# Quick Note

I’m trying something new! This blog post is available in two places, both here and on a Jupyter notebook. Over there, you can tweak and execute my source code, using it as a sandbox for your own explorations. Over here, it’s just a boring ol’ webpage without any fancy features, albeit one that’s easier to read on the go. Choose your own adventure!

Oh also, CONTENT WARNING: I’ll briefly be discussing sexual assault statistics from the USA at the start, in an abstract sense.

# Introduction

[5:08] Now this might seem pedantic to those not interested in athletics, but in the athletic world one percent is absolutely massive. Just take for example the 2016 Olympics. The difference between first and second place in the men’s 100-meter sprint was 0.8%.

I’ve covered this argument from Rationality Rules before, but time has made me realise my original presentation had a problem.

His name is Steven Pinker.

He looks at that graph, and sees a decline in violence. I look at that chart, and see an increase in violence. How can two people look at the same data, and come to contradictory conclusions?

Simple, we’ve got at least two separate mental models.

Finding the maximal likelihood, please wait ... done.
Running an MCMC sampler, please wait ... done.
Charting the results, please wait ...


All Pinker cares about is short-term trends here, as he’s focused on “The Great Decline” in crime since the 1990’s. His mental model looks at the general trend over the last two decades of data, and discards the rest of the datapoints. It’s the model I’ve put in red.

I used two seperate models in my blog post. The first is quite crude: is the last datapoint better than the first? This model is quite intuitive, as it amounts to “leave the place in better shape than when you arrived,” and it’s dead easy to calculate. It discards all but two datapoints, though, which is worse than Pinker’s model. I’ve put this one in green.

The best model, in my opinion, wouldn’t discard any datapoints. It would also incorporate as much uncertainty as possible about the system. Unsurprisingly, given my blogging history, I consider Bayesian statistics to be the best way to represent uncertainty. A linear model is the best choice for general trends, so I went with a three-parameter likelihood and prior:

This third model encompasses all possible trendlines you could draw on the graph, but it doesn’t hold them all to be equally likely. Since time is short, I used an MCMC sampler to randomly sample the resulting probability distribution, and charted that sample in blue. As you can imagine this requires a lot more calculation than the second model, but I can’t think of anything superior.

Which model is best depends on the context. If you were arguing just over the rate of police-reported sexual assault from 1992 to 2012, Pinker’s model would be pretty good if incomplete. However, his whole schtick is that long-term trends show a decrease in violence, and when it comes to sexual violence in particular he’s the only one who dares to talk about this. He’s not being self-consistent, which is easier to see when you make your implicit mental models explicit.

# Pointing at Variance Isn’t Enough

Let’s return to Rationality Rules’ latest transphobic video. In the citations, he explicitly references the men’s 100m sprint at the 2016 Olympics. That’s a terribly narrow window to view athletic performance through, so I tracked down the racetimes of all eight finalists on the IAAF’s website and tossed them into a spreadsheet.

Rio de Janeiro Olympic Games, finals
Athlete  Result  Delta
bolt    9.81   0.00
gatlin    9.89   0.08
de grasse    9.91   0.10
blake    9.93   0.12
simbine    9.94   0.13
meite    9.96   0.15
vicaut   10.04   0.23
bromell   10.06   0.25


Here, we see exactly what Rationality Rules sees: Usain Bolt, the current world record holder, earned himself another Olympic gold medal in the 100m sprint. First and third place are separated by a tenth of a second, and the slowest person in the finals was a mere quarter of a second behind the fastest. That’s a small fraction of the time it takes to complete the event.

Race times in 2016, sorted by fastest time
Name             Min time         Mean             Median           Personal max-min
-----------------------------------------------------------------------------------------------------
gatlin                        9.8         9.95         9.94         0.39
bolt                         9.81         9.98        10.01         0.34
bromell                      9.84        10.00        10.01         0.30
vicaut                       9.86        10.01        10.02         0.33
simbine                      9.89        10.10        10.08         0.43
de grasse                    9.91        10.07        10.04         0.41
blake                        9.93        10.04         9.98         0.33
meite                        9.95        10.10        10.05         0.44


Here, we see what I see: the person who won Olympic gold that year didn’t have the fastest time. That honour goes to Justin Gatlin, who squeaked ahead of Bolt by a hundredth of a second.

Come to think of it, isn’t the fastest time a poor judge of how good an athlete is? Picture one sprinter with a faster average time than another, and a second with a faster minimum time. The first athlete will win more races than the second. By that metric, Gatlin’s lead grows to three hundredths of a second.

