Machine-learned ASAS Classification Cat. (MACC)

In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy.

Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS, Cat. II/264), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated.