The importance of understanding toilets and politics

tl;dr: Understand things before you have an opinion

In today’s world, there is much discussion about fake news, about political movements becoming more extreme and about a divided society. Unfortunately, I rarely hear anyone discuss why our society is diverging and what can be done to prevent this. So please bear with me while I introduce a psychological issue which is a promising tool for cooling off and understanding the origin of heated political disagreements.

 

Picture a flush toilet and ask yourself: how well do you understand how this toilet works? Maybe rate it from 1-7? Are you above the average, which would presumably go for a 3 or 4? Now please stop reading and explain to yourself how that toilet you (hopefully) use every day works! Go through every step before reading on.

Did you explain where the water comes from, why there is water down there in the first place, how the toilet knows how much water to flush, how it refills the correct amount,…? Do you still think you understand it as well as you assessed a few seconds ago?

You may be under the illusion of explanatory depth, termed and examined by Leonid Rozenblit and Frank Keil in 2002, in short, IOED. People feel they understand complex phenomena with far greater precision, coherence and depth than they really do.

One of the most important reasons for IOED is the confusion of higher and lower levels of analysis. Most complex systems are hierarchical in terms of explanations of their natures. In explaining a cell phone, one might describe the components such as a camera, buttons, loudspeakers and apps. If then asked what a camera is, you might start explaining flashes, apertures, lenses etc. The illusion of explanatory depth occurs when we gain a surface layer understanding and then stop asking any questions!

Another reason for IOED is the rarity of production: we rarely give explanations and therefore have little information on past successes and failures which would help us classify our knowledge. In contrast, we often tell narratives of events or retrieve facts; hence it is often easy to assess our average level of knowledge in these cases by inspection of past performance.

 

In case I you are still reading (thanks, I guess), you may be starting to wonder why I am boring you with toilets. To be fair, you will (almost) never need to know how these things work, this is simply the division of cognitive labor and ultimately how our society can function on a high level.

But of course, the IOED extends well beyond toilets, to how we think about scientific fields, mental illnesses, economic markets, politics and virtually anything we are capable of (mis)understanding. Not understanding how toilets work is one thing, not understanding the history of Jerusalem and all the involved parties and still having a strong opinion on how this should be handled – that is a very different thing. Today, the IOED is profoundly pervasive, given our access to infinite information which we consume in large quantities – however, most do this in a superficial manner. Most of us consume knowledge widely, but not deeply!

Fortunately, understanding the IOED allows us to combat political extremism. In 2013, Philip Fernbach and colleagues demonstrated that the IOED underlies people’s policy positions on issues like single-payer health care, a national flat tax, and a cap-and-trade system for carbon emissions. As in Rozenbilt and Keil’s studies, Fernbach first asked people to rate how well they understood these issues, and then asked them to explain how each issue works and subsequently re-rate their understanding of each issue. In addition, participants rated the extremism of their attitudes on these issues both before and after offering an explanation. Both self-reported understanding of the issue and attitude extremity dropped significantly after explaining the issue – people who strongly supported or opposed an issue became more moderate. These studies suggest that IOED awareness is a powerful tool for cooling off heated political disagreements.

 

In a time where income inequality, urban-rural separation and strong political polarization have fractured us over social and economic issues, recognizing our own (at best) modest understanding of these issues is a first step to bridging these divisions.

The next time you are having an intense debate about Trump’s politics, unconditional basic income or your educational system, take a step back and contemplate on whether you are in a position of real understanding or rather throwing superficial arguments at one another.

And as always, stay curious!

 

For deeper insights on this topic, I can highly recommend Dr. Fernbach’s book, “The Knowledge Illusion: Why We Never Think Alone“, Keil and Rozenblit’s original paper: “The misunderstood limits of folk science: an illusion of explanatory depth” and, of course, the Wikipedia page on flush toilets!

The Conjunction Fallacy

You think your rational, right? Given all facts and enough time to decide, you can always come up with the correct solution?

Well, really sorry to break it to you, but you are far from being a rational boolean agent! In fact we humans are sh*t at statistical decisions.

In this, and maybe some upcoming posts I will explain some cases where humans don’t act according to simple logic. These flaws may come in handy, once you are aware of them! Obviously, the best approach is to test these fallacies on you, the reader.
So, assume following description of Heiner is true. Given these facts, order the 6 options below by their probability meaning the most probable option first and then ending with the least probable option:

Heiner is 34 years old. He is intelligent, but unimaginative, compulsive, and generally lifeless. In school, he was strong in mathematics but weak in social studies and humanities.

No complaints about the description, please, this experiment was done in 1974. 

