Contributed Talk - Splinter EScience
Friday, 17 September 2021, 10:40 (virtual ESc)
Practical application of t-distributed stochastic neighbor embedding in classifying chromospheric spectra
Meetu Verma, Gal Matijevič, Carsten Denker, Andrea Diercke, Ekaterina Dineva, Horst Balthasar, Robert Kamlah, Ioannis Kontogiannis, Christoph Kuckein, and Partha S. Pal
Leibniz Institute for Astrophysics Potsdam (AIP)
Observational solar physicists are nowadays confronted with huge amounts of data, initially, this mainly concerned images but also spectra enter the realm of Big Data. The number of spectra accumulated at a medium-size telescope such as the Vacuum Tower Telescope (VTT) at Tenerife easily reaches up to millions over a single observing day. Hence, machine learning tools are required to identify and classify spectra with minimal human intervention. Our exploratory work provides the framework and some ideas on how t-distributed stochastic neighbor embedding (t-SNE) can be adapted to the classification of chromospheric spectra, identifying those spectra related to eruptive events.