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AstroPT: Scaling Large Observation Models for Astronomy (2405.14930v1)

Published 23 May 2024 in astro-ph.IM, astro-ph.GA, and cs.LG

Abstract: This work presents AstroPT, an autoregressive pretrained transformer developed with astronomical use-cases in mind. The AstroPT models presented here have been pretrained on 8.6 million $512 \times 512$ pixel $grz$-band galaxy postage stamp observations from the DESI Legacy Survey DR8. We train a selection of foundation models of increasing size from 1 million to 2.1 billion parameters, and find that AstroPT follows a similar saturating log-log scaling law to textual models. We also find that the models' performances on downstream tasks as measured by linear probing improves with model size up to the model parameter saturation point. We believe that collaborative community development paves the best route towards realising an open source `Large Observation Model' -- a model trained on data taken from the observational sciences at the scale seen in natural language processing. To this end, we release the source code, weights, and dataset for AstroPT under the MIT license, and invite potential collaborators to join us in collectively building and researching these models.

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Summary

  • The paper demonstrates that increasing model size enhances performance in astronomical tasks, with saturation observed near 89M parameters.
  • The paper employs causal autoregressive training using 16x16 pixel patches and a Huber loss to generate semantically rich embeddings from 8.6M galaxy images.
  • The paper emphasizes open-source collaboration and highlights future research directions to integrate multimodal data for advanced astronomical analysis.

An Overview of 'AstroPT: Scaling Large Observation Models for Astronomy'

The paper "AstroPT: Scaling Large Observation Models for Astronomy" details the development and findings associated with AstroPT, an autoregressive transformer model specifically devised for astronomical use. AstroPT has been pretrained on galaxy observations from the DESI Legacy Survey DR8, preparing it for various downstream tasks within the field of astronomical data analysis. This research echoes the trends observed in natural language processing, emphasizing the scalability of neural networks and the utility of autoregressive pretrained transformers in this context.

Development and Methodology

AstroPT was constructed with a specific focus on leveraging the characteristics and challenges inherent in astronomical data. The paper narrates the training of foundation models with parameter counts ranging from 1 million to 2.1 billion, empirically demonstrating that the performance improves with size until a saturation point is reached. This finding is in line with the established scaling laws for neural networks found in textual data settings.

The training dataset comprised 8.6 million galaxy images, efficiently processed through a causal autoregressive training protocol. A noteworthy aspect of AstroPT's methodology is its ability to effectively utilize multimodal data sources, which is pertinent given the diverse data types present in observational sciences. The use of 16 × 16 pixel patches as tokens and the application of a Huber loss function are distinctive methodological choices that underline the model's adaptability to large-scale astronomical datasets.

Results and Observations

The results obtained from AstroPT indicate a significant correlation between model size and task performance, with the saturation occurring near 89 million parameters. The paper employs linear probing to assess the embeddings' scientific value by predicting galaxy properties and morphologies, showing improved performance with larger models. This substantiates their claim of semantically meaningful learning from the pretraining routine. Remarkably, emergent abilities were observed, suggesting that model capacity significantly affects the complexity of learnable tasks.

The paper also highlights the efficiency benefits of causal transformers in pretraining time due to their widespread adoption and adaptability for autoregressive generative tasks. Specifically, AstroPT's design enables it to be a robust tool in assessing diverse scientific tasks, stretching its utility beyond the immediate dataset it was trained on.

Implications and Future Directions

AstroPT's development and its open-source availability are strategic in encouraging collaboration for scaling and applying large observation models. By sharing the model weights, dataset, and code openly, the authors emphasize collective endeavor and progression in the domain. This accessibility aligns well with the open science movement, promoting further research and adaptation of such models in observing sciences.

In terms of implications, the potential for integrating multimodal data sources to overcome token data shortages presents a promising frontier in model training. This approach can catalyze advancements in cross-modal foundations in scientific inquiry, enabling the fusion of textual and observational data for enhanced analytical capability.

The paper suggests future directions, including exploring more information-dense observational modalities and further dissecting scaling laws within this context. These pursuits could extend the applicability of autoregressive models in astronomy and other areas reliant on large, intricate datasets.

Conclusion

AstroPT stands as a technological testament to the adaptability and utility of autoregressive models in the astronomical domain. The model serves as a bridge, demonstrating how neural scaling principles from NLP can be effectively transferred and applied to observational sciences. By choosing a deliberately community-focused development strategy, the research encourages further exploration and refinement of large observation models, promising wider-reaching impacts in both theoretical and practical capacities within and beyond astronomy.

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  1. [R] AstroPT: Scaling Large Observation Models for Astronomy (11 points, 2 comments)
  2. AstroPT: Scaling Large Observation Models for Astronomy (11 points, 2 comments)