Database of Experimental Results

SModelS stores all the information about the experimental results in the Database. Below we describe both the directory and object structure of the Database.

Database: Directory Structure

The Database is organized as files in an ordinary (UNIX) directory hierarchy, with a thin Python layer serving as the access to the database. The overall structure of the directory hierarchy and its contents is depicted in the scheme below (click to enlarge):

_images/DatabaseFolders.png

As seen above, the top level of the SModelS database categorizes the analyses by LHC center-of-mass energies, \(\sqrt{s}\):

  • 8 TeV
  • 13 TeV

Also, the top level directory contains a file called version with the version string of the database. The second level splits the results up between the different experiments:

  • 8TeV/CMS/
  • 8TeV/ATLAS/

The third level of the directory hierarchy encodes the Experimental Results:

  • 8TeV/CMS/CMS-SUS-12-024
  • 8TeV/ATLAS/ATLAS-CONF-2013-047
  • …
  • The Database folder is described by the Database Class

Experimental Result Folder

Each Experimental Result folder contains:

  • a folder for each DataSet (e.g. data)
  • a globalInfo.txt file

The globalInfo.txt file contains the meta information about the Experimental Result. It defines the center-of-mass energy \(\sqrt{s}\), the integrated luminosity, the id used to identify the result and additional information about the source of the data. Here is the content of CMS-SUS-12-024/globalInfo.txt as an example:

sqrts: 8.0*TeV
lumi: 19.4/fb
id: CMS-SUS-12-024
prettyName: \slash{E}_{T}+b
url: https://twiki.cern.ch/twiki/bin/view/CMSPublic/PhysicsResultsSUS12024
arxiv: http://arxiv.org/abs/1305.2390
publication: http://www.sciencedirect.com/science/article/pii/S0370269313005339
contact: Keith Ulmer <keith.ulmer@cern.ch>, Josh Thompson <joshua.thompson@cern.ch>, Alessandro Gaz <alessandro.gaz@cern.ch>
private: False
implementedBy: Wolfgang Waltenberger
lastUpdate: 2015/5/11

Data Set Folder

Each DataSet folder (e.g. data) contains:

Data Set Folder: Upper Limit Type

Since UL-type results have a single dataset (see DataSets), the info file only holds some trivial information, such as the type of Experimental Result (UL) and the dataset id (None for UL-type results). Here is the content of CMS-SUS-12-024/data/dataInfo.txt as an example:

dataType: upperLimit
dataId: None

For UL-type results, each TxName.txt file contains the UL map for a given simplified model (element or sum of elements) as well as some meta information, including the corresponding constraint and conditions. The first few lines of CMS-SUS-12-024/data/T1tttt.txt read:

txName: T1tttt
conditionDescription: None
condition: None
constraint: [[['t','t']],[['t','t']]]
figureUrl: https://twiki.cern.ch/twiki/pub/CMSPublic/PhysicsResultsSUS12024/T1tttt_exclusions_corrected.pdf
validated: True
axes: 2*Eq(mother,x)_Eq(lsp,y)
publishedData: True

The second block of data contains the upper limits as a function of the BSM masses:

upperLimits: [[[[400.0*GeV, 0.0*GeV], [400.0*GeV, 0.0*GeV]], 1.815773*pb],
[[[400.0*GeV, 25.0*GeV], [400.0*GeV, 25.0*GeV]], 1.806528*pb],
[[[400.0*GeV, 50.0*GeV], [400.0*GeV, 50.0*GeV]], 2.139336*pb],
[[[400.0*GeV, 75.0*GeV], [400.0*GeV, 75.0*GeV]], 2.472143*pb],
	...

As we can see, the UL map is given as a Python array with the structure: \([[\mbox{masses},\mbox{upper limit}], [\mbox{masses},\mbox{upper limit}],...]\).

Data Set Folder: Efficiency Map Type

For EM-type results the dataInfo.txt contains relevant information, such as an id to identify the DataSet (signal region), the number of observed and expected background events for the corresponding signal region and the respective signal upper limits. Here is the content of CMS-SUS-13-012-eff/3NJet6_1000HT1250_200MHT300/dataInfo.txt as an example:

dataType: efficiencyMap
dataId: 3NJet6_1000HT1250_200MHT300
observedN: 335
expectedBG: 305
bgError: 41
upperLimit: 5.681*fb
expectedUpperLimit: 4.585*fb

For EM-type results, each TxName.txt file contains the efficiency map for a given simplified model (element or sum of elements) as well as some meta information. Here is the first few lines of CMS-SUS-13-012-eff/3NJet6_1000HT1250_200MHT300/T2.txt:

txName: T2
conditionDescription: None
condition: None
constraint: [[['jet']],[['jet']]]
figureUrl: https://twiki.cern.ch/twiki/pub/CMSPublic/PhysicsResultsSUS13012/Fig_7a.pdf
validated: True
axes: 2*Eq(mother,x)_Eq(lsp,y)
publishedData: False

As seen above, the first block of data in the T2.txt file contains information about the element (\([[[\mbox{jet}]],[[\mbox{jet}]]]\)) in bracket notation for which the efficiencies refers to as well as reference to the original data source and some additional information. The second block of data contains the efficiencies as a function of the BSM masses:

efficiencyMap: [[[[312.5*GeV, 12.5*GeV], [312.5*GeV, 12.5*GeV]], 0.00109],
[[[312.5*GeV, 62.5*GeV], [312.5*GeV, 62.5*GeV]], 0.00118],
[[[312.5*GeV, 112.5*GeV], [312.5*GeV, 112.5*GeV]], 0.00073],
[[[312.5*GeV, 162.5*GeV], [312.5*GeV, 162.5*GeV]], 0.00044],
	...

As we can see the efficiency map is given as a Python array with the structure: \([[\mbox{masses},\mbox{efficiency}], [\mbox{masses},\mbox{efficiency}],...]\).

Database: Object Structure

The Database folder structure is mapped to Python objects in SModelS. The mapping is almost one-to-one, except for a few exceptions. Below we show the overall object structure as well as the folders/files the objects represent (click to enlarge):

_images/DatabaseObjects.png

The type of Python object (Python class, Python list,…) is shown in brackets. For convenience, below we explicitly list the main database folders/files and the Python objects they are mapped to:

Database: Binary (Pickle) Format

At the first time of instantiating the Database class, the text files in <database-path>. are loaded and parsed, and the corresponding data objects are built. The efficiency and upper limit maps themselves are subjected to standard preprocessing steps such as a principal component analysis and Delaunay triangulation (see Figure below). The simplices defined during triangulation are then used for linearly interpolating the data grid, thus allowing SModelS to compute efficiencies or upper limits for arbitrary mass values (as long as they fall inside the data grid). This procedure provides an efficient and numerically robust way of dealing with generic data grids, including arbitrary parametrizations of the mass parameter space, irregular data grids and asymmetric branches.

_images/delaunay.png

For the sake of efficiency, the entire database – including the Delaunay triangulation – is then serialized into a pickle file (<database-path>/database.pcl), which will be read directly the next time the database is loaded. If any changes in the database folder structure are detected, the python or the SModelS version has changed, SModelS will automatically re-build the pickle file. This action may take a few minutes, but it is again performed only once. If desired, the pickling process can be skipped using the option force_load = `txt’ in the constructor of Database .