Data Selection for Signal Variables and Efficiency Optimization
Selections on data
Dong Ao
1
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
Used BDT method
•
Second one:
•
Used Xic real data(after first BDT
cuted) as input signal
•
input variables:
daughter particle’s ProbNN
3
Find the max cut efficiency
•
To get the max S/sqrt(S+B) of Omegac
4
—
S(number of signal)
—
S/sqrt(S+B)
—
S(number of signal)
—
S/sqrt(S+B)
Orginal data
BDT cut
5
BDT cut && pidBDT cut
6
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.
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Presentation Transcript
Selections on data Dong Ao 1
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
Used BDT method Second one: Used Xic real data(after first BDT cuted) as input signal input variables: daughter particle s ProbNN 3
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
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
