over time, we are hoping to see clear jumps in the data when the sunspot group became more complex and we expect this to be followed by the occurrence of solar flares.
There are a few ‘biases’ that we could not easily correct for with the previous dataset, that we hope to get a handle on this time. For instance, depending on a sunspot group’s position, it will look more squished as it nears the edge of the Sun. As mentioned in an earlier post, we are now using a projection technique to ‘de-squish’ the sunspot groups. Also, it is likely that the most complex sunspot groups are always the largest. However, humans might also be biased toward thinking bigger things are more complex, even when they are not. So, to help reduce this bias, we have ‘de-scaled’ the images so that all of the sunspot groups, big or small, will appear roughly the same size on your screen.
We think you will find it much easier to focus on complexity with this new data set.
Thanks for listening, and happy classifying!!
After our brief hiatus, we are about to launch the next phase Sunspotter this Friday! As we mentioned before, there will be >200,000 images. Ranking the sunspot group complexity of this new dataset will require literally millions of clicks. This time we are trying to learn about the evolution of complexity in sunspot groups.
Last time, our goal was to obtain a quantitative measure of sunspot group complexity- the results of this are exactly what I presented to the attendees of the 224th American Astrophysical Society meeting in Boston, MA, last week. My talk was well attended by sunspot and solar flare experts who asked a number of insightful questions about the data analysis method, and about how data was presented to the hard-working volunteers.
You can view the slides from the presentation on FigShare. I will be writing an in-depth post explaining the methods and analysis techniques used to get these results as we pull the journal paper together for publication.
Hope to see you all on Talk.Sunspotter.org soon!
Magnetograms are wondrous things, that when I stop to think about what they are, I can scarcely believe that they even exist! A magnetogram is an image, where color represents magnetic field strength. The idea that you can take a picture of a magnetic field completely goes against my intuition of how magnetic fields work. Prior to studying physics, I only knew magnetic fields as funny forces one feels when pushing/pulling two refrigerator magnets together/apart. How could one possible take a picture of an invisible force!? Well, I’m not going to lie- its not a simple process at all. But I’ll give the explanation my best shot..
I will space out this explanation over a few posts, so I can go into a bit of detail about the whole process- starting from the ‘basic’ physics concepts. …I’ve never met a physics that I thought was basic 🙂
While reading this, if you have any corrections (I’ve never read or written a physics discussion with out at least one mistake) or questions (no matter how ‘basic’ you think they are), please post them in the comments section or on Sunspotter Talk!
Magnetic fields put the ‘magnet’ in Magneto
I could try and describe magnetic fields in some arcane abstract way, but I will start with something everyone has at least heard of: light… A magnetic field is one component of a light wave; the other component is an electric field. If you remember nothing else from what follows, remember these three rules:
- a changing electric field generates a magnetic field; (formally Ampere’s Law)
…coiling an electrified wire around a magnet will cause it to spin = an electric motor
- a changing magnetic field generates an electric field; (formally Faraday’s Law)
…passing a magnet through a coil of wire will generate electricity = an electrical generator
- an accelerating (or decelerating) electric field generates light. (e.g., synchrotron and Bremsstrahlung radiation)
And what is light? Incredibly, light is an oscillating electric field and an oscillating magnetic field that perpetuate each other, as long as they travel at the speed of light, 300,000 km/s. This is a constant conversion of electric energy into magnetic energy and magnetic energy back into electric energy. The energy of light is determined by how fast the electric and magnetic field is oscillating. However, in quantum mechanics, light is represented as photons, which are light-wave ‘packets’ of energy, that behave like particles (e.g., they can bounce off of another particle, like an electron). But in classical mechanics, light behaves as a wave (e.g., two light waves can ‘interfere’ and cancel out or build in strength, just like water waves). The fact that light can simultaneously behave as both a particle and wave is known as ‘wave-particle duality‘. To make a magnetogram, you sometimes have to consider light as a particle, and sometimes as a wave.
Like a packet of crisps (chips), a photon packet of light is VERY calorie dense. Each photon has a specific amount of energy stored inside. If the conditions are right, a photon can deliver all of its energy to an atom (e.g., a hydrogen atom) by smashing into it. When it does this, the orbit of the electron going around the proton (in a hydrogen atom, for example) will become more energetic, and the photon will be completely absorbed by the atom. This is called photon absorption. On the other hand, an energetic electron in an atom can suddenly (sometimes randomly) become less energetic, and in doing so, release a photon. This is called photon emission, and releases photons at a very narrow range of energies that correspond to the change in energy of the electron’s orbit.
