GwintersCEB558
From MariachiWiki
This blog covers a whole semester course so is quite long. Too long. To shorten this page, I have broken this into smaller parts. On this page I have left a brief description of each day, with a link to the page that covers that day in more detail. I have also left a full description of the most recent day.
Week 1: January 29, 2008: Introduction
Go here for a full description of this week's activities.
The course is an introduction to research. I am taking the course because it uses the MARIACHI muon detectors extensively. We have 5 detectors at Smithtown HS East, and my objective here is to find out all sorts of things about the detectors: how they work, how to access the data, what we can do with the data, and the list goes on. After all, why should I have detectors at my school if I don't know how to use them?
Prof. Mike Marx started us off by talking about cosmic rays and detection of cosmic rays. He very kindly has lecture notes available here. He also told us about our first assignment, which is to start a blog for this course. Here it is.
 Mike with Cosmic Chris, who is counting cosmic rays |
Week 2: February 5, 2008: First Measurements, including Calibration
Go here for a full description of this week's activities.
Just as expected, we started in right away with measurements. After just a short introduction, we were given a few tasks:
- Determine the optimal operational voltage of the middle detector.
- Operating at optimal voltage, see what happens when we changed the configuration of the detectors.
- Again at optimal voltage, find the cosmic ray rate. Determine if changing the counting time affects the results.
We broke into groups and I worked with Mildred and Desiree. Nice group to work with - everybody contributed, and I think we all learned something. Our overall data file is here, and specific graphs from the file are described in the detailed page.
Week 3: February 12, 2008: Experimental Errors
Go here for a full description of this week's activities.
Prof. Mike Marx started with a presentation and talked about experimental errors: accuracy, precision, systematic errors, random errors.
We will want to present our estimation of the errors when we present our results. In the detailed description of this week's activities I apply some of the error analysis to our data from last week.
We then went into a discussion of possible experiments to do.
- Actual rate of cosmic rays
- How steep. Angle of incidence - measure rates at various angles.
- What is the accidental counting rate measured by two counters that did not actually see the same signal.
My group (Mildred, Desiree and I) decided to use the "Octagon" setup to look at the different count rates for different angles.
At the end when we were starting to leave (it was snowing outside and we were concerned about slippery conditions getting home), Mike showed us a video some people from Cern made and posted to YouTube: The Cernettes. Watch it to see how Particle Physicists spend their free time.
Week 4: February 19, 2008: Cosmic Ray Counts vs. Angle from Vertical
Go here for a full description of this week's activities.
Prof. Mike Marx started with a short lecture about cosmic rays.
After the short lecture, we broke into our previous groups and continued measurements from last week. Find our results from today here.
 Detectors in the 0-degree "vertical" orientation. |
 Countrate vs. angle, measured from vertical. |
We got the highest number of counts pointing straight up (what we called 0 degrees), and fewer counts as we rotated the detectors away from vertical as shown in the graph.
Mike says next week we can do more measurements of the same kind, extend what we are doing, or go off in another direction. The following week is our first set of presentations, where we will present to each other what we have done so far.
Week 5: February 26, 2008: Intro to Chi Squared
Go here for a full description of this week's activities.
Prof. Marx started with a lecture, which he kindly posted (here). He talked about error, by which he meant looking at the data to figure out whether the data is good or questionable. He said that a good way to look at data is to find the chi squared value for each data point:
In lecture, we looked at data from the group that is taking Cosmic Chris on a tour of the building, at our data, and at the data from the group that is trying to measure the absolute flux of muons through the detectors.
Our group then looked at the angular range that our detectors see in order to get an error bar for the horizontal axis on our graphs. And then we looked at our data using chi squared calculations. This week we took new measurements, this time pointing the octagon north instead of east.
We worked on a PowerPoint presentation, to be presented next week.
After class, I compared our data to data from SLAC (Stanford Linear Accelerator Center), using flux measurement instead of raw count rate data.
Week 6: March 4, 2008: Presentations
Each group presented results in presentations to the rest of us.
Cosmic ray showers: Karyn, Tom. They investigated the effect of horizontal separation on coincidence rates. They used two pairs of detectors, separated by a distance, and found that the coincidence rate had an inverse relationship with separation distance. They tried to fit the data a number of ways, testing whether the relationship was really inverse. An interesting direction for this investigation would be to determine whether the coincidence rates continue to decrease with an inverse relationship for large separation distance, or whether they fall to effectively zero at some point.
