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With growing data volumes from synoptic surveys, astronomers must become moreabstracted from the discovery and introspection processes. Given the scarcityof follow-up resources, there is a particularly sharp onus on the frameworksthat replace these human roles to provide accurate and well-calibratedprobabilistic classification catalogs. Such catalogs inform the subsequentfollow-up, allowing consumers to optimize the selection of specific sources forfurther study and permitting rigorous treatment of purities and efficienciesfor population studies. Here, we describe a process to produce a probabilisticclassification catalog of variability with machine learning from a multi-epochphotometric survey. In addition to producing accurate classifications, we showhow to estimate calibrated class probabilities, and motivate the importance ofprobability calibration. We also introduce a methodology for feature-basedanomaly detection, which allows discovery of objects in the survey that do notfit within the predefined class taxonomy. Finally, we apply these methods tosources observed by the All Sky Automated Survey (ASAS), and unveil theMachine-learned ASAS Classification Catalog (MACC), which is a 28-classprobabilistic classification catalog of 50,124 ASAS sources. We estimate thatMACC achieves a sub-20% classification error rate, and demonstrate that theclass posterior probabilities are reasonably calibrated. MACC classificationscompare favorably to the classifications of several previous domain-specificASAS papers and to the ASAS Catalog of Variable Stars, which had classifiedonly 24% of those sources into one of 12 science classes. The MACC is publiclyavailable at http://www.bigmacc.info.

Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey