Science

Planets evenly spaced on log scale

Posted on by John

The previous post was about Kepler’s observation that the planets were spaced out around the sun the same way that nested regular solids would be. Kepler only knew of six planets, which was very convenient because there are only five regular solids. In fact, Kepler thought there could only be six planets because there are only five regular solids.

The distances to each of the planets is roughly a geometric series. Ratios of consecutive distances are between 1.3 and 3.4. That means the distances should be fairly evenly spaced on a logarithmic scale. The plot below shows that this is the case.

Distance to planets in our solar system on log scale

The plot goes beyond the six planets known in Kepler’s day and adds four more: Uranus, Neptune, Pluto, and Eris. Here I’m counting the two largest Kuiper belt objects as planets. Distances are measured in astronomical units, i.e. Earth = 1.

Update: The fit is even better if we include Ceres, the largest object in the asteroid belt. It’s now called a dwarf planet, but it was considered a planet for the first fifty years after it was discovered.

Distances in our solar system including planets and dwarf planets

Extrasolar planets

Is this just true of our solar system, or is it true of other planetary systems as well? At the time of writing, we know of one planetary system outside our own that has 8 planets. Three other systems have around 7 planets; I say “around” because it depends on whether you include unconfirmed planets. The spacing between planets in these other systems is also fairly even on a logarithmic scale. Data on planet distances is taken from each system’s Wikipedia page. Distances are semimajor axes, not average distances.

This post just shows plots. See this follow up post for regression results.

Kepler-90

Kepler-90 is the only planetary system outside our own with eight confirmed planets that we know of.

Distances to planets in Kepler-90 system

HD 10180

HD 10180 has seven confirmed planets and two unconfirmed planets. The unconfirmed planets are included below, the 3rd and 6th objects.

Distances to planets in HD 10180 system

HR 8832

The next largest system is HR 8832 with five confirmed planets and two unconfirmed, numbers 5 and 6 below. It would work out well for this post if the 6th object were found to be a little closer to its star.

Distances to planets in HR 8832 system

TRAPPIST-1

TRAPPIST-1 is interesting because the planets are very close to their star, ranging from 0.01 to 0.06 AU. Also, this system follows an even logarithmic spacing more than the others.

Distances to planets in TRAPPIST-1 system

Systems with six planets

There are currently four known systems with four planets: Kepler-11, Kepler-20, HD 40307, and HD 34445. They also appear to have planets evenly spaced on a log scale.

Distances between planets in known systems with six planets

Like TRAPPIST-1, Kepler-20 has planets closer in and more evenly spaced (on a log scale).

More astronomy posts

Planets and Platonic solids

Johann Kepler discovered in 1596 that the ratios of the orbits of the six planets known in his day were the same as the ratios between nested Platonic solids. Kepler was understandably quite impressed with this discovery and called it the Mysterium Cosmographicum.

Kepler's Mysterium Cosmographicum

I heard of this in a course in the history of astronomy long ago, and have had in the back of my mind that one day I’d look into this in detail. How exactly do you fit these regular solids together? How well do the planetary ratios match the regular solid ratios?

Imagine the orbit of each planet being the equator of a spherical shell centered at the sun. The five regular solids fit snugly into the spaces between the shells. Between Mercury and Venus you can insert an octahedron. Its inradius is the distance of Mercury to the sun, and its circumradius is the distance of Venus to the sun. You can fit the other regular solids in similarly, the icosahedron between Venus and Earth, the dodecahedron between Earth and Mars, the tetrahedron between Mars and Jupiter, and the hexahedron (cube) between Jupiter and Saturn.

Here’s the data on the inradius and circumradius of each regular solid taken from Mathworld.

    |--------------+--------------+----------|
    | solid        | circumradius | inradius |
    |--------------+--------------+----------|
    | octahedron   |      0.70722 |  0.40825 |
    | icosahedron  |      0.95106 |  0.75576 |
    | dodecahedron |      1.40126 |  1.11352 |
    | tetrahedron  |      0.61237 |  0.20412 |
    | hexahedron   |      0.86603 |  0.50000 |
    |--------------+--------------+----------|

Here’s the data on average orbit radii measured in astronomical units taken from WolframAlpha.

    |---------+----------|
    | Planet  | Distance |
    |---------+----------|
    | Mercury |  0.39528 |
    | Venus   |  0.72335 |
    | Earth   |  1.00000 |
    | Mars    |  1.53031 |
    | Jupiter |  5.20946 |
    | Saturn  |  9.55105 |
    |---------+----------|

So how well does Kepler’s pattern hold? In the table below, “Planet ratio” is the radius ratios of Venus to Mercury, Earth to Venus, etc. “Solid ratio” is the circumradius to inradius ratio of the regular solids in the order given above.

    |--------------+-------------|
    | Planet ratio | Solid ratio |
    |--------------+-------------|
    |      1.82997 |     1.73205 |
    |      1.38246 |     1.25841 |
    |      1.53031 |     1.25841 |
    |      3.40419 |     3.00000 |
    |      1.83340 |     1.73205 |
    |--------------+-------------|

Not a bad fit, but not great either. I’ve heard that the fit was better given the data available to Kepler at the time; if Kepler had had more accurate data, he might not have come up with his Mysterium Cosmographicum.