Systematic errors: Greg, Lena, Pat. They looked at whether accidental coincidences occur. They found that accidental coincidences do occur, which lead them to look at the cosmic ray pulse shape and after-pulsing. They came to the conclusion that using more expensive equipment (especially PMT tubes) would reduce the after-pulsing, which would reduce the number of accidental coincidences.
Cosmic ray count rate: Harry, Tania, Joe. A good discussion of the data is found on Harry's wiki. Impressively, this group was trying to figure out an absolute cosmic ray flux.
Cosmic Chris and heights: Brad, James, Vin. They took Cosmic Chris to various places in the Physics building to find the relationship between the number of cosmic rays detected and the height above (or below) ground. Their data was fairly linear in the middle, with unexpectedly low count rates in the basement (by NW stairwell) and unexpectedly high count rates on the top floor (measured in the center of the building). The results gave rise to a discussion of the design of the building. Before taking more measurements, this group may want to look at the design of the building, expecting thick walls and ceilings to shield (or cause to decay) cosmic rays better than thin walls or ceilings.
Angular dependence: Desiree, Mildred and me. We talked about the results that I have posted above.
Week 7: March 11, 2008: Start Next Project
We started this week by discussing possible future projects. Most projects that we discussed evolved out of our previous projects. Then we picked groups, loosely, knowing that there would be a couple of people, missing today, that could join our groups. I picked a different topic: doing something with the existing data that my school (and several others around Long Island) is collecting. I want to know
- Where is the data, and how can I access it
- How to manipulate the data
- What kinds of analyses I can do with the data
I started looking at Cosmic Ray data collected at Smithtown High School. I found the data, available if you have access, or available on the hard drive of the school computer (at least I think so - I haven't looked). I copied one file containing 1 day of data, put it into Excel, and tried to make sense of it. There are date, timestamp, counts from detectors 1, 2, 3, 4 and 5 (in order), double coincidences 1-2 (which are stacked on top of each other), triple coincidences 1-2-3, 1-2-4, 1-2-5, and 4-fold coincidences and 5-fold coincidences.
I spent the rest of the class manipulating the date and timestamp into a form that Excel could understand. I didn't combine the date and time, so graphed a line graph (not XY scatter) of double coincidences 1-2 vs time. I have some work to do.
March 18, 2008 (no class)
Go here for a full description of this week's activities.
Stony Brook was not in session this week (spring break?), but since I will be missing a class or two later this spring I came to class anyway to make up the class. That turned out to be a good idea. Dr. Dima Vavilov was there, and since there were no other students demanding his attention, we had good discussions about what to do with the data. Unfortunately there were some network problems, so I couldn't go to some link that I wanted to. I did, however do a lot of work for "homework" during the week that followed. My favorite set of data is the graph below, but there is more data on the page where I have a full description.
What I managed to do this week was:
- Graph 1 week of count rate data together on one graph, and average it over approximately 30-minute intervals.
- I found a source of data for local barometric pressure, available in comma-delimited files.
- Graph the pressure data together with cosmic ray data of the same time interval for comparison.
- "Normalize" my countrate data by multiplying it by a factor of (p/po). Then reduce the pressure dependence further by multiplying countrate by a factor of (p/po)^2, where po is a standard pressure, and p is the pressure at the time that matches my countrate data.
Week 8: March 25, 2008: New Project
We started working on our new project. I had started last week (no class). I didn't actually do any new work on my project. Instead, I worked on a PowerPoint poster that I will use for a Technology Fair at my school next week. In line with the objective of the Technology Fair, I want to highlight the use of technology in school, and I think that this cosmic ray analysis is an excellent vehicle for introducing technology into the curriculum. A copy of the poster is found here, but for whatever reason I don't seem to be able to link to the current version, which I'm pretty sure I have successfully uploaded (several times!). I borrowed a scintillation detector and a Geiger counter from class for people to look at during the Technology Fair. I also had a PowerPoint presentation that looped from the animation of a cosmic ray shower above Chicago, to an image of the Live Display. Several people were interested in the project, but I have to say that almost nobody actually read the poster.
Note to self: make posters nice to look at with pictures and captions but not too many words.
Week 9: April 1, 2008: Technology Fair (and more analyses)
Go here for a full description of this week's activities.
I wasn't at class this week. Instead, I borrowed a detector and a Geiger counter and took the poster I had worked on last week to a Technology Fair at Smithtown High School. The intent of the fair was to show students that there are jobs in technology-related fields.