The mean, alas, is easily tugged around by outliers. If someone had an exceptionally good or bad race, they could easily shift their overall mean a decent ways from where the mean of every other result lies. The median is a lot more resistant to the extremes, and thus a fairer measure of overall performance. By that metric, Bolt is now tied for third with Trayvon Bromell.

We could also judge how good an athlete is by how consistent they were in the given calendar year. By this metric, Bolt falls into fourth place behind Bromell, Jimmy Vicaut, and Yohan Blake. Even if you don’t agree to this metric, notice how everyone’s race times in 2016 varies between three and four tenths of a second. It’s hard to argue that a performance edge of a tenth of a second matters when even at the elite level sprinters’ times will vary by significantly more.

But let’s put on our Steven Pinker glasses. We don’t judge races by medians, we go by the fastest time. We don’t award records for the lowest average or most consistent performance, we go by the fastest time. Yes, Bolt didn’t have the fastest 100m time in 2016, but now we’re down to hundredths of a second; if anything, we’ve dug up more evidence that itty-bitty performance differences matter. If I’d just left things at that last paragraph, which is about as far as I progressed the argument last time, a Steven Pinker would likely have walked away even more convinced that Rationality Rules got it right.

I don’t have to leave things there, though. This time around, I’ll make my mental model as explicit as possible. Hopefully by fully arguing the case, instead of dumping out data and hoping you and I share the same mental model, I could manage to sway even a diehard skeptic. To further seal the deal, the Jupyter notebook will allow you to audit my thinking or even create your own model. No need to take my word.

I’m laying everything out in clear sight. I hope you’ll give it all a look before dismissing me.

# Model Behaviour

Our choice of model will be guided by the assumptions we make about how athletes perform in the 100 metre sprint. If we’re going to do this properly, we have to lay out those assumptions as clearly as possible.

1. The Best Athlete Is the One Who Wins the Most. Our first problem is to decide what we mean by “best,” when it comes to the 100 metre sprint. Rather than use any metric like the lowest possible time or the best overall performance, I’m going to settle on something I think we’ll both agree to: the athlete who wins the most races is the best. We’ll be pitting our models against each other as many times as possible via virtual races, and see who comes out on top.
2. Pobody’s Nerfect. There is always going to be a spanner in the works. Maybe one athlete has a touch of the flu, maybe another is going through a bad breakup, maybe a third got a rock in their shoe. Even if we can control for all that, human beings are complex machines with many moving parts. Our performance will vary. This means we can’t use point estimates for our model, like the minimum or median race time, and instead must use a continuous statistical distribution.This assumption might seem like begging the question, as variance is central to my counter-argument, but note that I’m only asserting there’s some variance. I’m not saying how much variance there is. It could easily be so small as to be inconsequential, in the process creating strong evidence that Rationality Rules was right.
3. Physics Always Wins. No human being can run at the speed of light. For that matter, nobody is going to break the sound barrier during the 100 metre sprint. This assumption places a hard constraint on our model, that there is a minimum time anyone could run the 100m. It rules out a number of potential candidates, like the Gaussian distribution, which allow negative times.
4. It’s Easier To Move Slow Than To Move Fast. This is kind of related to the last one, but it’s worth stating explicitly. Kinetic energy is proportional to the square of the velocity, so building up speed requires dumping an ever-increasing amount of energy into the system. Thus our model should have a bias towards slower times, giving it a lopsided look.

Based on all the above, I propose the Gamma distribution would make a suitable model.

(Be careful not to confuse the distribution with the function. I may need the Gamma function to calculate the Gamma distribution, but the Gamma function isn’t a valid probability distribution.)

Three versions of the Gamma Distribution


It’s a remarkably flexible distribution, capable of duplicating both the Exponential and Gaussian distributions. That’s handy, as if one of our above assumptions is wrong the fitting process could still come up with a good fit. Note that the Gamma distribution has a finite bound at zero, which is equivalent to stating that negative values are impossible. The variance can be expanded or contracted arbitrarily, so it isn’t implicitly supporting my arguments. Best of all, we’re not restricted to anchor the distribution at zero. With a little tweak …

… we can shift that zero mark wherever we wish. The $b$ parameter sets the minimum value our model predicts, while α controls the underlying shape and β controls the scale or rate associated with this distribution. α < 1 nets you the Exponential, and large values of α lead to something very Gaussian. Conveniently for me, SciPy already supports this three-parameter tweak.

