draw_pico

Repository inherited from: https://github.com/ald77/ra4_draw

For examples of functionality see: src/core/test.cxx

Setup

Prerequisites: (Note, on UCSB servers, source set_env.sh can be used to fulfill these prerequisites, which is described below in the UCSB server section.)

• ROOT
• (Optional) Scons for building the library. Can also use the compile.py script instead.

Please run the below command to clone the repository

git clone [email protected]:richstu/draw_pico.git
cd draw_pico

Please run the below command to build the libraries.

./compile.py

or

scons

Setup on UCSB servers

If you use the USCB servers, you can use the below commands for the setup. This also sets up the environment for the batch system at UCSB

git clone --recurse-submodules [email protected]:richstu/draw_pico.git
cd draw_pico
source set_env.sh
./compile.py or scons

An alternative method is shown below:

git clone [email protected]:richstu/draw_pico.git
cd draw_pico
git submodule update --init --remote --recursive
source set_env.sh
./compile.py or scons

Higgsino useful commands

To make datacards and get limits:

To plot overlap between the boosted and resolved analysis:

./run/higgsino/plot_regions.exe #boosted overlap in resolved regions
./run/higgsino/plot_regions.exe --boo #resolved overlap in boosted regions

Write all the datacards:

./run/higgsino/write_kappa_datacards_priority.exe -o datacards/tchihh_onedim/ -m CN -1 -r 1 --unblind --unblind_signalregion
./run/higgsino/write_kappa_datacards_priority.exe -o datacards/tchihh_twodim/ -m N1N2 -2 -r 1 --unblind --unblind_signalregion
./run/higgsino/write_kappa_datacards_priority.exe -o datacards/t5hh_onedim/ -m T5HH -f -1 -r 1 --unblind --unblind_signalregion
./run/higgsino/write_kappa_datacards_priority.exe -o datacards/t5hh_twodim/ -m T5HH -2 -r 1 --unblind --unblind_signalregion

For comparison of signal yields to other tables, remember to use --recomet such that the yield is not averaged with the one obtained using GenMET. To make a datacard for the boosted case, use -t boosted. Use option --unblind to include data. To run on a particular point add, e.g. -p "700_1". For computers with smaller amounts of memory, there may not be sufficient memory to generate all the 2D T5HH datacards at once. In this case, you can split the job in half with the -s flag, which sets a lower/upper bound on the gluino mass.

./run/higgsino/write_kappa_datacards_priority.exe -o datacards/t5hh_twodim/ -m T5HH -2 -r 1 --unblind --unblind_signalregion -s "-2100"
./run/higgsino/write_kappa_datacards_priority.exe -o datacards/t5hh_twodim/ -m T5HH -2 -r 1 --unblind --unblind_signalregion -s 2100

Then to get a limit interactively:

./run/higgsino/scan_point.exe -f datacards/tchihh_onedim/datacard-TChiHH_mChi-700_mLSP-0_Tune_2016,2017,2018_priority1_resolved.txt

To get limits for the full scan in the batch, use the following script to generate batch commands.

./scripts/write_combine_cmds.py --card_dir datacards/tchihh_onedim -m CN
./scripts/write_combine_cmds.py --card_dir datacards/tchihh_twodim -m N1N2
./scripts/write_combine_cmds.py --card_dir datacards/t5hh_onedim -m T5HH
./scripts/write_combine_cmds.py --card_dir datacards/t5hh_twodim -m T5HH

These can be run locally with ./scripts/run_commands.py or submitted to the UCSB batch system with auto_submit_jobs.py. To combine outputs and make the limit plot

cat datacards/tchihh_onedim/scan_point*/limit*txt | sort >> tchihh_onedim_resolved_limits.txt
cat datacards/tchihh_twodim/scan_point*/limit*txt | sort >> tchihh_twodim_resolved_limits.txt
cat datacards/t5hh_onedim/scan_point*/limit*txt | sort >> t5hh_onedim_resolved_limits.txt
cat datacards/t5hh_twodim/scan_point*/limit*txt | sort >> t5hh_twodim_resolved_limits.txt
./run/higgsino/plot_limit.exe -f tchihh_onedim_resolved_limits.txt --drawData -t tchihh_onedim_resolved
./run/higgsino/limit_scan.exe -f tchihh_twodim_resolved_limits.txt -m N1N2 -t tchihh_twodim_resolved --unblind
./run/higgsino/plot_limit.exe -f t5hh_onedim_resolved_limits.txt --drawData -m T5HH -t t5hh_onedim_resolved
./run/higgsino/limit_scan.exe -f t5hh_twodim_resolved_limits.txt -m T5HH -t t5hh_twodim_resolved --unblind

