This tool allows you to take a series of ROOT ntuples, signal & background, apply a lot of cuts automatically, and figure out the most optimal selections to maximize significance. It comes packed with a lot of features

• generator script to create, what we call, a supercuts file containing all the rules to tell the script what cuts to apply and on which branches
• cut script which will take your signal, background, and supercuts; run them all; and output a series of files with the appropriate event counts for all cuts provided
• optimization script which will take your signal counts and background counts; run them all; and output a sorted list of optimal cuts
• hash look up script to reverse-engineer the cut for a given hash when you supply the supercuts file

Note: as part of making the script run as fast as possible, I try to maintain a low memory profile. It will not store (or remember) the cut used to create a significance value. Instead, we compute a 32-bit hash which is used to encode the cuts, and a way to "decode" the hash is also provided.

Table of Contents generated with DocToc

• Major Dependencies
• Quick Start
  • Installing
    • Using virtual environment
    • Without using virtual environment
    • On a CVMFS-enabled machine
    • Errors with root-numpy and TMVA
  • Using
    • Grab some optimization ntuples
    • Generate a supercuts template
    • Running the cuts
    • Calculating the significances
    • Looking up a cut (or two)
  • Profiling Code
  • Example Script
• Documentation
  • Top-Level
    • Parameters
  • Action:Generate
    • Required Parameters
    • Optional Parameters
    • Output
  • Action:Cut
    • Required Parameters
    • Optional Parameters
    • Output
  • Action:Optimize
    • Required Parameters
    • Optional Parameters
    • Output
  • Action:Hash
    • Required Parameters
    • Optional Parameters
    • Output
  • Action:Summary
    • Required Parameters
    • Optional Parameters
    • Output
  • Supercuts File
    • Defining a fixed cut
    • Defining a supercut
    • Example of a supercuts file
    • More Complicated Selections
• Authors

• PyROOT (which technically requires ROOT)
• numpy
• root_numpy

All other dependencies are listed in requirements.txt and can be installed in one line with pip install -r requirements.txt.

tl;dr - copy and paste, and off you go.

I use virtualenvwrapper to manage my python dependencies and workspace. I assume you have pip.

pip install virtualenvwrapper
echo "source /usr/local/bin/virtualenvwrapper.sh" >> ~/.bash_profile
source ~/.bash_profile

and then at this point, you can set up and install:

mkvirtualenv optimization
workon optimization
pip install root-optimize
rooptimize -h

Start a new environment with mkvirtualenv NameOfEnv and everytime you open a new shell, you just need to type workon NameOfEnv. Type workon alone to see a list of environments you've created already. Read the virtualenvwrapper docs for more information.

pip install root-optimize
rooptimize -h

First, set up ROOT, python, and pip using lcgenv:

lsetup root "lcgenv -p LCG_87 x86_64-slc6-gcc49-opt pip"
pip install --user virtualenvwrapper

which gets us virtual environments to work with. To make sure these are sourced correctly, you need to add $HOME/.local/bin to your path and $HOME/.local/lib/python2.7/site-packages to your python path:

echo 'export PATH=$HOME/.local/bin:$PATH' >> $HOME/.bash_profile
echo 'export PYTHONPATH=$HOME/.local/lib/python2.7/site-packages:$PYTHONPATH' >> $HOME/.bash_profile

Lastly, all that's left to do is have a function in your bash profile you can run to set up the paths correctly

venv(){
  export WORKON_HOME=$HOME/.virtualenvs
  export PROJECT_HOME=$HOME/Devel
  source $HOME/.local/bin/virtualenvwrapper.sh
}

which provides a venv function to allow you to use virtual environments for your python installations which help encapsulate your work. This should be run when you need to use your python setup, and should be called after an lsetup root (or lsetup python) command. It means that your site packages (eg: for tarballing) will be located in $HOME/.virtualenvs/<NAME>/lib/python2.7/site-packages where <NAME> is the name of the virtual environment you make.

Go ahead and close that shell down and start with a clean slate. Now, you simply need to follow the instructions in the Using virtual environment subsection above. In future shells, you can do the following pattern to set up python, ROOT, and then go into your virtual environment:

lsetup root "lcgenv -p LCG_87 x86_64-slc6-gcc49-opt pip"
venv
workon optimization
rooptimize -h

and you're good to go!

ROOT changed the TMVA version so to make root-numpy behave nicely, just run

NOTMVA=1 pip install root-numpy

which will install it without TMVA support. Then comment out the relevant line. root-numpy also requires numpy so you'll want to have that installed beforehand as well.

