I know that serious radio telescopes cost way more than any random person could afford to pay but I've certainly seen plans for smaller DIY homemade models.
The idea is to count the number of events (beta particles here) per time interval. Do this twice. If count A > count B, output a 1. If count A < count B, output a 0. If count A = count B, skip that result. Von Neumann came up with that trick.
Don't use the low-order bit of the count. That has a bias.
> Von Neumann’s originally proposes the following technique for getting an unbiased result from a biased coin :
> > If independence of successive tosses is assumed, we can reconstruct a 50-50 chance out of even a badly biased coin by tossing twice. If we get heads-heads or tails-tails, we reject the tosses and try again. If we get heads-tails (or tails-heads), we accept the result as heads (or tails).
Only practical use for me would be to confound markers trying to reproduce my RNG's in statistics assignments.
Your PRNG that reads from your telescope would need to compensate for this.
As far as using radiation to generate random numbers, check out https://www.fourmilab.ch/hotbits/
It reminds me of a story from Cryptonomicon in which a character mentions a secretary grabbing randomly spun number balls from a tumbling device while blindfolded (if I remember the objects right) and not liking the results when she had to write them down because they didn't look random enough to her, so she starts peeking and slightly "correcting", and thus ruins a number of one-time pads
https://www.idquantique.com/random-number-generation/product...
From the brochure:
“ Photons - light particles - are sent one by one onto a semi-transparent mirror and detected. The exclusive events (reflection - transmission) are associated to « 0 » - « 1 » bit values.”
Here's a document from 1997 that looks at some hardware RNGs and how they fail: http://www.robertnz.net/true_rng.html
2. The dice roll example is not uniform distribution, I think this is a common pitfall when generating random integers of a range. `randomNumber % 6` results in a slight bias towards 0 and 1, since 2^31 % 6 == 2, there are more numbers in the range [0, 2^31-1] that map to 0 and 1 than those that map to 2...5. To make it uniform, for example, you should always discard if `randomNumber < 2` and regenerate another number for use.
On first pass, Benford’s Law looks a lot like Zipf’s Law.
What differentiates Benford’s Law from Zipf’s Law?
(Since we can reasonably assume that randomNumber is a binary number, and thus would be balanced over 8 values instead of 6.)
[0] https://en.wikipedia.org/wiki/Linear_congruential_generator#...
The more I read it, the more confused the article appears to be (e.g. mersenne twister is NOT a good example of a modern or high quality PRNG). For more about secure random numbers in Linux, I'd suggest reading [0].
0: https://buttondown.email/cryptography-dispatches/archive/cry...
Some cryptosystems really do need uniform randomness (ECDSA) rather than just negligible probability of choosing values. Other cryptosystems depend on not reusing values, though the values could be predictable. Sometimes there are subtle shifts in these needs based on modes (AES/CBC vs AES/GCM is a good example).
Modern chips ranging from the one in the Raspberry Pi to Intel CPUs have them too.
Modern PRNG's can be tested with Dieharder, TestU01 or STS and benchmarked. This article only talks about primitive old LCG's (not any good one) or MT.
Even a truncated 128-bit LCG has far better properties.
See https://www.pcg-random.org/index.html
The homepage might come across as a a little overzealous (for example ChaCha quality listed as good rather than excellent), but generally has good points.
[0] https://blog.cloudflare.com/randomness-101-lavarand-in-produ...
[1] https://blog.cloudflare.com/lavarand-in-production-the-nitty...
* Creates a sequence of unique integers
* Uses prime numbers that are congruent to P = 3 (mod 4).
* A single iteration has noticable patterns but applying it twice already results in randomness that is sufficiently good for many use cases.
* It is "embarrassingly parallel", a simple mapping of randomValue = randomize(i). You can calculate unique and deterministic random numbers from input i in parallel threads with no sync between threads.
* Since it is a unique mapping of i to r, you can use it to shuffle data sets virtually instantly. Take the index of a value in the original array, and use it to compute the target index in the shuffled array.
* I've used it to shuffle up to 800 million items per second on a GPU, including the time it took to transfer the data from RAM to GPU. So without the IO, you could probably shuffle billions of values per second, probably mostly bound by GPU bandwidth. E.g., 700GB/s and each item is 70 bytes -> could perhabs shuffle 10 billion items per second.
Example from matlab: https://www.mathworks.com/help/stats/generate-random-numbers...