My intuition is that the Gamma distribution on the left, with α > 1 but not too big, is the best model for athlete performance. That implies an athlete’s performance will hover around a specific value, and while they’re capable of faster times those are more difficult to pull off. The Exponential distribution, with α < 1, is most favourable to Rationality Rules, as it asserts the race time we’re most likely to observe is also the fastest time an athlete can do. We’ll never actually see that time, but what we observe will cluster around that minimum.

# Running the Numbers

Enough chatter, let’s fit some models! For this one, my prior will be

which is pretty light and only exists to filter out garbage values.

Generating some models for 2016 race times (a few seconds each) ...
# name          	α               	β               	b
gatlin          	0.288 (+0.112 -0.075)	1.973 (+0.765 -0.511)	9.798 (+0.002 -0.016)
bolt            	0.310 (+0.107 -0.083)	1.723 (+0.596 -0.459)	9.802 (+0.008 -0.025)
bromell         	0.339 (+0.115 -0.082)	1.677 (+0.570 -0.404)	9.836 (+0.004 -0.032)
vicaut          	0.332 (+0.066 -0.084)	1.576 (+0.315 -0.400)	9.856 (+0.004 -0.013)
simbine         	0.401 (+0.077 -0.068)	1.327 (+0.256 -0.226)	9.887 (+0.003 -0.018)
de grasse       	0.357 (+0.073 -0.082)	1.340 (+0.274 -0.307)	9.907 (+0.003 -0.022)
blake           	0.289 (+0.103 -0.085)	1.223 (+0.437 -0.361)	9.929 (+0.001 -0.008)
meite           	0.328 (+0.089 -0.067)	1.090 (+0.295 -0.222)	9.949 (+0.000 -0.003)
... done.


This text can’t change based on the results of the code, so this is only a guess, but I’m pretty sure you’re seeing a lot of α values less than one. That really had me worried when I first ran this model, as I was already conceding ground to Rationality Rules by focusing only on the 100 metre sprint, where even I think that physiology plays a significant role. I did a few trial runs with a prior that forced α > 1, but the resulting models would hug that threshold as tightly as possible. Comparing likelihoods, the α < 1 versions were always more likely than the α > 1 ones.

The fitting process was telling me my intuition was wrong, and the best model here is the one that most favours Rationality Rules. Look at the b values, too. There’s no way I could have sorted the models based on that parameter before I fit them; instead, I sorted them by each athlete’s minimum time. Sure enough, the model is hugging the fastest time each athlete posted that year, rather than a hypothetical minimum time they could achieve.

Charting some of the models in the posterior drives this home. I’ve looked at a few by tweaking the “player” variable, as well as the output of multiple sample runs, and they all are dominated by Exponential distributions.

Dang, we’ve tilted the playing field quite a ways in Rationality Rules’ favour.

Still, let’s simulate some races. For each race, I’ll pick a random trio of parameters from each model’s posterior and feet that into SciPy’s random number routines to generate a race time for each sprinter. Fastest time wins, and we tally up those wins to estimate the odds of any one sprinter coming in first.

Before running those simulations, though, we should make some predictions. Rationality Rules’ view is that (emphasis mine) …

[9:18] You see, I absolutely understand why we have and still do categorize sports based upon sex, as it’s simply the case that the vast majority of males have significant athletic advantages over females, but strictly speaking it’s not due to their sex. It’s due to factors that heavily correlate with their sex, such as height, width, heart size, lung size, bone density, muscle mass, muscle fiber type, hemoglobin, and so on. Or, in other words, sports are not segregated due to chromosomes, they’re segregated due to morphology.

[16:48] Which is to say that the attributes granted from male puberty that play a vital role in explosive events – such as height, width, limb length, and fast twitch muscle fibers – have not been shown to be sufficiently mitigated by HRT in trans women.

[19:07] In some events – such as long-distance running, in which hemoglobin and slow-twitch muscle fibers are vital – I think there’s a strong argument to say no, [transgender women who transitioned after puberty] don’t have an unfair advantage, as the primary attributes are sufficiently mitigated. But in most events, and especially those in which height, width, hip size, limb length, muscle mass, and muscle fiber type are the primary attributes – such as weightlifting, sprinting, hammer throw, javelin, netball, boxing, karate, basketball, rugby, judo, rowing, hockey, and many more – my answer is yes, most do have an unfair advantage.