There is another very different way that light can be released. When anything glows (like a red-hot iron or an old fashioned light bulb) a lot of photons are being released, but in a different way than with ‘emission‘. When something glows because it is hot (like the surface of the Sun), it is usually releasing thermal radiation. When matter has a high temperature, it means that the particles it is made of are moving around really fast, and banging into each other. Remember Rule #3? Every time charged particles in the matter bang into one another, they undergoing deceleration or acceleration, like the balls bouncing around on a pool table, and they release light! Not surprisingly, the hotter the matter is, the more thermal radiation is being released. It turns out that when something glows at a certain temperature it will release a predictable fraction of its photons at each energy; we can use Planck’s Law to predict this. Thermal radiation releases photons at many different energies (a broad spectrum).
The main thing to remember is this: photon emission releases light in a narrow range of energies, while thermal radiation releases light waves in a broad range of energies.
Proton says to a Neutron, ‘I’ve lost my Electron!‘
The Neutron says, ‘Are you sure?‘
The Proton replies, ‘I’m positive!‘
The surface of the Sun is like an ocean, but instead of being made of water molecules, it is made of (mostly) hydrogen and some helium. Another difference between the solar surface and an ocean is that instead of a refreshing ~290 Kelvin the surface of the Sun is a blazing 5,700 Kelvin (…so hot that it glows yellow!). For this reason, it is a plasma (and not just a ‘gas’). It is a plasma because a significant fraction of the Sun’s hydrogen has been stripped of its electron, leaving just a lonely ionised proton (a positively charged particle), wandering around looking for an electron. This gives plasma some very interesting properties, which will be covered in a future blog post when we discuss the physics of sunspots.
The Sun isn’t just hydrogen, it also has a small amount of a lot of other elements. These other elements can also undergo photon emission, which produces photons of certain energies. We observe this light as emission lines. By studying emission lines, Helium was first discovered because we saw one of its lines when we looked at the light from the Sun (the name Helium comes from Helios, the Greek Sun god). Atoms or partially ionised atoms (that still have at least one electron) undergo photon emission and absorption all the time because of all the other particles and photons smashing into them.
When atoms or ions undergo photon emission in the presence of a magnetic field, something strange happens. And because of this we are able to measure magnetic fields on the Sun- but you’ll have to wait for The Making of a Magnetogram Part II to hear all about it!
As always if you have any questions/ comments /suggestions, we are eager to hear them!
Thanks for listening.
I am a space weather research scientist at the Met Office*, the national meteorological service for the United Kingdom. My job is to transition basic research to operational forecasting- in other words, I try to improve space-weather forecasts with new-and-improved science!
What do I mean by space weather? It basically describes the changing environmental conditions in near-Earth space. Magnetic fields, radiation, particles, and matter, which have been ejected from the Sun, can interact with the Earth’s upper atmosphere and surrounding magnetic field to produce a variety of effects.
There are streams of particles from the Sun constantly hitting the Earth via the solar wind, but the Earth experiences an increased impact during periods of high solar activity, when solar eruptions can occur in the form of solar flares and coronal mass ejections (CMEs). Solar flares are sudden releases of energy across the entire electromagnetic spectrum. They are hard to predict, and the energy can be detected in the Earth’s atmosphere as soon as 8.5 minutes after a solar flare (travelling at the speed of light). CMEs are often associated with flares, eruptions of large amounts of matter from the solar atmosphere. These can take days to reach Earth, carrying a local magnetic field from the Sun. Considering the short time frame for forecasting of flares compared to CMEs, it’s really important to have accurate alerts for big events. That’s where your help with Sunspotter comes in – by improving our understanding of the active regions that are the source of flares, we can hopefully improve our forecasts!But why do we care about space weather? In our increasingly technologically-dependent society, the impact of solar eruptive events can actually be quite severe. Some key sectors in need of accurate event forecasts include energy, aviation, satellite operation, marine, communications, rail, and defence. For example, interruptions to radio communications and GPS can occur due to eruptions, and power grids can also be disrupted. Particles accelerated during eruptions can also damage spacecraft and degrade electronics, and instruments often have to be switched-off or reset. It is important for satellite companies to receive accurate information on the likelihood of eruptions to ensure as little down-time as possible. In the aviation industry, flight crews, passengers, and onboard electronics are all under direct exposure to higher levels of radiation on transpolar flights. Even astronauts cannot take space walks during these events. Solar-activity monitoring systems are imperative to keep astronauts safe! There have been a number of events directly related to high solar activity in recent years. For example, during a particularly large event in 1989, the entire power grid of Quebec collapsed, causing a 9-hour blackout which effected 6million people. In December 2006, a powerful flare disrupted satellite-to-ground communications and GPS navigation system signals for about 10 minutes. The eruption was so powerful it actually damaged the solar X-ray imager instrument on the GOES 13 satellite that was taking images of it! An American telecommunications satellite, Galaxy 15, now widely known as ‘zombiesat’, ceased responding to commands in 2010. The manufacturer has theorised that solar activity was responsible for the satellite malfunctioning, although they could not settle on a single root cause. Check out the National Research Council or the Royal Academy of Engineering reports for more information on, and examples of, space weather impacts.
