Category: Physics

Over the winter break, I have picked up my little muon detector data side project that started in the spring term of 2020. A few days ago, I remarked on how I wanted to take account of “weather” in the data – variations around a daily expectation given seasonal conditions – and “seasons” – long-term trends in the data.

Based on almost 290 days of data since I began this project, it was clear that the counts vary with time with short-period and long-period trends. Let us define a muon “timeblock” as a quarter-hour (15 minute) duration of data-taking. In a single timeblock, the detector typically reports the passage of about 1000 muons. What 290 days of data has taught me is what many previous experiments have also seen: there are overall fewer muons per timeblock in colder months and more in warmer months. So there is something of an annual overall trend, sinusoidal in shape, as well as shorter-term variations (hourly, daily, over multiple weeks, etc.).

I have now captured the seasonal effect using s sine function model. This is fitted to all previous days of data, excluding the current one, to find the best model parameters that describe the past. Today’s rate is then extrapolated from the function, including the effect of model parameter statistical uncertainty from the fitting process. This allows me to identify “weather” conditions – deviations from the daily expectations.

It is currently “winter-like” now. That means we expect, on average, the muon rate to be below the yearly average. Yesterday, we saw additionally “colder” muons weather, with quite a lull in muon counts given the expected rate in each timeblock. Today, things appear to be “heating” back to the expectation, and we will see where the day takes us.

You can find the current muon weather at SMU on my computational lab site, SA-SO (the SMU All-Scale Observatory, my name for my virtual laboratory space at SMU): https://blog.smu.edu/saso/projects/muon-observatory/#Muon_Weather_Conditions

I am spending some time playing around with the cosmic ray muon data from an instrument in the SMU Physics Department. That instrument is located in the basement hallway of Fondren Science Building. I already setup a “dashboard” of information derived from the instrument, available here: https://blog.smu.edu/saso/projects/muon-observatory/. If you want to learn more about the instrument, cosmic rays, and muons, you can check out that dashboard.

I’ve wanted to take account of the variation, over the seasons, of the muon rate through the instrument. Previous research on cosmic ray muons (there is a lot of this, since cosmic rays were discovered a century ago) suggests this variation is due to the change in temperature (and thus density) of the stratosphere over the year. More dense air = more cosmic ray interactions = fewer decays to muons.

The effect should be roughly sinusoidal, so I am trying to incorporate a sine function fit (done) into the muon rate subtraction (in progress) to better account for daily expectations. Right now, my subtraction is just of the entire running average of the count of muons, which reveals an annual variation … but fails to capture daily expectations. So, basically, I isolated muon climate (long time periods) but not muon weather (short time periods) in my current graphs … and I want to capture weather.

If you want to learn more about muons, check out my dedicated lecture on the subject from my Modern Physics course:

I’ve written extensive python and C++ code to analyze the raw data from the muon detector. That detector provides two pieces of data: the time between two pulses in the detector, and the time at which the pulses were received (e.g. wall-clock time). This is then turned into the information on the dashboard using code. This employs Panda dataframes, ZFit and ROOT to do data modeling and extrapolation/interpolation, and Matplotlib to do graphing (among other things). It’s been my fun pandemic side project since March, 2020.