1. Clustering-based Studies on the upgraded ITS of the Alice Experiment
Jiang Bofang

2. My name:姜博放 （JIANG Bofang）
My country: China
My university: SJTU, Shanghai, China
My degree: Bachelor of Physics ( )
My work at CERN: Alice ITS, EP department
My supervisor: Antonello Di Mauro
My possible future: PhD in Physics
What I liked most: Have new friends from all over the world

3. Introduction: Upgraded Alice ITS
Aim: To improve impact parameter resolution
To improve tracking efficiency and momentum resolution at low p T
Approaches: Reduce pixel size , material budget, distance from beam& Increase granularity
New detectors: Pixel signal amplified (above threshold) and digitized at a pixel level
10 m2 active Si 12.5 G-pixel
First, please let me introduce the upgraded alice inner tracking system. As you can see in the picture, there are seven layers of silicon trackers with Giga-pixels totally covering 10 square meter. In order to Improve impact parameter resolution
Improve tracking efficiency and pT resolution at low pT, Alice come up with this new design, with smaller pixel size , material budget, closer distance to the beam and higher granularity. A brand new design is utilized that all the detectors are pixelized digitizer, which only register the spatial information of particles pass through the layers. Alice make the first and largest Monolithic Active Pixel Sensor, in another words, the sensor and the readout chip are merged in a single divise
Here we come to my project. The data acquisition principle is that a pixel will be amplified when it collect some charge above a trigger threshold, but there are a few percent of sensors which shows small areas with higher threshold
Monolithic Active Pixel Sensor
Inner Tracking System (ITS)

4. High threshold Problem
Possible explanation: Leakage current
Impact: Decrease charge collection and spatial resolution ????
A solution: Apply a bias to the sensor to improve global charge collection
Typically the threshold are hope to be uniform with some fluctuation, but when we measure them, we found some high threshold areas on some of the detectors. A possible reason is the leakage current during the construction. Obviously, such problem will lead to a lower spatial resolution and make it harder to collect the charges. In order to improve the charge collection efficiency, we can apply a voltage bias to the sensor but the shortage is it will aggravate the ununiform threshold. My work is to find and analyze the high threshold clusters, then do a detector classification by compare the performance of different ones.
Threshold map

5. Cluster approach: find high threshold clusters
Abnormal points set P={p1, p2, p3 …}
Create a first cluster C1
Adding an arbitrary point p1 to C1
while (point pi in P & p not in C1)
If (pi has a neighbor in C1)
Add pi to C1
Remove pi from P
end
If (no new point added after one loop)
Break
Create a cluster C2 …
I first develop an algorithm to find the clusters. As shown on left, I pick out the abnormal points set P, define a constant cluster distance, to determine the dispersion ratio in a cluster, then compare the distance of each two points in P. The only principle is, for each point in a certain cluster, it has at least one neighbor, whose distance is shorter than the cluster distance. Thus I create a cluster, scan the point set again and again to add points . the cluster definition will only end when no more unassociated points are found to satisfy the inter-distance relation. Hence I begin a new cluster. Isolated points will be abandoned. Finally I get all the clusters and here are some output information. Among which I use the area of cluster to do the detector classification, that is the smaller the clusters are, the better a detector is thought to be.
Output: Number of clusters
Area of each cluster
Average threshold
Shape of each cluster
Position of cluster centers
Threshold distribution in clusters

6. Simulation Part (O2) Generating particles Digitization
Then I attempt to prove the rationality of such ranking, I thus prove how the high threshold problem affect the track reconstruction. The method is insert the cluster map into alice simulation program o2. Here I display the workflow of the simulation. First we generate particles as the MC true events, then we add the detector response to obtain digitized hits on readout layers. Afterwards we do space reconstruction by treating the center of triggered pixels as the charge center. Hence we can do analysis the spatial resolution by compare the distance between the real charge center and reconstructed ones. Previously, the threshold is regarded as constant, what I modify is replacing the constant threshold by the experimental data. Therefore, we can compare the resolution chip by chip in order to do another ranking.
Reconstruction
Analyze spatial resolution

7. Results: Detector Classification
Detector Classification: simulation approach Vs. cluster approach
Baseline
Select the sensors with similar average threshold
Fail to see a clear correlation with current data
Percentage of problematic chips& pixels is limited
Here we have a result of the two ranking. I analyzed some chips in Alice ITS, and most of them don’t show high threshold problem, Thus I select some chips whose average threshold are similar, compare the relation between the cluster areas and the spatial resolution. Unfortunately we fail to see a clear correlation between the two parameter since the data and the cluster area is limited. However, I make the detector response closer to reality, the baseline is the resolution with constant threshold.

8. Conclusion& Outlook Summary Outlook
I developed an algorithm to find and analyse high threshold clusters on silicon detectors
I managed to simulate the detector response closer to the reality
Whether high threshold cluster have a negative correlation with spatial resolution needs to be verified
Outlook
Larger amount of simulation
More cluster parameters to be analyzed (number, distribution …)
Find the balance between “high threshold problem” and voltage bias
Optimize the spatial resolution / such ranking will help us to determine the position of different sensors since the inner layer need higher percision

9. Thank you！