Fully Automated Approaches to Analyze Large-Scale Astronomy Survey Data
  Observational astronomy has changed drastically in the last decade: manually
driven target-by-target instruments have been replaced by fully automated
robotic telescopes. Data acquisition methods have advanced to the point that
terabytes of data are flowing in and being stored on a daily basis. At the same
time, the vast majority of analysis tools in stellar astrophysics still rely on
manual expert interaction. To bridge this gap, we foresee that the next decade
will witness a fundamental shift in the approaches to data analysis:
case-by-case methods will be replaced by fully automated pipelines that will
process the data from their reduction stage, through analysis, to storage.
While major effort has been invested in data reduction automation, automated
data analysis has mostly been neglected despite the urgent need. Scientific
data mining will face serious challenges to identify, understand and eliminate
the sources of systematic errors that will arise from this automation. As a
special case, we present an artificial intelligence (AI) driven pipeline that
is prototyped in the domain of stellar astrophysics (eclipsing binaries in
particular), current results and the challenges still ahead.