To generate AN tables/plots:

./run/higgsino/an_plot_triggers.exe --unblind --year 2016 --string_options systematic,efficiency,cr
./run/higgsino/an_plot_triggers.exe --unblind --year 2017 --string_options systematic,efficiency,cr
./run/higgsino/an_plot_triggers.exe --unblind --year 2018 --string_options systematic,efficiency,cr
./run/higgsino/an_plot_triggers.exe --unblind --year run2
./run/higgsino/an_plot_selection.exe --unblind --year run2
./run/higgsino/an_plot_backgroundEstimation.exe --unblind --year run2
./run/higgsino/plot_kappas.exe -s search --scen mc -y run2 -o paper_style
./run/higgsino/an_plot_syst_ttbar.exe --unblind --year run2
./run/higgsino/an_plot_syst_zll.exe --unblind --year run2
./run/higgsino/an_plot_syst_qcd.exe --unblind --year run2
./scripts/datacard_syst_utils.py
./run/higgsino/plot_n_minus_1.exe --unblind --year run2 --sample search
./run/higgsino/plot_search_unblind.exe -u -a -o plot_baseline,paper_style,plot_in_btags,plot_in_btags_with_met_split
./run/higgsino/plot_kappas.exe -s search --scen data -y run2 -o paper_style,use_datacard_results,do_zbi --unblind
./scripts/plot_ra4style_results.py

To generate paper plots:

./run/higgsino/plot_search_unblind.exe -u -a -o plot_baseline,paper_style,plot_in_btags,plot_in_btags_with_met_split
./run/higgsino/plot_kappas.exe -s search --scen mc -y run2 -o paper_style
./run/higgsino/plot_kappas.exe -s search --scen data -y run2 -o paper_style,use_datacard_results,do_zbi --unblind
./scripts/plot_ra4style_results.py

To generate supplementary plots:

./run/higgsino/an_plot_triggers.exe -u -o plot,paper_style
./run/higgsino/an_plot_syst_zll.exe --year run2 --unblind -o paper_style
./run/higgsino/an_plot_syst_qcd.exe --year run2 --unblind -o paper_style
./run/higgsino/an_plot_syst_ttbar.exe --year run2 --unblind -o plot_data_vs_mc,paper_style
./run/higgsino/plot_kappas.exe --sample ttbar --scen data --unblind --year run2 -o paper_style
./run/higgsino/plot_kappas.exe --sample zll --scen data --unblind --year run2 -o paper_style
./run/higgsino/plot_kappas.exe --sample qcd --scen data --unblind --year run2 -o paper_style
./run/higgsino/plot_search_unblind.exe --year run2 -u --unblind_signal -o plot_in_btags,plot_in_btags_with_met_split,paper_style,supplementary
./scripts/plot_signal_efficiency.py
./run/higgsino/plot_phase_space.exe
./run/higgsino/plot_supplementary.exe -y run2 -o cutflow
./run/higgsino/plot_supplementary.exe -y run2 -o pie
./scripts/plot_ra4style_results.py --signal

Getting Higgsino cross-section (CN to N1N2) scale factors.

Step 1. Download cross-section files

scp -r lxplus:/afs/cern.ch/user/a/amete/public/EWKGauginoCrossSections_13TeV cross_section

Step 2. Fix bug in script

Set masses, xsecs, xsecUncs to 0 when initializing.

cd cross_section
sed -i 's/std::vector<double>\* masses;/std::vector<double>\* masses=0;/' get_gaugino.C
sed -i 's/std::vector<double>\* xsecs;/std::vector<double>\* xsecs=0;/' get_gaugino.C
sed -i 's/std::vector<double>\* xsecUncs;/std::vector<double>\* xsecUncs=0;/' get_gaugino.C

Step 3. Run script for all mass points.

model "CN" (mixing) or "N1N2" (no mixing).

cd cross_section
../scripts/print_higgsino_cross_sections.py -i /net/cms25/cms25r0/pico/NanoAODv7/nano/2016/SMS-TChiHH_2D -m CN
../scripts/print_higgsino_cross_sections.py -i /net/cms25/cms25r0/pico/NanoAODv7/nano/2016/SMS-TChiHH_2D -m N1N2
../scripts/print_higgsino_cross_sections.py -i /net/cms25/cms25r0/pico/NanoAODv7/nano/2016/SMS-TChiHH_2D -c

Zgamma useful commands