- Email: [email protected]
Fully automated emulsion analysis system
466
Nuclear Instruments and Methods in Physics Research B51 (1990) 466-472 North-Holland
FULLY AUTOMATED S. AOKI, K. HOSHINO, Department
EMULSION
ANALYSIS
M. NAKAMURA,
SYSTEM
K. NIU, K. NIWA and N. TORI1 *
of Physics, Nagoya University, Nagoya 464-01, Japan
Received 13 December 1989 and in revised form 16 March 1990
A fully automated emulsion analysis system has been developed by the Nagoya University Group, which has brought revolutionary progress to the analysis of nuclear emulsion plates. A description of this analysis system is given and its application to emulsion-counter hybrid experiments is introduced.
1. Introduction Nuclear emulsion plates record tracks of charged particles with an accuracy of less than 1 pm while keeping their three dimensional structure. When we analyze these tracks under a microscope, we observe a tomographic image within the 5 to 10 ym of the microscope’s depth of focus, which is much thinner than the typical thickness (70 to 500 Pm) of emulsion plates. Raising and lowering the focal plane of the microscope’s objective lens through whole emulsion depth, we can reconstruct the three dimensional structure of tracks in our mind. Thus, the process of track recognition in emulsion plates is much different from that used in other visual detectors such as a bubble chamber, a streamer chamber etc., which record the information onto a projected image with a deep depth of focus. Accordingly, the approach to automatic track recognition in emulsion plates needs to be different from that of other visual detectors. Following the model of humans’ recognition process, we have developed an approach to automatic recognition for penetrating (perpendicular and/or sloping) tracks by piling up several tomographic images at different depths [l].
for the video image processing of the microscopic image of emulsion plates. These processors free the host computer from real-time operations of these two aspects, which lead to troublesome control problem in usual computer control approaches. The overall configuration of the analysis system is shown in fig. 1. The “DOMS Interface” had already been developed by the Nagoya University Group in the 1970s for a semi-automated emulsion analysis system. It controls a dc-motor to drive the stage of the microscope dynamically, while reading out the position with a linear encoder to an accuracy of 1 Pm in each direction of three dimensions. The “Track Selector” is a type of video image processor. Its basic operation is shown in fig. 2. It has 16 frame memories, which are able to work individually. Changing the focal plane from one to another of 16 layers which imaginarily slice one emulsion plate at regular intervals, the tomographic image of grains only in-focus at each depth is stored into each frame memory after some processing for binarization. After reading the
Track
2. Overview of the system
Selector In practice, an integrated combination of mechanical control and video image processing is necessary for automatic track recognition. We realized this integration by developing two types of intelligent front-end processors for each operation [2]. One is the “DOMS Interface” [3] for the mechanical control of the microscope’s optical system, the other is the “Track Selector”
3 dimensional with
* Now at Totsuka Works, Hitachi Co., Ltd., Yokohama, Japan. 0168-583X/90/$03.50
Mot.or Driven
lpm Read-Out
Linear
.
Stage Encoder
Fig. 1. Configuration of the fully automated emulsion analysis system.
0 1990 - Elsevier Science Publishers B.V. (North-Holland)
S. Aoki et al. / Fully automated
Real
Track
time videa
Software
mode
Processes
Recognition
Fig. 2. Basic operation of the Track Selector.
images of all layers, the Track Selector recognizes tracks with desired angle by checking the coincidence rate of 16 layers at every pixel.
46-l
emulsion analysis system
video signal is processed in real-time. That means, all processes for each frame are completed in 33 ms (30 frames per second). Each frame memory works independently, and the stored signal of each frame can be shifted to any direction of four sides. After storage of all the signals of 16 layers, the digital signals in all frame memories are summed up like analog signals. If there is a penetrating track which makes hits at the same position of each layer, it makes a sharp peak (fig. 3). In order to detect a sloping track, the frame memories are shifted regularly in proportion to a desired angle before the summation. Tracks are recognized by discrimination of pulse height of this summed up signal. Fig. 4b shows discriminated signal superimposed on raw video image. In order to measure the position on the TV screen, the number of pulses for horizontal synchronization is counted for the vertical direction, and the number of clock pulses of 12 MHz corresponding to a width of a pixel is counted for the horizontal direction. Regarding the discriminated signal as a trigger pulse, the coordinates are recorded into a register.