In order to monitor and forecast space weather, we generally use ground-based and satellite instrumentation. The solar surface and atmosphere is observed in near-real time to detect any new active regions that may become the source of large events. These observations, such as the MDI images used in Sunspotter, can help determine whether an eruption may be a threat if it is Earth-directed. The Earth’s atmosphere is also monitored to detect changes related to solar wind variations, as well as short-term impacts of solar eruptions. Ongoing scientific research is crucial to determine the fundamental physical processes involved in driving space weather, such as solar magnetic fields. The more that is known about these processes, the more models can be improved to accurately predict when a flare or eruption will occur. So keep clicking those active regions and help improve our warning systems!
*Disclaimer: all statements in this post are my own, and not those of the Met Office.
… Then tell us so!
Why I ❤ Citizen Science
The Sunspotter volunteers have been working tirelessly at classifying all of our sunspot data, and it shows! We are already near 250,000 classifications, making this dataset around 70% completed. After this one is done, we will be releasing a new dataset that includes over 200,000 sunspot group images, and much more meta data.
Also, we have been experimenting with ways to ‘de-project’ the data so that sunspot groups near the edge or ‘limb’ of the Sun do not appear squashed due to foreshortening- but, more on that when we launch the new dataset…
While working on this project, our team has had the privilege of interacting with some of the Sunspotter volunteers. If you haven’t tried it yet, pop over to talk.sunspotter.org and give us a shout! The volunteers taking advantage of Talk have been asking us some fascinating questions about the science behind Sunspotter, why the data looks the way it does, and about our ideas for how the data will be analysed later on. Honestly, it has provoked us to think much more deeply about this project than we would have otherwise.
I think that this is one of the hidden benefits of starting a citizen science project: your grandad’s/grandma’s data analysis algorithm is no longer a lifeless automaton. Now, it is a group of people that can provide you with amazing feedback on the work!
The Cursed Pay-Wall
Over the course of interacting with volunteers on Talk, there have been many discussions about the science behind Sunspotter where a specific research paper came up in the conversation. Unfortunately, many solar and space-physics researchers have not yet discovered the likes of arxiv.org, where you can upload a ‘pre-print’ manuscript of your science journal paper for everyone to read for free. So, often when a Sunspotter volunteer goes to check out a specific paper (perhaps by using the astrophysics-paper search engine, adsabs), they are hit with a ‘Pay-Wall’. If you have not encountered this yet, it occurs when an individual tries to access a science paper on-line, using a computer that is not on a university, research laboratory, or library campus (that has access to the given journal). Often the publisher requires a fee of ~$30 to access a single paper!
It is my personal opinion (and the opinion of many others) that at the very least, the general public (non-working scientists) should be able to freely access any scientific research paper in which the work was paid for by public funding. The vast majority of research is paid for by public money, but the majority of scientific papers published today are not Open Access. On the bright side, there is a growing movement to make science fundamentally freely accessible for everyone.
That said, when a research paper comes up in a Sunspotter conversation, that is not openly accessible, I have taken to emailing the author directly to request that they upload their paper to arXiv to make it available for free. Furthermore, I have begun to email the main astrophysics journal publishers to request that they provide open access to volunteers of citizen science projects. The American Geophysical Union (AGU) has already responded very positively to our request, and is working to provide such access! Furthermore, the folks at Zooniverse got the Monthly Notices of the Royal Astronomical Society (MNRAS) to agree make all citizen science-based research in their journals automatically open-access! Cheers to AGU and MNRAS!
These journals are clearly thinking ahead, and hopefully the other journals will see the light soon!
To help us in our mission of making science more open (especially for citizen science volunteers), please vote in our poll above, to declare your interest (or disinterest) in free access to journal papers. If this is an issue that Zoo-ites feel strongly about, we would like to know. For one thing publishers will take our requests more seriously if we can show that there is substantial support.
Again, we at Sunspotter really appreciate all the time, effort, and interest that you volunteers have given us!
16 Years Staring at the Sun (without sunglasses)
The data we are using for this stage of Sunspotters comes from the Michelson Doppler Imager (MDI; Scherrer, 1995) instrument, which is on-board the Solar and Heliospheric Observatory (fact sheet; shown in images above).
SOHO orbits the Sun between the Sun and Earth at the first Lagrange point (L1). Enjoy a video of SOHO being launched into space on board an ATLAS rocket. An L1 orbit allows uninterrupted views of the Sun, without the Earth or Moon getting in the way. The MDI instrument was turned off in 2011, but successfully took data for about 16 years.
Around 60,000 magnetic images of the Sun’s surface were beamed back to Earth over this time interval, and have allowed the study of the magnetic properties of sunspots and the Sun as a whole over more than an entire 11-year solar cycle. In this project we take advantage of these features to study the magnetic complexity of sunspot groups over a long timescale and with regards to eruptive activity.
The current dataset used in Sunspotter includes cut-out images that are based on the locations of sunspot groups determined by hand (by the National Oceanic and Atmospheric Agency and the US Air Force; Figure 14). The current dataset includes around 10,000 images, and will allow us to determine the relationship between sunspot group magnetic complexity and other magnetic properties, such as magnetic area, flux, polarity imbalance, and the length of the polarity separation line (separating positive and negative regions of a given sunspot group image).
Making Sense of the Data
We have processed the data in a specific way to aid volunteers in comparing the sunspot group images. The thick white line shows the limb of the Sun (beyond the limb lies outer space). MDI provides us with images of the whole disk of the Sun. We have cut out images of sunspot groups centered on a set of human detections, as explained above. The cut-outs end up being all different sizes, so we buffer out the smaller ones with generated noise to make them a uniform size.
Within the images you will see blobs of white and black. White areas represent magnetic fields oriented toward the observer, and black areas represent magnetic fields oriented away from the observer. If you could put a bar magnet on the Sun, the magnet would look white when facing one way, and black when facing the other way.
However, not everything you see in these images is a magnetic field (or necessarily even a physical feature, for that matter). The following images show some examples of odd looking stuff you’ll find in the data. We have tried to explain the cause of each observed feature, below.
You will probably notice that the sunspot groups have a rectangular boundary. This is due to the detection algorithm we used to extract each sunspot group. We get rid of all the stuff that we do not consider to be a part of the sunspot group in question. It is an arbitrary choice of what to keep and what to get rid of, but one that has to be made- at least it is done in a uniform manner. You will be able to see a ‘context’ image of what exists outside of this rectangular area after making a classification and going to the ‘Profile’ tab. We buffer out the images to the same field of view so that one can compare the scale of the features in the images. We are discussing the possibility of removing the scale information in the next batch of data (to remove the potential bias that larger spot groups would always be judged to be more complex- which should not always be the case…).
Very rarely, a scratchy speckled pattern will be seen, like that shown in the image above. This is due to a large eruption being launched from the Sun, that actually hit our spacecraft! The scratches and speckles are energetic particles (e.g., protons), travelling close to the speed of light that hit our camera and were recorded, while we were trying to take a picture of the Sun. Its kind of like trying to film a tornado in Kansas, and your film crew keeps getting hit by airborne cows. Movie of the ‘Halloween Storm’ in 2003; pay attention at ~8 seconds into the movie.
The black line seen at the bottom of the feature in this image is likely due to MDI’s camera not recording certain pixels. Once in a while a large block of data (not shown here) may go missing, and this tends to occur when the ground station on Earth was in the process of receiving data and an interruption occurred, resulting in the loss…like having dropped cell-phone call.
These images show sunspot groups during their emergence phase. The first and last images show pairs of sunspots (one positive/white, and the other negative/black) bursting through the solar surface. They often show a characteristic double-C shape, like two lions roaring at each other, face-to-face. The middle image shows a sunspot pair emerging (in the top left corner of the image) into an already established sunspot group (the stuff in the center of the image). One explanation for the double-C is that it is the result of helical magnetic fields passing through the solar surface (all you can see is the cross-section). Here is the best example I have ever seen, as noted by Sunspotter volunteer, @artman40.
