How Low Can We Go: Minimum Spectroscopic Requirements for SN Subtype Classification

Willow Fortino

After first light for the Rubin Observatory, the Legacy Survey of Space and Time will discover millions of transient events and hundreds of supernovae (SNe) each night. As a result, spectrographs around the world will have to make difficult decisions about which transients will get resource intensive spectroscopic follow-ups. Our work aims to discover the minimum SNR, spectral resolving power, R = Δlambda/lambda, and their combination, at which spectral classification is still possible for specific SNe and SN subtypes, including subtypes of Core Collapse SNe: IIP, IIL; SN Ibc: IIb, Ib, Ic, Ic-broad; and interacting SNe: Ibn and IIn. We developed a rigorous method to degrade spectra to simulate future low resolution observations from existing high resolution data. We have applied existing classifier models to the synthetic low-resolution spectra, including DASH (Muthukrishna et al. 2019), a convolutional neural network-based classifier. Finally, we are now developing a new, transformer-based neural network classifier (Vaswani et al. 2017) specifically tuned to low-resolution spectral classification. These results will inform future spectrograph design and help alleviate the stress that Rubin will cause on existing facilities, as well as provide the community with a new state of the art tool for SNe classification.

This poster will be displayed on Wednesday and Thursday.

Career Stage: 
Grad Student