3. The function of the Track Selector Fig. 3 schematically shows the transition of a video signal (one scanning line) of a microscopic image of an emulsion plate, which is processed step by step in the Track Selector. In the raw video signal, in-focus grains form tiny spikes, and dusts and heavily ionizing tracks usually form broader peaks. At first, the signal is differentiated in order to pick up only sharp signals of in-focus grains. Additionally, the differentiation cancels the effects of vague shadow and unevenness of lighting. Discriminating the differentiated signal with proper threshold, leading and trailing edges are detected to recognize the width of each signal. Using this pulse width, the signal is filtered after sampling with the clock of 12 MHz corresponding to l/512 of one scanning line of the video signal. The video signal has already been digitized vertically with 512 scanning lines. The video signal is digitized two dimensionally after this horizontal sampling. The field of view is 160 X 160 pm2 with an objective lens of 50 magnification, and hence one pixel corresponds to about 0.3 x 0.3 pm2. A digitized image of a grain, then, typically occupies 3 x 3 pixels. Before storage into a frame memory of 512 X 512 X 1 bit, each signal is expanded one pixel along its circumference, in order to avoid inefficiency of track recognition caused by distortion etc. Fig. 4a shows stored signal superimposed on raw video image. All of these processes including expansion are carried out by hardware, thereby the
Differentiated
WA#-~---
leading
Edge
Tailing
Edge
Pulse
Widt.h
Expanded
Analog
u
ru
u
I.
u
u
-u-~-fl-~li-u-uu’d-u-u-
u
U
Sum
Precise angletrack
Fig. 3. Step by step transition of a video signal (schematic).
S. Aoki et al. / Fully automated emulsion analysis system
468
on raw Fig. 4. (a) Stored signal of one layer superimposed track signal superimpose1 d on vide o image. (b) Discriminated raw video image.
__
Tracks recognized Tracks recognized Tracks coinciclwg
Like the processes for the binarization of tomographic image, all processes for the summation, the discrimination and the recording into the register are operated in the real-time video mode in the Track Selector. Consequently, this series of processes are also done in 33 ms for all over the field of view. By recording into the register, the data become arithmetical. A group of coordinates (typically 5 to 7) are recorded for each track, because the size of a pixel is smaller than a grain making a track. Finally, the group of the coordinates are clusterized by the software. The Track Selector sends the host computer the central coordinates of the cluster of each track. It takes a much shorter time to process the video images than to control the optical system mechanically and to communicate with the host computer, because most of processes are carried out by hardware in the Track Selector. If we utilized only software techniques, it would take an enormous time to accomplish this series of video image processes. As described above, the Track Selector converts an emulsion plate of 16 layers of track detectors separated by 5 to 30 lt.rn from each other. Moreover, each layer has complete and precise two-dimensional submicron spatial resolution. Additionally, the recognition of tracks in this track detectors is performed with very high efficiency by the appropriate hardware of the Track Selector. We tested the efficiency of track recognition of this analysis system using tracks penetrating perpendicularly
at downstream side at uptream side at both s!d?s
I’
10
pulse Fig. 5. (a) Number
of recognized
tracks
and the coincidence rate of both sides at each threshold the summed up signal.
height
15
i layers)
level. (b) Pulse height
spectrum
of
S. Aoki et al. / Fully automated emulsion analysis system
an emulsion plate. The emulsion plate, which has 330 urn of emulsion layer on each side of 70 urn polystyrene base, was oriented perpendicular to the beam from a high-energy accelerator, and exposed with 3 X lo5 tracks/cm2 of beam density. The parallelity of beam tracks was better than 1 rnrad. We applied the analysis system to pick up the beam tracks at each side of the same field of view independently. We appraised the efficiency by checking the coincidence rate of tracks on both sides. If the efficiency in a single side is 7, the coincidence rate should be q2. Fig. 5a shows the result. The number of frame memories used for this test was 15 out of 16. The horizontal axis corresponds to the threshold number of layers for discrimination. The vertical axis corresponds to the number of recognized tracks and the coincidence rate. Because of the existence of slightly sloping tracks, the number of recognized tracks gradually decreases with increasing threshold. The solid line shows the correspondence of the number of tracks recognized on the downstream side, the dotted line shows that on the upstream side, and the dashed line shows that on both sides. The crosses show the coincidence rate. The coincidence rate remain above 90% for the threshold level between 5 to 11. It is over 95% for a level of 7-8. This means that the efficiency of track recognition in a single side is higher than 97% with proper threshold. The net efficiency must be better than this value, because the coincidence is obstructed by not only inefficiency but also the difference of the optical condition of both sides. Fig. 5b shows the differentiation of the number of tracks of fig. 5a. This gives us the pulse height spectrum of the summed up signal. We have clear separation in the region from 5 to 9 layers between the signal made by penetrating tracks and the pedestal noise from random coincidences. This also shows the high eftciency of track recognition by this system.