Often, you will notice that circular blobs of a given polarity will have an edge that appears to be of the opposite polarity. It will always be the edge facing the limb (edge) of the Sun. These circular blobs are sunspots, and because their magnetic fields fan out at their boundaries (penumbrae), this can cause a ‘false’ polarity to be observed when the sunspot is near the limb of the Sun, because of the way that we measure magnetic fields with this instrument. The ‘true’ polarity of a sunspot is the one that is on the edge facing away from the limb of the Sun. In the images above, the first sunspot is ‘truly’ positive/white, the middle one is ‘truly’ positive/white, and the last one is ‘truly’ negative/black.
The ‘hollowed-out’ artifact seen in the leading sunspot is due to a saturation effect, as noted by Sunspotter volunteers @Quia and @Mjtbarrett. The problem is that for MDI, much of the data was processed on-board the spacecraft. Because of the way that the processing was designed from the beginning, the model used to calculate the magnetic fields broke down for very large fields (>2000 gauss), resulting in the saturation. Unfortunately, the effect isn’t even linear, so even at lower fields (1k-1.5k G) you start to see problems. Also, the non-linearity makes it hard to correct for.
A paper on the effect is available. In a future blog post, we will explain the making of a magnetograms, and also touch on the saturation problem.
Often, there will be nothing to see at all. This is usually because the sunspot group that was recorded by human observers at the beginning of the day has already progressed beyond the edge of the Sun by the time our spacecraft took the data. And since we are relying on human observers to pick out sunspot groups in the current data set, sometimes we end up with nothing…
Last but not least: this is an image of a particularly large sunspot group that released many significant eruptions (some of the largest that we know of!). Note the elongated strip of negative (black) magnetic flux, sandwiched between the two areas of positive (white flux). This sandwiching may have caused the shearing and stretching of the fields to the point of breakage. The double polarity separation line pattern seen here (tracing the dividing line between the white and black areas) is of particular interest to me, as I am convinced it that it is often a sign that a large eruption could occur.
If you come across any other interesting artifacts or patterns in the data, save them by going to the ‘Profile’ tab in the main sunspotter interface. There is plenty to discover about what makes sunspots go boom!
Sunspots emerge in groups, and their configuration varies in complexity (as you can see when you click through a few classifications). But, what does complexity really mean?
Allow me to start where many of my research projects start- Wikipedia 🙂 -which defines complexity as follows:
- Complexity characterises something with many parts in intricate arrangement.
- ‘Complexity science’ is the study of the phenomena that emerge from a collection of interacting objects.
- Displaying variation without being random.
In our case, something = a sunspot group, parts = sunspots, phenomena = eruptions emerging from a group of sunspots… and so on. Since we are using magnetograms to study sunspot groups, we define the ‘parts’ as regions of a given polarity (positive/white or negative/black) within a given sunspot group. Thus, to describe a sunspot group as simple (the opposite of complex), we consider whether a straight line could be drawn, so that all the white areas of the image are on one side and black on the other.
Panel A in the above image shows a simple ‘bipolar’, with basically one area of white and one of black (an even more simple configuration would be all white or all black). A more complex region, like the one shown in the panel B, would require a more curvy (possibly more than a single line) to divide the image into white and black regions. An image of countless white and black spots (like that shown in the rightmost panel) appears to be random (like noise), and thus simple. So, true complexity lies between the two extremes of being uniformly white/black and being composed of random speckles of white and black. Unfortunately, computers have a hard time finding that sweet spot of true complexity, but humans can spot it quite easily– looks like 3.5 billion years of evolution for the human brain was worth the wait!
The top row of images shows the configuration of the magnetic field in the solar atmosphere above the sunspot groups; hot, bright gas is confined to magnetic loops, much like iron filings being confined to the magnetic field of a bar magnet. The bottom row shows the magnetic ‘footprint’ of the above magnetic structures.
There is currently a sunspot group ‘classification’ system which is used as a ‘proxy’ or place-holder for complexity, since the real thing hasn’t been measured yet. Experts at observatories around the world sort images of sunspot groups into several classes. The most simple class is ‘alpha’: one polarity of magnetic field (bottom-left panel.). Slightly more complex is ‘beta’: two polarities, or bipolar. A classification of ‘gamma’ is more complex, and indicates that the polarities are mixed together (i.e., essentially that a single, fairly straight line cannot divide the regions of white and black). There are other variations of these basic classifications, that are part of the ‘Hale’ or ‘Mt. Wilson’ classification scheme. The Hale scheme relies on both magnetograms and visible light images of sunspot groups.
