When I was younger, I remember to read cyberpunk comics quite a lot. They explain a vision of the future that is improbable, but in many ways it get stuff right. Imagine aligning this with real word science. Imagine hearing from a superhero how his powers came to him. Imagine having a scientist name on the movie credits.
It doesn't need to make everything scientifically accurate, but explaining the fundamentals can engage more people to enter science.
Yesterday I was watching a new movie from Netflix called 'hacker'. The movie is awful, but it starts showing how Stuxnet should work, and that is pretty awesome. This is cool because I know the fundamentals of Stuxnet.
If they break the 4th wall and show something that could happen for real, it could bring more emotions to the movie.
I still remember finding part 1 in the used books store with my dad around the age of 10-11 for like $2. Now I'm in my early 30's and all 3 parts are just a handful of books away from my physics and philosophy books on my book shelf :)
which are pretty great.
We're currently heading into cyberpunk in basically every aspect except for the anarchy. More like totalitarian cyberpunk. It's left to see whether tech gives us the means for a semblance of anarchy, but I'm not getting my hopes up.
It seemed biased but still covered the basics well, I thought, not that I'm a good judge.
Spooky quantum effect, there!
I didn't feel the need to click anything.
I think it'd be more accurate to say "interact" instead of "collide" – the electron could still be far away from the charged particle. More generally, bremsstrahlung also occurs when an electron's velocity vector (not necessarily its modulus) changes, i.e. when the electron changes direction, like in a synchroton.
> In fact the name "bremsstrahlung" means "braking radiation," if memory serves.
That's correct :)
eg: (a+b)^2 = a^2 + b^2 + 2ab
That 2ab is an interference term so a different process can get mixed in (quantum mechanically speaking). And we may not experimentally be able to disentangle it.
Assuming that there were no experimental errors, you can use the measure of standard deviation to express roughly what % chance a measurement is due to a statistical anomaly vs. a real indication that something is wrong.
To put some numbers to this, a measurement 1 sigma from the prediction would mean that there is roughly a 84% chance that the measurement represented a deviation from the prediction and a 16% chance that it was just a statistical anomaly. Similarly:
> 2 sigma = 97.7%/2.3% chance of deviation/anomaly
> 3 sigma = 99.9%/0.1% chance of deviation/anomaly
> 4.2 sigma = 99.9987%/0.0013% chance of deviation/anomaly
Which is why this is potentially big news since there is a very small chance that the disagreements between measurement and prediction are due to a statistical anomaly, and a higher chance that there are some fundamental physics going on that we don't understand and thus cannot predict.
edit: Again, this assumes both that there were no errors made in the experiment (it inspires confidence that they were able to reproduce this result twice in different settings) and that there were no mistakes made in the predicition itself, which as another commenter mentions eleswhere, is a nontrivial task in and of itself.
This is worth repeating a lot when explaining sigma (even in a great and comprehensive explanation such as yours): Statistical anomalies are only relevant when the experiment itself is sound.
Imagine you are trying to see whether two brands of cake mix have different density (maybe you want to get a good initial idea whether they could be the same cake mix). You can do this by weighing the same amount (volume) of cake mix repeatedly, and comparing the mean value for weight measurements of either brand. That works well, but it totally breaks down if you consistently use a glass bowl for one brand, and a steel bowl for the other brand. You will get very high units of sigma, but not because of the cake mix.
No, this is a p-value misinterpretation. Sigma has to do with the probability that, if the null hypothesis were true, the observed data would be generated. It does not reflect the probability that any hypothesis is true given the data.
The null hypothesis is that there are no new particles or physics and the Standard Model predicts the magnetic charge of a muon. A 4.2 sigma result means that given this null hypothesis prediction, the chances that we would have observed the given data is ~0.0013% (chance this was a statistical anomaly). Since this is a vanishingly small chance (assuming no experimental errors), we can reasonably reject the hypothesis that the Standard Model wholly predicts the charge of a muon.
- still looking for a better link than the Book… I’ll update this later
[1] http://klaus-volkamer.de/wp-content/uploads/2014/11/1994-Vol...
[1] https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1....
Seems like all he was initially doing in the 80’s was dig into the 2 out of 10 experiments from Landolt that failed to confirm a conservation of mass
[1] http://klaus-volkamer.de/wp-content/uploads/2014/11/1994-Vol...
[2] https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1....