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Comparison between simulations and measurements in the LHC with heavy ions T. Mertens, R. Bruce, J.M. Jowett, H. Damerau,F. Roncarolo.

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Presentation on theme: "Comparison between simulations and measurements in the LHC with heavy ions T. Mertens, R. Bruce, J.M. Jowett, H. Damerau,F. Roncarolo."— Presentation transcript:

1 Comparison between simulations and measurements in the LHC with heavy ions T. Mertens, R. Bruce, J.M. Jowett, H. Damerau,F. Roncarolo

2 Outline Introduction Comparison of different IBS Models Measured data and simulation input Comparing the simulation with single bunch data Comparing the simulation with averaged bunch data Side note on Protons Conclusion and outlook T. Mertens5/2/20112

3 Introduction Goal is to simulate Ion runs in 2010 during physics Different IBS models available Which fills should we try to simulate? Is all the necessary data to compare with simulation available? 5/2/2011T. Mertens3

4 Comparison of different IBS Models[1] Model Summary ModelDescription Piwinski Smooth (Piwi) Uses Piwinski’s formulas as described on page 126 of “The Accelerator Handbook” assuming vertical Dispersion to be zero. Uses a smooth Lattice approximation Piwinski Lattice (PiwLat) Uses Piwinski’s formulas as described on page 126 of “The Accelerator Handbook” assuming vertical Dispersion to be zero. Uses optical functions in the Lattice elements and sums growth rates over all the elements in the accelerator. Piwinski Modified Lattice (modPiwLat) Uses Piwinski’s formulas as described on page 126 of “The Accelerator Handbook” assuming vertical Dispersion to be zero. Uses optical functions in the Lattice elements and sums growth rates over all the elements in the accelerator. Also takes derivatives of the horizontal Beta and horizontal Dispersion into account Interpolation (Interpolat) Uses tri-linear interpolation on a lattice in an external file. This file can be generated using any IBS model of choice! Here we used a stand-alone software version of the modPiwLat Model to calculate the IBS growth rates on such a lattice. Bane (Bane) High Energy approximation using Bane’s Approximation Function (Reference : SLAC-PUB-9226. A simplified Model of Intrabeam Scattering, 2002. Stanford Linear Accelerator Center.) Nagaitsev (Nagaitsev) Based on Bjorken-Mtingwa but expressed in Carlson’s Elliptic Integrals to calculate the IBS growth rates. Does not take Vertical Dispersion into account. (Reference : S. Nagaitsev. Intrabeam scattering formulas for fast numerical evaluation. Physical Review Special Topics – Accelerators and Beams, 2005. PhysRevSTAB.8.064403.) 5/2/2011T. Mertens4

5 5/2/2011T. Mertens5 Comparison of different IBS Models[2] Simulations Input 1 = Process is on 0 = Process is off Normalized Emittances

6 Comparison of different IBS Models[3] T. Mertens5/2/20116 Coupled = Full coupling between horizontal and vertical plane, growth rate for both planes set equal

7 Comparison of different IBS Models[4] T. Mertens5/2/20117

8 Comparison of different IBS Models[5] Decided to use Nagaitsev Based on Carlson’s Elliptic Integral ( Reference: Numerical recipes in Fortran, page 1130 ) Does not include Vertical Dispersion Depends on Coulomb Logarithm, set to 20 for the simulations here ( Reference: S.K. Mtingwa J.D. Bjorken. Intrabeam scattering. Part. Acc., 13:115–143, 1983 ) 5/2/2011T. Mertens8 We hope to get rid of this in the future.

9 Measured data and simulation input [1] Selecting Ion Fills to Study Duration of STABLE beam mode T. Mertens5/2/20119

10 Measured data and simulation input [2] Selecting Ion Fills to Study All required data available? T. Mertens5/2/201110

11 Measured data and simulation input [3] Selecting Ion Fills to Study Final selection of Fills we simulated 5/2/2011T. Mertens11 FillN bunches Beam 1 N bunches Beam 2 N bunches colliding in ATLAS/CMS Fill Length in Physics 1494121 1136.5 h 15041211201127.25 h 1511121 11310 h 1514121 1136.5 h

12 Measured data and simulation input [4] Single bunch for each beam – Select a bunch in beam 1 and the bunch in beam 2 that collides with this first bunch in ATLAS/CMS – Extract the data for these 2 bunches – Use data at the beginning of STABLE mode to set initial conditions for the simulation Averaged data – Select the bunches colliding in ATLAS/CMS from beam 1 and beam 2 – Extract the data and average it over the selected bunches – Use these averages at the beginning of STABLE mode to set initial conditions for the simulation T. Mertens5/2/201112

13 Comparing simulation with single bunch data[1] Bunch length for bunch 2 Fill 1494 Bunch length for bunch 3 Fill 1494 T. Mertens5/2/201113

14 Comparing simulation with single bunch data[2] Intensity for bunch 2 Fill 1514 Intensity for bunch 4 Fill 1514 T. Mertens5/2/201114

15 Compare simulation with averaged bunch data[1] Uncorrected data Luminosity from ATLAS Luminosity from (just 2 bunches colliding) Bunch length data BQM Intensity data FBCT Transverse data from BSRTS corrected as (F. Roncarolo) Note : correction factors different in horizontal and vertical plane but the same for all fills Corrected data Luminosity from ATLAS Bunch length data BQM Intensity data FBCT Transverse data from BSRTS corrected so that luminosity from ATLAS and simulated luminosity match. Note: same correction factor used for both planes here (can be improved!) but not the same for all fills -> Fill dependent! T. Mertens5/2/201115

16 Compare simulation with averaged bunch data[2] T. Mertens5/2/201116 Careful : sigma's are at ATLAS IP, take Beta’s into account!

