Assignment #1: Identifying Stable Data Run Periods
We need to inspect the data rates recorded underground, to determine if they are reasonable and consistent.  This will 1st be done by histogramming the rates to see if they fluctuate around a stable average.

As an example, this histogram displays, minute by minute, the 2-fold coincidences counted at the Base Station PC.  Note the zero is "suppressed", i.e., the vertical axes starts at 500, not 0.  We have focussed in on the very top of the graph.  These are actually minor fluctuations riding atop a nearly 600 tall base.  We see 1 bin as low as 562, and 1 as high as 657; most are close to the 610 counts/minute average (marked by a blue horizontal line).  We don't see sudden spikes up to 800, or drops down to 400.  We will discuss (in assignment 2) how large fluctuations could get and still be considered "reasonable.  For now we begin with a visual inspection.
 Minute-by-minute 2-fold Coincidences

A similar plot, showing the 2-fold counts hour-by-hour for the RFHS data.  One detector module (read out through channels 0 and 1) gave rates between 1200-1400 counts/hour (see red  oval) that varied smoothly over time.  The other, during the same data period, had much higher counts (2000-3000) and much choppier behavior.  In fact, looking at the counts collected in the individual channels, 0 - 4, show both 0 and 1 remaining stable, while both 2 and 3 show the same strange behavior.
 Hour-by-hour 2-fold counts of RFHS data
Channel 0
 Channel 1
 Channel 2
 Channel 3
Channel 0
 Channel 1 Channel 2 Channel 3
We might imagine the 2nd module happened to be set up close enough to some electrical equipment (fan?  transformer?
junction box?) to be sensitive to noise it generated.  Perhaps adjacent channels of the power supply shared the problem.  Or perhaps those 2 detectors were being over-driven when operated at the same high voltage as the others.  In any event, we will tend to trust data from the 1st module, and suspect data from the 2nd.

You need to check all the underground data to see which channels and run periods look satisfactory.  We will base this initial conclusion on the same procedure.  Build and print histograms.  Inspect them for smooth, relatively flat average behavior.  Flag those with very choppy (and suspisciously high) results.

The underground data is available through links on the Henderson Project homepage.  The left column of links are to zipped files of raw data (named for the original folders as saved on the PC by the student teams).  You are welcome to check these files out.  Except for the date and time stamped at the beginning of each line, though, information is encoded in hexadeciminal:
11/17/04 15:41:27 76EC5743 01 25 01 24 00 01 00 01   00000000 8 +0000
11/17/04 15:41:34 876693EF  80 01 00 01 37 01 00 01   00000000 A +0000
11/17/04 15:41:34 876693F0  00 01 00 01 01 32 00 01   00000000 8 +0000
11/17/04 15:41:34 87A6783B 80 01 00 01 00 01 37 01   00000000 A +0000
11/17/04 15:41:34 87A6783C 00 01 00 01 00 01 01 25   00000000 8 +0000                                Raw  data file from AHS folder
11/17/04 15:41:34 88EEE962  80 01 00 01 2C 38 2C 32 00000000 8 +0000
11/17/04 15:41:35 89C492B7 A6 01 25 3B 00 01 00 01  00000000 A +0000
11/17/04 15:41:35 89C492B8 01 23 00 01 00 01 00 01   00000000 8 +0000
11/17/04 15:41:35 8A495B18 80 01 00 01 3C 01 33 01  00000000 A +0000
I'll post an explanation of how this is interpretted later.  For now, you can at least determine the duration of each run by checking out the time-stamp on the 1st and last recorded event.  The right column links you to the simple text output of a program that decodes the info and reports the number of coincident hits recorded.  Its organized into tab separated columns, so in fact can be opened with a spreadsheet program like Excel.

