1. Start-to-End Simulations for the TESLA LC
A Status Report Nick Walker DESY
TESLA collaboration Meeting, Frascati, 26-28th May 2003

2. A ‘Mixed-Bag’ of Topics
Software tools (MERLIN advertisement)
Beam-based alignment of the TESLA linac
DFS vs Ballistic Alignment
S2E simulations of luminosity performance

3. Simulation and Simulation Tools
Much progress made towards ‘true’ S2E simulations during TRC studies
Codes used:
PLACET (linac)
MERLIN (LET)
LIAR (linac)
DIMAD (BC, BDS)
MAD (BC, BDS)
ELEGANT (BC)
GUINEAPIG (for beam-beam) 90

4. Software tools MERLIN C++ class library Used to simulate Models:
Bunch Compressor
Main Linac
BDS
DR (A. Wolski, LBNL)
Models:
Single-bunch wakefields
Full 3D alignment errors
Girders and complex geometries
Diagnostics & tuning algorithms
Thin-spoiler scattering (used for halo and collimation studies)
Synchrotron radiation
Control system-like interface

5. Software tools Two tracking modes: New MATLAB interface
MERLIN C++ class library
Two tracking modes:
Particles (ray tracing, 2nd O TRANSPORT)
Slice macro-particles (linac, LIAR/PLACET)
New MATLAB interface
More details later in this talk
Powerful and Flexible
Allows rapid code development
Not for the faint hearted!

6. TESLA Linac Alignment A Little History
Many studies for CDR and TDR, most based on Dispersion Free Steering (DFS)
P. Tenenbaum (SLAC) made an independent study using LIAR for EPAC [Tenenbaum, Brinkmann, Tsakanov]
PT’s results suggested ~140% emittance growth on average using this method! [budget: 50%]
Culprit was assumed to be cavity tilts (300mr RMS), but is (I believe) actually BPM resolution (10mm RMS)

7. DFS Find an orbit (trajectory) that minimises dispersion
changing the lattice – beam energy match;
measure difference orbit;
using known lattice model, calculate (fit) orbit correction to minimise difference orbit;
measured difference
linear model
quadrupole offsets

8. DFS Find an orbit (trajectory) that minimises dispersion
changing the lattice – beam energy match;
measure difference orbit;
using known lattice model, calculate (fit) orbit correction to minimise difference orbit;
measured difference
linear model
quadrupole offsets
random

9. DFS Find an orbit (trajectory) that minimises dispersion
changing the lattice – beam energy match;
measure difference orbit;
using known lattice model, calculate (fit) orbit correction to minimise difference orbit;
measured difference
linear model
quadrupole offsets
random
upstream ‘jitter’

10. DFS: Problems Fit is ill-conditioned
with BPM noise DF orbits have very large unrealistic amplitudes.
Need to constrain the absolute orbit
minimise
Sensitive to initial launch conditions (steering, beam jitter)
need to be fitted out or averaged away

11. DFS for TESLA
The effect of upstream beam jitter on DFS simulations for the TESLA linac.
1 sy initial jitter
10 mm BPM noise
45.0
with incoming jitter fitted out
40.0
no jitter
35.0
norm. vertical emittance (nm)
budget
30.0
25.0
uncorrected cavity tilts cause problems for TESLA
20.0 50
100
150
200
250
300
350
Quadrupole #
average over 100 random machines

12. Ballistic Alignment
Turn of all components in section to be aligned [magnets, and RF]
use ‘ballistic beam’ to define straight reference line (BPM offsets)
Linearly adjust BPM readings to arbitrarily zero last BPM
restore components, steer beam to adjusted ballistic line 62

13. Ballistic Alignment 62

14. New Simulations using PLACET and MERLIN
14 quads per bin (7 cells, Df = 7p/3)
RMS Errors:
quad offsets: 300 mm
cavity offsets: 300 mm
cavity tilts: 300 mrad
BPM offsets: 200 mm
BPM resolution: 10 mm
CM offsets: 200 mm
initial beam jitter: 1sy (~10 mm)
New transverse wakefield included (~30% reduction from TDR) [Zagorodnov and Weiland, PAC2003]
wrt CM axis