A:  Heiner is an accountant.
B:  Heiner is a physician who plays poker for a hobby.
C:  Heiner plays jazz for a hobby.
D:  Heiner is an architect.
E:  Heiner is an accountant who plays jazz for a hobby.
F:  Heiner climbs mountains for a hobby.

Take a moment to rank these six propositions by probability. Write them down so you can`t cheat.

In a very similar experiment conducted by Tversky and Kahneman in 1982, 92% of 94 undergraduates at a well-known American university gave an ordering with A > E > C, did you do too? The ranking E > C was also displayed by 83% of 32 grad students in the decision science program of Stanford Business School, all of whom had taken advanced courses in probability and statistics.

There is a certain logical problem with saying that Heiner is more likely to be an account who plays jazz, than he is to play jazz.  The conjunction rule of probability theory states that, for all X and Y, P(X&Y) <= P(Y).  That is, the probability that X and Y are simultaneously true, is always less than or equal to the probability that Y is true. Violating this rule is called a conjunction fallacy.

Imagine a group of 100,000 people, all of whom fit Heiner’s description (except for the name, perhaps).  If you take the subset of all these persons who play jazz, and the subset of all these persons who play jazz and are accountants, the second subset will always be smaller because it is strictly contained within the first subset.

Why would highly educated students with knowledge of statistic still fail this test? Why did you fail the test even though you knew it was a test? One explanation would be a misunderstanding of the statements, a problem with wording and framing. Maybe you understood “A:  Heiner is an accountant” as “Heiner is an accountant but he doesn’t play Jazz”.

It is then possible that “E: accountant & plays jazz” > “A: accountant & doesn’t play Jazz”. Another problem is that many people will maybe mix probability and plausibility – meanings “what is plausible”, and “whether there is evidence”. But we’ll talk about this later, some more tests first.


Tversky and Kahneman (1983), played undergraduates at UBC and Stanford for real money:

Consider a regular six-sided die with four green faces and two red faces. The die will be rolled 20 times and the sequences of greens (G) and reds (R) will be recorded.  You are asked to select one sequence, from a set of three, and you will win $25 if the sequence you chose appears on successive rolls of the die.

1.  RGRRR
2.  GRGRRR
3.  GRRRRR

65% of the subjects chose sequence 2, which is most representative of the die, since the die is mostly green and sequence 2 contains the greatest proportion of green rolls.  However, sequence 1 dominates sequence 2, because sequence 1 is strictly included in 2.

This clears up possible misunderstandings of “probability” as stated above, since the goal was simply to get the $25.


Another experiment from Tversky and Kahneman (1983) was conducted at the Second International Congress on Forecasting in July of 1982. The experimental subjects were 115 professional analysts, employed by industry, universities, or research institutes. Two different experimental groups were respectively asked to rate the probability of two different statements, each group seeing only one statement:

“A complete suspension of diplomatic relations between the USA and the Soviet Union, sometime in 1983.”

“A Russian invasion of Poland, and a complete suspension of diplomatic relations between the USA and the Soviet Union, sometime in 1983.”

Estimates of probability were low for both statements, but significantly lower for the first group (1%) than the second (4%). Since each experimental group only saw one statement, there is no possibility that the first group interpreted (1) to mean “suspension but no invasion”.
This excludes the first explanation I stated!

So, adding more detail or extra assumptions can make an event seem more plausible, even though the event necessarily becomes less probable.

There are many other fallacies and psychological effects that show how limited and non-bayesian our brain actually is. Though this one really hit me hard!

Take a bit of time the next days to figure out where this effect is important. When a politician tells you how he wants to achieve something it seems more reachable, right? In politics and economics predicting the future is important but basically no one is good at it. By adding of details and explanations everything seems more probable. Especially the last test example with the USA is impressive. It almost seems like our brain is happy not to think about why something will happen and happily accepts the explanation of a polish invasion, even though this makes the whole statement less probable!


The reason why this fallacy is growing in relevance is this new technology, the internet (Neuland).

Thanks to the vast amount of information (or misinformation) on the internet, we are all able to build stories, ideas and “facts” on the fly. Nowadays, we can all easily be misguided by confirmation bias, our natural tendency to search for information that confirms our beliefs and to ignore that which threatens our beliefs.

 The problem is that the abundance of blocks makes it very easy to string together stories supporting A, B, and C. The internet makes readily accessible vast numbers of marginally relevant or manifestly false details about almost everything and everybody. Because of this, confirmation bias and the conjunction fallacy are very easy traps to fall into. Untrue stories are believable not only because of our partisanship and our confirmation bias, but also because of the proliferation of information on the internet. Anyone can now pick and choose from the internet’s vast trove of “facts” to colorfully embellish a simple story and make it superficially plausible.


