The source code of this project is hosted on my SNIaSpectrumNN Github repository.

About

This project provides a collection of models that predict various properties of SNe Ia (e.g., Si II velocity, pseudo-equivalent widths, line strengths) given only their optical spectrum. Each model shares a common transformer-based autoencoder backbone that learns a compact representation of the spectrum, which is then used by task-specific output heads.

Architecture

• Base Encoder: A transformer autoencoder with gated residual network layers that learns spectral features.
• Pre-training: The autoencoder is pre-trained on spectrum reconstruction to learn meaningful representations.
• Task-Specific Heads: After pre-training, the encoder backbone is fine-tuned with different output heads for specific prediction tasks.

Example

Coming soon!