Another widely used scheme is called the McIntosh system The drawback to using these schemes for indicating complexity is that there are only a few broad classes and it is not entirely clear how these classes should be ordered in terms of complexity. On the other hard, we want a continuous measure of complexity, like a scale from 1-100, with arbitrary precision; basically a meter stick for sunspot group complexity.
Lots of previous work (Abramenko 2005; McAteer et al. 2005; Ireland et al. 2008; Conlon et al. 2008; Hewett et al. 2008) has sought to design an algorithm that could indicate the complexity of a sunspot group. A common method utilises the concept of ‘fractals’. The fractal dimension of a 2D object can range between 1 (a line) and 2 (a square). It has been found sunspot groups with certain fractal dimensions are somewhat more likely to produce flares. More recently, Georgoulis (2012) has shown that the association is marginal at best. Moreover, fractality is incredibly hard to interpret physically.
Other proxies for complexity consider the magnetic connectivity between different areas within a sunspot group (Georgoulis & Rust 2007; Ahmed et al. 2010). Properties based on connectivity scale much better with sunspot group flaring than fractal dimension. However, the relationship between these properties and ‘complexity’ is not clear. Also, these properties cannot be accurately determined away from the center of the observed solar disk, due to foreshortening (since the Sun is a sphere, the sunspot groups get squashed at the edges).
This plot shows the Hale class of a large sample of sunspot groups (colored symbols), the size of each group (horizontal, X-axis) and the magnitude of the largest solar flare produced by each group (vertical, Y-axis). The point is that larger and more complex sunspot groups produce larger flares, which is very important in solar flare prediction, and supports our motivation for running Sunspotter.
By the way, you may enjoy some interesting TED talks related to the idea of complexity that put this abstract concept into a more practical light:
- Nicolas Perony on the interface between Puppies and Complexity Theory
- Stephen Wolfram on using Chaos theory to achieve a ‘Theory of Everything’
- Benoit Mandelbrot on the discovery of Fractals
Complexity is difficult to put into words. If you have an ideas about complexity you would like to share, lets discuss it on Talk or the comments section below!
Hello citizen scientists! As leader of the Sunspotter science team, it is my job to welcome you to the project. If you would like to learn something about solar activity, and what makes the Sun such an interesting system to study, then I’m sure you will enjoy following this blog. And, as the project develops, we will keep you up to date on what we are learning from your classifications.
I am a solar astrophysicist and have spent the last few years investigating what makes sunspots tick. Sunspots are like animals – they are born, they grow and change, and then slowly they decay and fade away, leaving barely a trace (the trace they leave is important, but I’ll explain that in a future blog post). They live for a couple of weeks, and although their lives are short, from time to time they release the most powerful (in energy per time) events in the solar system: solar flares. It is my job to study the evolution of sunspots to determine when and why they release flares, in an effort to predict them; much like a weather forecaster on Earth, I try to forecast space weather. This is important work because adverse space weather resulting from solar flares can jeopardise our GPS system, affect the health of our astronauts, inhibit long range radio communications, endanger our power grid, and the list goes on…
If you have already tried a few classifications, you may have noticed that the images of sunspots shown do not look like a dark spot on a bright Sun, they look more like a mix of black and white on a gray background. This is because they are maps of the sunspot magnetic fields (a.k.a., magnetograms). Magnetic fields are what make the Sun and especially sunspots (where the strongest magnetic fields are) so interesting.
We use computer programs to automatically track sunspots and calculate their various physical properties. For example, we determine the area they cover on the Sun’s surface, their maximum magnetic field values, their total magnetic flux (magnetic field times area) and so on. We have found that the properties most likely to help us predict the future occurrence of eruptions are related to the line(s) separating the black and white areas of the magnetogram, known as a polarity separation line. For example, a sunspot group is much more likely to flare if it has a long line separating the white and black areas, rather than a short one.
It is also known that the more complex a sunspot group is, the more likely it is to flare. Thus, we seek to reliably measure the complexity of sunspot groups. And this is where you come in … and where computers fail, since they cannot yet determine the complexity of an image in a meaningful way. Humans on the other hand, have a pretty easy time of indicating which is the more complex of a pair of images. Scientists have been trying (pretty unsuccessfully) to come up with a way to represent the complexity of sunspot groups for a long time. The Sunspotter project might just be the golden ticket!
So, please help us reliably measure the complexity of our sunspot group images. The results of this project could lead to significant advances in our ability to predict flares. This is only phase 1 … stay tuned for more!