Beam Gas Vertex – Beam monitor 02.10.2017 Benedikt Würkner

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) 02.10.2017 Benedikt Würkner

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 02.10.2017 Benedikt Würkner

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 02.10.2017 Benedikt Würkner

02.10.2017 Benedikt Würkner

02.10.2017 Benedikt Würkner

02.10.2017 Benedikt Würkner

02.10.2017 Benedikt Würkner

02.10.2017 Benedikt Würkner

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) 02.10.2017 Benedikt Würkner

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 02.10.2017 Benedikt Würkner

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 02.10.2017 Benedikt Würkner

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 02.10.2017 Benedikt Würkner

Output of the Vertexing algorithm Simulation Run Run1911 02.10.2017 Benedikt Würkner

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) 02.10.2017 Benedikt Würkner

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 02.10.2017 Benedikt Würkner

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) 02.10.2017 Benedikt Würkner

Plot type for visualization Simulation Reconstructed Run2485 02.10.2017 Benedikt Würkner

02.10.2017 Benedikt Würkner

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 02.10.2017 Benedikt Würkner