Learn About Monte Carlo

Monte Carlo simulations use random numbers and probabilities to draw statistical conclusions of complex phenomena.

Example

Perhaps a simple example would shed some light on how the Monte Carlo method works. Suppose we are interested in determining how many coins, out of 10, would land heads up when tossed. We could write a Monte Carlo program that would randomly generate 10 characters: each character being either H (for heads) or T (for tails). Then, we could count how many of these characters were designated as H.

The number of Hs would not necessarily tell us how many coins, out of 10, would land heads up, just as throwing 10 coins and counting the heads would not tell us how many heads would land face up next time. Instead, it is just an example of what could happen.

Say the simulation determines the very unlikely result that only 2 coins landed heads up. This result would not be very useful when trying to estimate how many coins will land heads up next time. To account for this, we could run the simulation many times and then take the average result. Alternatively, we could have our Monte Carlo program generate many (much more than 10) random characters (either H or T, again) and count the Hs. These results are much more trustworthy, and with them, we can calculate the probability of getting heads in a coin toss.

CDMS

Maybe for that simple example it would be easier to just throw the pennies a few times and count the heads ourselves. But imagine if we wanted to figure out how many heads would land face up in scenario where flipping a coin is difficult. Suppose we were asked to flip a billion gold coins!

These difficult-to-perform scenarios are the kind of scenarios CDMS at Case uses the Monte Carlo method to simulate. Instead of spending extraordinary amounts of money and time to test how something works, we can write Monte Carlo simulations that we can use to draw statistical conclusions!

Monte Carlo simulations have been especially useful in the development of our detector shielding. We can use these simulations to estimate number unwanted background particles that will reach our detectors with different kinds of shielding. Without Monte Carlo, trying to determine how well a hypothetical shielding scheme would work would require spending much time and money to build and test the shielding.

Estimating background particles is also useful in our data analysis as well as other areas of design.

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