Jet variables
Presented by Kaifu Lam
Mar 1, 2017
The Exercise
> To re-plot the variables and understand the variables
from this paper:
– Jet Flavor Classifcation in High-Energy Physics with Deep
Neural Networks – Sep 2016
> The dataset is created by simulation modeling light
and heavy jets of pp collisions in ATLAS detector.
– Details for data generation?
> The dataset contains high level variables and mid –
low level variables
– Mid / Low level variables are provided in the end of this PPT
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Jet pT
From paper
Kaifu regenerated
jet momentum transverse to the beam line
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ROC Curve
Jet eta
From paper
>
Kaifu regenerated
ROC Curve
Wolffram dictionary: Pseudorapidity is a function of the production angle with
respect to a beam axis. It is a good approximation of the true relativistic rapidity
when a particle is relativistic. It is defined as:
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Track 2 d0 significance
From paper
Kaifu regenerated
ROC Curve
tells how much a track "misses" the original interaction point by. We can rank b
the tracks by this significance and then take the second highest one as a proxy
for all the track information.
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Track 3 d0 significance*
From paper
Kaifu regenerated
same as above, but for the third-highest ranked track.
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ROC Curve
Track 2 z0 significance
From paper
Kaifu regenerated
ROC Curve
d0 is transverse to the beam line, z0 is along it, so this is the complementary
coordinate to the one above.
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Track 3 z0 significance
From paper
Kaifu regenerated
same as above, but for the third-highest ranked track.
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ROC Curve
Number of tracks over d0 threshold
From paper
Kaifu regenerated
ROC Curve
rather than taking the nth highest d0 or z0, we can count the number of tracks in
which the value is over some threshold. In our case this is 1.8.
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Jet probability
From paper
Kaifu regenerated
ROC Curve*
this is an algorithm which ATLAS uses to combine the d0 significance of all the tracks
in the jet. It's basically a product of the likelihood that each track comes from the
interaction point, so a lower value means the track is less likely to be from a
displaced vertex (our signal).
ROC curve not correct due to different x range of histograms
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Jet width eta
From paper
Kaifu regenerated
the "width" of the track distribution for this jet, in the eta coordinate
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ROC Curve
Jet width phi
From paper
Kaifu regenerated
ROC Curve
same as above, but in the "phi" coordinate. The detector has approximate cylindrical
symmetry, so we don't expect any interesting behavior as a function of phi, but
the width of jets in this coordinate can be interesting.
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Vertex significance
From paper
Kaifu regenerated
ROC Curve
the reconstructed vertex displacement divided by the uncertainty in the displacement.
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Number of secondary vertices
From paper
Kaifu regenerated
ROC Curve
Dan Guest: number of reconstructed vertices. This should only be 1 in this particular
file, since I've set the vertex reconstruction code to only build one vertex, but it
may change in the future.
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Number of secondary vertex tracks
From paper
Kaifu regenerated
ROC Curve*
the number of tracks associated to this reconstructed vertex (as opposed to the
interaction point). A reconstructed vertex with a lot of tracks is unlikely to be a
fluke.
ROC curve not correct due to different x range of histograms
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Delta R to vertex
From paper
Kaifu regenerated
ROC Curve
this is basically the angular separation between the vertex and the jet. It's in our weird
(eta, phi) coordinates, so it also has the weird name (delta R).
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Vertex mass*
From paper
Kaifu regenerated
same mass as in the medium-level variables.
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ROC Curve
Vertex energy fraction*
From paper
Kaifu regenerated
ROC Curve
what fraction of the energy in all the tracks in the jet is contained in tracks associated
to the displaced vertex.
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Mid / Low Level variables
Gaps / Improvements
> Re-do binning to match original x axis range in
charts
> Feed data into DNN
> Understand how the dataset was generated
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Special Thanks!
Julian Collardo
Dr. Sam Meehan
Prof. Shih-Chieh Hsu
Most plot comments are quoted from Dan Guest’s Github
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