Download presentation

Presentation is loading. Please wait.

Published byDanna Cheers Modified over 2 years ago

1
Behaviour of MicroMega chambers in magnetic field: analysis of H2 June data Outline: (0) Introduction (1) Data set used and noise filtering (2)Cluster size and length (3) TPC behaviour (4)Space resolutions and offsets. 1

2
(0) Introduction Effect of the magnetic field on electron drift: where v 0 d is the drift velocity when B = 0. If B perp. to E (H2 data) at the nominal MM working point. is the “Lorentz angle” In NSW B<0.3 T < 0.24 B term can be neglected (unless a sizeable E B is there). Displacements in the ExB direction of typical sizes: up to hundreds of micron >> typical mechanical systematics 2

3
(1) Data set used and noise filtering 3 beam: p=150 GeV/c T1 – T2 T3 – T4 B field side view Magnetic field orthogonal to Electric field Xstrip readout (vertical coordinate) particle bending non-negligible ( displacement ≈ 50 m×B(T) btw. T1 and T3 ) T1, T2: 400 m pitch, 5mm gap, HV mesh = 500(?) V; HV drift = 300 V, Ar-CO 2 93-7 T3, T4: 400 m pitch, 10 mm gap, HV mesh = 500(?) V; HV drift = 600 V, Ar-CO 2 93-7

4
Full dataset used (June test-beam) |B| (T)+10°-10°+20°-20° 0.7340731972737299 0.27345732472797305 0.57348732772867308 17353733372907313 Pre-filter done based on FFT recipe (see following) Strips are selected using the standard selection: (charge threshold = 80) Times obtained using risetime fit (slope > 0.15) Extended cluster definition (see following) Resolution: core (T1-T3)/√2 (not completely correct…) 4

5
NoiseFilter CGatti NoiseFilter extracts an FFT value per chamber. High FFT means “noisy” event. Typical distributions are shown here (run 7453): T1 T2 T3 T4 5

6
6 June test-beam (run 7353) July test-beam (run 7486) June H2 data are “more noisy” than H8 July data T1 T2

7
FFT tails in different chambers are correlated : cut based on T1 and T3 chambers only: Events are accepted if FFT(T1)<4.5 && FFT(T3)<4.5 FFT(T1) FFT(T3) 7 Typical rejection ≈ 20%: 20kevts 15-16 kevts

8
8 Most plots in the following: |B| = 0|B| = 0.2 T (average NSW) |B| = 0.5 T (extreme NSW) |B| = 1 T (“crash” test) Dataset A: bending “track-side” -10° and -20° data Dataset B: bending “opposite-side” +10° and +20° data

9
9 (2) Number of strips: dataset A -10° T1 T3 average #strips 0-strips events “singular” configuration @ |B|=0.2 T increase of width increase of “empty events” fraction (particularly strong for T1 data)

10
10 Number of strips: dataset A -20° T1 T3 average #strips 0-strips events “singular” configuration @ 0.2<|B|<0.5 T increase of width increase of “empty events” fraction but less evident than at 10 o.

11
11 Number of strips: dataset B +10° T1 T3 average #strips 0-strips events No “singular” configuration average #strips almost constant BUT increase of width increase of “empty events” fraction (particularly strong for T1 data)

12
12 Average cluster charge vs. |B|: General decrease with increasing |B|. Dataset ADataset B

13
13 Cluster length and #holes: T3 – dataset B +10° cluster length Number of holes The cluster definition has to be changed to include “scattered” clusters. For TPC (see following) I require 2<#strips<16 nholes<15 CONCLUSION: clusters are spread but maintains approximately the same number of strips; the overall charge decreases

14
Dataset B: T3 time spectra General trend: increase of drift time +10° data:+20° data: 14

15
15 Maximum drift time: summary T3 T1 Effect of singluarities evident in Dataset A data (-10° and -20°) N.B. In TPC the v drift is held at its nominal value of 47 m/ns (it should be adjusted accordingly)

