an easy to use scanning tool with machine learning abilities
xBIT
is a tool for performing parameter scans in beyond the Standard Model (BSM) theories. It's written in python
and fully open source. The main focus was to provide an easy to use tool to perform the daily tasks of a BSM phenomenologist: exploring the parameter space of new models. It was developed under the impression of the SARAH/SPheno framework, but should be useable with other tools that use the SLHA format to transfer
data. Out-of-the-box it also supports MicrOmegas
for dark matter scans, HiggsBounds
and HiggsSignals
for checking the Higgs properties and Vevacious for testing the vacuum stability. Classes for other tools can be defined which are then automatically included in the scans. Also new scan classes can be added in a modular way.
In order to improve the efficiency of the parameter scans, the recently proposed 'Machine Learning Scan' (MLS) approach is included. For this purpose, xBIT
trains artificial neural networks which are generated by linking pyTorch
.
A scan is started via
python3 xBIT INPUTFILE
where the INPUTFILE contains all information to define a scan. An example for a CMSSM scenario looks like
{
"Setup": {
"Settings": "MSSM.json",
"Name": "m0m12_grid",
"Type": "Grid",
"Cores": 1
},
"Included_Codes": {
"HiggsBounds": "True",
"HiggsSignals": "True",
"MicrOmegas": "True",
"Vevacious": "False"
},
"Variables": {
"0": "np.geomspace(100, 10000, num=10)",
"1": "np.linspace(200, 500, num=10)",
"2": "np.linspace(10, 20, num=2)"
},
"Blocks": {
"MODSEL": {
"1": 1,
"2": 1,
"3": 1
},
"SMINPUTS": {
"2": 1.166370E-05,
"3": 1.187000E-01,
"4": 9.118870E+01,
"5": 4.180000E+00,
"6": 1.735000E+02,
"7": 1.776690E+00
},
"MINPAR": {
"1": "2.0*Variable[0]",
"2": "Variable[0]",
"3": "Variable[2]",
"4": 1.0,
"5": "Variable[1]"
},
"SPhenoInput": {
...
}
}
}
This package is completely independent of another scanning tool which comes with a bunch of 'BITs', but follows in many respects a philosophy with is orthogonal to this other tool.