469
Fig. 6. Example of result of the vertex scanning. (a) All of tracks picked up. (b) Only tracks connected.
4. Applications to experiments At first, this analysis system was applied as trial to the vertex scanning in a heavily exposed emulsion plate of an emulsion-counter hybrid experiment CERN WA75 [4]. Emulsion plates used in this experiment has the same configuration as described before. Tracks produced in the emulsion target were measured by the vertex detector at the downstream, which included several SSDs (Silicon Stripe Detectors). The analysis system scans tracks with predicted angles in the region of about 500 X 500 urn2 around the predicted position of the reconstructed vertex. This is done for each plate stacked in downstream of the predicted vertex. All kind of tracks with predicted angles are picked up at one reading-out time. At present, it takes 5 min to scan 10
kinds of tracks in one plate, including the time for the mechanical control and for the communication. One example of the vertex scanning is shown in fig. 6. Detected tracks at each plate are shown projected onto a plane perpendicular to the beam. In this case, the angular allowance of picking up tracks is about 2 or 3 mrad for each kind of track. The target was exposed so heavily (track density is close to lo6 tracks/cm2), that many background tracks having similar angle are also picked up in addition to the real track (fig. 6a). But, if we only select those tracks that are precisely connected to tracks in the next plate, the background tracks become much less and the real tracks can be detected (fig. 6b). We can see two pairs of tracks converging at the lower left and upper right (marked by circles) in ad-
470
S. Aoki et al.
\k ‘: ;X ;. L
r................
,
,_ .
.-.................................................
/
i
,e.. \ i: f!rY
,: :r..
i:> ...,_ _._.____._..:
.
.
..
i
. . . . . . . . . ..__.._
Fig. 7. Example of result of the exit point scanning. (a) Original prediction (only SSDs’ information). (b) Improved prediction (added the result of the exit point scanning). dition to three tracks converging at the center (marked by a square: predicted primary vertex), though we still have some background tracks. Those pairs were confirmed as a pair of decay candidates of neutral charmed particle by manual scanning with the semi-automated emulsion analysis system. The main advantage of this application is the realization of completely bias-free scanning over a huge (microscopically-speaking) region, maintaining a high efficiency of track recognition even among the jungle of high density background tracks. This application of the
method is specially powerful for searching for neutral decays. Next, we applied this analysis system to the exit point scanning in the analysis of another emulsioncounter hybrid experiment Fermilab E653 [5]. In this experiment, we put an emulsion plate with thick base at the most downstream of the emulsion target, in order to improve the angular resolution of tracks there. The plate haa 70 pm of emulsion layer on each side of the lucite base with the thickness of 300 urn. In this case, 16 frame memories of the Track Selector are assigned to
471
S. Aoki et al. / Fully automated emulsion analysis system
both emulsion layers (8 for each side). When the signals are piled up, the frame memories are shifted taking into account the thickness of the base. The angles and the positions of tracks produced in the emulsion target are measured by 18 planes (6 groups of three projections) of the SSDs at the downstream. If the position of a track is predicted accurately enough against the track density and the angular allowance of picking up track, the number of background tracks would be reasonably small. In this experiment, the number of background tracks is less than two in the case of tracks with slope bigger than 10 mrad. (The less background tracks are found for tracks with the bigger slope.) Once the exit points are measured in the emulsion plate, the vertex can be reconstructed much more accurately. Fig. 7 shows an example of this improvement. Fig. 7a is drawn using only the information from the SSDs. Fig. 7b shows the improved result including the information of the exit points of tracks measured by this analysis system. In fig. 7b, the solid lines are confirmed as tracks coming from the primary interaction, and the dotted lines are confirmed as tracks from a pair of decay candidates of charmed particles. By manual scanning with the semi-automated emulsion analysis system, it was confirmed that one dotted line with “ + ” mark comes from one of decay candidate and the other two dotted lines come from the other decay candidate. From this exit point scanning, we appraised the recognition efficiency of sloping track and the improvement brought by this scanning. When we find a primary interaction in the emulsion target, we can confirm which tracks are coming from the primary vertex among the tracks