… human morphology due to puberty is the primary determinant of race performance. Since our bodies change little after puberty, that implies your race performance should be both constant and consistent. The most extreme version of this argument states that the fastest person should win 100% of the time. I doubt Rationality Rules holds that view, but I am pretty confident he’d place the odds of the fastest person winning quite high.

The opposite view is that the winner is due to chance. Since there are eight athletes competing here, each would have a 12.5% chance of winning. I certainly don’t hold that view, but I do argue that chance plays a significant role in who wins. I thus want the odds of the fastest person winning to be somewhere above 12.8%, but not too much higher.

Simulating 15000 races, please wait ... done.

Number of wins during simulation
--------------------------------
gatlin                       5174 (34.49%)
bolt                         4611 (30.74%)
bromell                      2286 (15.24%)
vicaut                       1491 (9.94%)
simbine                       530 (3.53%)
de grasse                     513 (3.42%)
blake                         278 (1.85%)
meite                         117 (0.78%)


Whew! The fastest 100 metre sprinter of 2016 only had a one in three chance of winning Olympic gold. Of the eight athletes, three had odds better than chance of winning. Even with the field tilted in favor of Rationality Rules, this strongly hints that other factors are more determinative of performance than fixed physiology.

But let’s put our Steven Pinker glasses back on for a moment. Yes, the odds of the fastest 100 metre sprinter winning the 2016 Olympics are surprisingly low, but look at the spread between first and last place. What’s on my screen tells me that Gatlin is 40-50 times more likely to win Olympic gold than Ben Youssef Meite, which is a pretty substantial gap. Maybe we can rescue Rationality Rules?

In order for Meite to win, though, he didn’t just have to beat Gatlin. He had to also beat six other sprinters. If pM represents the geometric mean of Meite beating one sprinter, then his odds of beating seven are pM7. The same rationale applies to Gatlin, of course, but because the geometric mean of him beating seven other racers is higher than pM, repeatedly multiplying it by itself results in a much greater number. With a little math, we can use the number of wins above to estimate how well the first-place finisher would fare against the last-place finisher in a one-on-one race.

In the above simulation, gatlin was 39.5 times more likely to win Olympic gold than meite.
But we estimate that if they were racing head-to-head, gatlin would win only 62.8% of the time.
(For reference, their best race times in 2016 differed by 1.53%.)


For comparison, FiveThirtyEight gave roughly those odds for Hilary Clinton becoming the president of the USA in 2016. That’s not all that high, given how “massive” the difference is in their best race times that year.

This is just an estimate, though. Maybe if we pitted our models head-to-head, we’d get different results?

Wins when racing head to head (1875 simulations each)
----------------------------------------------
LOSER->       gatlin      bolt   bromell    vicaut   simbine de grasse     blake     meite
gatlin                   48.9%     52.1%     55.8%     56.4%     59.5%     63.5%     61.9%
bolt                               52.2%     57.9%     55.8%     57.9%     65.8%     60.2%
bromell                                      52.4%     55.3%     55.0%     65.2%     59.0%
vicaut                                                 51.7%     52.2%     59.8%     59.3%
simbine                                                          52.3%     57.7%     57.1%
de grasse                                                                  57.0%     54.7%
blake                                                                                47.2%
meite

The best winning percentage was 65.8% (therefore the worst losing percent was 34.2%).


Nope, it’s pretty much bang on! The columns of this chart represents the loser of the head-to-head, while the rows represent the winner. That number in the upper-right, then, represents the odds of Gatlin coming in first against Meite. When I run the numbers, I usually get a percentage that’s less than 5 percentage points off. Since the odds of one person losing is the odds of the other person winning, you can flip around who won and lost by subtracting the odds from 100%. That explains why I only calculated less than half of the match-ups.

I don’t know what’s on your screen, but I typically get one or two match-ups that are below 50%. I’m again organizing the calculations by each athlete’s fastest time in 2016, so if an athlete’s win ratio was purely determined by that then every single value in this table would be equal to or above 50%. That’s usually the case, thanks to each model favouring the Exponential distribution, but sometimes one sprinter still winds up with a better average time than a second’s fastest time. As pointed out earlier, that translates into more wins for the first athlete.

# Getting Physical

Even at this elite level, you can see the odds of someone winning a head-to-head race are not terribly high. A layperson can create that much bias in a coin toss, yet we still both outcomes of that toss to be equally likely.