I grab a set of optimization ntuples from dropbox and extract them

tar -xzvf TA07_MBJ10V1.tar.gz

A straightforward example is simply just

rooptimize generate "Gtt_0L_a/fetch/data-optimizationTree/user.lgagnon:user.lgagnon.370101.Gtt.DAOD_SUSY10.e4049_s2608_r6765_r6282_p2411_tag_10_v1_output_xAOD.root-0.root"

which will create a supercuts.json file for you to edit so that you can run the optimizations. As a more advanced example, I only wanted to generate a file using a subset of the branches in my file as well as setting some of them to be a fixed cut that I would configure, so I ran

rooptimize generate "Gtt_0L_a/fetch/data-optimizationTree/user.lgagnon:user.lgagnon.370101.Gtt.DAOD_SUSY10.e4049_s2608_r6765_r6282_p2411_tag_10_v1_output_xAOD.root-0.root" --fixedBranches multiplicity_topTag* -o dump.json -b -vv --skipBranches *_jet_rc*

which will write branches that match multiplicity_topTag* to have a fixed cut when I eventually run optimize over it; and will also skip branches that match *_jet_rc* so they won't be considered at all for cuts.

After that, we just specify all of our ROOT files. The script takes advantage of TChain and *nix file handling, it will automatically handle multiple files specified either as a pattern or just explicitly writing them out. We will group every output by the DID passed in, so please try not to deviate from the default sample names or this breaks the code quite badly.

rooptimize cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_a -b
rooptimize cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_b -b
rooptimize cut TA07_MBJ10V1/*_1L/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_1L -b

By default, we use TTree::Draw in order to calculate the number of events passing a given cut. We will also attempt to parallelize the computations as much as possible. In cases where you have a fast computer and the ntuples are reasonably small (can fit in memory), you might benefit from using a numpy boost by adding the --numpy flag like so

rooptimize cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_a -b --numpy
rooptimize cut TA07_MBJ10V1/*_0L_a/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_0L_b -b --numpy
rooptimize cut TA07_MBJ10V1/*_1L/fetch/data-optimizationTree/*.root --supercuts=supercuts_small.json -o cuts_1L -b --numpy

After that, we just (at a bare minimum) specify the signal and bkgd json cut files. The following example takes the 0L_a files and calculates significances for two different values of luminosity

rooptimize optimize --signal 37* --bkgd 4* --searchDirectory=cuts_0L_a -b --o=significances_0L_a_lumi1 --lumi=1
rooptimize optimize --signal 37* --bkgd 4* --searchDirectory=cuts_0L_a -b --o=significances_0L_a_lumi2 --lumi=2

and this will automatically combine background and produce a significances file for each signal DID passed in.

When the optimizations have finished running, you'll want to take the given hash(es) and figure out what cut it corresponds to, you can do this with

rooptimize hash e31dcf5ba4786d9e8ffa9e642729a6b9 4e16fdc03c171913bc309d57739c7225 8fa0e0ab6bf6a957d545df68dba97a53 --supercuts=supercuts_small.json

which will create outputHash/<hash>.json files detailing the cuts involved.

This is one of those pieces of python code we always want to run as fast as possible. Optimization should not take long. To figure out those dead-ends, I use snakeviz. The requirements.txt file contains this dependency. To run it, I first profile the code by running it:

python -m cProfile -o profiler.log rooptimize cut TA06_MBJ05/*_0L/fetch/data-optimizationTree/*.root --supercuts=supercuts.json -o cuts_0L -b --numpy

then I use the snakeviz script to help me visualize this

snakeviz profiler.log

and I'm good to go.

See example_script.sh for an idea how how to run everything in order to produce a plot of significances.

rooptimize

or

rooptimize -h

usage: rooptimize [-h] [-a] {generate,cut,optimize,hash,summary} ...

Author: Giordon Stark. vX.Y.Z

positional arguments:
  {generate,cut,optimize,hash,summary}
                              actions available
    generate                  Write supercuts template
    cut                       Apply the cuts
    optimize                  Calculate significances for a series of computed
                              cuts
    hash                      Translate hash to cut
    summary                   Summarize Optimization Results

optional arguments:
  -h, --help                  show this help message and exit
  -a, --allhelp               show this help message and all subcommand help
                              messages and exit

This is the top-level. You have no power here.

There is only one required position argument: the action. You can choose from

• generate
• cut
• optimize
• hash
• summary

We also provide an optional argument -a, --allhelp which will print all the help documentation at once instead of just the top-level -h, --help.

Generate helps you quickly start. Given the ROOT ntuples, generate a supercuts.json template.

usage: rooptimize generate --signal=signal.root [..] --bkgd=bkgd.root [...] [options]

• --globalMinVal is just an aesthetic feature to make it easier to identify the "true" minimum of your ntuples. I often output -99.0 in case there is (for example) no 4th jet, or I could not calculate some substructure information, this allows me to automatically chop off the low end of a branch to get a better calculation of the percentiles

• --fixedBranches and --skipBranches can take a series of strings or a series of patterns

  --fixedBranches multiplicity_jet multiplicity_topTag_loose multiplicity_topTag_tight

  or

  --fixedBranches multiplicity_* pt_jet_rc8_1

  which aims to make life easier for all of us.

This script will generate a supercuts json file. See Supercuts File for more information.

Cut helps you by generating the cuts from a supercuts file and applying them to create an output file of counts. Process ROOT ntuples and apply cuts.

usage: rooptimize cut <file.root> ... [options]

Note that weights are applied in order of prominance and specificity: weighted events are applying the monte-carlo event weights (from the generators themselves). Scaled events are with the mc weights applied but also scaled using the sample weights (the ones that differ from sample to sample) and this does not include luminosity at this stage. The calculation of significance includes the luminosity scale factor.

The output is a directory of json files which will look like

{
    ...
    "09a130622e1e6345b83739b3527eccb1": {
        "raw": 90909,
        "scaled": 90909.0,
        "weighted": 2.503
    },
    ...
}

This code will group your input files by DIDs and will try its best to do its job to group your sample files.

Optimize helps you find your optimal cuts. Process cuts and determine significance.

usage: rooptimize optimize  --signal=signal.root [..] --bkgd=bkgd.root [...] [options]

Note: You can specify multiple backgrounds and multiple signals. Each signal will be run over separately and each background will be merged and treated as a single background.

Note: this will search for files under the search_directory option, default is cuts to search for files produced by rooptimize cut.

The output is a directory of json files which will look like

[
    ...
    {
        "hash": "7595976a84303a003f6a4a7458f12b8d",
        "significance_raw": 7.643122000999725,
        "significance_scaled": 4.382066929290212,
        "significance_weighted": 18.34212454602254,
        "yield_raw": { ... },
        "yield_scaled": { ... },
        "yield_weighted": { ... }
    },
    ...
]

if a significance was calculated successfully or

[
    ...
    {
        "hash": "c911af35708dcdc51380ebbde81c9b1e",
        "significance_raw": -3,
        "significance_scaled": -1,
        "significance_weighted": -3,
        "yield_raw": { ... },
        "yield_scaled": { ... },
        "yield_weighted": { ... }
    },
    {
        "hash": "b383cea24037667ffb6136d670a33468",
        "significance_raw": -2,
        "significance_scaled": -1,
        "significance_weighted": -2,
        "yield_raw": { ... },
        "yield_scaled": { ... },
        "yield_weighted": { ... }
    },
    {
        "hash": "095414bacf1022f2c941cc6164b175a1",
        "significance_raw": 9.421795580339449,
        "significance_scaled": -2,
        "significance_weighted": 20.37611073465684,
        "yield_raw": { ... },
        "yield_scaled": { ... },
        "yield_weighted": { ... }
    },
    ...
]

if the number of events in signal or background did not pass the --insignificance minimum threshold set. The significance will always be flagged as a negative number depending on the insignificance observed. The table below summarizes these cases:

Note that --max-num-hashes determines how many hashes you will actually see in these output files.

Hash to cut translation. Given a hash from optimization, dump the cuts associated with it.

usage: rooptimize hash <hash> [<hash> ...] [options]

The hash subcommand will create an output directory with multiple json files, one for each hash, containing details about the cut applied. Unlike a standard supercuts file, the hash will only output dictionaries of 4 elements

Optimize results to summary json. Given the finished results of an optimization, produce a json summarizing it entirely.

usage: rooptimize summary --searchDirectory significances/ --massWindows massWindows.txt [options]

The summary subcommand will create an output json file containing a list of dictionaries, one for each signal DID used in optimization. It will contain the following variables in each dictionary:

This will look something like:

[
    ...
    {
        "bkgd": 5.656293846714225,
        "did": "370102",
        "hash": "dc41780c77207a9a5dcf6b97b0cac5ac",
        "m_gluino": 900,
        "m_lsp": 400,
        "m_stop": 5000,
        "ratio": 79.33950854202577,
        "signal": 448.76757396759103,
        "significance": 29.78897424015455
    },
    ...
]

This is a potentially large JSON file that tells the optimize, hash, and generate commands the rules of your cuts.

• The optimize command uses it to generate a series of cuts to apply to your ntuples, then hash these cuts and store them with their calculated significance.
• The hash command uses it to recompute the hash and find the cuts that match up to the hashes you need to decode.
• The generate command creates this file for you based on your ntuples to help you get started.

The file will always contain a list of objects (dictionaries) for each branch that you care about cutting on.

A fixed cut is a single cut on a single branch. This is like taking a partial derivative where you fix one thing and vary others. In this case, we fix a branch defined by a fixed cut.

The simplest example is when we want to use a single fixed cut on a single branch. Your object will look like

[
    ...
    {
        "selections": "multiplicity_jet > {0}",
        "pivot": 3,
    },
    ...
]

This says we would like a fixed cut on multiplicity_jet requiring that there are more than 3 jets (eg: the rule we obey is value > 3).

