The Orion star-formation complex as a training set for machine learning techniques
Abstract
As the field of star-formation follows astronomy into the era of big data, we are now faced with the challenges behind truly reliable and unbiased applications of artificial intelligence and machine learning techniques. One of these challenges is quantifying the uncertainties behind machine learning applications and evaluating how the uncertainties on our prior knowledge on the field of star-formation impact the performance of such algorithms. The Orion star-formation complex is arguably the most well-studied star-forming region in the sky. In this study, we examine the region under the framework of the NEMESIS (New Evolutionary Model for Early stages of Stars with Intelligent Systems) project - which aims to reshape our understanding of star-formation by employing artificial intelligence methods to explore astronomical big-data (e.g., from Spitzer, Herschel, AKARI and soon JWST). Towards benchmarking the Orion complex as a training set for machine learning techniques, we present a synoptic catalogue of Young Stellar Objects in the region. This catalogue combines data from large surveys with literature data products, covering the whole electromagnetic spectrum from X-rays to radio and has been curated with particular attention to both time-domain and variability aspects.
- Publication:
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44th COSPAR Scientific Assembly. Held 16-24 July
- Pub Date:
- July 2022
- Bibcode:
- 2022cosp...44.1792A