Rapidity etc.

• arXiv:1802.05356 (hep-ph) Quit Using Pseudorapidity, Transverse Energy, and Massless Constituents

Say we have a track-jet consisting of two tracks, both at eta = 2. One has phi = 0.4 the other phi = -0.4
These tracks hit the detector elements at eta=2.
Now we build a jet as usual, a few lines of ROOT code:

root [0] TLorentzVector p1, p2, j;
root [1] p1.SetPtEtaPhiM(10,2,-0.4,0)
root [2] p2.SetPtEtaPhiM(10,2,+0.4,0)
root [3] j = p1 + p2;
root [4] j.Eta()
(double) 2.0794904
root [5] j.Rapidity()
(double) 2.0000000

On line 3 we build the jet as usual as the 4-vector sum of its constituents.
Note that the jet eta is plain wrong!
It claims the jet is at eta = 2.08. This is biased away from the correct geometrical value which is 2.0 (where the detector hits are, and all jet constituents).
It is hence not ideal to cut on jet |eta|. We should make our acceptance selection based on |y|.
The issue here is that we use 4-vector addition to build the jet, and then we use eta which is not Lorentz invariant (differences in y are invariant under boost along z-axis, certainly not for eta). One crude fix would be not to use 4-vector addition to calculate eta. For topoclusters, a weighted sum is used. A better fix is to use "proper physics" (4 vector addition) but move away from quantities that are not Lorentz invariant (eta and ET).
Also note that eta vs y is a quite small effect. The example above is pretty extreme.

• ATL-PHYS-INT-2014-018 Recommendations of the Physics Objects and Analysis Harmonisation Study Groups 2014
• wiki: Pseudorapidity

• ATL-PHYS-INT-2010-129 Performance of Jet Algorithms in the ATLAS Detector
• arXiv:0906.1833 (hep-ph) Towards Jetography

Pileup

• arXiv:1801.09721 (hep-ph) Pileup mitigation at the LHC: a theorist's view (252 pages, 101 figures. Report originally presented as a habilitation thesis)
• arXiv:1810.07988 (hep-ph) Pileup mitigation at the Large Hadron Collider with Graph Neural Networks.

Min-bias & underline events

• arXiv:1202.0901 (hep-ph) Min-Bias and the Underlying Event at the LHC.

Machine Learning

• arXiv:1806.11484 (hep-ex) Deep Learning and its Application to LHC Physics

HE/HL LHC

• ATLAS Expected Performance at HL-LHC Workshop on the physics of HL-LHC, and perspectives at HE-LHC on 30 Oct - 1 Nov, 2017
• HL/HE LHC Meeting 4-6 April 2018, Fermilab

• arXiv:1903.05825 (hep-ph) Timing information at HL-LHC: Complete determination of masses of Dark Matter and Long lived particle.

Lectures

• Slac Summer Institute 2016 ”New Horizons on the Energy Frontier”, focusing on the Run 2 LHC, its upgrades and possible future e+e- and hadron colliders.

CERN Yellow Report

• CERN Yellow Reports
• arXiv:1708.01046 (hep-ph) Neutrino Physics (CERN Yellow Report CERN 2015-005, pp. 85-142)
• arXiv:1708.01117 (hep-ph) 2014 European School of High-Energy Physics (CERN Yellow Report CERN-2016-003, 4 lectures, 123 pages)
• arXiv:1708.00772 (hep-ph) Introduction to Supersymmetry (CERN Yellow Report CERN 2016-003, pp. 95-123)
• arXiv:1708.02150 (hep-ph) 8th CERN - Latin-American School of High-Energy Physics (9 lectures, 270 pages, published as CERN Yellow Report 2016-005)
• arXiv:1708.00794 (hep-ph) Higgs Physics (CERN Yellow Report CERN 2016-005, pp. 143-158)
• arXiv:1708.00800 (hep-ph) Physics Beyond the Standard Model (CERN Yellow Report CERN 2016-005, pp. 159-164)

ATLAS 25-year book

• ATLAS: A 25-Year Insider Story of the LHC Experiment
• Chap 1
• Chap 2
• Chap 3
• Chap 4
• Chap 6
• Chap 7
• Chap 8
• Chap 9
• Chap 10
• Chap 11
• Chap 12
• Contribution list

Physics Beyond Colliders study

• arXiv:1901.09966 (hep-ex) Physics Beyond Colliders at CERN: Beyond the Standard Model Working Group Report

-- KenjiHamano - 2016-09-21