A supercut is our term for an object that generates more than 1 cut on the defined branch. A fixed cut will generate 1 cut, but a supercut can generate a boundless number of cuts.

Note: the direction in which cuts are generated can be controlled by running cuts in increasing values (start < stop, step > 0) or decreasing values (start > stop, step < 0).

There are two main examples we will provide to show the different cuts that could be generated.

[
    ...
    {
        "selections": "multiplicity_jet < {0}",
        "st3": [
            [2, 7, 2]
        ]
    },
    ...
]

This says we would like a supercut on multiplicity_jet where the pivot values are 2, 4, 6 obeying the rule that value < pivot. This supercut will generate 3 cuts:

• value < 2
• value < 4
• value < 6

in that order.

[
    ...
    {
        "selections": "multiplicity_jet > {0}",
        "st3": [
            [3, 1, -1]
        ]
    },
    ...
]

This says we would like a supercut on multiplicity_jet where the pivot values are 3, 2 obeying the rule that value > pivot. This supercut will generate 2 cuts:

• value > 3
• value > 2

in that order.

Here is an example supercuts.json file

[
    {
        "selections": "multiplicity_jet > {0}",
        "st3": [
            [2, 15, 1]
        ]
    },
    {
        "selections": "multiplicity_jet_largeR > {0}",
        "st3": [
            [3, 1, -1]
        ]
    },
    {
        "selections": "multiplicity_topTag_loose > {0}",
        "pivot": [1]
    }
]

How do we interpret this? This file tells the code that there are 3 branches to apply cuts on: multiplicity_jet, multiplicity_jet_largeR, and multiplicity_topTag_loose. Each object {...} represents a branch. In order:

• This is a supercut. 13 cuts will be generated for multiplicity_jet starting from 2 to 15 in increments of 1. This means the cut values (pivot) used will be 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 (inclusive start, exclusive end - adhere to python standards). The signal_direction specifies where we expect the signal to be. > means to cut on the right so we only want to keep events with value > pivot.
• This is a supercut.2 cuts will be generated for multiplicity_jet_largeR starting from 3 to 1 in incremenets of -1. This means the cut values (pivot) used will be 3, 2 (inclusive start, exclusive end - adhere to python standards). The signal_direction specifies where we expect the signal to be. < means to cut on the left so we only want to keep events with value < pivot.
• This is a fixed cut. 1 cut will be used for multiplicity_topTag_loose with a pivot = 1 and signal_direction = >. This means we will only select events with value > 1 always. The pivot will be fixed. One could also fix the cut by providing start, stop, step such that it only generates 1 cut, but the script will not identify this as a fixed cut for you when you look up the cut using hash.

This supercuts file will generate 26 total cuts (13*2*1 = 26). Each cut will have an associated hash value and an associated significance which will be recorded to an output file when you run optimize.

If you wish to provide a fixed cut (the pivot does not change), you simply need to specify the pivot instead. Taking the example shown above, you might have something like

[
    {
        "selections": "multiplicity_jet >= {0}",
        "pivot": [4]
    },
    {
        "selections": "multiplicity_jet_largeR > {0}",
        "st3": [
            [3, 1, -1]
        ]
    },
    {
        "selections": "multiplicity_topTag_loose > {0}",
        "pivot": [1]
    }
]

which tells the code to always apply a cut of multiplicity_jet >= 4 always.

One can certainly provide more complicated selections involving multiple pivots and multiple branches. In fact, this makes our optimization increasingly more flexible and faster than any other code in existence. If you use --numpy, we use the numexpr package to provide the parsing of the more complicated selection strings (they have examples of what you can do). If you do not use --numpy, we default to use ROOT and TTree::Draw to make the cuts, which means a standard TCut or TFormula can be used for your selection. Either way, you still need to specify placeholders for your pivots.

[
    ...
    {
        "selections": "(mass_jets_largeR_1 > {0} & mass_jets_largeR_2 > {0} & mass_jets_largeR_3 > {0}) >= {1}",
        "st3": [
            [50, 2000, 50],
            [0, 4, 1],
        ]
    },
    ...
]

is an example of a perhaps more complicated selection that can be done. In this case, we are determining how many of the 3 leading jets pass a mass cut, but also applying a cut on that count. In this case, the {0} pivot placeholder refers to the first st^3 option: [50, 2000, 50] which is to vary the first pivot {0} from 50 GeV to 2 TeV in 50 GeV steps. The {1} pivot placeholder refers to the second st^3 option: [0, 4, 1] which is to vary the second pivot {1} from 0 to 4 in steps of 1. This will allow us to iterate over all possible values of pivots (the product of [50, 2000, 50] X [0, 4, 1]).

• Giordon Stark
• Aviv Cukierman