This in itself is a whole other topic I should maybe not squeeze into this post, but anyway, can’t erase it now…

There are many, many other situations in life the conjunction fallacy applies to. So the next time you are tempted to believe something or someone, trust mathematics not yourself!

And as always, stay curious!

Superintelligent Artificial Intelligence


“This is all speculation and is solely an intectual game!”, you might say. Well, then sit back and enjoy me wasting my time on irrelevant topics
. But maybe, just maybe I can convince you that we have to take this more serious and consider actions to take?

Tl;dr: Too long; didn’t read: We will all die!


Introduction

General intelligent minds have been anticipated since the 1940s. But like some other technologies (looking at you, nuclear fusion!), the expected arrival date moves forward as time goes by. What was to be in 20 years in 1950 is to be in 25 years now in 2016.

An agent is super intelligent when he is better at solving a wide range of “intellectual problems” than any human being. These problems could range from solving a Rubix Cube, to setting up the best 3-year strategy for your company or understanding how superconductivity at 290 °C could work.

Today a agent can only barely solve the first problem but the other two are far beyond its scope. Still, it is impressive how far our intelligent computers have come. John McCarthy once said: “As soon as it works, no one calls it AI anymore”

In the 50s experts were of the opinion that a machine beating a man at chess is surely the ultimate triumph of AI – which it wasn’t.  We have beaten humans in several games e.g. Chess – 1997 Deep Blue  ; Joepardy – 2010 by IBM’s Watson and DeepMinds AlphaGo beat a world class Go player in March 2016!

Almost everyone would agree, however, that general intelligence as defined above goes beyond beating humans at a particular game. Donald Knuth once remarked: “AI are good at doing the thinking (chess, maths, logics…), but they fail at doing things, animals and humans do without thinking.” I find this thought very intuitive: We can create things that mimic our “out loud” thinking, such as calculating or solving a simple puzzle but we can’t reproduce anything more subtle.

Paths to superintelligence

Over the last decades, experts have thought of many different ways to achieve post human intelligence. I will present the most common ideas briefly and then move on with an depth analysis of artificial computer intelligence from a seed AI.

Whole Brain emulation project: This idea is fairly straightforward. Take a brain, slice it into really thin pieces, scan them and then emulate an exact copy on a computer. When done correctly, we will have transferred the humans brain to an emulated (not simulated!) brain. (Is that the same person you might ask? Well, stop getting of topic!!!) This project is interesting in the way that we know/think it works. There is no: “We need to the figure out how to figure out how to achieve that” The Whole Brain emulation is going to take some advances in imaging and emulation and will cost a lot money but in the end, it could provide a working brain in a computer that we can then change and improve.

Another rather controversial plan is simple biological selection: figure what genes lead to higher intelligence, create a society where only the most intelligent humans mate, improve their offspring by selective ex vitro fertilization (designer babies) and then wait and repeat. Ignoring very serious moral issues, this method is simply very slow and doesn’t promise accelerated IQ growth like other methods.

Simulated Evolution: Again, this concept is easy to understand. Start with seed code simulated in an environment that challenges the agent to evolve by changing its own code. By letting every “generation” solve a wide range of problems you can direct the evolution process towards a smarter agent.

Seed AI: Code a seed AI based on values and motivation as well as copying human learning processes, help it develop, teach it new techniques then teach it to develop itself and work on it, till it has reached human level intelligence and you’ve got yourself an artificial intelligence teaching itself to become smarter.

So, let us propose the agent reaches human or above human level intelligence as experts propose will eventually happen. What effect would this have?

Intelligence Explosion

Now the thing a human-intelligent AI can do that we can’t do is self-improvment. It can change its code or add hardware in order to become better at solving the problems it wants to overcome. This self-improvement is obviously an exponential growth. Think of that graph showing the world population from 0 A.D. to today. The last 150 years look like an explosion in comparison to the rest! This is what would happen to a self-improving agent.

I may remark at this point that it is important to talk about how fast such an explosion from below human level to above human level intelligence occurs. If this happens very slowly meaning years, maybe even decades then we might have a controllable competition for the first human intelligent AI between nations and/or companies. A fast takeoff on the other hand seems less desirable because we have less to no control of the situation. A fast takeoff or even a medium one, would entail the creation of a decisive advantage of one agent over all others, possibly creating a singleton = a single global decision-making agency. This isn’t unprecedented as for example with the nuclear bomb in 1949. USA could have put all efforts into stopping any other nations development of a nuclear bomb, creating a worldwide monopoly.

Cognitive Superpowers

We should not limit our thinking of what an AI can do and what it can’t and we shouldn’t project todays PCs onto the agent. We have an idea how a human with an IQ of 130 is considered smart and will be more likely to excel at academics than somebody with an IQ of 90. But when considering an agent with an IQ of 6000; we have no idea what that means!

