Proceedings of the 25th Linear Accelerator Meeting in Japan (July 12-14, 2000, Himeji, Japan)[13P-37]

Recording System of the Beam-Energy Stability for the KEKB Injector LinacT. SUWADA
Accelerator Laboratory
KEK, High Energy Accelerator Research Organization
1-1 Oho, Tsukuba, Ibaraki, 305-0801 Japan
Abstract
A new recording system of the beam-energy stability based on a software filtering algorithm was developed forelectron and positron beams of the KEKB injector linac. This diagnostics software is useful for monitoring any beam
instability during beam injection. It also allows software operators to know of any sudden beam instabilities by using
several graphical plots when and where these are generated along the linac. This report describes in detail the method andthe availability for this recording system based on beam tests.
1. Introduction
The KEKB injector linac[1] is required to supply
certain amounts of single-bunch electron beams (8×109e-/bunch) and positron beams (4×109 e+/bunch) for theKEKB high-energy (electron) and low-energy (positron)rings[2], respectively. High-current primary electronbeams (6×1010e-/bunch) are also required to produce
certain amounts of positron beams. Since the KEKB is afactory machine, well-controlled operation of its injector
linac is required for keeping the injection rate as high as
possible and for maintaining a stable operation. For thispurpose, beam diagnostic and monitoring tools areessentially important. Of those tools, the beam-position
monitors (BPMs) are important to keep the orbits of thebeams stable; especially, the beam positions and chargesof the primary high-current electron beams must be wellcontrolled in order to suppress any beam blowup
generated by large transverse wake-fields. Although thebeam orbits are automatically controlled by the beamposition feedback system of the linac[3] in daily
operations, sudden position and charge changes haveoften been observed by the BPMs at energy-analyzing
sections. Such instabilities are frequently accompanied
by unexpected beam instabilities. The linac operators
worry about such sudden instabilities because they can
not know where the beam instabilities are generated
along the linac, except for clear phenomena, forexample, tripped-off of the high-power klystrons. Ofcourse, data-logging system for any devices works
during linac operation; for example, the RF monitor
system records any fluctuation of the phase andamplitude of the high-power klystrons. However, sucha logging system can not observe fast changes caused bypulse-to-pulse fluctuations, because the nominal
loggings are performed on the order of minutes. TheBPMs are only useful tools for fast logging on the orderof a second. New software has been developed tomonitor the beam-energy stabilities at the energyanalyzing sections by using the BPM data for operationdiagnostics. In this report, some experimental examples
are given regarding the logging-system structure.

2. Beam-energy monitoring system
2.1 Beam-position monitor system
The hardware and software systems of the BPMs havealready been reported elsewhere[4]. Here, only the datataking system is briefly described. Ninty BPMs installed
along the linac are controlled by eighteen monitor
stations, each of which comprises a front-end computer(VME/OS-9 with a 68060 microprocessor at 50MHz), a
signal-digitizing system (an oscilloscope with asampling rate of 5GHz and a bandwidth of 1 GHz) arelocated on the linac klystron gallery at a nearly equalinterval along the beam line. Each monitor station cancontrol twelve BPMs at maximum. Trigger-pluses
synchronized with the linac beam are provided to all of
the oscilloscopes at a 0.67 Hz cycle. This rate islimited by the communication throughput between afront-end computer and an oscilloscope through a GPIB
line. Unix workstations and the front-end computerscommunicate with each other through the network
system of the linac, and receive the beam positions andcharges from the eighteen front-end computers and store
ten recent data for each front-end on shared-memory
regions. The data servers for BPMs use the data on theshared-memory.
2.2 Recording software of the BPM data
The beam energies are observed at two energy analizing
sections (the center of the 180-degree arc and the beamswitch yard) where large horizontal dispersions are made.
An application software was coded by using theinterpreter software “SAD”[5]. This software gets all of
the BPM data synchronized by the beam trigger withinseveral seconds. The selected target BPMs are located atthe energy-analyzing sections. If sudden changes of thebeam positions or charge at the target BPM occur, an
event flag for the BPM is stored in memory. The events
are judged as a sudden change separated from fluctuationsof noise by using a software filter algorithm. Suchevents are succeedingly searched one-by-one for upstream
BPMs, and judged by the same filter algorithm. If any
step changes are not observed at the most upstream−386−

2.3 Software filtering algorithm
The beam positions and charges detected by the BPMs
normally fluctuate in time series because of the electricaland environmental noise for signal detections.
Asoftware filtering algorithm was developed in order toseparate any beam signals with a sudden (step) change
from those with nominal fluctuations. An ε-separating
nonlinear digital filter (ε-filters)[6] is a useful softwarealgorithm for this purpose, that is, for a function ofrejecting high-frequency noise with small amplitudes andof keeping sudden changes of the signals. A block
diagram of the ε-filter function is shown in Fig.1.