16
16 (3) TPC event gallery-1: |B|=1, =+10 o

17
17 TPC event gallery-2: |B|=1, =+10 o

18
TPC T1 angles: Dataset A – T1 -10° data:-20° data: 18 “Angle inversion”: at 0.2 T @ -10° at 0.2 ÷ 0.5 T @ -20°

19
TPC T3 angles: Dataset A – T3 -10° data:-20° data: 19 “Angle inversion”: at 0.2 T @ -10° at 0.2 ÷ 0.5 T @ -20°

20
TPC T3 angles: Dataset B – T3 +10° data: +20° data: 20 Increase of the angle due to Lorentz angle effect

21
21 Peak angle from TPC vs. |B| (Dataset B data – previous slide). Data (red points) are compared with expections based on geometrical considerations: |B| (T) +20° data +10° data

22
(4) TPC x half resolution: Dataset A -10° data:-20° data: 22 Bad x half resolution: @-10° |B| = 0.2 T @-20° |B| = 0.5 T

23
TPC x half resolution: Dataset B +10° data:+20° data: 23 @20° resolution is worsening for |B|≥0.5 T

24
Centroid resolutions: Dataset A -10° data:-20° data: 24 Good centroid resolution: @-10° |B| = 0.2 T @-20° |B| = 0.5 T

25
25 x half and centroid resolutions: summary Dataset B Dataset A

26
26 NSW operation regions “singular belt” Summary: a pictorial view “Singular belt” = Points where Lorentz Angle ≈ Track inclination

27
27 T1 T3 Offset (T1-T3): depends on |B| due to the different gap size of T1 and T3 sketch of a track crossing T1 and T3 both immersed in the same B-field

28
28 Try x 0 in place of x half x half is affected by a systematics, the effect of the magnetic field being a rotation of the track with x 0 as “pivot”. x 0 shouldn’t be affected. Since T1 and T3 have a different gap (5mm vs. 10 mm) a B-dependent offset in x half is expected but not in x 0. x0x0 x0x0 x half

29
29 TPC: comparison btw x 0 and x half measurements (Dataset B data) Offset clearly reduced BUT worse resolution (as expected) +10° data +20° data

30
30 TPC: comparison btw x 0 and x half measurements (Dataset A data) -10° data -20° data

31
31 Study of back-to-back configuration: mTPC on the four chambers, than combine and check. (T1+T2)/2 vs. (T3+T4)/2 l x comb (1) = (x half (T1) + x half (T2))/2 x comb (2) = (x half (T3) + x half (T4))/2 then: x comb (1) – x comb (2) distribution resolution and offset.

32
32 Look @ 0 T data: resolution improves for centroid, not for x half. Why ? I expect that the resolution on x comb is roughly √2 times better than resolution on x half red = T1 – T3 blue = T1T2 – T3T4

33
33 Offsets = average values of xcomb(1) - xcomb(2): The offset should be reduced to the the effect of the particle bending Offset are reduced to tipical slopes of 350 m/T: I expect this slope if p=150 GeV/c and l = 60 cm. Are these numbers correct ?

34
34 Summary and conclusions The operation of MM in magnetic field requires a careful knowledge of the field map and a careful calibration procedure providing O(100 m) corrections; TPC works fine with acceptable resolution in the full |B|- plane apart from specific “singularities” ( =-10 o, |B|=0.2 T and =-20 o, |B|≈0.4T) where the Lorentz angle “compensates” the track inclination. In the singularities the centroid helps to recover resolution (but the combination should be based on clusterlength rather than #strips); Using x 0 rather than x half reduces the offset but spoils the resolution. Back-to-Back doublets show no improvements on resolution but reduction of the offset probably consistent with track bending.

Similar presentations

OK

Jonathan BouchetBerkeley School on Collective Dynamics 1 Performance of the Silicon Strip Detector of the STAR Experiment Jonathan Bouchet Subatech STAR.

Jonathan BouchetBerkeley School on Collective Dynamics 1 Performance of the Silicon Strip Detector of the STAR Experiment Jonathan Bouchet Subatech STAR.

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on power transmission elements Ppt on elements and compounds Ppt on anti rigging voting system Ppt on struts framework in java Project ppt on student information system Performance based pay ppt online Ppt on computer malwares wiki Ppt on role of construction industry in indian economy Ppt on primary and secondary data Ppt on wild animals for grade 1