Here are some superpowers to consider: Intelligence amplification (make yourself smarter); strategizing (achieve goals and overcoming opponents), social manipulation, computer hacking, technology research and economic productivity just to name a few. Having these abilities make it possible to overcome everyone else even if that is humanity itself!

Doomsday for humanity

Here is a simple scenario:

Researchers make a seed AI, which becomes increasingly helpful at improving itself. This is called the pre-criticality phase. Next, we enter the recursive self-improvement phase which means the seed AI is the main force for improving itself and brings about an intelligence explosion. It perhaps develops all superpowers it didn’t already have. In a covert preparation, the AI makes up a robust long term plan, pretends to be nice, and escapes from human control if need be. This may include breaking through potential barriers, by getting access to the internet, persuade whole organizations etc. Now the agent has full control and moves on the overt implementation where the AI goes ahead with its plan, perhaps killing the humans at the outset to remove opposition. Once we are on this track, there is no way to stop it.

The Motivation Problem

The whole story boils down to this question: Can we control the agent in a way that its actions will benefit us in a way we want? This may seem like an easy job, right? Just put it into the code.

Unfortunately, it is not easy at all. Consider we can hard code and define the goal of a seed AI, and it can’t change it then this still doesn’t change anything. Let me give you some examples:

Final goal: Make us Happy. – AI implants electrodes stimulation the pleasure center in every human being. This is an example for so-called perverse instantiation because the AI does what you ask, but what you ask turns out to be most satisfiable in unforeseen and destructive ways.

Other examples fall under the category of Infrastructure profusion: in pursuit of some goal, an AI redirects most resources to infrastructure, at our expense:
The AI is supposed to solve the Riemann hypothesis. Considering the AI doesn’t find the solution right away, the AI might build up infrastructure and computing power. That means exploiting everything on earth, then building Van Neumann probes and mining all neighboring solar systems in an epic cosmic endeavor and so on. Humans are the first thing such an agent would get rid of.

Sure, there are clever people coming up with clever ideas to solve the motivation problem to control the AI and not to end up destroying mankind by mistake. The scope of what these methods are and how they could help is well beyond this already very long blog post. But the real problem remains!

Closing remarks

If we create something super intelligent and we haven’t found a solution to the motivation problem, then humanity will most likely end. This is in many ways hard to grasp and sounds like fantasy, even to me. But the people who know the most about this topic think there is an 80% probability we will have created AI until 2075; that’s still in my lifetime!

What I am trying to say, I think, is that our society needs to think and talk more about this. Even though it seems far away, it is not something we can be serious enough about!


And as always, stay curious!

Rainbows

When the Sir Isaac Newton explained the colours of the rainbow with refraction the poet John Keats was horrified. Keats complained (through poetry of course) that a mathematical explanation robbed these marvels of nature of their magic.

Whether you, dear reader, agree with Keats’ view or not, it is time to deep dive into the mathematical explanation, requiring just basic geometry of lines and circles. As we will see, the explanation is just as elegant as the rainbows themselves.
When sunlight enters a droplet from any angle some light is reflected and some is refracted into the raindrop. We most commonly encounter refraction when we look at a straw in a glass, it seems distorted and cut off. How much light is refracted is determined by the refraction index Latex formula which is simply Latex formula; the speed of light in vacuum divided by the speed of light in the new medium which makes n a number between one and (usually) two.
Due to this underlying mechanism, the angle of the light beam changes according to Snell’s law Latex formula , where Latex formula is the geometry1refractive index of the first medium (air, Latex formula = 1) and Latex formula is the incidental angle. The secondary angle is a bit smaller because the refractive index of water is around 1,34.  From here some of the sunlight reflects off the back side of the raindrop and then leaves the raindrop through the “bottom” where the light is refracted again, same as when it entered the raindrop.

Now, depending on where the light enters the raindrop it will exit the raindrop at different angles. We can calculate the total deflection D by adding up all grey angles Latex formula. We then calculate the derivative for the incidental angle to find the minimum. This minimum angle has the most intense light and creates what we can see as a rainbow. Remember that we are plotting the total deflection, the angle between the sunlight and you looking up will be 180°-D. This ends up to be around 42° and does not depend on the size of the water droplet.

observerSupposedly Descartes (mostly known for his philosophy) figured all this out graphically, but he did not understand why the rainbow showed different colors. He didn’t know that every medium has a different refractive index for each color which is a wavelength in the electro-magnetic spectrum, Blue’s refractive index is around 1,342 while red’s is around 1,331 resulting in different deflection angles.