reconstructed by the SSDs. Those confirmed tracks must be found in the exit point scanning at the most downstream of the emulsion target. Thus we can determine the efficiency. Fig. 8 shows the result. The horizontal axis corresponds to the slope measured by the SSDs. The vertical axis corresponds to the number of tracks and the efficiency. The dashed line shows the number of tracks confirmed at the primary vertex, the dotted line shows that of tracks found in the exit point scanning by the analysis system, and the solid line shows the efficiency. We used only tracks with momentum higher than 4 GeV/c to get reliable result. The statistical error is rather big for tracks with the slope bigger than 100 mrad. The efficiency is about 90% or higher about tracks with the slope bigger than 10 mrad. The inefficiency at small slope is not caused by the scanning miss of the analysis system, but caused by the confusion with so many background beam tracks. To see the gain of this exit point scanning, we compared the distribution of the apparent impact parameter of tracks with respect to the reconstructed primary vertex. The samples are the tracks confirmed at
100
80
__I , I
~~~~scanned .. recognized
w Ii0 P. 2 .z " .A 5 40
20
i.,-1_, .A,
-7 L_. ‘-‘L_.__3 --L_
0
slo;
( mrad )
Fig. 8. Efficiency of the sloping track recognition. primary vertex and found in the exit point scanning. Ideally, the impact parameters of these tracks are 0. We used only tracks with momentum higher than 8 GeV/c, in order to avoid the effects by the multiple scattering in the emulsion target. Fig. 9a shows the impact parameter determined by the information only from the SSDs. Fig. 9b shows the improved result obtained by adding the position information from the exit point scanning to the angle information from the SSDs. For example, in the case of charmed particles, the impact parameter of daughter tracks will be a few X 10 pm to a few x 100 pm, because the impact parameter is distributed around CT (c: the light velocity, T: the lifetime). Consequently, using the information of the exit point scanning by this analysis system, we can easily discriminate between the daughter tracks and tracks from the primary vertex. This fact can be used to select the decay candidate events more efficiently.
5. Conclusion The fully automated emulsion analysis system has brought a revolutionary progress to the analysis of nuclear emulsion plates. As described above, many advantages have been achieved for the emulsion-counter hybrid experiment. With this system, the reading-out of the data from emulsion plates has become much speedier and fully
S. Aoki et al. / Fully automated emulsion analysis system
total
r,,,,,,,,,,,, h,_
@ ”
189
impact parameter
tracks
: 985
total
,,,,1 2@E
(pm)
!@3 0
impact
parameter
180
tracks
: 985
20@
(pm)
Fig. 9. Measured impact parameter distribution, (a) Only SSDs’ information. (b) Added the result of the exit point scanning.
automatic. Typically (like the exit point scanning of E653), it takes 5 to 10 min to process one event. This speed corresponds to 6 to 10 events per hour and about 200 events per day, because this fully automated analysis system can work 24 hours a day. This number corresponds to a few X lo4 events per year per system. (This system can work 7 days a week.) When we wish to analyze an event, we only put an emulsion plate on this analysis system, like putting a magnetic tape on a tape driver to analyze the counter data. Emulsion plates are transformed by this fully automated analysis system into the compact, precise and two dimensional track detector arrays with submicron spatial resolution. Moreover, emulsion plates can store the tracks of charged particles for up to the 106/cm2. In the case of an emulsion plate with the size of 25 x 25 cm*, it can store about 6 x lo8 tracks. This amount of information corresponds to 10” bytes (10 gigabytes). This density of information of emulsion plates as a storage media is as high as that of optical disks.
Acknowledgement We would like to express special thanks to Mr. Abe who designed and made the actual circuit for the Track
Selector at Hamamatsu Television (now Hamamatsu Photonics) Co. Ltd. We greatly appreciate toleration to use the data of CERN WA75 collaboration and Fermilab E653 collaboration. This work is financially supported by Japanese Ministry of Education, Science and Culture with Grantin-Aid for Specially Scientific Research (‘81, ‘82) and Grant-in-Aid for Specially Promoted Project Research (‘833’86).
References (11 K. Niwa, K. Hoshino and K. Niu, Proc. Int. Cosmic Ray Symp. on High Energy Phenomena (Cosmic Ray Lab., Univ. Tokyo, 1974) p. 149. [2] S. Aoki et al., Nucl. Tracks and Radiat. Meas. 12 (1986) 249; S. Aoki, M.Sc. Thesis, Nagoya University (1987) in Japanese. [3] S. Aoki and K. Niwa, Cosmic Ray Study 27, 4 (1984) 153 in Japanese. [4] S. Aoki et al., Nucl. Instr. and Meth. A274 (1989) 64. [5] K. Kodama et al., Nucl. Instr. and Meth. A289 (1990) 146.
