Drift chambers and pattern recognisation
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Drift Chambers and Pattern recognisation. Fabrizio Cei Pattern recognition ideas INFN & Pisa University Hajime Nishiguchi Kapton foils for DC cathode University of Tokyo Johny Egger DC status and tests PSI. DC Status and Tests. Hood and inner cathodes Print foils p M1 test

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Drift Chambers and Pattern recognisation

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Drift chambers and pattern recognisation

Drift Chambers and Pattern recognisation

  • Fabrizio CeiPattern recognition ideas INFN & Pisa University

  • Hajime NishiguchiKapton foils for DC cathode

    University of Tokyo

  • Johny EggerDC status and tests

    PSI


Dc status and tests

DC Status and Tests

  • Hood and inner cathodes

  • Print foils

  • pM1 test

    • Drift time measurement

    • Cathode read out

  • Next future


Drift chambers and pattern recognisation

Cathode hood

Delivery and first tests:

second halfof July


Inner cathodes

Inner cathodes

  • measurement of deformation with applied forces

  • simulate forces (level arm) with wires through the gas holes

  • eventually stabilize free edges with tiny foam support(rohacell)


Drift chambers and pattern recognisation

Print foils

Good results of pM1 tests

hood-foil tests with mounting tool

Inner cathode foil mounting test

possible foil pattern deformations

Final design of the foil pattern

Ordering

if all requests are satisfied by the manufactory


Drift chambers and pattern recognisation

H. Nishiguchi

Kapton foils for DC cathodes

Requirements

Low-Mass: reduce resolution deterioration due to multiple scattering

Kapton film 12.5 µm thick;

Aluminum deposition 50 nm thick (NOT copper !! )

Accuracy: precise position reconstruction with vernier pad method requires manufacture accuracy ~ ±20 µm and deposition by “Etching”.

Situation

Sample foils with3 Japanese companies suffered frommany problems, buta sample foil which satisfies all requests (except cost) was built. Contacts with european factories under way from PSI group.


Drift chambers and pattern recognisation

pM1 test

with the 4 test chambers

Anode 1, 2

plan 0 to 4

digital Scope

8 channels

500 MHz

500 points, 8 bits

Cathodes of anode 2

plan 2,3

FLASH ADC

8 channels

100 MHz

500 points, 8 bits

24 Cathodes

Anode 1,(2,3)

LRS ADC

24 double channels

Anode 3

plan 0 to 4

AD811

Read out


Drift chambers and pattern recognisation

Parallel beam + tilted chambers

Not staggered


Drift chambers and pattern recognisation

DC tilted by a1

No Field

P= 158 & 258 MeV/c

1.00 & .60 Tesla

P= 158 & 258 MeV/c

Dparallel < 1 %

DC tilted by a2

s = .21 ns


Drift chambers and pattern recognisation

Behavior of amplifiers in the 1 Tesla magnetic field

  • no measurable difference on induced test pulses

    • time definition : Dt = .12 ns ( s = .8 ns)

    • pulse integral : DI < .04 % (s = .16 % )

  • -no measurable effect in the magnetic field even with magnetic SMD capacitors and resistances ( contacts)

    • approximate the field distortion with the new „non magnetic“ SMD elements, cables, connectors


Drift chambers and pattern recognisation

Drift time

Subtract y = b1 + b2*(sin[w*(t-t0)]): fit of the 225 first points

pM1 run : w = 2p* 50 MHz

Look for the first n=4,5 adjacent points above threshold

Linear fit of first n signal points and n last background points (=0)

threshold


Drift chambers and pattern recognisation

(Fit-measured time) against drifttime

Drift time

t3 against t0

Sqrt( S(Fit-measured time)2 ) = > 2.0 ns2.8 ns(~.1 mm)

1.0 ns < s(Fit-measured time) < 2.1 ns

Difference between 2 colors in t1 shows misalignment of anode w2p1 of ~.1 mm


Drift chambers and pattern recognisation

u1-u2

u1+u2

u1 u2

d1d2

d1-d2

d1+d2

Cathode readout

Anode has a shielding effect of a few percents

Cathode u1 = n1*Anode * (1+c2 * sin[c3*x] + e * c2*sin2 [c3*x])

Cathode u2 = n2 *Anode * (1-c2 * sin[c3*x] - e * c2*sin2 [c3*x])

Cathode d1 = n3 *Anode * (1+c2 * cos[c3*x] + e * c2*cos2 [c3*x])

Cathode d2 = n4 *Anode * (1-c2 * cos[c3*x] - e * c2*cos2 [c3*x])

e value for traces on 1 side of the anode is the opposite of the value for traces on the other side


Drift chambers and pattern recognisation

e and the cathode normalization factors ni are correlated in the calibration procedure

