1. ECMbias in the presence of Beamstrahlung
Arik Florimonte, UCSC
Mike Woods, SLAC
WORKSHOP  Machine-Detector Interface  at the International Linear Collider
Jan. 6-8, 2005 SLAC
M. Woods, SLAC
MDI SLAC

2. Wakefields, Disruption and y-z Kink instability
Linac wakefields generate “banana” beam distortions (larger for NLC than for TESLA)
NLC example
TESLA example
Disruption parameter is ratio of
bunch length to focal length:
Disruption Parameter
NLC-500
TESLA-500 Dx
0.16
0.23 Dy
13.1
25.3
(larger for TESLA than for NLC)
M. Woods, SLAC
MDI SLAC

3. Summary of ECM Bias studies
w/ Beamstrahlung OFF
Wakefields Disruption Y-Z Kink instability
E-Spread + E-Z correlation + Y-Z Kink instability ECM Bias
E1 and E2 are beam energies measured by the
energy spectrometers. (ISR and beamstrahlung
are turned off for this study.)
Summary of ECMbias
LC Machine Design
<ECMbias>
(Dy = 0)
s(ECMbias)
Max(ECMbias)
vary Dy, hy
WARM-500
+520 ppm
170 ppm
+1000 ppm
COLD-500
+50 ppm
30 ppm
+250 ppm
M. Woods, SLAC
MDI SLAC

4. Summary of ECMbias NLC TESLA NLC’ LC Machine Design <ECMbias>
(Dy = 0)
s(ECMbias)
Max(ECMbias)
vary Dy, hy
WARM-500
+520 ppm
170 ppm
+1000 ppm
COLD-500
+50 ppm
30 ppm
+250 ppm
NLC'-500
0 ppm
10 ppm
M. Woods, SLAC
MDI SLAC

5. Definition of ECMbias (Beamsstrahlung OFF)
E1 and E2 are beam energies measured by the
energy spectrometers. (ISR and beamstrahlung
are turned off for this study.)
Definition of ECMbias (Beamsstrahlung ON)
ECM for NLC-500
Vary cutoff energy
from GeV
M. Woods, SLAC
MDI SLAC

6. M. Woods, SLAC
MDI SLAC

7. ECM Bias versus Vertical Offset
of Colliding Beams
M. Woods, SLAC
MDI SLAC

8. Tails are similar in both E1+E2 and
Study of distributions for i) ECM (cannot measure this)
ii) E1-E2 (closely related to Bhabha acolinearity)
NLC
NLC w/ random-ordered
energy
NLC
NLC w/ random-ordered
energy
Negligible difference
in 2 distributions
Clear difference
in 2 distributions
NLC
NLC w/ random-ordered
energy
Tails are similar in both E1+E2 and
E1-E2 distributions
NLC
NLC w/ random-ordered
energy
M. Woods, SLAC
MDI SLAC

9. Tails are similar in both E1+E2 and
Study of distributions for i) ECM (cannot measure this)
ii) E1-E2 (closely related to Bhabha acolinearity)
TESLA
TESLA w/ random-
ordered energy
TESLA
TESLA w/ random-
ordered energy
Clear difference
in 2 distributions
Negligible difference
in 2 distributions
TESLA
TESLA w/ random-ordered
energy
Tails are similar in both E1+E2 and
E1-E2 distributions
TESLA
TESLA w/ random-ordered
energy
M. Woods, SLAC
MDI SLAC

10. Study of distributions for i) ECM (cannot measure this)
ii) E1-E2 (closely related to Bhabha acolinearity)
NLC’
NLC’ w/ random-ordered
energy
NLC’
NLC’ w/ random-ordered
energy
Clear difference
in 2 distributions
Competing/cancelling
ECM bias contributions
NLC’
NLC’ w/ random-ordered
energy
Tails are similar in both E1+E2 and
E1-E2 distributions
NLC’
NLC’ w/ random-ordered
energy
M. Woods, SLAC
MDI SLAC

11. Summary of ECMbias without Beamsstrahlung
LC Machine Design
<ECMbias>
(Dy = 0)
s(ECMbias)
Max(ECMbias)
vary Dy, hy
WARM-500
+520 ppm
170 ppm
+1000 ppm
COLD-500
+50 ppm
30 ppm
+250 ppm
NLC'-500
0 ppm
20 ppm
<50 ppm
Summary of ECMbias in presence of Beamsstrahlung
LC Machine Design
<ECMbias>
(Dy = 0)
s(ECMbias)
Max(ECMbias)
vary Dy, hy
WARM-500
+960 ppm
150 ppm
ppm
COLD-500
+150 ppm
30 ppm
+350 ppm
NLC'-500
~0 ppm
20 ppm
<50 ppm
→ Energy spectrometers and Bhabha acolinearity alone are not sufficient to
correct for this bias. Need beam dynamics modeling and other input
from annihilation data, disrupted energy measurements, …
M. Woods, SLAC
MDI SLAC