Informative, concise, and full of excellent diagrams. Be safe - please don't stare at the sun without a proper filter!!
Bees & Bombs is a wonderful animated gif blog. Check out his other work!
This is amazing! It works for Baltimore. :)
Eventually one learns to do this without even reading the article. :)
I thought this would be a simple tutorial...
while this is extremely funny, still, it’s showing perfectly what is Houdini capable of. Made in 1 day :o
Note: he shared source file in the description - if interested
One of my favorite phenomena in math is how you can make a teeny tiny change to a problem statement and end up with a completely different problem in terms of difficulty!
Today I learned about a rather remarkable open problem in mathematics, which looks tantalizingly easy. The question was posed by Ron Graham.
Consider the following recursively defined sequence:
Innocent question: is this sequence unbounded? Surprisingly, the answer to this is unknown—at least according to the source article dating from 2000, Unbounded orbits and binary digits by M. Chamberland and M. Martelli.
Here is a very similar problem where we know the answer.
The question is the same, is this sequence bounded or not? In other words, is it possible that the sequence never goes above a certain number?
Try to solve it! It is not easy but you don’t need any kind of advanced tools.
The zoomed in image of the sun with the Earth superimposed is super helpful.
Welcome to the summer American Astronomical Society (AAS) meeting in Austin, Texas! Astrobites is attending the conference as usual, and we will report highlights from each day here. If you’d like to see more timely updates during the day, we encourage you to search the #aas230 hashtag on twitter. We’ll be posting once a day during the meeting, so be sure the visit the site often to catch all the news!
Plenary Session: George Ellery Hale Prize, The Solar Magnetic Field: From Complexity to Simplicity (and Back) (by Benny Tsang)
The morning plenary session started with the George Ellery Hale Prize presentation of our speaker Manfred Schüssler (Max Planck Institute for Solar System Research) for his “outstanding contributions over an extended period of time to the field of solar astronomy”. Eugene Parker, who first discovered the magnetism and polarity of sunspots and who we named NASA’s new Sun-touching spacecraft after, was the first scientist to have received this honor. Today Schüssler led us on a journey to disentangle the Sun’s complex magnetic field with simple models — can this really be done?
Zoomed-in images showing the complex structures within structures on the Sun’s surface. [NAOJ, JAXA, NASA]
To get a sense of the level of complexity in the magnetic structures of the Sun, let’s first take a look at some images. On the seemingly simple and boring surface, we see tiny features around sunspots (middle panel) and granules (hot, rising pockets of gas; right panel). In addition, all these are highly turbulent and dynamical, so we are faced with the challenge of explaining a hierarchy of time-varying complexities on a wide range of scales.
Numerical simulations have tried to reproduce the observed features by including physics at different scales — from the near-surface layer, to the deeper layer where the magnetic field is believed to be created, to the whole convection zone. Although simulations are not perfect in reproducing all features, Schüssler stressed that they offer an otherwise unavailable 3D view of the Sun, which allows new questions to be asked. Among all, the small-scale dynamo model shows the most promising prospects for explaining most of the observed small-scale structures. This dynamo process is so fundamental that it is believed to prevail even when the first generation of stars were born.
The Sun can be quite predictable in its own way. The highly regular, 11-year cycle of sunspot activity and the 22-year field direction reversal are two examples. Such regularities can be understood by the Babcock and Leighton (BL) model pretty well, which describes the activities as driven by the twisting of magnetic field lines in the Sun by its rotation. That said, the full picture of Solar magnetism is still far from being complete. As an example, Schüssler noted that the emergence of magnetic field deeper in the Sun (flux emergence) assumed in the BL model seems to be extremely complex in and of itself. Future scientists, I think we could really use some help here.
Press Conference: Bending & Blending (by Benny Tsang)
The last press conference of this AAS meeting featured two speakers and had a rather enigmatic title: Bending and Blending. To summarize in one sentence, it was about the bending of light by a white dwarf, and the blending of a suite of versatile tools for better data visualization.
