Author: Vital Fernandez
Physical Science Research
Schmidt AI in Science Fellow Vital Gutiérrez Fernández is applying machine learning to the chemical and dynamical analysis of the light from star-forming regions.
When stars are born, especially massive and young ones, they emit intense light and energy. This UV light is so powerful that it strips electrons from atoms in the surrounding gas, a process called ionization. The photons produced in these ions as the electrons move back to their orbits have very distinctive wavelengths, which produce the wonderful colors seen in astronomical photometry. Moreover, these emissions are so intense that they can be observed across cosmological ages, up to the very first galaxies over 13 billion years ago.
Due to the fact that each element emits light at unique wavelengths, we can measure the chemical composition of this gas. For example, in the local universe, ionized hydrogen is responsible for red colors, while oxygen ions produce blue and red photons. Moreover, as in the case of sound, the light’s shade changes because of the motion of the object: if the gas is moving toward us, the emission lines shift to shorter wavelengths. In the opposite case, the wavelengths become longer as the object is moving away from us.
Thanks to the new generation of astronomical telescopes and instruments, the field has entered a new age of Big Data. In Vital’s research, he is developing machine learning classifiers to automatically distinguish these shapes in the photon distributions to interpret the observations as if they were done by a human. This work is being used by state-of-the-art surveys with the James Webb Space Telescope to diagnose the quality of the data and provide new scientific insights.