# Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning

arXiv: https://arxiv.org/abs/2305.11869 · HTML: https://mlsanity.com/paper/2305.11869/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 19 May 2023.

## Builds on (most cited first, 4 of 4)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [MetNet: A Neural Weather Model for Precipitation Forecasting](https://mlsanity.com/paper/2003.12140/) (2020)

## Built on by (most cited first, 3 of 3)

- [Connect Later: Improving Fine-tuning for Robustness with Targeted Augmentations](https://mlsanity.com/paper/2402.03325/) (2024)
- [Maven: A Multimodal Foundation Model for Supernova Science](https://mlsanity.com/paper/2408.16829/) (2024)
- [Simulation-Based Pretraining and Domain Adaptation for Astronomical Time Series with Minimal Labeled Data](https://mlsanity.com/paper/2510.12958/) (2025)

## Abstract (arXiv)

Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the science return of these observations in the absence of spectroscopic information, we must accurately extract key parameters, such as SN redshifts, with photometric information alone. We present Photo-zSNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera C. Rubin Legacy Survey of Space and Time (LSST) data as well as observed SDSS SNe. We show major improvements over predictions from existing methods on both simulations and real observations as well as minimal redshift-dependent bias, which is a challenge due to selection effects, e.g. Malmquist bias. Specifically, we show a 61x improvement in prediction bias <Delta z> on PLAsTiCC simulations and 5x improvement on real SDSS data compared to results from a widely used photometric redshift estimator, LCFIT+Z. The PDFs produced by this method are well-constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.