There are so many further interesting facts to point out but I will try to keep the list short:

26273684822_26a9df07f0_zIn theory there are many more orders of the rainbow, each one reflects light once more inside of the rain trop. The second order rainbow which has its colors reversed is the only one you can frequently see with bare eyes and is located about 10° above the first order rainbow.

Rainbows seen from the ground can only occur in the morning or the evening due to the 42°-degree angle between you and the sun. If the sun is higher than 42° -degrees, the rainbow will be below the horizon (unless you are up high and looking down). Seen from an airplane, rainbows are full circles directly opposite of the sun!

seawaterrainbowLast but not least, seawater has a higher refractive index than rain water, so the radius of the seabow is a bit smaller, making for some crazy photos!

 

 


So next time you see one of these colorful arcs appear in the sky, try to remember the elegant math behind what you’re seeing!

And as always, stay curious!

Hawai’i

Now, other than Kiruna, I do not have to explain where Hawai’i is or why you would want to there, do I? So this will be more like a travel blog. Before we start with “travelstuffs” its time for some SCIENCE, so bear with me.

Hawai’i as a region subsists of several islands emerging out of the water in the middle of the pacific. The leading and widely accepted theory on stationary hotspots proposed by geophysicist Tuzo Wilson as late as 1963 explains the existence of the Hawai’in islands. Another well-known example of a hotspot is Yellowstone. Imagine hot magma moving upward from the lower part of the earth’s core up to the lithosphere which is the region just below the earth’s crust.

source: wikimedia
source: wikimedia

These hotspots are stationary; the crust above though moves ever so slowly (about as fast as your fingernails grow), creating a line of volcanos behind the current location of the hotspot.

Hwaiin seamount chain source: wikimedia
Hwaiin seamount chain
source: wikimedia

The whole Hawai’i seamount chain is almost 6000 kms long! Of course the old volcano islands are worn down by erosion and eventually disappear below the waster surface.

Although this explanation should be enough for now, I do need to point out that there are many open questions and different theories concerning this topic.

The two most popular and populated islands are Big Island and O’ahu. I will be concentrating on O’ahu.

Before we begin here, I would like to mention your chance of seeing at least one rainbow per day is pretty high (not the license plate of Hawai’i, the real ones). Rainbows are fascinating! So after you have red how and why they occur you can boast about your knowledge whenever you see one on Hawai’i.


So, for a beginning we can start with some classical stuff: Surfing. Chances are, you haven’t tried this “exotic” sport. If you are ever going to try, you should when you’re on O’ahu. Depending on the time of year, you can learn surfing on “small” waves on the north beach or on Waikiki beach which is the most famous beach in Honululu. Surf lesson will cost you around 50-80$ per hour so best do group lessons and find some options to get the lowest price. It’s fairly easy to get some first results (cinda standing on the board) after an hour or two. (By the way, did you know that water waves are really complicated to explain and need some extensive theories to roughly model them? Maybe I’ll do an article about that sometime)

 

hanauma-bay
Source: flickr commons

Another classic thing to do is snorkeling at Hanauma Bay. You need to arrive at least before 8 a.m. because the bay has a maximum capacity of tourists in order to preserve the corals. The earlier you come the less other tourists and the calmer the water but more importantly less sun. Snorkeling with your back exposed to the midday sun is not a good idea, believe me, I tried it :/. You may also want to keep the tide in mind. If it is low tide you will have fairly limited depth of water which can cause you to hit the corals while you are swimming. Also, if you buy some snorkeling gear beforehand it may be cheaper than to rent equipment at the bay itself.

You should definitely do some hiking around the island. From Diamond head you have a very nice view onto Waikiki beach. Inland there are several hikes to nice waterfalls through the rain forest. Or you can climb the 1048 steps up Koko head which allows for a great view on non-cloudy days.

Lastly I would recommend npcthe National pacific cemetery and or the pearl harbor museum. I know you probably don’t like museums but these two are a fast way to learn about the WWII pacific war. You don’t need to go on the pearl harbour tour that brings you to the sunken USS Arizona.
It’s just a sunken ship and you can’t really see anything. But the museum part of pearl harbor and the cemetery which is free show the whole marine war against the Japanese. As a European we only concentrate on how bad Hitler was and how the Allies kicked ass in Europe. I knew nothing about the pacific war before I was on O’ahu and it was really fascinating.

To do all the things mentioned above plus some other obvious and easy to find tourist destinations you will probably need around a full week.

So as you see, there is more to do on Hawaii than chilling on the beach and boasting on Facebook about you being in Hawaii. You can still do that, just don’t miss out on the other good stuff.

And as always, stay curious!

Rolling-shutter-effect

You may have encountered rolling-shutter-effect (RSE) when taking photos of a fast moving object, like propellers or a car on the highway. Propellers and cars get distorted or straight lines bend; so what’s going on here?

