sPHENIX tracking discussion

[Anthony Frawley <afrawley AT fsu.edu>]

Hi Zhongling.

It looks very good. A few minor comments only:

Titile: Clustering hits of Time Projection Chamber

->

Title: Clustering hits of the Time Projection Chamber

TPC is the major tracking

->

The TPC is the major tracking

such as Au+Au collisions with pileup, due

->

such as Au+Au collisions with event pileup from multiple beam crossings, due

Cheers

Tony

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From: sPHENIX-tracking-l <sphenix-tracking-l-bounces AT lists.bnl.gov> on behalf of Zhongling Ji via sPHENIX-tracking-l <sphenix-tracking-l AT lists.bnl.gov>
Sent: Tuesday, April 18, 2023 10:39 PM
To: sphenix-tracking-l AT lists.bnl.gov <sphenix-tracking-l AT lists.bnl.gov>
Subject: [Sphenix-tracking-l] QM poster abstract for TPC NN clustering

 

Dear All,

    Attached is my poster abstract for TPC NN clustering.
    Though I haven't gotten a performance advantage over the existing clustering method, I will talk about the motivation and technical implementation of NN clustering at sPHENIX. In the meanwhile before QM, I will improve its performance and hopefully I can achieve better performance before QM.

Titile: Clustering hits of Time Projection Chamber by machine learning and artificial neural networks at sPHENIX
Abstract: The Time Projection Chamber (TPC) at sPHENIX coverages pseudorapidity $|\eta| <$ 1.1. TPC is the major tracking detector and plays a key role in jet and heavy-flavor measurements. Charged particles passing through the TPC ionize electrons, and their positions are calculated using electron-drift time. These ionized electrons produce hits that form clusters for track reconstruction. The traditional method of grouping connected hits into clusters, known as connected component analysis (CCA), becomes less effective in high-multiplicity events, such as Au+Au collisions with pileup, due to effects like $\delta$-electrons. A neural network (NN) clustering, which uses an NN to predict the cluster position based on the distribution of hits, is supposed to improve the clustering performance. We simulate high-multiplicity events and sPHENIX detector responses and train the NN to predict the associated truth cluster position based on the distribution of the reconstructed hits. I will show the implementation of NN clustering at sPHENIX and our plan to enhance its performance by improving truth-information association and fine-tuning the parameters of the NN.

Best regards,
Zhongling