Calibration procedure not yet optimal : distributions are not flat

0.34 mm < s(Fit-measured value) < .60 mm

0.11 mm < s(Fit-measured value) < .35 mm

June 27

July 8


Cross talks

Cross talks

a) to next plane < 1%

b) To adjacent anode: 1) small on integrals

2) important for timimg

c) to near cathode of adjacent anode: Important

Corrections to b) and c) well defined by measurement of events without signal on the adjacent anode 

„Domino „ read out on all channels is important

1000 points, 500 MHz, 10 bits is ideal


Present and near future plane

Present and near future plane

  • Analysis of pM1 run(rich sample of data)

    • Magnetic field

    • Anode shielding, cross talk

    • Exotic events

  • Tests

    • Foils

    • Amplifiers and prints

    • High rates

  • Ordering

    • Foils

    • Frames and prints

    • Amplifiers and cables

  • Construction

  • Test with first elements(maybe within this year)


Drift chambers and pattern recognisation

Positron Tracker

2002

2003

2004

2005

Medium Prototype

Charge division & Cosmics

Full Prototype

FP

FP

Full Detector (18 DC)

Design

Manufactoring

Assembly

Test

Milestone


Drift chambers and pattern recognisation

Fabrizio

Ideas for pattern recognition

Pattern recognition

problem in MEG:

DC integration

time~ 1 ms

Positron rate on drift chambers~ 5 MHz

Some superimposed

tracks in each DC

readout.

Two turns

(arather

frequentcase)


Assumptions

Assumptions

  • Chambers are formed by two independent sensitive elements;

  • From each of them a three-dimensional information of the

    crossing point(“hit”) can be extracted

    DC signals were already analysed up to this level

  • Wiresandsignal formationnot simulated.


Drift chambers and pattern recognisation

Strategy outline

  • Define a segment of track as two pairs of hits (ora pair and an isolated hit)withintwo adjacent chambers(one wedge);

  • Perform afastandreliable estimation of the positron momentum associated to any segment;

  • Look at any couple of segments which havethe same pair of hits(orthe same isolated hit)as end (one segment) and beginning (the other);

  • Select the couples of segments having compatible momentum components and total momentum;

  • Within this sample, join the couples of segments which satisfy the geometrical requirements for a good track;

  • Save all “tracks” formed by a minimum number of segments (4 or 5);

  • Absolute timing informationnot used (by now).


Drift chambers and pattern recognisation

Not enough time for details; only

Fast momentum estimation

Technique:

principal component analysis

in quadratic approximation:

Minimize:

to determine ai, bij.

hi = one of the three spatial coordinates of one hit.

Sum overthe hits (3 or 4) in one wedge associated to a segment.

Training by MC events: sample of 5 x 105 Michel positron tracks,

corresponding to ~ (1  3) x 104 tracksin each wedge.


Drift chambers and pattern recognisation

Tracks with  4 segments

Momentum reconstruction in one

segment minus true value

N.B. This is NOT

a momentum reconstruction.

py

px

Black: true

Red: estimated

pz

p

pz

MeV

Total momentum


Drift chambers and pattern recognisation

Linking segments

  • Any chamber (except #1 and

  • #17) is a part of two“wedges”

  • we can link two segments in

  • adjacent wedges requiring that

  • they have one common point.

  • Further selections:

  • compatibility of momentum vectors in adjacent segments;

  • compatibility of new segment

    with extrapolated track.

Segments with the same

starting point.


Drift chambers and pattern recognisation

Example of reconstructed tracks


Drift chambers and pattern recognisation

Algorithm performances

Michel positronsin the solid angle covered by the spectrometer or isotropically over the whole detector; no significant differences; positron momentum> 30 MeV;

# of correctly reconstructed tracks

Efficiency = ———————————————

# of generated tracks


Drift chambers and pattern recognisation

A “by-product”: momentum estimation

Not a momentum

reconstruction,

but a reasonable

starting point for

more refined tracking

algorithms (like

MINUIT).


Conclusions

Conclusions

  • Algorithm looks to work well; e> 95 % for 5 mixed tracks & > 91 % for 10; more efficient for higher momentum tracks (encouraging !).

  • Performances be only slightly(~ 1 %) affected by worsening the

    z resolution or reversing the scanning direction.

  • Technical note soon.

  • Suggestions (to investigated):

    begin the search from outermost radii & try to recover isolated hits.

  • Possible improvements:

    • perform a cuts fine tuning;

    • follow all possible links between segments;

    • use absolute timing information as linking tool;

    • perform a better track extrapolation;

    • include random noise;

    • insert signal propagation in the simulation code …


P m1 tests with four test chambers and various readout digiscope 500 mhz flash other adcs

pM1 testswithfour test chambersand various readout(digiscope 500 MHz, flash & other ADCs ..)

No Field

P= 158 & 258 MeV/c

1.00 & .60 Tesla

P= 158 & 258 MeV/c

Dparallel < 1 %

Measurement of

four DC times

DC tilted by a1

DC tilted by a2

s = .21 ns

Trigger timing resolution


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