TauPOG-recommonded tau corrections

This repository contains the scale factors (SFs) and energy scales recommended by the TauPOG. More detailed recommendations can be found on this TWiki page: https://twiki.cern.ch/twiki/bin/viewauth/CMS/TauIDRecommendationForRun2

Summary of available SFs

This is a rough summary of the available SFs for DeepTau2017v2p1:

Tau component	genmatch	DeepTau2017v2p1 VSjet	DeepTau2017v2p1 VSe	DeepTau2017v2p1 VSmu	energy scale
real tau	5	vs. pT, or vs. DM	– (*)	– (*)	vs. DM
e -> tau fake	1, 3	–	vs. eta	–	vs. DM and eta
mu -> tau fake	2, 4	–	–	vs. eta	– (±1% unc.)

(*) An extra uncertainty is recommended if you use a different working point (WP) combination than was used to measure the SFs, see the TWiki.

The gen-matching is defined as:

• 1 for prompt electrons
• 2 for prompt muons
• 3 for electrons from tau decay
• 4 for muons from tau decay
• 5 for real taus
• 6 for no match, or jets faking taus. For more info on gen-matching of taus, please see here. Note that in nanoAOD this is available as Tau_GenPartFlav, but jet or no match correspond to Tau_GenPartFlav==0 instead of 6.

The SFs are meant for the following campaigns:

Year label	MC campaign	Data campaign
2016Legacy	RunIISummer16MiniAODv3	17Jul2018
2017ReReco	RunIIFall17MiniAODv2	31Mar2018
2018ReReco	RunIIAutumn18MiniAOD	17Sep2018/22Jan2019

Usage

Please install the correctionlib tool to read these SFs. There are several ways to install, but the best way is via python3, for example,

source /cvmfs/sft.cern.ch/lcg/views/LCG_99/x86_64-centos7-gcc8-opt/setup.sh
git clone --recursive https://github.com/cms-tau-pog/correctionlib.git
cd correctionlib
python3 -m pip install .
python3 -c 'import correctionlib._core; import correctionlib.schemav2' # test

Find out the content of the tau.json using

gunzip POG/TAU/2018_ReReco/tau.json.gz
correction summary POG/TAU/2018_ReReco/tau.json

An example is given in examples/tauExample.py. You can load the set of corrections as follows in python as

import correctionlib as _core
cset = _core.CorrectionSet.from_file("tau.json")
corr1 = cset["DeepTau2017v2p1VSjet"]
corr2 = cset["DeepTau2017v2p1VSe"]
corr3 = cset["tau_trigger"]
corr4 = cset["tau_energy_scale"]

And then on an event-by-event basis with reconstructed tau objects, you can do

sf1 = corr1.evaluate(pt,dm,genmatch,wp,syst,"pt")
sf2 = corr2.evaluate(eta,genmatch,wp,syst)
sf3 = corr3.evaluate(pt,dm,"etau",wp,"sf",syst)
tes = corr4.evaluate(pt,eta,dm,genmatch,"DeepTau2017v2p1",syst)

Where ‘syst='nom’,'up'or'down'`. A C++ example can be found here.

Alternative way to load the JSON files (including gunzip'ed):

import correctionlib as _core
fname = "tau.json.gz"
if fname.endswith(".json.gz"):
  import gzip
  with gzip.open(fname,'rt') as file:
    data = file.read().strip()
  cset = _core.CorrectionSet.from_string(data)
else:
  cset = _core.CorrectionSet.from_file(fname)

References

The TauPOG JSON files are created from https://github.com/cms-tau-pog/correctionlib