By the way, notice some repetition in the solid ratios. The implied equalities are exact. The icosahedron and dodecahedron have the same ratio of circumradius to inradius because they are dual polyhedra. The same is true for the cube and the octahedron. Also, the ratio of 3 for the tetrahedron is exact.

Update: What if Kepler had known about more planets? The next planet ratios in our table above would be 2.01, 1.57, and 1.35. None of these is close to any of the solid ratios.

More polyhedra posts

Gravitational attraction of stars and cows

Posted on by John

cow

One attempt at rationalizing astrology is to say that the gravitational effects of celestial bodies impact our bodies. To get an idea how hard the stars and planets pull on us, let’s compare their gravitational attraction to that of cows at various distances.

Newton’s law of gravity says that the gravitational attraction between two objects is proportional to the product of their masses and inversely proportional to the square of the distance between them. From this we can solve for how far away a cow would have to be to have the same gravitational attraction.

d_{\text{cow}} = d_{\text{star}} \sqrt{ \frac{m_{\text{cow}}}{m_\text{star}} }

For round numbers, let’s say a cow weighs 1000 kg. (Strictly speaking, that’s a more typical weight for a bull than a cow.) Then Jupiter has as much gravitational tug on a person as does a cow about two feet away.

Regulus, the brightest star in Leo, has the same pull as a cow about 5 miles away. Alpha Centauri, the closest star to Earth (other than the sun, of course) has about the same pull as a cow 18 miles away.

Calculations were based on average distance. The distance to planets changes over time, but so does the distance to a given cow, and the relative change in the latter over time is potentially much larger.

|----------------+--------------|
| Body           | Cow distance |
|----------------+--------------|
| Jupiter        |     0.57 m   |
| Venus          |     2.45 m   |
| Mars           |    10.06 m   |
| Regulus        |     7.81 km  |
| Alpha Centauri |    28.70 km  |
|----------------+--------------|

Could you read on Pluto?

Posted on by John

Reading at sunset

I heard somewhere that Pluto receives more sunlight than you might think, enough to read by, and that sunlight on Pluto is much brighter than moonlight on Earth. I forget where I heard that, but I’ve done a back-of-the-envelope calculation to confirm that it’s true.

Pluto is about 40 AU from the sun, i.e. forty times as far from the sun as we are. The inverse square law implies Pluto gets 1/1600 as much light from the sun as Earth does.

Direct sun is between 30,000 and 100,000 lux (lumens per square meter). We’ll go with the high end because that’s an underestimate of how bright the sun would be if we didn’t have an atmosphere between us and the sun. So at high noon on Pluto you’d get at least 60 lux of sunlight.

Civil twilight is roughly enough light to read by, and that’s 3.4 lux. Moonlight is less than 0.3 lux.

60 lux would be comparable to indoor lighting in a hall or stairway.

Are coffee and wine good for you or bad for you?

Posted on by John

wine coffee

One study will say that coffee is good for you and then another will say it’s bad for you. Ditto with wine and many other things. So which is it: are these things good for you or bad for you?

Probably neither. That is, these things that are endlessly studied with contradictory conclusions must not have much of an effect, positive or negative, or else studies would be more definitive.

John Ioannidis puts this well in his recent interview on EconTalk:

John Ioannidis: … We have performed hundreds of thousands of studies trying to look whether single nutrients are associated with specific types of disease outcomes. And, you know, you see all these thousands of studies about coffee, and tea, and all kind of —

Russ Roberts: broccoli, red meat, wine, …

John Ioannidis: — things that you eat. And they are all over the place, and they are always in the news. And I think it is a complete waste. We should just decide that we are talking about very small effects. The noise is many orders of magnitude more than the signal. If there is a signal. Maybe there is no signal at all. So, why are we keep doing this? We should just pause, and abandon this type of design for this type of question.

Misplacing a continent

Posted on by John

There are many conventions for describing points on a sphere. For example, does latitude zero refer to the North Pole or the equator? Mathematicians tend to prefer the former and geoscientists the latter. There are also varying conventions for longitude.

Volker Michel describes this clash of conventions colorfully in his book on constructive approximation.

Many mathematicians have faced weird jigsaw puzzles with misplaced continents after using a data set from a geoscientist. If you ever get such figures, too, or if you are, for example, desperately searching South America in a data set but cannot find it, remember the remark you have just read to solve your problem.

More geography posts

Mercury and the bandwagon effect

Posted on by John

Mercury

The study of the planet Mercury provides two examples of the bandwagon effect. In her new book Worlds Fantastic, Worlds Familiar, planetary astronomer Bonnie Buratti writes

The study of Mercury … illustrates one of the most confounding bugaboos of the scientific method: the bandwagon effect. Scientists are only human, and they impose their own prejudices and foregone conclusions on their experiments.

Around 1800, Johann Schroeter determined that Mercury had a rotational period of 24 hours. This view held for eight decades.