This doesn’t really contradict Rationality Rules’ claim that fractions of a percent in performance matter, though. Each of these athletes differ in physiology, and while that may not have as much effect as we thought it still has some effect. What we really need is a way to substract out the effects due to morphology.

If you read that old blog post, you know what’s coming next.

[16:48] Which is to say that the attributes granted from male puberty that play a vital role in explosive events – such as height, width, limb length, and fast twitch muscle fibers – have not been shown to be sufficiently mitigated by HRT in trans women.

According to Rationality Rules, the physical traits that determine track performance are all set in place by puberty. Since puberty finishes roughly around age 15, and human beings can easily live to 75, that implies those traits are fixed for most of our lifespan. In practice that’s not quite true, as (for instance) human beings lose a bit of height in old age, but here we’re only dealing with athletes in the prime of their career. Every attribute Rationality Rules lists is effectively constant.

So to truly put RR’s claim to the test, we need to fit our model to different parts of the same athlete’s career, and compare those head-to-head results with the ones where we raced athletes against each other.

     Athlete First Result Latest Result
0      blake   2005-07-13    2019-06-21
1       bolt   2007-07-18    2017-08-05
2    bromell   2012-04-06    2019-06-08
3  de grasse   2012-06-08    2019-06-20
4     gatlin   2000-05-13    2019-07-05
5      meite   2003-07-11    2018-06-16
6    simbine   2010-03-13    2019-06-20
7     vicaut   2008-07-05    2019-07-02


That dataset contains official IAAF times going back nearly two decades, in some cases, for those eight athletes. In the case of Bolt and Meite, those span their entire sprinting career.

Which athlete should we focus on? It’s tempting to go with Bolt, but he’s an outlier who broke the mathmatical models used to predict sprint times. Gatlin would have been my second choice, but between his unusually long career and history of doping there’s a decent argument that he too is an outlier. Bromell seems free of any issue, so I’ll go with him. Don’t agree? I made changing the athlete as simple as altering one variable, so you can pick whoever you like.

I’ll divide up these athlete’s careers by year, as their performance should be pretty constant over that timespan, and for this sport there’s usually enough datapoints within the year to get a decent fit.

bromell vs. bromell, model building ...
year	α	β	b
2012	0.639 (+0.317 -0.219)	0.817 (+0.406 -0.280)	10.370 (+0.028 -0.415)
2013	0.662 (+0.157 -0.118)	1.090 (+0.258 -0.195)	9.970 (+0.018 -0.070)
2014	0.457 (+0.118 -0.070)	1.556 (+0.403 -0.238)	9.762 (+0.007 -0.035)
2015	0.312 (+0.069 -0.064)	2.082 (+0.459 -0.423)	9.758 (+0.002 -0.016)
2016	0.356 (+0.092 -0.104)	1.761 (+0.457 -0.513)	9.835 (+0.005 -0.037)
... done.

----------------------------------------------
LOSER->   2012   2013   2014   2015   2016
2012         61.3%  67.4%  74.3%  71.0%
2013                65.1%  70.7%  66.9%
2014                       57.7%  48.7%
2015                              40.2%
2016

The best winning percentage was 74.3% (therefore the worst losing percent was 25.7%).


Again, I have no idea what you’re seeing, but I’ve looked at a number of Bromell vs. Bromell runs, and every one I’ve done shows at least as much variation, if not more, than runs that pit Bromell against other athletes. Bromell vs. Bromell shows even more variation in success than the coin flip benchmark, giving us justification for saying Bromell has a significant advantage over Bromell.

I’ve also changed that variable myself, and seen the same pattern in other athletes. Worried about a lack of datapoints causing the model to “fuzz out” and cover a wide range of values? I thought of that and restricted the code to filter out years with less than three races. Honestly, I think it puts my conclusion on firmer ground.

# Conclusion

Texas Sharpshooter Fallacy: Ignoring the difference while focusing on the similarities, thus coming to an inaccurate conclusion. Similar to the gambler’s fallacy, this is an example of inserting meaning into randomness.

Rationality Rules loves to point to sporting records and the outcome of single races, as on the surface these seem to justify his assertion that differences in performance of fractions of a percent matter. In reality, he’s painting a bullseye around a very small subset of the data and ignoring the rest. When you include all the data, you find Rationality Rules has badly missed the mark. Physiology cannot be as determinative as Rationality Rules claims, other factors must be important enough to sometimes overrule it.