Nuclear Instruments and Methods in Physics Research B51 (1990) 466-472 North-Holland
FULLY AUTOMATED S. AOKI, K. HOSHINO, Department
EMULSION
ANALYSIS
M. NAKAMURA,
SYSTEM
K. NIU, K. NIWA and N. TORI1 *
of Physics, Nagoya University, Nagoya 464-01, Japan
Received 13 December 1989 and in revised form 16 March 1990
A fully automated emulsion analysis system has been developed by the Nagoya University Group, which has brought revolutionary progress to the analysis of nuclear emulsion plates. A description of this analysis system is given and its application to emulsion-counter hybrid experiments is introduced.
1. Introduction Nuclear emulsion plates record tracks of charged particles with an accuracy of less than 1 pm while keeping their three dimensional structure. When we analyze these tracks under a microscope, we observe a tomographic image within the 5 to 10 ym of the microscope’s depth of focus, which is much thinner than the typical thickness (70 to 500 Pm) of emulsion plates. Raising and lowering the focal plane of the microscope’s objective lens through whole emulsion depth, we can reconstruct the three dimensional structure of tracks in our mind. Thus, the process of track recognition in emulsion plates is much different from that used in other visual detectors such as a bubble chamber, a streamer chamber etc., which record the information onto a projected image with a deep depth of focus. Accordingly, the approach to automatic track recognition in emulsion plates needs to be different from that of other visual detectors. Following the model of humans’ recognition process, we have developed an approach to automatic recognition for penetrating (perpendicular and/or sloping) tracks by piling up several tomographic images at different depths [l].
for the video image processing of the microscopic image of emulsion plates. These processors free the host computer from real-time operations of these two aspects, which lead to troublesome control problem in usual computer control approaches. The overall configuration of the analysis system is shown in fig. 1. The “DOMS Interface” had already been developed by the Nagoya University Group in the 1970s for a semi-automated emulsion analysis system. It controls a dc-motor to drive the stage of the microscope dynamically, while reading out the position with a linear encoder to an accuracy of 1 Pm in each direction of three dimensions. The “Track Selector” is a type of video image processor. Its basic operation is shown in fig. 2. It has 16 frame memories, which are able to work individually. Changing the focal plane from one to another of 16 layers which imaginarily slice one emulsion plate at regular intervals, the tomographic image of grains only in-focus at each depth is stored into each frame memory after some processing for binarization. After reading the
Track
2. Overview of the system
Selector In practice, an integrated combination of mechanical control and video image processing is necessary for automatic track recognition. We realized this integration by developing two types of intelligent front-end processors for each operation [2]. One is the “DOMS Interface” [3] for the mechanical control of the microscope’s optical system, the other is the “Track Selector”
3 dimensional with
* Now at Totsuka Works, Hitachi Co., Ltd., Yokohama, Japan. 0168-583X/90/$03.50
Mot.or Driven
lpm Read-Out
Linear
.
Stage Encoder
Fig. 1. Configuration of the fully automated emulsion analysis system.
0 1990 - Elsevier Science Publishers B.V. (North-Holland)
S. Aoki et al. / Fully automated
Real
Track
time videa
Software
mode
Processes
Recognition
Fig. 2. Basic operation of the Track Selector.
images of all layers, the Track Selector recognizes tracks with desired angle by checking the coincidence rate of 16 layers at every pixel.
46-l
emulsion analysis system
video signal is processed in real-time. That means, all processes for each frame are completed in 33 ms (30 frames per second). Each frame memory works independently, and the stored signal of each frame can be shifted to any direction of four sides. After storage of all the signals of 16 layers, the digital signals in all frame memories are summed up like analog signals. If there is a penetrating track which makes hits at the same position of each layer, it makes a sharp peak (fig. 3). In order to detect a sloping track, the frame memories are shifted regularly in proportion to a desired angle before the summation. Tracks are recognized by discrimination of pulse height of this summed up signal. Fig. 4b shows discriminated signal superimposed on raw video image. In order to measure the position on the TV screen, the number of pulses for horizontal synchronization is counted for the vertical direction, and the number of clock pulses of 12 MHz corresponding to a width of a pixel is counted for the horizontal direction. Regarding the discriminated signal as a trigger pulse, the coordinates are recorded into a register.
