What is the application of pseudorandomness in astrophysics? What are the advantages of using pseudorandom numbers in Monte Carlo simulation instead of random numbers? What are other applications of pseudorandomness in astrophysics? Many thanks.
The primary advantage of pseudorandom numbers is speed (as Cristiano mentioned) and convenience: they're what are available in almost any software package or library, whereas true random number generators are not. As long as you use a pseudorandom number generator (PRNG) with good statistical qualities and are not worrying about true cryptographic randomness, this is perfectly acceptable.
In addition to Monte Carlo simulations, pseudorandom number are used for many different applications, including all sorts of physical simulations (cosmological, stellar evolution, planet formation, etc.), statistical analyses (bootstrap resampling), initial conditions for fits, updating parameters in genetic algorithms, sample selection, etc.
A personal example: I've worked on two different studies where we were making classifications of different aspects of galaxies, based on their individual isophote plots or radial surface-brightness profiles. Rather than worrying about the possibility of subtle biases based on possible knowledge of individual galaxies (&quot;Oh, it's NGC 1097, I'd expect that galaxy to be...&quot; or &quot;Well, the list is alphabetically ordered, so the middle galaxies will have NGC prefixes, and those will mostly be really luminous galaxies, so...&quot;), we set up a blind analysis system, where the galaxies were presented to us in a random order, without names (with the actual identifications automatically recorded where we couldn't see them until the whole thing was over). The randomness of the order was, of course, really pseudorandom order.
This is how things like Galaxy Zoo work, for example: the individual galaxies that citizen scientists classify are (pseudo)randomly selected. (And for one of the projects I mentioned above [Kruk et al. 2019], we set up the classification process using the &quot;Zooniverse&quot; framework that Galaxy Zoo developed).
A separate advantage is that if you save the number used as the seed for the pseudorandom number generator, you can use that to regenerate the exact set of pseudorandom numbers used in the analysis/simulation/whatever, which can be very useful for debugging purposes, and also for allowing others to replicate your results. (E.g., you run some kind of relatively short/simple simulation, and derive some results and figures from that. How can someone else trust that your results and figures accurately represent the simulation? Provide them with the code and the PRNG seed, and then can generate the same simulated/random-sampling results and check. Plus, they can vary the seed and make sure you didn't cheat -- or fool yourself -- by reporting atypical results. Here's a Github site for a recent paper of mine that includes both the code for a survey simulation and the seed number I used for the figure in the paper.)