In the 1880’s, Giovanni Schiaparelli determined that Mercury was tidally locked, making one rotation on its axis for every orbits around the sun. This view also held for eight decades.

In 1965, radar measurements of Mercury showed that Mercury completes 3 rotations in every 2 orbits around the sun.

Studying Mercury is difficult since it is only visible near the horizon and around sunrise and sunset, i.e. when the sun’s light interferes. And it is understandable that someone would confuse a 3:2 resonance with tidal locking. Still, for two periods of eight decades each, astronomers looked at Mercury and concluded what they expected.

The difficulty of seeing Mercury objectively was compounded by two incorrect but satisfying metaphors. First that Mercury was like Earth, rotating every 24 hours, then that Mercury was like the moon, orbiting the sun the same way the moon orbits Earth.

Buratti mentions the famous Millikan oil drop experiment as another example of the bandwagon effect.

… Millikan’s value for the electron’s charge was slightly in error—he had used a wrong value for the viscosity of air. But future experimenters all seemed to get Millikan’s number. Having done the experiment myself I can see that they just picked those values that agreed with previous results.

Buratti explains that Millikan’s experiment is hard to do and “it is impossible to successfully do it without abandoning most data.” This is what I like to call acceptance-rejection modeling.

The name comes from the acceptance-rejection method of random number generation. For example, the obvious way to generate truncated normal random values is to generate (unrestricted) normal random values and simply throw out the ones that lie outside the interval we’d like to truncate to. This is inefficient if we’re truncating to a small interval, but it always works. We’re conforming our samples to a pre-determined distribution, which is OK when we do it intentionally. The problem comes when we do it unintentionally.

Photo of Mercury above via NASA

Freudian hypothesis testing

Posted on by John

Sigmund Freud

In his paper Mindless statistics, Gerd Gigerenzer uses a Freudian analogy to describe the mental conflict researchers experience over statistical hypothesis testing. He says that the “statistical ritual” of NHST (null hypothesis significance testing) “is a form of conflict resolution, like compulsive hand washing.”

In Gigerenzer’s analogy, the id represents Bayesian analysis. Deep down, a researcher wants to know the probabilities of hypotheses being true. This is something that Bayesian statistics makes possible, but more conventional frequentist statistics does not.

The ego represents R. A. Fisher’s significance testing: specify a null hypothesis only, not an alternative, and report a p-value. Significance is calculated after collecting the data. This makes it easy to publish papers. The researcher never clearly states his hypothesis, and yet takes credit for having established it after rejecting the null. This leads to feelings of guilt and shame.

The superego represents the Neyman-Pearson version of hypothesis testing: pre-specified alternative hypotheses, power and sample size calculations, etc. Neyman and Pearson insist that hypothesis testing is about what to do, not what to believe. [1]

I assume Gigerenzer doesn’t take this analogy too seriously. In context, it’s a humorous interlude in his polemic against rote statistical ritual.

But there really is a conflict in hypothesis testing. Researchers naturally think in Bayesian terms, and interpret frequentist results as if they were Bayesian. They really do want probabilities associated with hypotheses, and will imagine they have them even though frequentist theory explicitly forbids this. The rest of the analogy, comparing the ego and superego to Fisher and Neyman-Pearson respectively, seems weaker to me. But I suppose you could imagine Neyman and Pearson playing the role of your conscience, making you feel guilty about the pragmatic but unprincipled use of p-values.

* * *

[1] “No test based upon a theory of probability can by itself provide any valuable evidence of the truth or falsehood of a hypothesis. But we may look at the purpose of tests from another viewpoint. Without hoping to know whether each separate hypothesis is true or false, we may search for rules to govern behaviour in regard to them, in following which we insure that, in the long run of experience, we shall not often be wrong.”

Neyman J, Pearson E. On the problem of the most efficient tests of statistical hypotheses. Philos Trans Roy Soc A, 1933;231:289, 337.

Simulating seashells

Posted on by John

In 1838, Rev. Henry Moseley discovered that a large number of mollusk shells and other shells can be described using three parameters: k, T, and D.

simulated seashell

First imagine a thin wire running through the coil of the shell. In cylindrical coordinates, this wire follows the parameterization

r = ekt
z = Tt

If T = 0 this is a logarithmic spiral in the (r, θ) plane. For positive T, the spiral is stretched so that its vertical position is proportional to its radius.

Next we build a shell by putting a tube around this imaginary wire. The radius R of the tube at each point is proportional to the r coordinate: R = Dr.

The image above was created using k = 0.1, T = 2.791, and D = 0.8845 using Øyvind Hammer’s seashell generating software. You can download Hammer’s software for Windows and experiment with your own shell simulations by adjusting the parameters.

See also Hammer’s book and YouTube video:

Publishable

Posted on by John

For an article to be published, it has to be published somewhere. Each journal has a responsibility to select articles relevant to its readership. Articles that make new connections might be unpublishable because they don’t fit into a category. For example, I’ve seen papers rejected by theoretical journals for being too applied, and the same papers rejected by applied journals for being too theoretical.

“A bird may love a fish but where would they build a home together?” — Fiddler on the Roof