2. Beam Gas Vertex – Beam monitor
Benedikt Würkner

3. Overview What is the BGV How does it work Software efforts
Clustering
Tracking
Vertexing
Different approaches to the required result
Data quality due to cuts (and introduced bias)
Data visualization (plot types)
Benedikt Würkner

4. BGV – Beam Gas Vertex(ing)
A beam profile monitor
Based on LHCb detector technology
Non destructive method - doesn’t influence the beam
Currently fully functional demonstrator built in point 4
Built by the beams department with support from the LHCb Group and EPFL Lausanne
Benedikt Würkner

5. Working principle
Uses collisions of beam-particles (protons, ions) with residual gas atoms in the beam pipe
Uses two layers of detector modules to reconstruct the tracks of the particles created by the collisions
Reconstruct the vertex(ices) from these tracks
Accumulate the measured vertices to calculate the beam profile
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6.
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7.
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8.
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9.
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10.
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11. Software - Clustering
Each Layer consists of 1024 fibers with 250μm diameter
On the backside there are again 1024 strips at an angle of 2º
Particles passing through a layer produce light which travels along the fiber to a SiPMs
Each channel gives an analog signal to the readout board
Channels above a pre-defined limit trigger a cluster search: Neighboring channels are checked and multiple channels are combined into a cluster
Now available in the firmware of the Readout- system. Data acquisition speedup ~5x and processing speed increased by about ~3x (independently)
Benedikt Würkner

12. Software – Tracking(1)
Generally: Find 8 clusters in the 8 top or bottom planes and fit a line through them.
0. Take cluster in plane 8 and create window from there
Take the first cluster on the first plane (0)
Define search window for second (angled=1) plane
If cluster is found continue, otherwise return to 1.
Define generous search window and find cluster in plane 2 (90º angled)
Define search window and find cluster in plane 3
Benedikt Würkner

13. Software – Tracking(2)
Extrapolate to second detector panel Find starting cluster in layer 8 (actually no, this is already known from step 0) and repeat the algorithm from the first detector panel Fit line that has the closes distance to these 8 clusters (which represent lines in 3D) If the fit of the line is good (chi2 low enough) accept the track, otherwise discard it
Benedikt Würkner

14. Software – Vertexing
Find point in 3D space where multiple tracks originated from
Take the first track
compare to all until a distance below a limit is found
Calculate point between them and compare this point to all future tracks.
If another track is found that is below the limit calculate the central point between the tracks and continue
At the end of the loop declare the central point as a vertex
Store all the vertices, their statistics give the beam spot
Point in between
Vertex
distance
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15. Output of the Vertexing algorithm
Simulation Run
Run1911
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16. ɸ
d (Distance Of Closest Approach) x y
DOCA-tPhi approach
Many events have only few tracks, needed to find a method that works in that case that isn’t as precision-dependent as vertexing.
DOCA-tPhi method (Distance Of Closes Approach) is a standard method for estimating the global detector offset from the beam
Uses only the minimal distance of a track from the z-axis and the angle of the track in a x-y plane projection
Is independent of the resolution of the DOCA measurement (cancels out in equation)
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17. DOCA-tPhi approach (vis)
Plotting the distance from the z-axis over the angle distribution
Results for MC simulation (perfectly aligned detector) look 100% flat (as expected)
Results for offset MC beam look like a misaligned detector (cannot be distinguished)
Creating the profile of this data and fitting x*sin(tPhi)-y*cos(tPhi) = DOCA
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18. For real data 1.8 million events
Adaptive filter around the expected shape
Removes small artifacts that are track fit errors
Only shows expected reconstruction problems (explained later)
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19. Plot type for visualization
Simulation
Reconstructed
Run2485
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20.
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21. Summary The BGV is a beam profile monitor based on a physics detector
It is currently in working conditions with software improvements to be done
Clustering works well, Tracking needs some effort to make Vertexing better
Clustering now even better, Tracking currently being improved
Other investigated methods show promise of multiple different methods to get the desired result
Benedikt Würkner