And, at long last, I can call bullshit on this (emphasis mine):

[17:50] It’s important to stress, by the way, that these are just my views. I’m not a biologist, physiologist, or statistician, though I have had people check this video who are.

Either Rationality Rules found a statistician who has no idea of variance, which is like finding a computer scientist who doesn’t know boolean logic, or he never actually consulted a statistician. Chalk up yet another lie in his column.

# Argument to Extreme Moderation

EssenseOfThought has already covered the way Rationality Rules plunges head-first into this fallacy, but I don’t think they truly captured how far he takes it. I mean, he said this with a straight face:

[2:53] The truth on the matter, however, as where it normally is, between the two extremes, and within this video I’m going to do my utmost best to show this to be the case.

# Sexism Poisons Everything

That black hole image was something, wasn’t it? For a few days, we all managed to forget the train wreck that is modern politics and celebrate science in its purest form. Alas, for some people there was one problem with M87’s black hole.

A woman was involved! Despite the evidence that Dr. Bouman played a crucial role or had the expertise, they instead decided Andrew Chael had done all the work and she was faking it.

So apparently some (I hope very few) people online are using the fact that I am the primary developer of the eht-imaging software library () to launch awful and sexist attacks on my colleague and friend Katie Bouman. Stop.

Our papers used three independent imaging software libraries (…). While I wrote much of the code for one of these pipelines, Katie was a huge contributor to the software; it would have never worked without her contributions and

the work of many others who wrote code, debugged, and figured out how to use the code on challenging EHT data. With a few others, Katie also developed the imaging framework that rigorously tested all three codes and shaped the entire paper ();

as a result, this is probably the most vetted image in the history of radio interferometry. I’m thrilled Katie is getting recognition for her work and that she’s inspiring people as an example of women’s leadership in STEM. I’m also thrilled she’s pointing

out that this was a team effort including contributions from many junior scientists, including many women junior scientists (). Together, we all make each other’s work better; the number of commits doesn’t tell the full story of who was indispensable.

Amusingly, their attempt to beat back social justice within the sciences kinda backfired.

As openly lesbian, gay, bisexual, transgender, queer, intersex, asexual, and other gender/sexual minority (LGBTQIA+) members of the astronomical community, we strongly believe that there is no place for discrimination based on sexual orientation/preference or gender identity/expression. We want to actively maintain and promote a safe, accepting and supportive environment in all our work places. We invite other LGBTQIA+ members of the astronomical community to join us in being visible and to reach out to those who still feel that it is not yet safe for them to be public.

As experts, TAs, instructors, professors and technical staff, we serve as professional role models every day. Let us also become positive examples of members of the LGBTQIA+ community at large.

We also invite everyone in our community, regardless how you identify yourself, to become an ally and make visible your acceptance of LGBTQIA+ people. We urge you to make visible (and audible) your objections to derogatory comments and “jokes” about LGBTQIA+ people.

In the light of the above statements, we, your fellow students, alumni/ae, faculty, coworkers, and friends, sign this message.

[…]
Andrew Chael, Graduate Student, Harvard-Smithsonian Center for Astrophysics
[…]

Yep, the poster boy for those anti-SJWs is an SJW himself!

So while I appreciate the congratulations on a result that I worked hard on for years, if you are congratulating me because you have a sexist vendetta against Katie, please go away and reconsider your priorities in life. Otherwise, stick around — I hope to start tweeting

more about black holes and other subjects I am passionate about — including space, being a gay astronomer, Ursula K. Le Guin, architecture, and musicals. Thanks for following me, and let me know if you have any questions about the EHT!

If you want a simple reason why I spend far more time talking about sexism than religion, this is it. What has done more harm to the world, religion or sexism? Which of the two depends most heavily on poor arguments and evidence? While religion can do good things once in a while, sexism is prevented from that by definition.

Nevermind religion, sexism poisons everything.

… Whoops, I should probably read Pharyngula more often. Ah well, my rant at the end was still worth the effort.

# Happy Emmy Noether Day!

Whenever anyone asks me for my favorite scientist, her name comes first.

At a time when women were considered intellectually inferior to men, Noether (pronounced NUR-ter) won the admiration of her male colleagues. She resolved a nagging puzzle in Albert Einstein’s newfound theory of gravity, the general theory of relativity. And in the process, she proved a revolutionary mathematical theorem that changed the way physicists study the universe.