RSE, photo credit: flickr.com
RSE, photo credit: flickr.com

Most smartphone and digital cameras use so called Complementary Metal-Oxide-Semiconductor (CMOS) sensors while many other reflex cameras use CCD pixels (charge-coupled device). I won’t go into any detail on the physical difference between these because in order to understand our RSE we need to look at the light exposure.

shutter_anim_01
photo credit: jasmcole.com

With a CCD camera light exposure is what you would expect it to be: shine light on all pixels for x amount of time, then save that and move on. This is called global exposure. However, with the cheaper CMOS pixels the camera scans in horizontal line, usually from top to bottom. If any object in your frame is moving (or if you are) pixels will be exposed at different times thus objects will appear at different places in one snapshot.

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photo credit: jasmcole.com

Depending on how fast your object is moving in comparison to your cameras screening different patterns will appear

Try this with your smart phone the next time you’re on the highway (not as the driver) and try to figure out how your camera scans; left to right or top to bottom?

And as always, stay curious!

Polar lights

Polar lights are one of my favorite phenomena. They are colorful, hard to catch and still not fully understood! But let’s dive into the science behind them!

If this is too much physics for you, skip to the tl;dr a the bottom of the page for some practical takeaways.


We need to start out somewhere very hot, the sun. Here convections of charged particles (plasma) in the outer parts of the sun cause strong magnetic fields. Sometimes these fields move outwards creating rings that act like rubber bands. These will snap now and then, blowing out great amounts of plasma. These events are called solar flares, or when they are big: coronal mass ejections. The frequency of such events is linked to the suns 11-year cycle as the suns inverts its whole magnetic alignment though the intensity of each cycle varies.Sunspot_Numbers
We may even head into another Maunder-minimum which would be unfortunate for the science community and everyone who wants to see some aurora. The next peak in sun activity is set for 2023.

As you probably have guessed, these solar flares are part of the so called solar wind which ultimately causes the polar lights. This constant stream of mostly electrons, protons and some nuclei such as helium moves towards earth at speeds between 400 and 700 km/s (250 to 430 mps). Now a complicated process of deflecting, magnetic recombination, particle acceleration and other crazy stuff starts. The whole process hasn’t been understood fully and many effects (or theories) have been shown to play a role but how they all interact with each other is mostly unknown! I will try to map out the clearest elements of this complicated process.

Structure_of_the_magnetosphere-en.svg
Earth’s Magnetosphere

Coming from the sun we encounter the bow shock about 12.000 km (7500 miles) away from earth. Most low energy particles are diverted by the earths outer most magnetic fields and move around to the magnetotail. Some particles move into the polar cusp, creating the so called day-light aurora which we can obviously not see with our eyes. Other particles will move into one of the two Van Allen radiation belts. These belts hold great amounts of plasma and act as a reservoir for the aroura but are also constantly washed away and then refilled by stronger solar flares.

Fields_in_magnetic_bottlesThese belts as well as the plasma moving into earth’s atmosphere act in as an first approximation like magnetic bottle. These “bottles” are two magnetic mirrors placed together to create a trap for charged particles. In case of earth’s field, the poles act as the mirrors. When an electron moves in a helical (corkscrew) path along the magnetic field he will eventually approach a pole. Here the magnetic fields become denser and thus creating a backwards force on the particles due to the Lorenz force.

Aurora_australis_ringNow there are several conflicting but also non-conflicting theories about why particles on the night side are being accelerate towards the poles which I won’t go into in detail. Fact is that sometimes particles have enough energy to come spiraling as close as 80km (50 miles). This creates a so called auroral zone as seen in this image of the south pole from one of NASA’s satellites. This auroral zone will move towards the equator as long as the particles have enough energy, they will then move to the pole again. So once you’ve experienced some strong polar lights moving southward (if you’re on the north hemisphere), then wait for them to come back!

IMG_4965
Typical green curtains

Generally, we can differ between discrete, often shaped like curtains, and diffuse aurora. The most iconic and frequently seen aurora are green curtains at 100-150km (60-90 miles) due to fairly high concentrated oxygen emitting light at a wavelength of 557.7 nm. These curtains have a sharp cut of at 100km due to a fast concentration drop of oxygen. Slightly below the curtains one may spot some blue due to Nitrogen molecules being the dominant light source. These two colors are considered discrete aurora due to concentration drops or electrons with more or less discrete energy distributions. The most common diffuse aurora is the red emission of oxygen at high levels of altitude. These can be hard to spot by eye because of the dominant green curtains, but can often been seen on the horizon or in pictures due to the cameras better sensitivity (in comparison to our eyes) to red.

