These wiki pages describe our work in using the LSST Software Stack (LSS) to reduce and analyse GOTO data. Included in this wiki are:

• instructions on how to install the LSST stack;
• guides for providing the LSST stack with information about GOTO's cameras (via obs_goto);
• guides to configure the data reduction and analysis steps (again, via the obs_goto);
• the commands used to instruct the LSST stack to reduce and analyse GOTO data;
• data products of the LSST stack and how to access them;
• results from testing the LSST output on simulated data.

The LSS is being developed to process all of the data that will be generated by the LSST. This includes:

• Logging incoming data and organising the filesystem;
• Master Flat, Bias, Dark construction;
• Instrument signature removal (i.e., flat-fielding, bias-subtraction);
• Astrometric and photometric calibration;
• Background subtraction;
• Source detection;
• PSF measurement;
• Measuring aperture, PSF, Kron, CModel, Petrovian, de Vaucoulier etc. photometry;
• Image alignment, warping, and coaddition to obtain deeper data;
• Forced photometry;
• Image differencing.

It may therefore help to overcome many of the challenges facing our processing of GOTO data. We have managed to get all of the above processes working on simulated GOTO data at some level, but the outputs remain untested and, therefore, unreliable. There are vast numbers of configuration parameters that need to be adjusted to obtain optimal results (if such an end-point is even possible). The major difficulties of using the LSS to process GOTO data are a critical lack of documentation for its use and the possibility that some of its algorithms may not be suited to GOTO data (in particular, GOTO's large pixel size and resulting PSF undersampling).

Having said that, all the LSS is freely available to download and edit on GitHub under a GNU Public Licence, enabling one to delve into the code to determine what it is doing and make changes, if necessary. The LSS is primarily written in Python, but all of the intense calculations are carried out in C++ to boost performance. It is very rare, however, that one needs to delve into the C++ code.

Finally, there is an online community of LSS developers who are welcoming and who respond to queries quickly. The LSS GitHub repository is available at https://github.com/lsst while the community forum is at https://community.lsst.org/.