Modelling of synchrotron, inverse Compton and Bremsstrahlung emission from a distribution of electrons

This service implements a model of synchrotron, inverse Compton and Bremsstrahlung emission from high-energy electrons, using the formulae from Ref. Blumenthal&Gould Rev. Mod. Phys. 42, 237 (1970) [BG]). The data analysis can be launched either using the MMODA fronend interface or through a Python API from e.g. a Jupyter notebook on a user laptop.

For this modelling service, the main parameter panel generic for all analysis services (selection of the source of interest and time interval) is not used, as there are no observaitons involved. The instrument-specific parameter panel allows to select define the source differential spectrum dN/dE as an analytical function expressed through a conventional Python syntaxis. Standard Numpy functions are allowed (like exp or log). Otherwise, the electron differential spectrum can be uploaded as an ascii file (energy in eV vs dN/dE, space separated, see example). The background photon field for the inverse Compton scattering is either a black body with a temperatrue defined by the parameter T and grey-body factor defined by the normalisaiton of the black body, or it can also be defined by an analytical function (the spectral energy density in units of 1/(eV cm3)), or it can be given as a text file (two column: energy vs. spectral energy density, space separatedsee example)).

Pressing the Submit button initiates the modelling. The product display panel that appears upon the completion of the modelling script shows the resulting spectra of the synchrotron, inverse Compton and Bremsstrahlung emission as a picture in png format and as an astropy table. Clicking on the View buttons next to the data product names provides a possibility to view and dowonload the data product files. The "Query parameters" button provides the metadata with the analysis parameters. The "API code" button displays the Python API code that can be copy-pasted into a python code (e.g. on the user laptop) to request the data product. The same API code can also be launched in an online Jupyter lab environment on a collaborative data science platform renkulab.io, using the "View on Renku" button. Finally, clicking on the Jupyter icon one can see the workflow used for generation of the countmap.

Python notebook for the modelling can be found at renkulab.io and in a related GitLab repository.