1. Marling and Stroud High Particle Physics Society

2. Data Processing What do we do with the numbers?

3. Pre-Calculation Considerations: Sidereal Days  Earth rotates both around the sun and its own axis.  Every day it makes one rotation around itself.  Every year it makes one revolution around the sun.  However, taking both of these into account and using the stars as reference point, the Earth appears to make one extra rotation around itself per year.  This gives the sidereal year and the celestial time system which will be used frequently for the astrophysical calculations.

4. Time and Angles  Our first step to locating the source of the cosmic radiation is to split the sky up into 4 quadrants (as illustrated).  Calculate the radiation from each quadrant via vector decomposition.  This requires the time to be converted to an angle (alpha), where ‘t’ is the time in solar hours and ‘T’ is the amount of hours in a sidereal day.

5. Using Vector Decomposition  For each time ‘t’ the data can be treated as a vector.  Alpha is the angle of the vector and the number of events is the magnitude.  This allows the data to be split into its 2 independent components using the second formula.  As each DataX and DataY can be positive or negative, the data has been split into 4 groups, for the 4 quadrants.  Summing over this data will help us detect the direction of the cosmic rays but further refinement is required…

6. Weighting  To analyse the data, a whole number of complete periods are required. This can be quite impractical.  To solve this we weight the data, making vales at the start and end less valuable.  The weighting function should be a pure Gauss-curve. However this curve is infinitely long therefore we will use an approximation, a raised cosine (the third formula).  To use the weighting factor, each number of coincidences (at a time ‘t’) should be multiplied by its respective weighting factor. This should be done before vector decomposition.

7. Weighting

8. Analysing the Final Data  To turn all this data into a final answer, we must take the sums of the 2 sets of data (x and y) and recompose them as vectors.  The angle between these 2 sums can be calculated using standard trigonometry.  However, this result may not be accurate, and a final formula, that compares the resultant radiation with the total radiation, can be used to find the standard deviation ( as a percentage).

9. Automation Isn’t this all just a bit too tedious?

10. Background Info  Coded in python 2.7.9  Written using python xy in the Spyder development environment.  Compiled into a Windows.exe file using cx_freeze.  Modules used:  Tkinter and tkMessageBox  Datetime  Os and shutil  Math, numpy and pylab  Csv  urllib

11. The GUI Status Screen- Tells the user which files have been downloaded Input start and end date of data collection Input the stations that you want to get data from Control the program from here

12. The Output- Folder Structure

13. The Output- Files

15. Code- Constants and Setup

16. Code- Looping and Calculations

17. Code- Final Analysis

18. What's Next?  Program returns the time (or angle) at which the event took place.  This, combined with the position of the detector, can be used to find the location of the source on a star chart.  This requires additional software.  It is a manual process and it is difficult to compare star charts.  Thus a python compatible piece of virtual observatory software is required for further automation.

19. Our Results- Sample  There are many stations that have data for a large time span.  We shall take a sample of the stations at Eindhoven for the dates 12/01/13 to 19/01/13. All these stations were active constantly for this time period. StationAlpha (degrees)Deviation (%) 80013.743563609033.84648164658 80020.08106905537960.845310045434 800421.8730023782.08510888992 800514.63828743981.91793671168 80062.5082200334813.8259604359 800738.75242452250.757092194196 800817.7040603413.54404325151 800986.99042304632.55522768076

20. Our Results- Conclusion  All these station have similar locations.  Most have fairly low deviations and thus are quite reliable.  Expectation: similar values for alpha.  A variety of values for alpha mean we cannot pinpoint a location for the source.  Result: Inconclusive.

21. Cloud Chamber Workshop

22. Beginnings  Ran a workshop for younger students with the intention to inspire a pursuit in physics and join the PPS  Decided the subject of the workshop: detection of muons via cloud chambers, which links closely to the HiSPARC assignment Example of a muon track

23. The Process  Decided to run 4 sessions due to the popularity of the workshop  Each session was run by 4 members of the HiSPARC group, with about 30 students per session

24. The Process Cont’d  The sessions started a brief presentation on theory and a practical demonstration on how to setup the experiment  Questionnaires assessing pupils interest in physics were handed out at the beginning and end of sessions  There was an increase of interest in taking physics further after the workshop

25. The Process Cont’d  Students set up the apparatus  Observed cloud formation  Completed tasks during formation delays  Observed muons passing through  Dry ice was crushed, increasing surface contact area

26. Cern Trip  Visited the LHC at Cern  Spoke to physicists about pushing the boundaries of physics  Saw the equipment used to work on the LHC and toured the LHC building  This gave us a deeper understanding of cosmic rays

27. Where we are now  Attaching the detector to the roof of the DT block and start analysing data  Creating a 3D model to represent the findings  Recruiting new 6 th formers to help run the detector