What’s New¶
Since the publication of SModelS v1.0 in December 2014, the code base has undergone significant structural changes. The major novelties of this release are as follows:
New in Version 1.1.3:¶
- Support for covariance matrices and combination of signal regions (see combineSR in parameters file)
- New plotting tool added to smodelsTools (see Interactive Plots Maker)
- Path to particles.py can now be specified in parameters.ini file (see model in parameters file)
- Wildcards allowed when selecting analyses, datasets, txnames (see analyses, txnames and dataselector in parameters file)
- Option to show individual contribution from topologies to total theory prediction (see addTxWeights in parameters file)
- URLs are allowed as database paths (see path in parameters file)
- Python default changed from python2 to python3
- Fixed lastUpdate bug, now giving correct date
- Changes in pickling (e.g. subpickling, removing redundant zeroes)
- Added fixpermissions to smodelsTools.py, for system-wide installs (see Files Permissions Fixer)
- Fixed small issue with pair production of even particles
- Moved the code documentation to the manual
- Added option for installing within the source folder
New in Version 1.1.2:¶
- Database update only, the code is the same as v1.1.1
New in Version 1.1.1:¶
- C++ Interface
- Support for pythia8 (see Cross Section Calculator)
- improved binary database
- automated SLHA and LHE file detection
- Fix and improvements for missing topologies
- Added SLHA-type output
- Small improvements in interpolation and clustering
New in Version 1.1.0:¶
- the inclusion of efficiency maps (see EM-type results)
- a new and more flexible database format (see Database structure)
- inclusion of likelihood and \(\chi^2\) calculation for EM-type results (see likelihood calculation)
- extended information on the topology coverage
- inclusion of a database broswer tool for easy access to the information stored in the database (see database browser)
- the database now supports also a more efficient binary format
- performance improvement for the decomposition of the input model
- inclusion of new simplified results to the database (including a few 13 TeV results)
- Fastlim efficiency maps can now also be used in SModelS