It had been a while since I did any work on my project, so during the week (after the Technology Fair) I took another look at the data. I looked at the relationship between cosmic ray counts and pressure, and found (using Excel) the relationship between the two to be a linear relationship where the best linear fit to the data is given by the equation:
Counts = -0.03406 * pressure + 5355
This gives a decrease in countrate of 0.18% for an increase of 1 millibar of atmospheric pressure, comparable to results from both Joe Willie and the U of Adelaide, who both arrived at -0.2% decrease in count rate for an increase of 1 millibar of atmospheric pressure.
Once I subtract the best fit line from the counts data I get a roughly straight line that still has some small features. These may be due to temperature, humidity, actual cosmic ray flux, or who-knows-what.
 Cosmic ray counts vs. pressure: a linear relationship |
 Difference between count rate and linear fit to pressure. |
Week 10: April 8, 2008: Presentations
Go here for a full description of this week's activities.
My presentation is here. It is a summary of the work that I have described over the previous weeks, regarding retrieving and analyzing cosmic ray data from my high school.
Brad, Desiree and James' presentation: Measuring speed of the muons from cosmic ray showers, and the speed of signals in the cables.
Karyn and Tom's (weekly blog here) presentation: Looking at cosmic ray showers, a continuation of their previous investigation.
Greg, Lena, Mildred (weekly blog here), and Patrick's presentation: Angular dependence of delayed detections.
Harry (weekly blog here), Tania, and Joe's presentation: energy deposited in scintillator by cosmic rays.
Vincent: Cosmic Chris. Vincent took Cosmic Chris all around the math and physics buildings, taking readings.
Week 11: April 15, 2008: Start Last Project
Go here for a full description of this week's activities.
This week we started out last project for the course. In my case, I continued with the same project, but other groups may have decided to change directions. Brad, from Bay Shore HS, has joined me in my quest to analyze some of the data that has been accumulating at the high schools. During class time I mostly read a couple of papers and worked with Brad to access data from Bay Shore HS. Since I didn't accomplish much during class (other than reading, but there isn't much to show for that), I did some analysis in the week before our next class.
First I looked at the pressure dependence of 3-fold, 4-fold and 5-fold coincidences, and compared the relative changes in count rates. I used the same week of data: 3/3/08-3/9/08, with the same atmospheric pressure as before.
Graphs are below.
The percent change for each of the 3-fold, 4-fold and 5-fold coincidences were similar, certainly similar within the 1 significant figure quoted elsewhere. Although the 3-fold coincidences had a larger absolute decrease in number of counts per unit of pressure, since there were more 3-fold counts detected, the percent change was similar to the percent change of the 4-fold and 5-fold coincidences.
 3-fold coincidences. Slope is -0.0049 cts/Pa or -0.57%/hPa |
 4-fold coincidences. Slope is -0.0012 cts/Pa or -0.64%/hPa |
 5-fold coincidences. Slope is -0.0015 cts/Pa or -0.64%/hPa |
 1-2 coincidence vs outside temperature |
 1-2 coincidence corrected for both P and T |
I started to look at the temperature dependence of the cosmic ray data. Graphing the corrected 1-2 coincidence cosmic ray data (corrected for pressure dependence, as outlined above) vs. outside temperature yielded one of the above graphs. The 2-fold coincidence rate does appear to decrease with increased outside temperature. Subtracting the apparent temperature dependence yields the last graph, which shows some fine structure which may (or may not) be periodic. Mike and others suggest that the periodicity may coincide with the earth's rotation (12 hour period).
Week 12: April 22, 2008 - Temperature Dependence
Go here for a full description of this week's activities.
There was no lecture today - we just got to work on our projects. Brad made some progress. He had found data for Bay Shore HS for most of the week that I had analyzed: 3/5/08-3/9/08 (missing 3/3 and 3/4, presumably due to some technical problem at the time). Brad imported the count rate data into Excel, averaged count rates over 30-minute time intervals, found pressure data for the same time interval, averaged the pressure data over 30-minute intervals, and graphed count rate and pressure on the same graph. It looked great - very similar to my graph from 3/18. This is good news, as we were hoping for similar results over the same time interval.
