IceCube

This service provides data analysis for puclicly available IceCube neutrino telescope based on SkyLLH data analysis tools (see SkyLLH documentation for description of the tools). The data are described by IceCube collaboration (2021).

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. The main parameter panel generic for all analysis services allows to select the source of interest, based on its name or coordinates:

For the specific case of IceCube, it is not possible to set the start and stop times for the analysis. Instead, it is possible to choose a set of fixed-time observation periods onto which the IceCube data is divided: IC40, IC59, IC79, IC86-I, IC86-II-VII, the time spans of these periods are specified in the IceCube publicaiton (IceCube collaboration, 2021).

The instrument specific parameter panel allow to select one of the three available data product types: Image, Spectrum or Lightcurve.

Selecting one of the available data product types reveals additional parameters specific to this data product.

The image parameter panel allows to specify the energy range the image size (Radius parameter) and pixel size (pixsize parameter). The image that will be produced is a Test Statistic (TS) value map around the reference source position. The TS values will be computed for the powerlaw spectral model of a source placed at the center of each pixel. It is possible to either fix the slope of the spectrum or leave it free, using a Fixed_slope / Free_slope switch.

Upon each data analysis request, IceCube dataset and Instrument Response Functions are downloaded from the IceCube website.

The product display panel that appears upon the completion of data analysis shows the image together with a set of buttons that provide a possibility for further manipulations of the data product:

The "Qeury 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.

Similar to the Image, the spectrum can be requested either for a fixed spectral slope, or leaving the slope free in the spectral fitting, using the Fixed_slope / Free_slope switch. For the "Fixed slope" choice, it is possible to set the spectral slope setting the Slope parameter. For the fixed spectral slope, the analysis builds a likelihood profile to find the best-fit number of counts from the source and the error intervals (defined as the boundaries of the interval in which the TS value decreases by 1 with respect to the maximum, the 90% upper bound is defined at the upper boundary of the interval at which the TS value decreases by 2.7). The counts are converted to the flux normalisation using the calculate_fluxmodel_scaling_factor funciton of skyllh. In the case of "free slope" choice, TS values are calculated as a function of the source counts and spectral slope and 68% confidence contours are defined as the level at which TS decreases by 2.3 with respect to the maximal value. If the maximal TS value exceeds 6, the best-fit spectrum is plotted together with the 68% confidence range "butterfly". Otherwise, an upper limit on the powerlaw type spectra is shown. This upper limit is shown as a curve that is a tangent to the maximal possible powerlaw spectrum (at 90% confidence level) for each spectral slope in the range between 1 and 5.

For the lightcurve, the entire ten-year time span of IceCube public data is divided into intevals corresponding to the IceCube observational periods (from IC40 to IC86-VII). For each period, the source flux is extracted assuming fixed spectral slope.

Python notebooks for image, spectrum and lightcurves can be found at renkulab.io and in a related GitLab repository.