deep-REMAP: Probabilistic Parameterization of Stellar Spectra Using Regularized Multi-Task Learning
This provides an automated solution for astronomers to characterize stars efficiently, though it is incremental as it builds on existing deep learning methods for a specific domain.
The paper tackles the challenge of analyzing large volumes of stellar spectra by developing deep-REMAP, a deep learning framework that predicts stellar atmospheric parameters from spectra, achieving a precision of approximately 75 K in effective temperature on validation data.
In the era of exploding survey volumes, traditional methods of spectroscopic analysis are being pushed to their limits. In response, we develop deep-REMAP, a novel deep learning framework that utilizes a regularized, multi-task approach to predict stellar atmospheric parameters from observed spectra. We train a deep convolutional neural network on the PHOENIX synthetic spectral library and use transfer learning to fine-tune the model on a small subset of observed FGK dwarf spectra from the MARVELS survey. We then apply the model to 732 uncharacterized FGK giant candidates from the same survey. When validated on 30 MARVELS calibration stars, deep-REMAP accurately recovers the effective temperature ($T_{\rm{eff}}$), surface gravity ($\log \rm{g}$), and metallicity ([Fe/H]), achieving a precision of, for instance, approximately 75 K in $T_{\rm{eff}}$. By combining an asymmetric loss function with an embedding loss, our regression-as-classification framework is interpretable, robust to parameter imbalances, and capable of capturing non-Gaussian uncertainties. While developed for MARVELS, the deep-REMAP framework is extensible to other surveys and synthetic libraries, demonstrating a powerful and automated pathway for stellar characterization.