# Preliminary Report on Mantis Shrimp: a Multi-Survey Computer Vision Photometric Redshift Model

arXiv: https://arxiv.org/abs/2402.03535 · HTML: https://mlsanity.com/paper/2402.03535/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 5 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer](https://mlsanity.com/paper/2507.11711/) (2025)

## Abstract (arXiv)

The availability of large, public, multi-modal astronomical datasets presents an opportunity to execute novel research that straddles the line between science of AI and science of astronomy. Photometric redshift estimation is a well-established subfield of astronomy. Prior works show that computer vision models typically outperform catalog-based models, but these models face additional complexities when incorporating images from more than one instrument or sensor. In this report, we detail our progress creating Mantis Shrimp, a multi-survey computer vision model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. We use deep learning interpretability diagnostics to measure how the model leverages information from the different inputs. We reason about the behavior of the CNNs from the interpretability metrics, specifically framing the result in terms of physically-grounded knowledge of galaxy properties.