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Behaviour of MicroMega chambers in magnetic field: analysis of H2 June data Outline: (0) Introduction (1) Data set used and noise filtering (2)Cluster.

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Presentation on theme: "Behaviour of MicroMega chambers in magnetic field: analysis of H2 June data Outline: (0) Introduction (1) Data set used and noise filtering (2)Cluster."— Presentation transcript:

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  T3, T4: 400  m pitch, 10 mm gap, HV mesh = 500(?) V; HV drift = 600 V, Ar-CO

4 Full dataset used (June test-beam) |B| (T)+10°-10°+20°-20°  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  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” |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” 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 ° at 0.2 ÷ °

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

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 |B| = 0.2 |B| = 0.5 T

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

24 Centroid resolutions: Dataset A -10° data:-20° data: 24 Good centroid |B| = 0.2 |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 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.


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