The standard paradigm describing the formation of stars and planets, introduced almost 40 years ago, is being challenged by discoveries suggesting that (i) the mass reservoir for star formation continuously grows during the protostellar collapse, (ii) the growth of stellar embryos is not continuous but likely episodic, (iii) the formation of planets occurs synchronously rather than sequentially to their host stars and (iv) the co-evolution of proto-star/planet systems occurs faster than assumed.
Thanks to many surveys from ground and space facilities, star formation now enters the Big Data era. Gaia now provides photometry, spectroscopy, parallax, proper motions, and radial velocities for millions (up to a bit more than a billion, for some parameters) of sources that allow a much more precise determination of the populations of star forming regions, and thus, their age and luminosity. Observations at infrared and millimeter regimes (Spitzer and Herschel, IRAM, NOEMA, VLA, ALMA), which are essential for the characterization of protostars, await to be complemented with crucial information in X-rays from e.g., XMM-Newton, and the ongoing e-ROSITA all-sky survey.
Nowadays higher quality and larger volumes of data, more advanced computing methods and higher computing power exist, which allow us to revisit the standard star-formation paradigm and the evolutionary scheme of young stars, using new machine learning techniques applicable to big data.
This ISSI team brings together experts in star formation, survey data, big data analysis, and machine learning to use multi-wavelength data to derive a new evolutionary scheme for young stellar evolution.