— Science Press Pkg (@scipak) June 7, 2017
Kailash Sahu (Space Telescope Science Institute) led the discussion of a truly exceptional microlensing event. One of the crucial tests of Einstein’s theory of general relativity is the bending of light around massive objects. Unlike typical gravitational lensing by clusters of galaxies, microlensing events are caused by objects with stellar or planetary masses. Sahu’s team observed a foreground white dwarf (Stein 2051 B) deflecting light of a background star. By analysing the images formed by this “white dwarf lens”, they estimated its mass to be 0.675 times the mass of the Sun (with ~7% error). Until this discovery, all mass estimates of white dwarfs have relied on binary systems. Sahu’s discovery opened up a new way to measure white dwarfs’ masses, which could empower many new discoveries in astronomy. [Full press release]
Aside: If you wish to do your personal gravitational lensing observation, there’s a chance during the upcoming total Solar eclipse event on Aug 21. We can all be part of it!
Kent showing examples of visualization projects by astronomers. This includes the making of protoplanetary disks, galaxy mergers, N-body simulations, and a fly-through of a 3D source catalog!
Next, Brian Kent (National Radio Astronomy Observatory) illustrated the multi-purpose, well-documented, scientific data visualization tool he built, known as Blender. Data from multi-wavelength observations and advanced supercomputer simulations have been growing in both size and complexity. Not only is visualization required to help communicate new discoveries to the general public, but scientists themselves rely heavily on efficient visualization tools to make discoveries in the first place. Recently Kent has even combined Blender with Google Spatial Media to “put data in the hands of the audience” — data visualization on users’ mobile devices. We can start making our own scientific art pieces now by following the tutorials and reading the new Blender book! [Full press release]
Plenary Session: CANDELS: A Cosmic Quest for Distant Galaxies Offering Live Views of Galaxy Evolution (by Benny Tsang)
Inventor of photometric redshift measurement David Koo (University of California, Santa Cruz) told the story of the cosmic quest to understand galaxy formation. Having recently retired to “finally do research full-time”, Koo started by clarifying a common question about the CANDELS program — the name ‘CANDELS’ is indeed an intentional misspelling to avoid generic results on search engines. CANDELS is a Hubble Space Telescope legacy survey with an unprecedented amount of data, providing both wide and deep coverage of galaxies. The entire image database consists of 250,000 galaxies from redshift of 1.5 to 8.
A small patch of the Hubble Ultra Deep Field image showing variations of environments and galaxy types within just a single image. [Image credit: HUDF/HST]
With complementary coverage by Herschel and Spitzer (infrared), Chandra (X-ray), and GALEX (ultraviolet), we earn the bread and butter for galaxy evolution, e.g. stellar mass, size, star formation rate, and morphology. In particular, the addition of the X-ray band provides important hints about galaxies’ central supermassive black holes. An important component of the CANDELS program is the inclusion of theorists working with N-body and hydrodynamical simulations. By reproducing observed galaxies from first principles, simulations allow us to track them back in time (like rewinding a movie) to see the processes of their evolution.
Throughout the talk Koo filled the entire hall with his warmth, and he didn’t hesitate to give thanks to his team. Besides the principal investigators Sandra Faber (who has won the Bruce Gold Medal, the “cosmology Nobel prize”) and Henry Ferguson, he also thanked astronaut Andrew Feustel for installing the camera that made CANDELS possible! With the prospects of new telescopes such as JWST, ALMA, SKA, and those of decades to come, Koo echoed Casey on Day 1, envisioning that detailed mapping of gas and dust is the future of astronomical observations.
Authors: S. J. Bolton, A. Adriani, V. Adumitroaie, M. Allison, J. Anderson, S. Atreya et al.