3. The function of the Track Selector Fig. 3 schematically shows the transition of a video signal (one scanning line) of a microscopic image of an emulsion plate, which is processed step by step in the Track Selector. In the raw video signal, in-focus grains form tiny spikes, and dusts and heavily ionizing tracks usually form broader peaks. At first, the signal is differentiated in order to pick up only sharp signals of in-focus grains. Additionally, the differentiation cancels the effects of vague shadow and unevenness of lighting. Discriminating the differentiated signal with proper threshold, leading and trailing edges are detected to recognize the width of each signal. Using this pulse width, the signal is filtered after sampling with the clock of 12 MHz corresponding to l/512 of one scanning line of the video signal. The video signal has already been digitized vertically with 512 scanning lines. The video signal is digitized two dimensionally after this horizontal sampling. The field of view is 160 X 160 pm2 with an objective lens of 50 magnification, and hence one pixel corresponds to about 0.3 x 0.3 pm2. A digitized image of a grain, then, typically occupies 3 x 3 pixels. Before storage into a frame memory of 512 X 512 X 1 bit, each signal is expanded one pixel along its circumference, in order to avoid inefficiency of track recognition caused by distortion etc. Fig. 4a shows stored signal superimposed on raw video image. All of these processes including expansion are carried out by hardware, thereby the
Differentiated
WA#-~---
leading
Edge
Tailing
Edge
Pulse
Widt.h
Expanded
Analog
u
ru
u
I.
u
u
-u-~-fl-~li-u-uu’d-u-u-
u
U
Sum
Precise angletrack
Fig. 3. Step by step transition of a video signal (schematic).
S. Aoki et al. / Fully automated emulsion analysis system
468
on raw Fig. 4. (a) Stored signal of one layer superimposed track signal superimpose1 d on vide o image. (b) Discriminated raw video image.
__
Tracks recognized Tracks recognized Tracks coinciclwg
Like the processes for the binarization of tomographic image, all processes for the summation, the discrimination and the recording into the register are operated in the real-time video mode in the Track Selector. Consequently, this series of processes are also done in 33 ms for all over the field of view. By recording into the register, the data become arithmetical. A group of coordinates (typically 5 to 7) are recorded for each track, because the size of a pixel is smaller than a grain making a track. Finally, the group of the coordinates are clusterized by the software. The Track Selector sends the host computer the central coordinates of the cluster of each track. It takes a much shorter time to process the video images than to control the optical system mechanically and to communicate with the host computer, because most of processes are carried out by hardware in the Track Selector. If we utilized only software techniques, it would take an enormous time to accomplish this series of video image processes. As described above, the Track Selector converts an emulsion plate of 16 layers of track detectors separated by 5 to 30 lt.rn from each other. Moreover, each layer has complete and precise two-dimensional submicron spatial resolution. Additionally, the recognition of tracks in this track detectors is performed with very high efficiency by the appropriate hardware of the Track Selector. We tested the efficiency of track recognition of this analysis system using tracks penetrating perpendicularly
at downstream side at uptream side at both s!d?s
I’
10
pulse Fig. 5. (a) Number
of recognized
tracks
and the coincidence rate of both sides at each threshold the summed up signal.
height
15
i layers)
level. (b) Pulse height
spectrum
of
S. Aoki et al. / Fully automated emulsion analysis system
an emulsion plate. The emulsion plate, which has 330 urn of emulsion layer on each side of 70 urn polystyrene base, was oriented perpendicular to the beam from a high-energy accelerator, and exposed with 3 X lo5 tracks/cm2 of beam density. The parallelity of beam tracks was better than 1 rnrad. We applied the analysis system to pick up the beam tracks at each side of the same field of view independently. We appraised the efficiency by checking the coincidence rate of tracks on both sides. If the efficiency in a single side is 7, the coincidence rate should be q2. Fig. 5a shows the result. The number of frame memories used for this test was 15 out of 16. The horizontal axis corresponds to the threshold number of layers for discrimination. The vertical axis corresponds to the number of recognized tracks and the coincidence rate. Because of the existence of slightly sloping tracks, the number of recognized tracks gradually decreases with increasing threshold. The solid line shows the correspondence of the number of tracks recognized on the downstream side, the dotted line shows that on the upstream side, and the dashed line shows that on both sides. The crosses show the coincidence rate. The coincidence rate remain above 90% for the threshold level between 5 to 11. It is over 95% for a level of 7-8. This means that the efficiency of track recognition in a single side is higher than 97% with proper threshold. The net efficiency must be better than this value, because the coincidence is obstructed by not only inefficiency but also the difference of the optical condition of both sides. Fig. 5b shows the differentiation of the number of tracks of fig. 5a. This gives us the pulse height spectrum of the summed up signal. We have clear separation in the region from 5 to 9 layers between the signal made by penetrating tracks and the pedestal noise from random coincidences. This also shows the high eftciency of track recognition by this system.
