1. Jet variables
Presented by Kaifu Lam
Mar 1, 2017

2. 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 2

3. Jet pT From paper Kaifu regenerated ROC Curve
jet momentum transverse to the beam line 3

4. 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: 4

5. 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. 5

6. Track 3 d0 significance* From paper Kaifu regenerated ROC Curve
same as above, but for the third-highest ranked track. 6

7. 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. 7

8. Track 3 z0 significance From paper Kaifu regenerated ROC Curve
same as above, but for the third-highest ranked track. 8

9. 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. 9

10. 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 10

11. Jet width eta From paper Kaifu regenerated ROC Curve
the "width" of the track distribution for this jet, in the eta coordinate 11

12. 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. 12

13. Vertex significance From paper Kaifu regenerated ROC Curve
the reconstructed vertex displacement divided by the uncertainty in the displacement. 13

14. 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. 14

15. 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 15

16. 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). 16

17. Vertex mass* From paper Kaifu regenerated ROC Curve
same mass as in the medium-level variables. 17

18. 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. 18

19. Mid / Low Level variables

20. Gaps / Improvements
Re-do binning to match original x axis range in charts
Feed data into DNN
Understand how the dataset was generated 20

21. Special Thanks! Julian Collardo Dr. Sam Meehan Prof. Shih-Chieh Hsu
Most plot comments are quoted from Dan Guest’s Github 21