15. Ballistic Alignment Less sensitive to model errors beam jitter
average over 100 seeds

16. Ballistic Alignment
We can tune out linear <yd> and <y’d> correlation using bumps or dispersion correction in BDS
initial energy spread (3%)
cavity tilts (27 deg phase in first twenty cells)
average over 100 seeds

17. 100 Random Machines 94% 85% dispersion corrected
60% achieved average of 26 or less (>80% for dispersion correction)

18. Ballistic Alignment: Problems
Controlling the downstream beam during the ballistic measurement
large beta-beat
large coherent oscillation
Need to maintain energy match
scale downstream lattice while RF in ballistic section is off
use feedback to keep downstream orbit under control
large linac apertures a + for TESLA

19. S2E Simulations of Dynamic Errors [Ground Motion]
collaborative effort between
Glen White (QMUL,UK)
Nick Walker (DESY)
Daniel Schulte (CERN)
bulk of the work
primary objectives:
the ‘banana’ effect and its correction
intra-train fast feedback
intra-train lumi optimisation using fast lumi monitor

20. Bananas TESLA: high disruption regime:
long. correlated emittance growth causes excessive luminosity loss (‘banana’ effect)
Brinkmann, Napoly, Schulte, TESLA-01-16

21. Bananas TESLA luminosity as a function of linac emittance growth
Note: Dey will contain a correlated component due to wakefields
D. Schulte. PAC03, RPAB004

22. Beam-Beam Issues
Rigid bunch approximation
D. Schulte. PAC03, RPAB004

23. Beam-Beam Issues GUINEAPIG result ‘banana effect’
Now optimise (scan) collision offset and angle (collision feedback)
D. Schulte. PAC03, RPAB004

24. Beam-Beam Issues
optimise beam-beam offset
D. Schulte. PAC03, RPAB004

25. Beam-Beam Issues optimise beam-beam offset and angle
OK for ‘static’ effect
dynamic effects still a problem
D. Schulte. PAC03, RPAB004

26. Simulating the Dynamic Effect
LINAC
BDS IR
IP FFBK
Realistic simulated ‘bunches’ at IP
linac (PLACET, D.Schulte)
BDS (MERLIN, N. Walker)
IP (GUINEAPIG, D. Schulte)
FFBK (SIMULINK, G. White)
bunch trains simulated with realistic errors, including ground motion and vibration
All ‘bolted’ together within a MATLAB framework by Glen White (QMC)

27. Simulating the Dynamic Effect
Intra-train fast feedback
modelled realistically using
bunches from PLACET+MERLIN simulations
realistic beam-beam simulation using GUINEAPIG
Angle feedback kicker modelled correctly in MERLIN

28. Simulating the Dynamic Effect
LINAC (PLACET, inc. multi-bunch effects)
‘static’ alignment errors randomly added
RMS values chosen to give design emittance growth (10nm) on average
BDS (MERLIN)
no static errors currently included
Ground motion
first pass: effect of random 70nm RMS quadrupole jitter
full correlated ground motion models implemented

29. Simulating the Dynamic Effect
First 500 bunches of single bunch train modelled (~18%)
Fast feedback
first corrects angle [BPM] and offset [beam-beam kick] <50 bunches
attempt lumi optimisation by scanning offset and angle
fast lumi monitor correctly modelled by tracking pairs (produced by GUINEAPIG)

30. Simulating the Dynamic Effect
IP beam angle
IP beam offset

31. Simulating the Dynamic Effect
21034 cm-2s-1
Only 1 seed: need to run many seeds to gain statistics!

32. By-product of Dynamic Studies
database of beam-beam events (GUINEAPIG)
quasi realistic beams from linac/BDS simulations
contains
spent e± beam
beamstrahlung
e± pairs etc.
useful for HEP detector studies