It’s been a century since the July 23, 1918, unveiling of Noether’s famous theorem. Yet its importance persists today. “That theorem has been a guiding star to 20th and 21st century physics,” says theoretical physicist Frank Wilczek of MIT. […]

Although most people have never heard of Noether, physicists sing her theorem’s praises. The theorem is “pervasive in everything we do,” says theoretical physicist Ruth Gregory of Durham University in England. Gregory, who has lectured on the importance of Noether’s work, studies gravity, a field in which Noether’s legacy looms large.

And as luck would have it, today was the day she was born. So read up on why she’s such a critical figure, and use it as an excuse to remember other important women in science.

# Ridiculously Complex

Things have gotten quiet over here, due to SIGGRAPH. Picture a giant box of computer graphics nerds, crossed with a shit-tonne of cash, and you get the basic idea. And the papers! A lot of it is complicated and math-heavy or detailing speculative hardware, sprinkled with the slightly strange. Some of it, though, is fairly accessible.

This panel on colour, in particular, was a treat. I’ve been fascinated by colour and visual perception for years, and was even lucky enough to do two lectures on the subject. It’s a ridiculously complicated subject! For instance, purple isn’t a real colour.

Ok ok, it’s definitely “real” in the sense that you can have the sensation of it, but there is no single wavelength of light associated with it. To make the colour, you have to combine both red-ish and blue-ish light. That might seem strange; isn’t there a purple-ish section at the back of the rainbow labeled “violet?” Since all the colours of the rainbow are “real” in the single-wavelength sense, a red-blue single wavelength must be real too.

It turns out that’s all a trick of the eye. We detect colour through one of three cone-shaped photoreceptors, dubbed “long,” “medium,” and “short.” These vary in what sort of light they’re sensitive to, and overlap a surprising amount.

Your brain determines the colour by weighing the relative response of the cone cells. Light with a wavelength of 650 nanometres tickles the long cone far more than the medium one, and more still than the short cone, and we’ve labeled that colour “red.” With 440nm light, it’s now the short cone that blasts a signal while the medium and long cones are more reserved, so we slap “blue” on that.

Notice that when we get to 400nm light, our long cones start becoming more active, even as the short ones are less so and the medium ones aren’t doing much? Proportionately, the share of “red” is gaining on the “blue,” and our brain interprets that as a mixture of the two colours. Hence, “violet” has that red-blue sensation even though there’s no light arriving from the red end of the spectrum.

To make things even more confusing, your eye doesn’t fire those cone signals directly back to the brain. Instead, ganglions merge the “long” and “medium” signals together, firing faster if there’s more “long” than “medium” and vice-versa. That combined signal is itself combined with the “short” signal, firing faster if there’s more “long”/”medium” than “short.” Finally, all the cone and rod cells are merged, firing more if they’re brighter than nominal. Hence where there’s no such thing as a reddish-green nor a yellow-ish blue, because both would be interpreted as an absence of colour.

I could (and have!) go on for an hour or two, and yet barely scratch the surface of how we try to standardize what goes on in our heads. Thus why it was cool to see some experts in the field give their own introduction to colour representation at SIGGRAPH. I recommend tuning in.

# It Is Friday, After All

I was sitting down to write a weighty post about child separation, while reminding myself of another post I’d promised on the subject, and eyeing up which Steven Pinker post I should begin work on, all of which is happening as I’m juggling some complex physics and computational problems, and-

You know what? Here’s a video of someone dunking oranges in a fish tank, in an excellent demonstration of the scientific method. [Read more…]

# Art Break

I thought my latest series on Carol Tavris would be the last time I’d be writing about sexual assault for a while. It’s not. Hell, I won’t even get a break from Carol Tavris. So while those posts are cooking, let us detour to happier things.

# The Total Package

Mano Singham and PZ Myers aren’t that interested in eclipses. I’m sort of the opposite, as a group of us drove 13 hours to reach totality, arriving only an hour and a half before the eclipse started… and departing for the return trip fifteen minutes after totality ended. Why the heck would anyone go to such extreme lengths for a few minutes of darkness?

## The Corona

The solar corona is the hottest part of the sun we can see, far hotter than the surface, and we don’t know why that is. Despite being so hot, the corona is also very diffuse and thus the cooler chromosphere blasts out far more light than it does. This means you can’t see it if any part of the sun is visible, and the physics of choronographs means they block off significantly more of the corona than the Moon does during an eclipse.

While that’s all very nice and intellectual, there’s also something satisfying about looking up in the sky and seeing what looks like Albert Einstein being consumed by a black hole.

