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Using MICE to verify simulation codes?

Using MICE to verify simulation codes?. Andreas Jansson. Intro. Recently, there has been a lot of discussion about what the follow-up cooling channel experiment should be. I have come to the conclusion that to answer this question, we need to do some studies that could also benefit MICE.

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Using MICE to verify simulation codes?

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  1. Using MICE to verify simulation codes? Andreas Jansson

  2. Intro • Recently, there has been a lot of discussion about what the follow-up cooling channel experiment should be. • I have come to the conclusion that to answer this question, we need to do some studies that could also benefit MICE. • In particular, I am interested in the question whether MICE (or any other cooling channel experiment) can actually test simulation codes. • If MICE can’t do it, not clear that it will be easier in the follow-up. MICE analysis meet

  3. Possible follow-up experiments MICE rebuilt as FOFO snake (Alexahin) MANX w LHe, no RF (Muons Inc) Mag. ins. Guggenheim section (Palmer) HCC w. HP H2 RF (Palmer) LiH wedge as MICE phase III (Rogers) MICE analysis meet

  4. R&D goals • Three types of goals for a cooling channel experiment • Demonstrate that the simulated cooling channel conditions can be created in reality. • Demonstrate cooling (emittance out < emittance in) • Experimentally validate simulation codes and models. • Note that • If A and C are achieved, in principle this implies B. • Achieving B does not imply C (or A). • MICE will do A and B, can it do C? • In fact, can C be done in any cooling channel experiment? MICE analysis meet

  5. How to “Demonstrate cooling”? • MICE method: • Measure single tracks and form a beam off-line. Calculate emittance in and out of this beam. • A particular challenge is to make sure the off-line generated beam is properly matched • Bad matching can easily mask the small cooling effect. • Need a method to assign weights to the tracks, and make sure there are no voids in the initial distribution. • Significant progress on this recently, although perhaps not yet a done deal. • The method developed for MICE can probably be adapted for used in any future 6D cooling channel experiment. MICE analysis meet

  6. How to “Verify simulations” • Stochastic process -> simple track-by-track comparison not possible. • Look at distributions of ensembles of tracks with similar (identical) initial values, and compare to these to MC of representative ensembles • Information about alignment errors, field errors, average energy loss in absorbers will appear as deviations in the mean values. • Information about energy straggling, tails of scattering distribution will appear as deviations in the distributions (sigmas or even shape) • How to select the ensembles? • Implies binning tracks with similar initial 6D phase space coordinates • For good resolution, bin size should be small compared to the effect to be measured (distribution of the ensembles at exit). • 6D means a very large number of bins. • For decent statistics (say ~500 tracks per bin), need a huge number of measured tracks. • Many orders of magnitude more than to accurately measure cooling. • Even with large data set, sensitivity might not be high enough to resolve effect. MICE analysis meet

  7. A comparison recipe • First, need a good recipe for how to compare to simulations (the following comes from a discussion with Chris Rogers) • For each measured track, run a MC of ~1000 particles with identical initial conditions (measured initial 6D phase space coordinates). • Calculate the difference between the measured exit coordinates and the mean of the MC distribution. • Normalize the deviation using the 6D covariance matrix of the MC tracks. • Calculate the average deviation and the distribution of normalized deviations in each 6D bin. • Distribution should have r.m.s=1, no correlations, and predictable tails. • The idea of this recipe is to minimize the effect of the bin size • Each track is compared to its own MC, rather than a representative MC or the bin (e.g. evenly distributed tracks or tracks starting in the bin center) MICE analysis meet

  8. MANX example: ensemble transmission Transmission for different input phase space coordinates MICE analysis meet

  9. MANX example: ensemble exit mean values Exit mean values for particles starting on the xy plane MICE analysis meet

  10. MANX example: ensemble exit emittance Exit emittance for different initial phase space coordinates MICE analysis meet

  11. The next step/Conclusions • Would like input from you. • Is there a better way to do this type of analysis? • Would you be interested in working on it (perhaps someone already is)? • Would like to implement this recipe for MICE and run a MC study of the sensitivity using “planted errors”. • This should tell us something about whether it is realistic to achieve such a test in a follow-up experiment. • Even if the sensitivity is not good enough to measure e.g. dE/dx, this type of analysis might help to understand (and fix) alignment and field errors. MICE analysis meet

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