First Author’s Institution: Southwest Research Institute, San Antonio, USA
Status: Published in Science, [open access]
When we gaze upon the night sky, we can often spot Jupiter. We have also launched several missions to explore Jupiter, like Pioneer, Voyager, and Galileo. Yet all we have “seen” stops at the cloud tops. Now our understanding is going to change by NASA’s Juno mission, which provides us the privilege to have a glimpse through the clouds. The primary science goal of Juno is to measure the deep composition and internal structure, so that we can better understand the formation and evolution of Jupiter and planetary formation in general. Juno takes elliptical orbits around Jupiter to minimize the damage from the radiation belt. Once every 53 days, Juno accomplishes a close flyby and takes as many photos as possible. Today’s paper brings some exciting new results from first few close passes of Juno probe.
Figure 1. The close-up, three-color images of the north and south poles of Jupiter obtained 27 August 2016. The circular features are cyclones, range from 200 to 1400 km in diameter. Source: featured paper.
Before Juno, we never had a close look at Jupiter’s polar regions. Figure 1 shows the snapshots of north and south poles taken by the visible-light camera, JunoCam, with a resolution of 50 km. From this amazingly detailed image, we see that the familiar band and belt zonal structure vanishes at about 60° latitude. It is replaced by ovals and spiral-like features, which are revealed to be cyclones and waves in the temporal sequence of shots. It is now very interesting and challenging to explain the dynamical transition from bands to cyclones and the differences from Saturn’s poles (e.g. Saturn has north polar hexagon).
Figure 2. Jupiter’s brightness temperatures for all six channels, obtained during Juno’s first two passes of Jupiter in 2016. The brightness at each wavelength depends on the mean temperature of the atmospheric layer where the main emission is from, which in turn is determined by the molecular absorption. The frequencies of channels 1 to 6 are 0.6, 1.2, 2.6, 5.2, 10, and 22 GHz, respectively. The white circles indicate the footprint sizes for channels 3 to 6. Source: featured paper.
Figure 3. latitude-altitude map of ammonia: The blue band at the top is where ammonia is condensing and the abundance is low. The high abundance at the equator is interpreted as it is transported from the deep atmosphere at pressures of 100 bars or more. Source: featured paper.
MWR (the microwave radiometer) measures the thermal emission coming from below the cloud deck, which provides very powerful eyesights through the clouds with its large antenna. Figure 2 displays the brightness temperatures measured by MRW at different wavelengths, corresponding to emission flux from different depths. The team argues that the ~50 K variations of brightness temperature are not due to physical temperature since the equatorial wind would have been much greater in that case. Instead, they are caused by the variations of microwave opacity, and ammonia is the dominant microwave opacity source. Therefore, the authors work out a global ammonia abundance that best matches the observed brightness temperatures, as shown in Figure 3. Again, surprisingly, ammonia is not well-mixed, as predicted by the equilibrium chemistry. There is an equatorial plume lifting ammonia up and descending at higher latitude, resembling a Hadley cell on Earth. If the ammonia distribution is indeed driven by the circulation below the cloud layer, we definitely need some new ideas and models to understand what drives the circulation!
By measuring the Doppler shift of radio signals, Juno can also set constraints on Jupiter’s gravity field. What they found now is the current interior models do not agree precisely with the Juno data. These can be contributed to several uncertainties such as the equation of state for hydrogen-helium mixtures, the presence of a core, the assumption of an adiabatic interior, and the differential rotation (different part of the body at different latitude/depth rotates with different angular velocity). We would get a better handle on this with further Juno measurements of smaller components of the gravity.
It is usually more exciting to see unexpected things. What Juno has found so far hints us that our models of giant planets may be a little too oversimplified. Juno will continue more close passes around Jupiter until 2018, so we can expect more surprises and puzzles to come!
These kinds of charts are all-too-common. Beware.
Someone sent me this chart via Twitter, as an example of yet another terrible pie chart. (I couldn't find that tweet anymore but thank you to the reader for submitting this.)
At first glance, this looks like a pie chart with the radius as a second dimension. But that is the wrong interpretation.
In a pie chart, we typically encode the data in the angles of the pie sectors, or equivalently, the areas of the sectors. In this special case, the angle is invariant across the slices, and the data are encoded in the radius.
Since the data are found in the radii, let's deconstruct this chart by reducing each sector to its left-side edge.
