Uploading an open office spreadsheet giving approximate current densities for the cooling channel using "as built" dimensions, keeping beta ~ 42cm at the centre of a LH2 absorber. Also included are the approximate diffuser configurations to give 3, 6 and 10mm beams at 200 MeV/c, and the expected energy lost in them.

These won't give perfectly matched beams, it's highly likely we can improve on them, but they should be sufficient to 'test the system' and get going.

I loop over all of the files and run the analysis separately for each file.

It is far more pedestrian and slow than a Chain, but it seems to work. I have attached an example analysis script (plot_virtual.py) that runs over all of the virtual planes and histograms the position, magnetic field, and muon momentum as a function of z. It is currently set up to loop over the first pass MC files contained in the parent directory relative to the working directory.

Note that some of the files are empty because they died prematurely for whatever reason (memory or wall time constraints). This script filters those files out of the analysis.

The simulations on the grid are in preparation. The local tests of the jobs need to be completed in the a similar manner to how they would be run on the grid.

The idea is to post a list (such as the one attached) to the web somewhere (CDB), containing references to all of the json documents to be used in the grid job (examples are also attached) that are derived from G4beamline. These documents should also exist somewhere that is web accessible. At submission a number is fed into the simulation script that then corresponds to a line in the list file as well as the web location of the json document list, so that each subjob of the submission downloads first the json document list and then the json document corresponding to the assigned line to the working space of the grid subjob. The output is then indexed by the list line number.

The issues that still need to be sorted out is where to place the G4beamline interface files. The possibilities are being explored by John Nugent right now.

The pass2 simulation has been updated so that there are now 1280 good runs out of 1418 available G4BL interface files. The tar-ball has the same name and URL.

I have completed an analysis of the updated simulation using the above simulation to evaluate the use of a cut on the single particle amplitude on the measured emittance. My notes (in the form of a set of slides) are attached. The executive summary is that a cut that accepts single particle amplitudes (as defined in the slides) between 16 mm and 48 mm in coordination with a cut that rejects pz > 210 MeV/c yields the best emittance change (of -0.23 ± 0.18 mm). Either an improvement in the emittance change (by reducing beta, if possible) or an order of magnitude improvement in statistics is required.

Ryan, do you know which geometry and magnet settings were used for the "pass 2" dataset?

I ran an analysis against the reconstructed dataset in order to complete MPB action "Do a blind analysis ...". I ran the analysis against

http://www.ppe.gla.ac.uk/~rbayes/MICE_6pi200_1/pass2_simulation_mausv0p9p2.tar.gz

• I note that tracker reconstruction does not have any error matrix associated with the tracks.
• I note that TOF is uncalibrated (presumably we don't have a calibration appropriate for this TOF geometry)

I constrained myself to the recon_events branch in the data structure (hence blind). I tried to do PID using uncalibrated TOF vs tracker momentum to reject pion impurities. I did a statistical weighting using the voronoi algorithm in x,px,y,py to generate matched 6 mm emittance beam followed by a statistical weighting to select energy with 5 MeV sigma gaussian distribution and mean 226 MeV (200 MeV/c pz). I measured exactly no cooling! Results below:

Number upstream 10060 weight 10268.6736441
Number downstream 10060 weight 10268.6736441
Emittance upstream: 6.26319650991
[[ 1228.81324105   -71.05796649    76.69789367  -740.27516681]
 [  -71.05796649   691.50433735   638.7959465     76.89672699]
 [   76.69789367   638.7959465   1192.80588937   -64.05287378]
 [ -740.27516681    76.89672699   -64.05287378   839.24662183]]
Emittance downstream: 6.29066900614
[[ 1598.90347602  -292.16254374   213.90940464   926.62538153]
 [ -292.16254374   913.4983059  -1056.10643249   -55.95572244]
 [  213.90940464 -1056.10643249  1738.57475123   -69.65882614]
 [  926.62538153   -55.95572244   -69.65882614   838.63803593]]

Full report in the analysis meeting.

