diff --git a/_episodes/05-MET101.md b/_episodes/05-MET101.md index d46a8879..e15a6d14 100644 --- a/_episodes/05-MET101.md +++ b/_episodes/05-MET101.md @@ -96,7 +96,7 @@ $$\textrm{PUPPI}~\vec{p}_{T}^{~miss} = - \sum_{i \in all~PF~Cands} w_i~\vec{p}_{ The figure below presents the MET distribution for both PF MET and PUPPI MET in events with leptonically decaying W bosons, demonstrating the improved performance achieved with PUPPI MET. - + > ## Remember > PUPPI MET is the default MET algorithm in Run~3. diff --git a/_episodes/06-METcalibration.md b/_episodes/06-METcalibration.md index a679ca9c..2e07c3ca 100644 --- a/_episodes/06-METcalibration.md +++ b/_episodes/06-METcalibration.md @@ -74,7 +74,7 @@ $$\vec{p}_{T}^{~miss,~Type-1} = - \sum_{i}^{nJets} \vec{p}_{T, jet}^{~corr} - \s
- +
We will revisit this in MET performance, but this figure shows a comparison between the MET scale for raw and Type-1 corrected MET.
@@ -103,10 +103,10 @@ The possible causes of this modulation include: The amplitude of the modulation increases roughly linearly with the number of pile-up interactions. For example, following plot shows the MET $\phi$ distribution without the XY correction in events with an electron and a muon where $t\bar{t}$+jets background dominates: - + After applying the correction the data/MC agreement improves: - + ## MET Uncertainty @@ -122,7 +122,7 @@ The physics objects that contribute the most are: The scale and resolution of each component must be systematically varied within their respective uncertainties. These variations are then propagated to the MET calculation to calculate their impact on the analysis. Following figure shows the distribution of the Type 1 corrected MET reconstructed by the PF algorithm in MC and in data along with the uncertainties in the ratio plot. - + ## Exercise 2.1 diff --git a/_episodes/07-METperformance.md b/_episodes/07-METperformance.md index a1f85508..9ec30851 100644 --- a/_episodes/07-METperformance.md +++ b/_episodes/07-METperformance.md @@ -47,7 +47,7 @@ Specifically, the mean of the distribution of the magnitude of $$q_{T} + u_{\par An example of the $$q_{T} + u_{\parallel}$$ and $u_{\perp}$ distributions is shown in the following plots. - + Use the distribution of the parallel and perpendicular components of the hadronic recoil to measure the MET scale and resolution - Get the mean of the parallel component to estimate MET scale. diff --git a/_episodes/08-AnomalousMET.md b/_episodes/08-AnomalousMET.md index 2ee6688b..43e63a7c 100644 --- a/_episodes/08-AnomalousMET.md +++ b/_episodes/08-AnomalousMET.md @@ -34,7 +34,7 @@ These anomalous MET events can arise from: In such events, the MET value may be much higher than expected and does not reflect true missing energy from invisible particles (like neutrinos or dark matter candidates).
- +
An example of identifying the source of anomalous MET.
@@ -46,7 +46,7 @@ These cleaning algorithms, or filters, run in separate processing paths, and the Analyzers can use this decision bit to filter out noisy events. These filters are specifically designed to reject events with unusually large MET values caused by spurious signals.
- +
MET $p_T$ and leading jet $\phi$ distributions, with and without the application of event filters.
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