The correlation between outside temperature and cosmic ray counts (last week's graph) was not very convincing, so we looked into the dependence of count rate on the temperature of the detector. Mr. Rich Lefferts tracked down equipment to make a temperature somewhat-controlled environment for three stacked detectors, as shown in the picture. We started the detector counting program, and the following morning Rich turned on the air conditioner. Over the course of about two days the temperature dropped from 21.9C to 16.6C, but it is not clear how linear that temperature change was (we weren't recording temperature during measurements). Again the correlation between temperature and count rate is not convincing, this time showing a small increase in efficiency with decrease in temperature. It would be nice to have a measure of the statistical error on the slope of the line.
 Temperature controlled "box" enclosing 3 detectors. The air conditioner "cooler" is to the right |
 Change in detector efficiency with temperature change. |
I worked on the R statistical package and was able to graph the linear relationship between the 1-2 detector coincidence rate vs. pressure, with linear regression. The graph and statistical information follow.
 Counts vs. pressure |
 Regression summary |
R admits to being not too good at manipulating data. They recommend that we massage the data into appropriate form using Java, perl or python. For now, with small amounts of data, it looks as though manual data manipulation (import into Excel, and then export to tab-delimited .txt file) would be easiest, but if we plan to do a lot of this then we should write a couple of scripts for general use.
It turns out that the R tutorials, manuals and Help function are all helpful; it just takes some slogging to find the right information.
Week 13: April 29, 2008 - More Data Analysis, Using R
Class period was another work day, no lecture. I took down the temperature box from last week (Rich thanks for finding the equipment for me, and Tom thanks for lending the detectors), worked with Brad on analyzing Bay Shore and Smithtown data (great work, Brad!), discussed temperature dependence with Prof. Mike Marx, and discussed the statistics with Dr. Dima Vavilov (thanks Dima!). During the following week I looked at the R statistical package some more and re-analyzed the data using R (and help from Dr. Helio Takai - thanks Helio!).
Using R Statistical Package
I spent a significant amount of time learning the R statistical package. R's advantages over using Excel are
- Graphing is easy and flexible
- Statistics are readily available
R's disadvantages (for me) are:
- It is hard to read in data
- Is is a scripting language, so data manipulations should be done in a separate scripting language (python, Java or pearl). This would be an advantage if I knew one of the scripting languages.
I massaged the counts and pressure data using Excel, and then saved the data to a tab-delimited text file which is readable by R. To read the data into a variable "x" I used the R command: x <- read.table("C:/filepath/filename.txt", sep = "\t", header = TRUE) That allows tab-separated data (sep = "\t") and the first row is headers ("header = TRUE"). Note that the slashes in the filename are forward slashes (/ or I could have used double backslashes \\) because whereas Windows likes backslashes, R follows Unix and uses \ as a control character.
Use the command str(x) to print information about the variable x. In this case, it looks like the following:
The date and time are in the vectors called x$date and x$time, respectively. To figure out the date + time in a format that R understands, I used the sequence that follows, which puts the date and time into a variable that I called T. It would be really nice to have a script to do this, so as to avoid having to go through the following steps every time we look at data.
- y <- as.integer(x$date/10000)
- mo <- as.integer((x$date - y*10000)/100)
- d <- as.integer(x$date - y*10000 - mo*100)
- h <- as.integer(x$time/10000)
- m <- as.integer((x$time - h*10000)/100)
- s <- as.integer(x$time - h*10000 - m*100)
- T <- ISOdatetime(y, mo, d, h, m, s, tz = "")
Now the data is in a table called x and the times and dates are in R-compatible format in a variable called T.
Another look at Pressure and Temperature Dependence
Note: I mangled the raw data the first time I created these graphs, which was evident in just too much unexpected structure in the graphs. The results that follow were re-done a week later, replacing the mangled data.
Having entered the data into R, I graphed the cosmic ray count rate obtained at Smithtown for the period 3/3/08-3/9/08. This is similar to what I had done using Excel a few weeks ago.
For the first graph I used the commands:
- plot(T, x$avg.C12, xlab="Date + Time (GMT)", ylab = "1-2 Coincidences (cts/min)")
- title("Cosmic Ray Data - Smithtown HS")
R provides statistics with the linear regression, as shown in the second graph. R gives the equation for the line as 
where cts is the number of counts per minute, and the pressure is in Pa. This corresponds to a decrease of 0.17% in count rate per hPa, still reasonably close to the -0.2%/hPa quoted elsewhere (see my discussion on 4/1/08). I wonder what other people's error was?