469
Fig. 6. Example of result of the vertex scanning. (a) All of tracks picked up. (b) Only tracks connected.
4. Applications to experiments At first, this analysis system was applied as trial to the vertex scanning in a heavily exposed emulsion plate of an emulsion-counter hybrid experiment CERN WA75 [4]. Emulsion plates used in this experiment has the same configuration as described before. Tracks produced in the emulsion target were measured by the vertex detector at the downstream, which included several SSDs (Silicon Stripe Detectors). The analysis system scans tracks with predicted angles in the region of about 500 X 500 urn2 around the predicted position of the reconstructed vertex. This is done for each plate stacked in downstream of the predicted vertex. All kind of tracks with predicted angles are picked up at one reading-out time. At present, it takes 5 min to scan 10
kinds of tracks in one plate, including the time for the mechanical control and for the communication. One example of the vertex scanning is shown in fig. 6. Detected tracks at each plate are shown projected onto a plane perpendicular to the beam. In this case, the angular allowance of picking up tracks is about 2 or 3 mrad for each kind of track. The target was exposed so heavily (track density is close to lo6 tracks/cm2), that many background tracks having similar angle are also picked up in addition to the real track (fig. 6a). But, if we only select those tracks that are precisely connected to tracks in the next plate, the background tracks become much less and the real tracks can be detected (fig. 6b). We can see two pairs of tracks converging at the lower left and upper right (marked by circles) in ad-
470
S. Aoki et al.
\k ‘: ;X ;. L
r................
,
,_ .
.-.................................................
/
i
,e.. \ i: f!rY
,: :r..
i:> ...,_ _._.____._..:
.
.
..
i
. . . . . . . . . ..__.._
Fig. 7. Example of result of the exit point scanning. (a) Original prediction (only SSDs’ information). (b) Improved prediction (added the result of the exit point scanning). dition to three tracks converging at the center (marked by a square: predicted primary vertex), though we still have some background tracks. Those pairs were confirmed as a pair of decay candidates of neutral charmed particle by manual scanning with the semi-automated emulsion analysis system. The main advantage of this application is the realization of completely bias-free scanning over a huge (microscopically-speaking) region, maintaining a high efficiency of track recognition even among the jungle of high density background tracks. This application of the
method is specially powerful for searching for neutral decays. Next, we applied this analysis system to the exit point scanning in the analysis of another emulsioncounter hybrid experiment Fermilab E653 [5]. In this experiment, we put an emulsion plate with thick base at the most downstream of the emulsion target, in order to improve the angular resolution of tracks there. The plate haa 70 pm of emulsion layer on each side of the lucite base with the thickness of 300 urn. In this case, 16 frame memories of the Track Selector are assigned to
471
S. Aoki et al. / Fully automated emulsion analysis system
both emulsion layers (8 for each side). When the signals are piled up, the frame memories are shifted taking into account the thickness of the base. The angles and the positions of tracks produced in the emulsion target are measured by 18 planes (6 groups of three projections) of the SSDs at the downstream. If the position of a track is predicted accurately enough against the track density and the angular allowance of picking up track, the number of background tracks would be reasonably small. In this experiment, the number of background tracks is less than two in the case of tracks with slope bigger than 10 mrad. (The less background tracks are found for tracks with the bigger slope.) Once the exit points are measured in the emulsion plate, the vertex can be reconstructed much more accurately. Fig. 7 shows an example of this improvement. Fig. 7a is drawn using only the information from the SSDs. Fig. 7b shows the improved result including the information of the exit points of tracks measured by this analysis system. In fig. 7b, the solid lines are confirmed as tracks coming from the primary interaction, and the dotted lines are confirmed as tracks from a pair of decay candidates of charmed particles. By manual scanning with the semi-automated emulsion analysis system, it was confirmed that one dotted line with “ + ” mark comes from one of decay candidate and the other two dotted lines come from the other decay candidate. From this exit point scanning, we appraised the recognition efficiency of sloping track and the improvement brought by this scanning. When we find a primary interaction in the emulsion target, we can confirm which tracks are coming from the primary vertex among the tracks