This leads to a different interpretation of the chart: it’s actually a simple bar chart, manipulated.
The process of the manipulation runs against what data visualization should be. It takes the bar chart (bottom right) that is easy to read, introduces slants so it becomes harder to digest (top right), and finally absorbs a distortion to go from inefficient to incompetent (left).
What is this distortion I just mentioned? When readers look at the original chart, they are not focusing on the left-side edge of each sector but they are seeing the area of each sector. The ratio of areas is not the same as the ratio of lengths. Adding purple areas to the chart seems harmless but in fact, despite applying the same angles, the designer added disproportionately more area to the larger data points compared to the smaller ones.
In order to remedy this situation, the designer has to take the square root of the lengths of the edges. But of course, the simple bar chart is more effective.
This is terrifying.
Ice in Antartica is in constant (very slow) motion, and as ocean waters warm, the flow of ice accelerates. The New York Times mapped the flows, showing where the ice is headed.
And, if you’re interested in how they did this, NYT graphics editor Derek Watkins provides the rundown.
I love this blog. "Horble Gray" is poetic. :)
2016 brought us the world’s blackest black, Vantablack, while 2017 has already introduced us to Harvard’s collection of the world’s rarest colours. We now have the first group of colours created by AI. Research Scientist, Janelle Shane, who took a neural network (for non-sciencey brains, that’s an artificial network made up of a number of computer systems), and tasked it with creating a unique set of colours with accompanying names. Janelle states, “for this experiment, I gave the neural network a list of about 7,700 paint colours along with their RGB values. (RGB = red, green, and blue colour values). Could the neural network learn to invent new paint colours and give them attractive names?”.
The answer was yes (well, almost!) Janelle created an algorithm for the network that completed two tasks: the creation of the RGB value of the colour and the selection of letters to form the colour name. The first results were promising, and the AI had managed to produce valid RGB values, however, the punchy and eye-catching names were lacking a little, and it seemed to be favouring brown, blue and grey hues.
The network developed further and could soon spell green and grey and was expanding its palette of colours, however it was failing to place the green and grey terms alongside the relevant colour.
Then the more creative (we’re not sure we’d be able to pronounce them!) names began.
Finally, the network reached a level of intelligence where colours matched names (almost!). You could see some of the below being right at home alongside the likes of Farrow & Ball’s Elephant’s Breath!
I really enjoy these elegantly animated geometry theorems.
No matter how you choose the red points, the three blue point will lie on a straight line.
The red line is known as the Pascal line of the hexagon.
This is terrifying. If you are scientifically literate, this is terrifying.
Listen to Wikipedia. Bells indicate additions, string plucks indicate subtractions. Pitch changes according to the size of the edit. Green circles show edits from unregistered contributors, and purple circles mark edits by bots. New users joining the site are punctuated by a string swell.
It sounds like modern minimal music. Very pretty stuff.
A Decline in Attention Span with the Rise of Mobile: A look at 60 million visits to my site 2010-7 [OC]
Interesting observations about declining attention span possibly being correlated with increasing use of mobile devices.
This is terrifying. If you are scientifically literate, this is more frightening than anything David Cronenberg ever imagined.
This is a fascinating article about differing views on what it means to be "genius".
“Scenius” is a term coined by musician and producer Brian Eno to counter “The Lone Genius Myth,” or the idea that innovation in art and culture comes from a few Great Chosen Ones. When Eno draws what the traditional model of genius looks like, he uses the example of the symphony orchestra, with God or the Muse at the very top of the triangle, and on descending levels, the composer, the conductor, the musicians, and, finally, the audience listening:
He then draws other organizations in our society that traditionally have hierarchical models:
When he gets to “scenius,” or what he calls the communal form of genius, he draws this:
Here’s what I wrote about it in my book, Show Your Work!:
There’s a healthier way of thinking about creativity that the musician Brian Eno refers to as “scenius.” Under this model, great ideas are often birthed by a group of creative individuals—artists, curators, thinkers, theorists, and other tastemakers—who make up an “ecology of talent.” If you look back closely at history, many of the people who we think of as lone geniuses were actually part of “a whole scene of people who were supporting each other, looking at each other’s work, copying from each other, stealing ideas, and contributing ideas.” Scenius doesn’t take away from the achievements of those great individuals: it just acknowledges that good work isn’t created in a vacuum, and that creativity is always, in some sense, a collaboration, the result of a mind connected to other minds.