17 Compare simulation with averaged bunch data[3] Determining Averages Bunch lengths : all bunches have same timestamp -> just average for each point in time FBCT : same procedure as for Bunch Lengths BSRTS : – Scans through the bunches : data for different bunches is at different moments in time! – Create an interpolation function for each bunch – Create a lattice of points in time – Calculate values of interpolation functions on time lattice – Use these values to calculate averages 5/2/2011T. Mertens17

18 Compare simulation with averaged bunch data[4] Determining Averages 5/2/2011T. Mertens18 Plots of the BSRTS interpolating functions for some of the bunches

19 Compare simulation with averaged bunch data[5] Determining Averages 5/2/2011T. Mertens19 Plots of the BSRTS interpolating functions for some of the bunches

20 Compare simulation with averaged bunch data[6] 5/2/2011T. Mertens20 Fill 1511

21 Compare simulation with averaged bunch data[7] Example 1 UncorrectedCorrected T. Mertens5/2/201121

22 Compare simulation with averaged bunch data[8] Example 1 UncorrectedCorrected T. Mertens5/2/201122

23 Compare simulation with averaged bunch data[9] Example 1 UncorrectedCorrected T. Mertens5/2/201123

24 Compare simulation with averaged bunch data[10] 5/2/2011T. Mertens24 Fill 1494

25 Compare simulation with averaged bunch data[11] Example 2 UncorrectedCorrected T. Mertens5/2/201125

26 Compare simulation with averaged bunch data[12] Example 2 UncorrectedCorrected T. Mertens5/2/201126

27 Compare simulation with averaged bunch data[13] Example 2 UncorrectedCorrected T. Mertens5/2/201127

28 Side note on Protons[1] 5/2/2011T. Mertens28 We are planning to use particle tracking to simulate proton runs. 2010 : used different approach – Assuming round beams calculate IBS growth rates on a Lattice (RF Voltage, Longitudinal Emittance, Transverse Emittance) using MAD-X – Choose initial point (Longitudinal and Transverse emittance) – Use iterative function (NestList command in Mathematica)

29 Side note on Protons[2] 5/2/2011T. Mertens29 Blue curves are the simulations based on the iterative function. Red curves are ATLAS Luminous Region Data

30 Side note on Protons[3] 5/2/2011T. Mertens30 Blue curves are the simulations based on the iterative function. Red curves are ATLAS Luminous Region Data

31 Conclusion and outlook Observations of comparison with particle tracking: – Transverse growth underestimated – Bunch length growth overestimated – Both are different expressions of same effect, when simulation would follow the transverse growth, bunch length would also agree better with data. Particle Tracking Simulation seems to be missing some effect(s) that makes transverse emittances grow faster than predicted by our IBS models. (hump?, particularly in vertical plane) Same observations can be made for protons. Would be interesting to do same comparison at injection energy without beams in collision. But more problems with data at injection : no BSRTS, BGI can not be trusted yet. Usually short periods of time at injection -> not much data available. Next step add hump model to simulation (Vertical? Beam 2? ) Try to compare particle tracking simulations for protons. T. Mertens5/2/201131

32 Back up 5/2/2011T. Mertens32

33 Correction Factors F. Roncarolo 5/2/2011T. Mertens33

34 Compare simulation with averaged bunch data Example 3 UncorrectedCorrected T. Mertens5/2/201134

35 Compare simulation with averaged bunch data Example 3 UncorrectedCorrected T. Mertens5/2/201135

36 Compare simulation with averaged bunch data Example 3 UncorrectedCorrected T. Mertens5/2/201136

37 Compare simulation with averaged bunch data Example 4 UncorrectedCorrected T. Mertens5/2/201137

38 Compare simulation with averaged bunch data Example 4 UncorrectedCorrected T. Mertens5/2/201138

39 Compare simulation with averaged bunch data Example 4 UncorrectedCorrected T. Mertens5/2/201139

40 Formulas Piwinski 5/2/2011T. Mertens40 For Piwinski SmoothFor Piwinski Modified

41 Formulas Bane 5/2/2011T. Mertens41

42 Formulas Nagaitsev[1] 5/2/2011T. Mertens42

43 Formulas Nagaitsev[2] 5/2/2011T. Mertens43

44 Formulas Nagaitsev[3] 5/2/2011T. Mertens44

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