Most of these spectral lines are “forbidden”. This is a misleading term; one should rather call them highly improbable, due the selection rules of quantum mechanics. “Normal” emissions function on a nanosecond timescale. The red emission of oxygen however is very slow (107s) therefore this color will only occur in very high altitudes (>150km) where the probability of colliding with another atom is low enough. Otherwise if an excited atom collides, it will transfer energy and will not emit any red light anymore. Of course now and then other colors will appear often due to an overlap of green and blue or red and green etc.

I hope you have enjoyed this little deep dive into the aurora. It is definitely a very active and interesting field of science, be curious and check it out on your own!

Too long, didn’t read:

The next peak of sun activity should be in 2023, so that’s definitely a year to plan some polar vacation. The process of particles like electrons moving from the sun down to our atmosphere is not fully understood. There are indicators for polar lights that you can look up online every night. If you happen to experience strong polar lights moving towards the equator don’t leave! They are probably going to come back to the poles in the next hours.

Ion thrusters

This technology sounds and looks like it is more fiction than it is science and “Ion engines” are common in the Star Wars universe on TIE-fighters (Twin Ion Engine), but are they real?

Short answer: Yes!
Long answer: Yeeeeeeeeeeees!

The first time ion propulsion has been mentioned is as early as 1906. From then it took a long path to the 1970’s to actually employ this technology as was done on American and on Soviet satellites. Deep Space 1 was NASA’s first interplanetary mission using ion thrusters in 1998.

So if this technology is real, then why aren’t we seeing anything of this when we watch rocket launches now and then?

To get a rocket off the ground and away from earth’s gravity it needs thrust which is the formal term for the force pushing you up. If a spacecraft has an effective thrust of 10 Newton it could accelerate an object with a mass of 1 kg into outer space (considering the gravitational constant g=10 m/s²). Ion thrusters simply have a very low thrust; we’re talking about a maximum of 250 mN! This could carry a rocket weighing about 25 grams. Or in other terms: It would take more than a day to accelerate your car to highway speed with an ion thruster, not taking any resistances into account (of course!).

1280px-Ion_engine.svg
photo credit: wikimedia.org

Before I get into why we even use this propulsion even though it is so weak, I want to take you through a simple explanation of how ion thrusters work. Generally, we can differentiate between electrostatic and electromagnetic thrusters, the former using the Coulomb force and the later using the Lorenz force as the main acceleration mechanism. There are, of course, several different variations but I will only explain one of the most common: The gridded electrostatic ion thruster.
As seen in the diagram, gas is vented into the ion chamber where it is ionized (kick out an electron) by fast electrons coming from the hot cathode on the left. Magnetic coils help hold the ions and electrons in place, as well as help ionize the gas. The ions then move to the positive grid on the right and are then accelerated to about 1 keV and shoot out of the chamber. A second cathode emits electrons into the ion beam in order to compensate for the positive charge leaving the spacecraft and stop the ions from being attracted back to the thruster.

For most thrusters, Xenon is used as a propellant gas. What you are looking for is an easy to ionize and heavy atom because the thrust is proportional to the mass of the accelerated ions. Xenon is also a noble gas and therefore doesn’t erode any parts of the thruster. However, Xenon is globally in short supply and expensive.

Now that we have a crude understanding, we can see how these thrusters are different from conventional rocket propellant. The biggest difference is the effective speed at which particles leave the spacecraft. For conventional rockets, e.g. the Space shuttle, the escaped velocity is around 4.400 m/s (16.000 km/h; 10.000 mph). For our standard ion thruster, the ions are accelerated to about 30.000 m/s! In space exploration the specific impulse of a propellant is very important, that is the total impulse delivered per unit of propellant consumed. If you accelerate your particles to high velocities, every little bit of mass you eject will have a relatively big impact in comparison to lower escape velocities. So ion thrusters have about ten times the specific impulse of conventional rockets which is really important because that simply means you need less propellant. Thus the overall mass of the spacecraft is smaller, reducing the amount of propellant again. This goes on until the spacecraft has no mass at all… just kidding.

Atlantis_taking_off_on_STS-27
Space Shuttle Atlantis; photo credit: wikimedia.org

There are two reasons conventional rockets don’t just use higher escape velocities as well. Firstly, conventional rockets are like big heat machines and a thing called Carnot’s law limits their exhaust velocity; secondly, the energy you need to accelerate stuff goes with the velocity squared and conventional rockets have limited energy reserves, ion thrusters though basically have unlimited electrical energy from their solar panels!


So in the end, ion thrusters are much more efficient, lighter and precise, but lack the ability to create strong thrust and can only be operated in outer space. A conventional rockets brings a spacecraft to outer space and then the ion thrusters take it from there in a well … slow manner.