What I love about the idea of scenius is that it makes room in the story of creativity for the rest of us: the people who don’t consider ourselves geniuses. Being a valuable part of a scenius is not necessarily about how smart or talented you are, but about what you have to contribute—the ideas you share, the quality of the connections you make, and the conversations you start. If we forget about genius and think more about how we can nurture and contribute to a scenius, we can adjust our own expectations and the expectations of the worlds we want to accept us. We can stop asking what others can do for us, and start asking what we can do for others.
To put it even more simply: Genius is an egosystem, scenius is an ecosystem.
Our world is an ecosystem in which our only real chance at survival as a species is cooperation, community, and care, but it’s being lead by people who believe in an egosystem, run on competition, power, and self-interest.
This was the message of the great feminist and pacifist Ursula Franklin, who said:
The dream of a peaceful society to me is still the dream of a potluck supper. The society in which all can contribute, and all can find friendship. Those who bring things, bring things that they do well. [We must] create conditions under which a potluck is possible.
When you think about your family, your friends, your neighborhood, your office, your city, your country, your world… are you operating as an ecosystem or an egosystem?
Which model we choose to operate under will determine the quality of our lives, and, arguably, our survival.
This is an amazingly detailed write-up about the subtle differences in two popular map systems. Well worth the read!
Google collects much of their own data to construct their maps, whereas Apple sources most of their data externally. This difference, coupled with varying cartography that changes over time, means an interesting contrast between the two map services. Justin O’Beirne took monthly screenshots for a year to look at the differences more closely.
A simple and elegant example of effective data visualization. (bar charts were ineffective, bump charts were much more effective)
Take a look at the following chart, and guess what message the designer wants to convey:
This chart accompanied an article in the Wall Street Journal about Wells Fargo losing brokers due to the fake account scandal, and using bonuses to lure them back. Like you, my first response to the chart was that little has changed from 2015 to 2017.
It is a bit mysterious the intention of the whitespace inserted to split the four columns into two pairs. It's not obvious that UBS and Merrill are different from Wells Fargo and Morgan Stanley. This device might have been used to overcome the difficulty of reading four columns side by side.
The additional challenge of this dataset is the outlier values for UBS, which elongates the range of the vertical axis, squeezing together the values of the other three banks.
In this first alternative version, I play around with irregular gridlines.
Grouped column charts are not great at conveying changes over time, as they cause our eyes to literally jump over hoops. In the second version, I use a bumps chart to compactly highlight the trends. I also zoom in on the quarterly growth rates.
The rounded interpolation removes the sharp angles from the typical bumps chart (aka slopegraph) but it does add patterns that might not be there. This type of interpolation however respects the values at the "knots" (here, the quarterly values) while a smoother may move those points. On balance, I like this treatment.
PS. [6/2/2017] Given the commentary below, I am including the straight version of the chart, so you can compare. The straight-line version is more precise. One aspect of this chart form I dislike is the sharp angles. When there are more lines, it gets very entangled.
Really nice dataviz and interpretation.
Interesting tool for exploring educational attainment vs. congressional district.
Nice visualization of something that can be very hard to grasp since we're just not accustomed to thinking on this scale.
The Kansas Budget Crisis Explained - What happened when a state attempted the most extreme tax restructuring in modern history?
This is a really nice video breakdown of how the state budget works and how it has changed over time.
This is a really nifty insight into how people draw pictures of common items. :)
A clever distortion field visualization of a subway map.
Beautiful example of how this open-source project has grown!
If you're not following this gif artist, do so immediately. He always posts elegant and creative geometry-based looping animations.