Building the configuration file

Before running the code, set the input config.json file with following entries:

{
    "debug":false,
    "partitions":["config/partitions_gerda_new.json","config/partitions_l200.json","config/partitions_mjd_new.json"],
    "events":    ["config/events_gerda.json","config/events_l200.json","config/events_mjd_new_part.json"],
    "output_path": "output/fit_mjd_l200_gerda_v2/",
    "overwrite":true,
    "bat_fit": {"nsteps": 1e6, "nchains": 6},
    "plot": {
            "fit_and_data": false,
            "bandfit_and_data": false,
            "scheme":"red",
            "alpha":0.3
        },
    "bkg_only": false,
    "signal": {"upper_bound":1000, "prior": "uniform"},
    "bkg": {"upper_bound":0.1,
             "prior": "uniform"
             },

    ...
}

where

• "debug": true if you want to display debug output on terminal;
• "partitions": list of partitions JSON inputs; it takes one entry per experiment;
• "events": list of events JSON inputs; it takes one entry per experiment;
• "output_path": path where to store outputs (logs, plots, mcmc results);
• "overwrite": true if you want to overwrite a previous fit with same output_path; if set to false but no fits were previously performed (ie there are no outputs to overwrite), the code will save the output of this fit;
• "bat_fit": settings for the BAT fit;
• "plot": settings for plotting; "fit_and_data": true plots fit line over data (and CI bands if "bandfit_and_data": true); "scheme":"red" and "alpha":0.3 are used for customizing output appearances;
• "bkg_only": true if we fit assuming no signal (S=0), false otherwise;
• "signal": select "upper_bound" for the prior and the "prior" shape (uniform, sqrt, ...);
• "bkg": select "upper_bound" for the prior and the "prior" shape (uniform, ...) there are several optional keys with details given below, if these are not provided the fit defaults to a flat background without correlations.

Moreover, the config requires the following block for nuisance parameters, ie energy scale (=energy bias and resolution) and efficiency:

    {
    ...
    "nuisance": { 
        "energy_scale" : {
            "correlated": true,
            "fixed":     false
            },
         "efficiency" : {
            "correlated": true,
            "fixed": false
            }
    }

In particular, you can set "correlated": true if you want to use one variable to correlate the nuisance parameters (eg to speed up the computation times), and "fixed": false if you want to include a prior for nuisance parameters (otherwise these parameters they will be fixed to their partition value and not constrained).

If a variable is correlated (either energy_scale or efficency), the code will search for a field in the fit_groups block of the partitions JSON file to use a correlated variable per each fit group. In particular, the field has to be specified as:

• "efficiency_group_name": "..."
• "energy_scale_group_name": "..."

Parameters are then added to the model called αr_$name (for resolution), αe_$name for efficiency and αb_$name for bias.

Background shape and correlation

There are several options to control the background in more detail. These can be added to the "bkg" section of the config: In particular:

• "correlated" adds a hierachical (correlated) background to the model, this key should have a dictonary giving details on the prior shape and ranges for example:

"correlated":{"mode":"lognormal","range":[0,0.1]}

The three options for the mode are 'lognormal', 'normal' or 'none'.While the range gives the range of the uniform prior on the \sigma_B parameter.

• "shape" changes the shape of the background from uniform. The user should provide a dictonary giving details on the shape:

for example:

"shape":{
            "name":"exponential",
            "pars":{"slope":[-10,10]}
        },

The "pars" subdictonary describes the range of the priors on the parameters of the model, currently implemented shapes are "uniform", "linear" and "exponential". These names correspond to functions in fitting.jl and logical conditions in get_bkg_pdf in likelihood.jl.

This will add parameters ${bkg_name}_slope or similar to the model (and then call them). This names therefore must correspond to the names in the functions in fitting.jl. To add a new shape simply define a new method in fitting.jl and a new logical condition in get_bkg_pdf in likelihood.jl.