Date   Time  ch0    ch1      ch2     ch3      01coinc  02coinc 03coi 12coinc 13coinc 23coinc 012coin 023coin 013coin 123coin 0123coin
4-Oct 14:00 4602 10688 12669 16911 3719       55         159    690       2511    8560      26         2           58         173          0
4-Oct 15:00 5604 13132 15198 20561 4537       61         220    827       3105   10287     32         3           84         220          0
4-Oct 16:00 5585 13052 15274 20677 4546       48         187    803       3102   10435     32         3           74         209          0
4-Oct 17:00 5448 12684 14980 19977 4338       62         182    818       2999   10160     41         3           87         196          0
4-Oct 18:00 5465 12624 14995 19799 4410       62         182    819       2907   10146     42         0           78         194          1                         Processed output from ES8100b folder
4-Oct 19:00 5747 12985 14875 19691 4625       58          214   849       2914   10018     38         2           78         187          0
4-Oct 20:00 5503 12708 14975 19669 4399       49          218   881       2875   10043     44         2           109       181          0
4-Oct 21:00 5583 12873 15012 19900 4500       47          216   852       2995   10058     38         1           75         214          1
4-Oct 22:00 5600 13142 15642 21025 4488       74          217   844       3184   10619     46         0           86         171          2
4-Oct 23:00 5610 13465 16054 21834 4495       65          222   812       3313   10903     30         4           89         220          1
5-Oct   0:00 5615 13364 15816 21412 4483       64          217   838       3277   10795     43         2           88         218          1

Each row reports hourly totals, channel by channel, starting from the time stamp on the left.  These numbers were collected with a 2-fold trigger requirement.  Notice the coincidence levels for the channel 0&1 and 2&3 coincidences are in the thousands; other pairings (02, 03, etc) give much smaller numbers.  You should remember detectors 0&1 and 2&3 were stacked together.  Muon tracks passing through such a stack should have triggered each detector in the pair.  The other (mis-)pairings give us some idea of the accidental coincidence rate.  Notice 3- and 4-fold coincident rates are very rare.

Performing the analysis
Start by building histograms of the counts reported for the 01 and 23 coincidences.  They may be generated by any means, but here is an outline of how it can be done in Excel.
1. Download the text
     a. Select a data output file from the list.
     b. Right click on the link
     c. "Save target as..."
     d. Save this as a file in your work area or desktop.
 
2. Open the output file in Excel.
     a. Right click on the file name or icon
     b. "Open with >" selecting "Microsoft Excel"
OR
 a. Click Start button: left, bottom screen menu bar
b. from "Programs " select "Microsoft Excel"
c. from "File" buttom, upper left
d. "Open file..." then browse to DEsktop for file.

3. Create histogram.    
      a.  Click the Chart/Histogram icon in the menu bar, or the "Insert" button selecting "Chart" from the list.
      b.  The default that comes up is a bar graph (histogram) like we  want.  Hit [ Next> ]
      c.  Highlight all the entries in the (for example) "01 coinc" column by clicking on the 1st cell (G2)
           and before you release the mouse button, scroll down through all the entries.
           This should fill the field with something like
=sheet!$G$2:$G$120
      d.  Click [ Next > ] and play with formatting the graph if you'd like.
      e.  Click [ Next > ] and save the graph on this (or any new sheet, if you want to keep the inital tables uncluttered.
      f. If zero has been suppressed, you can click on any of the y-axis labels, then in the pop-up window, under the "Scale" tab,
          change the "Minimum" to 0.
      g. These historgams can be copied into any Microsoft document (Word or PowerPoint).
      h. Double clicking on the plot allows you to make other format changes to the plots. 
      i.  You can also right click and "Cut" the graphs if you want to just start over.

4. If the 2-fold histograms show any funny structure, do the same thing for the "singles" rates in the 1st 4 columns to see if
you can identify problems in the individual channels.

5. Post your plots on a webserver, and circulate the URL (email me and all the SALTA schools)...or...paste the plots in a document and circulate that.  In either case, I will provide links to the plots from the main Henderson Project page.

Having everyone check the data is a good way to cross-check problems, and let everyone learn what good data looks like!
Any questions, suggestions, or difficulties contact me!  dclaes@unlhep.unl.edu
I'll post the 2nd assignment soon!
Dan