sevenstarknight / ssmm

Variable Star Signature Classification using Slotted Symbolic Markov Modeling

SSMM

Variable Star Signature Classification using Slotted Symbolic Markov Modeling

SSMM (Slotted Symbolic Markov Modeling) reduces time-domain stellar variable observations to classify stellar variables. The method can be applied to both folded and unfolded data, and does not require time-warping for waveform alignment. Written in Matlab, the performance of the supervised classification code is quantifiable and consistent, and the rate at which new data is processed is dependent only on the computational processing power available.

ascl:1807.032

See reference Johnston, K. B., & Peter, A. M. (2017). Variable Star Signature Classification using Slotted Symbolic Markov Modeling. New Astronomy, 50, 1-11.

The data are not included as part of this repository (as they are too big), see https://drive.google.com/drive/folders/0B-hW-bQbm2J9bExZemNnN2hGeTA?usp=sharing

for the data

Contact [email protected]; for more information