reconstructed by the SSDs. Those confirmed tracks must be found in the exit point scanning at the most downstream of the emulsion target. Thus we can determine the efficiency. Fig. 8 shows the result. The horizontal axis corresponds to the slope measured by the SSDs. The vertical axis corresponds to the number of tracks and the efficiency. The dashed line shows the number of tracks confirmed at the primary vertex, the dotted line shows that of tracks found in the exit point scanning by the analysis system, and the solid line shows the efficiency. We used only tracks with momentum higher than 4 GeV/c to get reliable result. The statistical error is rather big for tracks with the slope bigger than 100 mrad. The efficiency is about 90% or higher about tracks with the slope bigger than 10 mrad. The inefficiency at small slope is not caused by the scanning miss of the analysis system, but caused by the confusion with so many background beam tracks. To see the gain of this exit point scanning, we compared the distribution of the apparent impact parameter of tracks with respect to the reconstructed primary vertex. The samples are the tracks confirmed at
100
80
__I , I
~~~~scanned .. recognized
w Ii0 P. 2 .z " .A 5 40
20
i.,-1_, .A,
-7 L_. ‘-‘L_.__3 --L_
0
slo;
( mrad )
Fig. 8. Efficiency of the sloping track recognition. primary vertex and found in the exit point scanning. Ideally, the impact parameters of these tracks are 0. We used only tracks with momentum higher than 8 GeV/c, in order to avoid the effects by the multiple scattering in the emulsion target. Fig. 9a shows the impact parameter determined by the information only from the SSDs. Fig. 9b shows the improved result obtained by adding the position information from the exit point scanning to the angle information from the SSDs. For example, in the case of charmed particles, the impact parameter of daughter tracks will be a few X 10 pm to a few x 100 pm, because the impact parameter is distributed around CT (c: the light velocity, T: the lifetime). Consequently, using the information of the exit point scanning by this analysis system, we can easily discriminate between the daughter tracks and tracks from the primary vertex. This fact can be used to select the decay candidate events more efficiently.
5. Conclusion The fully automated emulsion analysis system has brought a revolutionary progress to the analysis of nuclear emulsion plates. As described above, many advantages have been achieved for the emulsion-counter hybrid experiment. With this system, the reading-out of the data from emulsion plates has become much speedier and fully
S. Aoki et al. / Fully automated emulsion analysis system
total
r,,,,,,,,,,,, h,_
@ ”
189
impact parameter
tracks
: 985
total
,,,,1 2@E
(pm)
!@3 0
impact
parameter
180
tracks
: 985
20@
(pm)
Fig. 9. Measured impact parameter distribution, (a) Only SSDs’ information. (b) Added the result of the exit point scanning.
automatic. Typically (like the exit point scanning of E653), it takes 5 to 10 min to process one event. This speed corresponds to 6 to 10 events per hour and about 200 events per day, because this fully automated analysis system can work 24 hours a day. This number corresponds to a few X lo4 events per year per system. (This system can work 7 days a week.) When we wish to analyze an event, we only put an emulsion plate on this analysis system, like putting a magnetic tape on a tape driver to analyze the counter data. Emulsion plates are transformed by this fully automated analysis system into the compact, precise and two dimensional track detector arrays with submicron spatial resolution. Moreover, emulsion plates can store the tracks of charged particles for up to the 106/cm2. In the case of an emulsion plate with the size of 25 x 25 cm*, it can store about 6 x lo8 tracks. This amount of information corresponds to 10” bytes (10 gigabytes). This density of information of emulsion plates as a storage media is as high as that of optical disks.
Acknowledgement We would like to express special thanks to Mr. Abe who designed and made the actual circuit for the Track
Selector at Hamamatsu Television (now Hamamatsu Photonics) Co. Ltd. We greatly appreciate toleration to use the data of CERN WA75 collaboration and Fermilab E653 collaboration. This work is financially supported by Japanese Ministry of Education, Science and Culture with Grantin-Aid for Specially Scientific Research (‘81, ‘82) and Grant-in-Aid for Specially Promoted Project Research (‘833’86).
References (11 K. Niwa, K. Hoshino and K. Niu, Proc. Int. Cosmic Ray Symp. on High Energy Phenomena (Cosmic Ray Lab., Univ. Tokyo, 1974) p. 149. [2] S. Aoki et al., Nucl. Tracks and Radiat. Meas. 12 (1986) 249; S. Aoki, M.Sc. Thesis, Nagoya University (1987) in Japanese. [3] S. Aoki and K. Niwa, Cosmic Ray Study 27, 4 (1984) 153 in Japanese. [4] S. Aoki et al., Nucl. Instr. and Meth. A274 (1989) 64. [5] K. Kodama et al., Nucl. Instr. and Meth. A289 (1990) 146.