(a)ε-Filter
Input

(b)

Output(a) High-amplitude low-passed signal(b) Small-amplitude noise signal
Fig.1. Block diagram of the ε-filter function.
The mathematical formulation is described in ref.6 indetail; here, only the principle and function are briefly
described. The ε-filter comprises a digital linear lowpass filter (LPF) and a non-linear filter (NLF). A
general manipulation for time-series data, like averaging,is a typical well-known LPF, and such a manipulation
can reject high-frequency components of signals withsmall amplitude. However, it also smooths any suddenchanges of the signals with a large amplitude at thesame time. An added function by the NLF, here the εfilter, does not smooth such a high-amplitude suddenchange of the signal greater than the threshold level,defined by ± ε . The threshold level (ε) should be
experimentally determined by the relation of the noiselevel to the amplitude of the step change. An example
is shown in fig.2 for the ε-filter function. Three
examples for data in time series were simulated by using(a) a step change, (b) a gradually slanted change and (c)two succesive sudden change signals superimposed on
Gaussian random signals. One can clearly see that atypical somoothing function on the base of the LPF
does not work well compared with the ε-filter;
especially, the LPF smoothing shows poor separation ofa signal comprising two succesive impulse-like changes(see example (c)).

(b)

(a)

Amplitude

BPM, we can speculate that the step change wasgenerated just after the BPM, and we can search fordevice failures within this region. Unfortunatelly, wecan not determine what kinds of devices are not properly
working, because all of the logging and monitoring
system including the beam and device parameters are notmutually connected with sufficient efficiently in thepresent linac control system. If the function is formed
to work completely, this system will be extended as amore reliable trouble-diagnostics system.

by ε-Filter(c)by LPFTime Series

Fig.2. Three examples of software-filtered signals whenusing the use of the ε-filter and LPF.
3. Beam experiment and its results
A beam-stability experiment has been performed using
high-current single-bunch electron beams of about 10
nC in the beam line up to the 180-degree arc, where thebeam energy was fixed by 1.7 GeV. These beams werenominal-injection beams to the KEKB ring under atypical operation condition. Under the operating
condition, several feedback system worked almost
stably. Figure 3 shows typical recorded BPM datameasured at the center of the 180-degree arc (SPR032),
which was called a target BPM. The recorded time wasabout forty hours. A couple of zero charge data weretaken just after the end of the injection in order toindicate the injection time;thus, the beam positions wereincorrect because of the signal detections for noiseevents. One can clearly see some sudden changes ((a),(b)and (c) in fig.3) for time series of the beam charge
detected at the target BPM. Three examples ((a), (b) and(c)) show a spike-like change, a step change withrelatively long interval instabilities and a step changewith short interval instabilities, respectively, whichwere clearly separated by using the ε-filters from thenoise fluctuations. After the step detections at the target
BPM, the software algorithm continuously searched forsuch beam instabilities generated at the same detection
time along the upream BPMs until no change wasfound. In these examples, the seed of the 1st instabilityof the example (a) started at the BPM (SPA1B8) justafter the buncher. On the other hand, the 2nd instability
was generated at the BPM (SPA1C5) just before the firstaccerelation structure. It is understood that for fhe 3rdinstability the seed was generated at the BPM at the endof sector B (no figure). The seeds for step instabilities(b) and (c) were also detected at the BPM (SPA1B8).
Especially, in example (b) the time trend for the verticalbeam position gradually retraces the nominal level after
the step change. The same gradual recovery of the beam

charge at the target BPM was also clearly seen according
to the recovery of the vertical position detected for theBPM(SPA1C5). Figure 6 shows an accumulation plotfor all step changes caused by the beam instabilities,
which were analyzed in real time. The counts of all thestep changes (caused by positions and charge) for all theupstream BPMs are accumulated in a plot for only a stepchange on the charge simultaneously detected at thetarget BPM. This plot shows that the step changes on
the charge are generated at almost same the counting rate
at any BPM location;on the other hand, those on thepositions are generated by a betatron-motion-like shape.
4. Conclusions
A new software low-pass filtering algorithm based onthe ε-filter was developed for detecting the beam-energy
instabilites. The performance and availavility of thisalgorithm were tested by beam experiments. Thismethod could effectively separate sudden step changes bythe beam instabilities with large amplitudes from noise
fluctuations. Its real-time processing also worked wellduring daily operations. This system will be extended asa more reliable trouble-diagnostics system by connectingwith other logging and monitoring systems including
the linac device parameters.

(b)

(c)

(a)

Fig.4. Recorded data at the BPM /SPA1B8 location.
(a)

(a)

References[1] I. Sato, et al., KEK Report 95-18 (1996).
[2] S. Kurokawa, et al., KEK Report 90-24 (1991).
[3] T. Suwada, et al., Procs. the 24th Linear Accelerator
Meeting in Japan, Hokkaido Univ., 1999, p.22.
[4] T. Suwada, et al., Nucl. Instrum. & Methods. A 440 No.2
(2000) p.307.
[5] http://www-acc-theory.kek.jp/SAD/sad.html[6] H.Harashima, et al., Denshi Tsushin Gakkai Ronbunshi
Vol.J-65-A No.4, p.297 (1982).

CountsFig.5. Recorded data at the BPM /SPA1C5 location.

(a)

(b)

(c)

BPM Location

Fig.3. Recorded BPM data at the center of the 180-degree
arc under a typical operating condition of hich-current
single-bunch electron beams.

Fig.6. Accumulation plot detected by all upstream
BPMs for any step changes in the beam positions andcharge.