Data Selection for Signal Variables and Efficiency Optimization

Utilizing a Boosted Decision Tree (BDT) method, this presentation discusses the selection of signal input variables for Xic particles, maximizing the S/sqrt(S+B) ratio for signal efficiency in particle physics analysis. The process involves using real data as input variables, cutting variables to improve efficiency, and assessing the impact on S/sqrt(S+B) ratios for different cuts, ultimately aiming to enhance the analysis of OmegacS particles.

• Data Selection
• Boosted Decision Tree
• Signal Efficiency
• Particle Physics Analysis
• Input Variables

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Uploaded on Sep 24, 2024 | 0 Views

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1. Selections on data Dong Ao 1

2. Used BDT method First one: Used Xic real data as input signal input variables: Xic_ENDVERTEX_CHI2, Xic_DIRA_OWNPV, daughter particle s PT, daughter particle s IPCHI2_OWNPV 2

3. Used BDT method Second one: Used Xic real data(after first BDT cuted) as input signal input variables: daughter particle s ProbNN 3

4. Find the max cut efficiency To get the max S/sqrt(S+B) of Omegac S(number of signal) S/sqrt(S+B) S(number of signal) S/sqrt(S+B) 4

5. Orginal data S S+B S/sqrt(S+B) 25273 420689 38.7 BDT cut S S+B S/sqrt(S+B) BDT>0.110 17704 105659 54.4675 BDT cut && pidBDT cut S S+B S/sqrt(S+B) pidBDT>0.01 16079 61673 64.746 5

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