The PAU survey: classifying low-z SEDs using Machine Learning clustering
We present an application of unsupervised Machine Learning clustering to the PAU survey of galaxy spectral energy distribution (SED) within the COSMOS field. The clustering algorithm is implemented and optimized to get the relevant groups in the data SEDs. We find 12 groups from a total number of 5234 targets in the survey at 0.01 < z < 0.28. Among
González-Morán, A. L. et al.
Fecha de publicación:
9
2023