# A New Task: Deriving Semantic Class Targets for the Physical Sciences

arXiv: https://arxiv.org/abs/2210.14760 · HTML: https://mlsanity.com/paper/2210.14760/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 13 references that are in the map.
- First posted on arXiv 26 Oct 2022.

## Builds on (most cited first, 1 of 1)

- [E(2) Equivariant Self-Attention for Radio Astronomy](https://mlsanity.com/paper/2111.04742/) (2021)

## Built on by (most cited first, 2 of 2)

- [PAPERCLIP: Associating Astronomical Observations and Natural Language with Multi-Modal Models](https://mlsanity.com/paper/2403.08851/) (2024)
- [GalaxAlign: Mimicking Citizen Scientists' Multimodal Guidance for Galaxy Morphology Analysis](https://mlsanity.com/paper/2411.19475/) (2024)

## Abstract (arXiv)

We define deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classification schemes in the physical sciences which can be severely abstracted and obfuscating. We address this task for upcoming radio astronomy surveys and present the derived semantic radio galaxy morphology class targets.