The pressure-corrected count rate looks like it has pretty random error. All the same, it is possible that there is still a correlation with temperature. One of the graphs that follows is of the count rate vs. outside temperature. R gives the equation for a linear fit to be 
, where the temperature is in C and the count rate has been corrected for pressure, as discussed above. The slope of the line is larger than the error, so is probably real, and may account for swings of 15 count/min over the course of the year, which corresponds to a little less than 1% of overall count rate.
Given the relationship with outside temperature, above, to correct for temperature I subtracted the fitted value (from the equation) from the actual (pressure-corrected) count rate.
 Cosmic rays detected over 1 week |
 Cosmic ray rate vs barometric pressure. Slope is -0.03351 |
 Count rate corrected for pressure dependence |
0.00058 cts/Pa, or -0.17% cts/hPa
Two looks at temperature dependence: without prior analysis, and after correcting for pressure dependence. |  Countrate vs outside temperature. Slope is about +3.5 cts / C |
 Pressure-corrected rate vs outside temperature. Slope is about -0.5 cts / C |
 3-fold coincidence vs. barometric pressure |
 4-fold coincidence vs. barometric pressure |
 5-fold coincidence vs. barometric pressure |
Statistics for some of the above graphs are below:
 Statistics for count rate vs barometric pressure |
 Statistics for count rate vs outside temperature |
 Statistics for pressure-corrected count rate vs outside temperature |
 Statistics for 3-fold coincidence with pressure |
 Statistics for 4-fold coincidence with pressure |
 Statistics for 5-fold coincidence with pressure |
Week 14: May 6 - Final Week
This was the final week, so each of us gave individual presentations on our work. I wasn't happy with some of my results from last week (which I included in my presentation) so in the week that followed I looked at the data again, and replaced some of the graphs from last week with ones that I think are correct. Last week's entry now has the reworked graphs. In addition to our presentations, we were asked to summarize what we got out of the course. Both my presentation and my summary are included below.
Presentations
Each of us gave individual presentations about our work. Since there were multiple people in each group, there was some overlap in the presentations, although it was instructive to see different people's summaries, and hear what they got out of the course. I have listed presentations here by working group, not necessarily in order of presentation. A few pictures of our working groups are shown below - scroll down to see happy students hard at work, or something like that.
Desiree: Speed of particles. Desiree and Mildred measured the speed of particles, using two different distances. They ended up measuring the speed of light remarkably accurately.
Mildred (Mildred's course wiki): Speed of particles. Mildred comes from a teaching language (Italian and Spanish) background. I was thoroughly impressed by how much she put into the course, learning and applying scientific ideas and skills that were far from her major. Well done, Mildred!
Karen: Effects of Shielding. Karen and Tom looked at the effects of shielding on the cosmic ray count rate. They used steel and lead plates/blocks.
Tom: Effects of Shielding. Tom talked about his work with Karen, then talked about small detectors from QuarkNet and QuarkNet e-labs on Cosmic Rays. I am supposed to be getting similar detectors, to be used in my classes at school, so was interested to see what he was able to do with them. It looks as though our current detectors are great because they measure and record continuously, and there are similar detectors in other schools for comparison. The QuarkNet detectors complement our current detectors, because they are small, movable, and can be configured according to an individual or group of students' experimental design. I was also interested in the e-labs, and hope to look into them and maybe implement them in my classes.
Lena: Accidental coincidence. Lena and Greg introduced a time delay by adding a length of cable to one of two superposed detectors. That way, any cosmic ray passing through both detectors were suffer a delay for one of the detectors, and the signals would not appear to be coincidental. They still found a lot of what they called accidental coincidence. After changing out detectors for a "better detector" they found less accidental coincidence, presumably because of less after-pulsing. They also looked at different path length caused by cosmic rays arriving from different directions.
Greg: Accidental coincidence (slides and notes). Greg included a discussion on systematic vs. statistical error. We had talked about statistical error in class, and we learned how to estimate the amount of statistical error associated with our measurements. Systematic errors were harder to estimate, being due to just about anything else. The systematic errors will get you every time. Greg also mentioned dispersion and attenuation of the signal traveling through the cable.
Vinny: Cosmic Chris. Vinny and James had taken Cosmic Chris around the Physics and Math buildings, the bridge between them, and the grounds surrounding them.
James: Cosmic Chris. James also mentioned Luis Alvarez, who had used cosmic ray count rates to detect open cavities in pyramids in Cairo.
