Kun Liu weekly discussion with students [ATLAS]

Wednesday 30 Nov 2022, 16:00 → 17:30 Asia/Shanghai

Online

1 Introduction

General items.

Speaker: Prof. Kun LIU (Tsung-Dao Lee Institute & Shanghai Jiao Tong University)

2 ttWML and ttHML analyses status

Speaker: Nihal BRAHIMI (Tsung-Dao Lee Institute & Shanghai Jiao Tong University)

ttWML analysis status:

• Large sets of unblinded results (inclusive and differential cross-sections) already available and presented at the Top+X meeting: https://indico.cern.ch/event/1205638/#2-topq-2019-30-update-on-unbli
• Overall positive feedback and requested checks are being followed-up
• The team is now targeting to have the closure of the analysis on Dec 8th at the Top plenary. This will allow to start the paper circulation before the winter break (LHC H/TopWG meeting target was missed). Results/checks are being finalized to be added to the INT note and paper draft, in order to bring a complete analysis to the closure as requested by the conveners.

ttHML analysis status:

• Most of the efforts focussed on defining MVA discriminants to be used in the SRs
• Working with Jialin to come up with a good discriminant using DNN. Multiple configurations tested so far. Main bottleneck is the ttW CR and ttH SR definitions. Large anti-correlation (70%) observed between NF(ttH) and NF(ttW) which is not desirable. We are most likely going to drop the 4D DNN option in favour of the 3D DNN setup from Chen. We can alternatively check the 4D setup where the nodes are ttH, ttW, ttZ and ttbar+other instead of ttH, ttW, ttZ and ttbar (benefit from the reweighting studies performed so far).
• First dedicated (mini/skimmed) ttHML ntuples production now mostly ready (few jobs to be retried). Main bottleneck for starting to check the samples is the limited eos space (HSG8 eos full). We are currently implementing a solution where the samples are saved on the Grid.

3 ttH analysis

Speaker: jialin li

1130_kungroup.pdf

4 photon ID efficiency measurement & Zgamma analysis

Speaker: Kang Liu

I have run though the example_zmass.py of R22 TnP and add the shower shape variables in it. But like the results of  example.py, the vector also have more than 14 element. I am still checking.

5 Zgamma combination

Speaker: yufeng wang

6 silicon tracker: HGTD

7 Non-academic items

Anything else?