# Histogram-based Drift Tubes Trigger Primitive Generator: local performance evaluation

### Approved Plots

Plot Caption

Efficiency of the bunch crossing identification with the histogram-based DT TPG algorithm (MMT), as a function of the local track angle $\phi_\textup{local}$, shown for low and high BX ID qualities, respectively 3/4 and 4/4, in each $\phi$ super-layer, and for two different quality thresholds after combining super-layer 1 and super-layer 3 with a logical or''. The denominator groups all muons associated via MC truth to at least one signal collected in the DT front-end, the numerator groups all candidate muons that are associated by the algorithm to the correct bunch crossing.

Efficiency of the track segment reconstruction at the correct bunch crossing with the histogram-based DT TPG algorithm (CHT), as a function of the local track angle $\phi_\textup{local}$, shown for different quality thresholds, each strictly comprised in the previous one. The denominator groups all muons associated via MC truth to at least one signal collected in the DT front-end, the numerator groups all track segments that are reconstructed at the correct bunch crossing.

Candidate muon time distribution with the histogram-based DT TPG algorithm (MMT) for different ranges of local track angles $\phi_\textup{local}$ in steps of 15$^\circ$, for two different quality thresholds after combining super-layer 1 and super-layer 3 with a logical or''. Both histograms are normalised to the area of the most inclusive threshold of a candidate muon compatible with a minimum of 3 hits out of 4 super-layers. Duplicate candidate muons within each $\phi$ super-layer are purged, multiple candidates per generated muon can arise by the union of both $\phi$ super-layers.

Summary of the the candidate muon time distribution with the histogram-based DT TPG algorithm (MMT) for different ranges of local track angle $\phi_\textup{local}$, for low and high BX ID qualities, respectively 3/4 and 4/4, in each $\phi$ super-layer. The central band represents the fraction of candidate muons identified at the correct bunch crossing, the upper and lower whiskers represent, respectively, the fraction of candidate muons which are assigned 1 BX later or earlier than the correct one, the empty area exceeding the length of whiskers represents candidate muons wrong by more than 1\ BX.

Track segment time distribution with the histogram-based DT TPG algorithm (CHT) for different ranges of local track angles $\phi_\textup{local}$ in steps of 15$^\circ$, for different quality thresholds. All histograms are normalised to the area of the most inclusive track segment quality threshold compatible with a minimum of 3 hits out of 4 super-layers. Duplicate track segments within each $\phi$ super-layer are purged, multiple candidates per generated muon can arise by the union of both $\phi$ super-layers.

Summary of the the track segment time distribution with the histogram-based DT TPG algorithm (CHT) for different ranges of local track angle $\phi_\textup{local}$, for different trigger primitive quality thresholds. The central band represents the fraction of Track segments identified at the correct bunch crossing, the upper and lower whiskers represent, respectively, the fraction of candidate muons which are assigned 1 BX later or earlier than the correct one, the empty area exceeding the length of whiskers represents Track segments wrong by more than 1\ BX.
PDF VERSION Track segment angular resolution with the histogram-based DT TPG algorithm (CHT) for different ranges of local track angles $\phi_\textup{local}$ in steps of 15$^\circ$, for different quality thresholds. Track segments are required to be identified at the correct BX. Distributions of residuals are fit to a weighted sum of Gaussian distributions, the $\sigma$ of the narrow Gaussian fitting the peak is reported together with its weight. All histograms are normalised to the area of the most inclusive track segment quality threshold compatible with a minimum of 3 hits out of 4 super-layers. Duplicate track segments within each $\phi$ super-layer are purged, multiple candidates per generated muon can arise by the union of both $\phi$ super-layers.

Summary of the the track segment angular resolution with the histogram-based DT TPG algorithm (CHT) for different ranges of local track angle $\phi_\textup{local}$, for different trigger primitive quality thresholds. Track segments are required to be identified at the correct BX. Distributions of residuals are fit to a weighted sum of Gaussian distributions, the reported resolution is the $\sigma$ of the narrow Gaussian fitting the peak.

Track segment position resolution with the histogram-based DT TPG algorithm (CHT) for different ranges of local track angles $\phi_\textup{local}$ in steps of 15$^\circ$, for different quality thresholds. Track segments are required to be identified at the correct BX. Distributions of residuals are fit to a weighted sum of Gaussian distributions, the $\sigma$ of the narrow Gaussian fitting the peak is reported together with its weight. All histograms are normalised to the area of the most inclusive track segment quality threshold compatible with a minimum of 3 hits out of 4 super-layers. Duplicate track segments within each $\phi$ super-layer are purged, multiple candidates per generated muon can arise by the union of both $\phi$ super-layers.

Summary of the the track segment position resolution with the histogram-based DT TPG algorithm (CHT) for different ranges of local track angle $\phi_\textup{local}$, for different trigger primitive quality thresholds. Track segments are required to be identified at the correct BX. Distributions of residuals are fit to a weighted sum of Gaussian distributions, the reported resolution is the $\sigma$ of the narrow Gaussian fitting the peak.

-- NicolaPozzobon - 2019-09-16

Topic attachments
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Topic revision: r1 - 2019-09-16 - NicolaPozzobon

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