Orateur
Peter Melchior
(Princeton University)
Description
I will present the neural network architect "spender", which is specifically designed for galaxy spectra at variable redshifts. Trained in 500k SDSS or DESI spectra, it is capable of automatically detecting highly meaningful outliers as well as making predictions of the physical state of the galaxies, thus serving as a summary for simulation-based inference approaches. Recently, my group has further demonstrated that the representations learned from optical spectra provide accurate prediction of IR photometry, a connection that is not captured by current physical SED modeling methods. I will also discuss extensions of this work to exoplanets and quasar spectra to demonstrate the strengths and versatility of this approach.
Author
Peter Melchior
(Princeton University)