Just to follow up on comments in MAUS meeting, I am using attached scripts to read data and get output reconstructed up to slabs but no space points. E.g. for "maus_output_root/maus_output_1.root" from Ryan's dataset:

number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 3 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 1 number of tof0 slabs: 0 number of tof1 slabs: 2 number of tof0 digs: 0 number of tof1 digs: 4
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 3
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2
number of tof0 sps: 0 number of tof1_sps: 0 number of tof0 slabs: 2 number of tof1 slabs: 2

Here is the first root file (of a thousand or so) so that you can have a play...

I have written an analysis algorithm in c++, largely because I have been having problems with xboa in terms of processing time, and I didn't find out about Adam's until after I had written it.

The attached tar file contains the program files. It should build in the MAUS environment with a simple make command after the tar file is unpacked.

The program uses a two step process to first identify events in a set of MAUS output files and place them into a "reduced" tree file. This reduces the space requirement of the batch analysis from 16 G to 1.8 G. The resulting events are then passed through a simple muon PID selection algorithm based on the TOF and the particle momenta at the absorber (based on the average momenta between reference planes). The TOF selection is fixed so that the TOF1 - TOF0 difference must be between 42 ns and 48 ns. The momentum range is flexible with respect to the software, but after discussion with Jaroslaw, I have been using a momentum range of 200 ± 5 MeV in my analyses. The results for the 6-200 MeV batch simulation are given below.

Upstream tracker measurements¶

• Summary

  Number of tracks = 3356

  mean X = 2.46407±0.631982

  mean Y = -3.58828±0.630931

  mean Z = 15049.7±0.566714

  mean Px = -5.3386±0.452144

  mean Py = 8.99147±0.47886

  mean Pz = 203.982±0.110495

• Covariance

  X	Y	Px	Py	Z	Pz
  1340.39	-44.3757	-35.8242	-736.47	-3.44362	46.1577
  -44.3757	1335.94	755.583	-30.4149	46.7451	13.1694
  -35.8242	755.583	686.08	-30.0475	42.0154	7.15832
  -736.47	-30.4149	-30.0475	769.555	31.429	-35.3736
  -3.44362	46.7451	42.0154	31.429	1077.83	12.8347
  46.1577	13.1694	7.15832	-35.3736	12.8347	40.974

• Emittance(x,y) = 6.05213±0.104471
• Beta(x,y) = 427.457±7.37873

Downstream tracker measurements¶

• Summary

  Number of tracks = 3356

  mean X = -10.367±0.565674

  mean Y = 16.638±0.633988

  mean Z = 18868.2±0.839434

  mean Px = -10.5996±0.492107

  mean Py = -5.8433±0.476276

  mean Pz = 187.887±0.130534

• Covariance Matrix

  X	Y	Px	Py	Z	Pz
  1073.88	-143.281	71.2935	677.234	78.629	33.7998
  -143.281	1348.91	-814.987	-117.019	-139.69	-23.9673
  71.2935	-814.987	812.72	45.0992	145.2	22.0473
  677.234	-117.019	45.0992	761.271	-22.1385	45.4622
  78.629	-139.69	145.2	-22.1385	2364.8	6.5534
  33.7998	-23.9673	22.0473	45.4622	6.5534	57.1834

• Emittance(x,y) = 5.91149±0.102044
• Beta(x,y) = 365.188±6.30384

I have not applied any correction to the emittance result shown.

I have also extracted the emittance and beta from the virtual planes with the same analysis so that the behaviour of the true particles can be accessed away from the tracker reference planes.

I have used this virtual plane analysis to explore the effect of the momentum cut. This shows that relaxing the cut has a dramatic effect on the measured emittance, increasing both the overall emittance of the sample and the emittance change across the absorber. The results are shown in the attached file "virtual_planes.pdf".

Now we have data this is no longer useful