# Image-Based Multi-Survey Classification of Light Curves with a Pre-Trained Vision Transformer

arXiv: https://arxiv.org/abs/2507.11711 · HTML: https://mlsanity.com/paper/2507.11711/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 51 references that are in the map.
- First posted on arXiv 15 Jul 2025.

## Builds on (most cited first, 17 of 17)

- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [Transformer Tracking](https://mlsanity.com/paper/2103.15436/) (2021)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [RAPID: Early Classification of Explosive Transients using Deep Learning](https://mlsanity.com/paper/1904.00014/) (2019)
- [AstroM3: A self-supervised multimodal model for astronomy](https://mlsanity.com/paper/2411.08842/) (2024)
- [ASTROMER: A transformer-based embedding for the representation of light curves](https://mlsanity.com/paper/2205.01677/) (2022)
- [Maven: A Multimodal Foundation Model for Supernova Science](https://mlsanity.com/paper/2408.16829/) (2024)
- [Deep multi-survey classification of variable stars](https://mlsanity.com/paper/1810.09440/) (2018)
- [Classifying Image Sequences of Astronomical Transients with Deep Neural Networks](https://mlsanity.com/paper/2004.13877/) (2020)
- [Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks](https://mlsanity.com/paper/2205.06758/) (2022)
- [Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves](https://mlsanity.com/paper/2201.08482/) (2022)
- [Deep-Learnt Classification of Light Curves](https://mlsanity.com/paper/1709.06257/) (2017)
- [Astroconformer: Inferring Surface Gravity of Stars from Stellar Light Curves with Transformer](https://mlsanity.com/paper/2207.02787/) (2022)
- [Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST](https://mlsanity.com/paper/2502.18558/) (2025)
- [Preliminary Report on Mantis Shrimp: a Multi-Survey Computer Vision Photometric Redshift Model](https://mlsanity.com/paper/2402.03535/) (2024)

## Abstract (arXiv)

We explore the use of Swin Transformer V2, a pre-trained vision Transformer, for photometric classification in a multi-survey setting by leveraging light curves from the Zwicky Transient Facility (ZTF) and the Asteroid Terrestrial-impact Last Alert System (ATLAS). We evaluate different strategies for integrating data from these surveys and find that a multi-survey architecture which processes them jointly achieves the best performance. These results highlight the importance of modeling survey-specific characteristics and cross-survey interactions, and provide guidance for building scalable classifiers for future time-domain astronomy.