Joe: Cosmic Ray lab for students. Joe had worked with Harry and Tania on energy deposition. His presentation diverged from the work they had done, and was a nice introduction to cosmic rays. He also presented a lab that he put together for use in his classes. The procedure was was very detailed (too detailed for me, but full of excellent detail).
Harry, (Harry's class wiki): Energy deposition. Harry has consistently posted well-thought discussions of his work and progress. His recent work was on the energy deposition in a scintillator detector due to a cosmic ray. Dr. Dima Vavilov had suggested that the energy was dependent on the distance from the PMT, so Harry et al. looked at a bunch of geometric effects. Harry found roughly a 2:1 ratio in energy deposited at a distance far from the PMT relative to close to the PMT. He also found a small peak at the high end of the energy spectrum, but Dima suggested that it was almost certainly a high energy dump (all higher energy counts landed in that one energy bin). Harry ended with an excellent list of so-far-mostly-unwritten documents that, once written, would help those of us working on cosmic ray measurements. He requested the following:
- Supplement on statistics, going beyond the brief discussion in class. Sounds like a good idea.
- Wiki work: a summary of how to upload various files. Something like this already exits here, and a page about converting files exists here, but maybe we should add some more details (and point students to it at the beginning of a course).
- LabView tutorial/manual/help (for measurements using the oscilloscope, I think): Dima is writing an oscilloscope tutorial which will probably address this.
Brad: Brad's presentation is here. Brad and I had worked together on analyzing cosmic ray data that is being collected at the high schools around Long Island. Brad had concentrated on comparing data from his school (Bay Shore HS) and mine (Smithtown HS), and got great results. Even better, he showed that it was relatively easy to compare data from different schools, which opens up the door for a lot of comparative analyses in the future. Brad also wrote a really nice Excel template that translates the recorded time into an Excel-compatible time and date, and calculates average countrates. Nice work, Brad!
I presented a summary of my results, described elsewhere in this wiki. At the time of the presentations I had some poor R analyses. I have since reworked the data, so have replaced the questionable graphs with graphs that I believe are valid. My (corrected) presentation is here.
Prof. Mike Marx recommended the book The Fly in the Cathedral: How a Group of Cambridge Scientists Won the International Race to Split the Atom, by Brian Cathcart, as an excellent introduction to the beginnings of nuclear physics, and a good read.
 Brad (left) with Prof. Marx' assistance, hamming it up for the camera |
 Tom T, hard at work (note the good book in hand). Karyn in absentia |
 Harry (front) and Joe, collecting and analyzing energy deposition data |
 Mildred with Desiree (at computer), measuring speed of particles |
 Patrick (left) and Greg (right), looking over their data with Dima. Lena in absentia |
 Cosmic Chris, who worked with Vin and James (camera shy) |
Summary
Mike asked us to summarize what we had learned from this course. In my case, I learned a lot.
- Where to find our cosmic ray data
- What the cosmic ray data looks like
- Where to find meteorological data, such as barometric pressure
- How to graph the data - using both Excel and R
- A few different kinds of questions that can be asked (and answered) from these measurements
- And I was right that I didn't have enough time for this course. But in the spirit of a story that has been making the e-rounds, time can always be found ...
Finally, thanks to a lot of people for making this course as good as it was. It was a pleasure working with Desiree and Mildred for my first project, and with Brad for my last project. Rich was invaluable in putting together a quick temperature box (and by the way has lots of ideas for class demos). Dima was the best - helpful (for everybody!) but not intrusive - thanks Dima! Helio Takai, although not mentioned here very often, read and commented on my wiki entries every week. His comments, suggestions and encouragement were invaluable (that means good; really good). Thanks Helio! And finally, thanks to Mike for a great course and the opportunity and support to look into some questions that I really wanted to look into - thanks Mike!
Epilogue
After this course was over, I introduced cosmic rays and data analysis to my classes at school. One of the classes came to the lab to perform experiments, analyze and present their results. Two other classes took existing cosmic ray data that the scintillation detectors at our school had been accumulating, and collaboratively analyzed the data to look for a small solar flare that was known to have occurred at the end of April of this year (2008). Both activities were hugely successful, both in terms of results and in terms of participation and learning on the part of the students. A description and links to the students' summaries of their results can be found here.
A similar activity will be (has been) introduced to students and high school teachers at the 2008 MARIACHI summer workshop. A preliminary description of the activity can be found here; it will (hopefully) be updated by the time of the workshop.