This definitely isn’t the only promising technology out there but it is the most practical at the time and has been and will be a crucial part of many space mission.

And as always, stay curious!

Cold beauty of Kiruna

If you are one of those people who don’t like destinies just because it is cold there, please stop reading (no just kidding… a bit). But to be fair: If your kind of vacation includes pools, beaches and pina coladas, then you probably shouldn’t waste the time reading on, unless of course you’re as curious as I am and are therefore ready to try something new.

So yea, Kiruna is kind of cool considering its location. Never underestimate distances in Sweden and Norway! The domestic flight from Stockholm to Kiruna took longer than the flight from Hamburg to Stockholm. Kiruna is considered the northernmost town in Sweden and is lies just a bit north of the polar circle. That is the boarder at which one can experience at least one day of total darkness and respectively one whole day of “sun”.

The temperature in October, November, March and April, which are the optimal months to go to Kiruna for obvious reasons explained soon, is about -5 °C on average (23F for silly people). It will be dry, so all you need is maybe two layers for your legs and 3 layers including a good jacket for your upper body.

Getting there is pretty much only possible by plane, with a 90 min flight from Stockholm. About two planes a day arrive and leave in Kiruna. When I went there in 2015 I flew in a propeller plane. So definitely get the right seat and take your camera, you wouldn’t want to miss out on these kind of photos.

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photo credit: wikimedia.org

If you have no Idea what is going on here, you should learn something about the Rolling-shutter-effect before moving on.

Now I must admit I have been a bad storyteller and have been blabbering about this cold place in the middle of nowhere. So why would you even go there?

 


Polar lights, aurora borealis (opposed to aurora australis at the south pole), appear due to charged particles erupted by the sun hitting our atmosphere. They excite atoms as high up as 150 km (93 miles) creating lights that can be seen by the human eye from below. These wonders of nature are strongest in the months I mentioned above. If this hand waving explanation isn’t enough (and it shouldn’t be), check out my in-depth explanation on polar lights.

aurora borealis

Before I had gone to Kiruna I had never taken more than a handful of photos for myself. I used to believe special moment in life shouldn’t be perceived trough a lens but through our naked eye, and I still believe this. But polar lights are actually slow enough to do both!

It is surely not the easiest object to start taking photography serious as a hobby but if you’re fascinated by natures beauty, then you can use that as a stepping stone for photography. If you’re already into photography but have never played around with night-time photos, then you should definitely do that! All you need is a decent reflex camera, optimally with a good light-sensitive chip, a tripod and a shutter release cable (so you don’t move the cam as you start the exposure), then you’re ready to play around with ISO, aperture width and exposure time. As a last notice, always try to integrate some ground into your picture. Our stupid brains need a reference point otherwise no one will know what they are looking at.

OLYMPUS DIGITAL CAMERA

This picture has just a “small” strip aurora and yet several people have asked me if they could use it for their purpose in magazines. So obviously it isn’t only about getting many bright lights into your camera. (Nice brag I snuck in back there, huh?).

Fortunately for us Kiruna has a lot to offer when it’s too bright for polar lights:

The “Institutet för rymdfysik” (don’t just skip that word, try to pronounce it!), the Swedish institute for Space Physics, has its headquarters is Kiruna. They can display a wide range of research subjects from Polar Atmospheric to Solar Terrestrial Physics and are one of the leading providers of Ion thrusters! Actually if you’re looking into doing your SpaceMaster here would be a great place to study. I mean come on, that’s almost as good as Master of Disaster.

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photo credit: wikimedia.org

Moving on to the next space related topic. ESRANGE is a scientific rocket range and research center for high altitude balloons and small rockets such and BEXUS and REXUS, which are projects that are only open to university students! The unique part about ESRANGE is, that they can reclaim the rockets with the experiments because they landcrash on a big area north of Kiruna (yes there is even more land up north) instead of crashing into water. Usually they also tell the Russians before they launch a fast rocket into the stratosphere.

Last but not least you should end your trip with a visit to the biggest iron ore mine in the world. The reason why someone would even consider building a town up there in the beginning of the 20th century is, well… iron. Today they extract gigantic amount of ore from deeper than 1300 meters. If you ever wake up at night or are still awake taking nice photos you may experience some tremors due to the explosion in the mine. The iron ore lies under the city of Kiruna if you go deeper. Thus the company running the ore has to pay half the city money to relocate to a safer part of the city, what iron(y)!


So to sum things up, I hope you could see from the few photos how beautiful it is. I would highly recommend going there as an excursion with your university buds (or anything similar). If you love an adventure, a change in climate and are mildly interested in space science and/or nighttime photography, then this is definitely a place to check out!

Warm travels and stay curious!