## Mercury

The planet Mercury is likely the last visible-eye planet discovered. Because it clings so tightly to it, you need to blot out the Sun by exploiting sunsets and sunrises, and even then you need a view close to the horizon and the planet in a certain orientation. A solar eclipse accomplishes the same, only during the middle of the day. I’m not convinced I actually saw Mercury yesterday, as it was faint and appeared in the wrong spot too close to the sun, but oh well.

## Sunsets on Every Horizon

I can verify this actually happens. The physics is pretty simple: the Moon’s shadow occupies a finite area. If you’re perfectly centred under it, every horizon is in the direction of a patch of earth which has at least some sunlight falling on it. That sunlight bounces back up into or scatters through the atmosphere, producing something that looks like a sunset. It is wicked cool!

The shadow of the Moon is quite fuzzy, so if you’re expecting to see a sharp line you’ll be disappointed. The best view is definitely from space, though an airplane will do in a pinch; on Earth, I could spot the Eastern horizon getting gradually lighter even as we were in totality.

As NASA puts it, “Shadow bands are thin wavy lines of alternating light and dark that can be seen moving and undulating in parallel on plain-coloured surfaces immediately before and after a total solar eclipse.” Scientists aren’t entirely sure what they are, but the best guess is atmospheric cells warping light in a similar way to stellar flicker. They aren’t guaranteed to show up, but I insisted on laying out a white blanket to make them more visible. We missed seeing them as totality was approaching, but as the Sun started peeking back I strongly suggested everyone stare at the blanket. And we saw them!

## A Chill In The Air

The Sun radiates a lot of heat our way, which is absorbed and scattered by the ground and atmosphere. Take away that source, and you’re just left with the radiation from said ground and atmosphere as it cools down. This is at its strongest during totality, and collectively we could feel the atmosphere was notably chillier just after the eclipse than it was in the lead up. I’m estimating the difference was about 5-10C.

## People Losing Their Shit

My photos of the eclipse were pretty lousy, as I didn’t have any money to invest in the proper gear. Derek Muller of Vertasium was much luckier, but his video is more notable for the audio; he, and everyone around him, were losing their minds as they reached totality. You don’t get that from a partial solar eclipse.

Don’t listen to Singham or Myers. Total solar eclipses are the coolest, and if one happens to fall near you I recommend you take full advantage.

# A Third One!

I know, I know, these are starting to get passé. But this third event brings a little more information.

For the third time in a year and a half, the Advanced Laser Interferometer Gravitational Wave Observatory (LIGO) has detected gravitational waves. […]

This most recent event, which we detected on Jan. 4, 2017, is the most distant source we’ve observed so far. Because gravitational waves travel at the speed of light, when we look at very distant objects, we also look back in time. This most recent event is also the most ancient gravitational wave source we’ve detected so far, having occurred over two billion years ago. Back then, the universe itself was 20 percent smaller than it is today, and multicellular life had not yet arisen on Earth.

The mass of the final black hole left behind after this most recent collision is 50 times the mass of our sun. Prior to the first detected event, which weighed in at 60 times the mass of the sun, astronomers didn’t think such massive black holes could be formed in this way. While the second event was only 20 solar masses, detecting this additional very massive event suggests that such systems not only exist, but may be relatively common.

Thanks to this third event, astronomers can set a stronger maximum mass for the graviton, the proposed name for any gravity force-carrying particle. They also have some hints as to how these black holes form; the axis of spin for these two black holes appear to be misaligned, which suggests they became binaries well after forming as opposed to starting off as binary stars in orbit. Finally, the absence of another signal tells us something important about intermediate black holes, thousands of times heavier than the Sun but less than millions.

The paper reports a “survey of the universe for midsize-black-hole collisions up to 5 billion light years ago,” says Karan Jani, a former Georgia Tech Ph.D. physics student who participated in the study. That volume of space contains about 100 million galaxies the size of the Milky Way. Nowhere in that space did the study find a collision of midsize black holes.

“Clearly they are much, much rarer than low-mass black holes, three collisions of which LIGO has detected so far,” Jani says. Nevertheless, should a gravitational wave from two Goldilocks black holes colliding ever gets detected, Jani adds, “we have all the tools to dissect the signal.”

If you want more info, Veritasium has a quick summary, while if you want something meatier the full paper has been published and the raw data has been released.

Otherwise, just be content that we’ve learned a little more about the world.