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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 size and length (3) TPC behaviour (4)Space resolutions and offsets. 1

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(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

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(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

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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

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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

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

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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

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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

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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)

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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.

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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)

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

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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

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

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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)

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

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

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

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

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

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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

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

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

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

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

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

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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

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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

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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

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

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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.

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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

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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 ?

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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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