AstroCLIP: A Cross-Modal Foundation Model for Galaxies (2310.03024v2)

Abstract: We present AstroCLIP, a single, versatile model that can embed both galaxy images and spectra into a shared, physically meaningful latent space. These embeddings can then be used - without any model fine-tuning - for a variety of downstream tasks including (1) accurate in-modality and cross-modality semantic similarity search, (2) photometric redshift estimation, (3) galaxy property estimation from both images and spectra, and (4) morphology classification. Our approach to implementing AstroCLIP consists of two parts. First, we embed galaxy images and spectra separately by pretraining separate transformer-based image and spectrum encoders in self-supervised settings. We then align the encoders using a contrastive loss. We apply our method to spectra from the Dark Energy Spectroscopic Instrument and images from its corresponding Legacy Imaging Survey. Overall, we find remarkable performance on all downstream tasks, even relative to supervised baselines. For example, for a task like photometric redshift prediction, we find similar performance to a specifically-trained ResNet18, and for additional tasks like physical property estimation (stellar mass, age, metallicity, and sSFR), we beat this supervised baseline by 19\% in terms of $R^2$. We also compare our results to a state-of-the-art self-supervised single-modal model for galaxy images, and find that our approach outperforms this benchmark by roughly a factor of two on photometric redshift estimation and physical property prediction in terms of $R^2$, while remaining roughly in-line in terms of morphology classification. Ultimately, our approach represents the first cross-modal self-supervised model for galaxies, and the first self-supervised transformer-based architectures for galaxy images and spectra.

• The paper introduces a cross-modal contrastive learning framework that fuses galaxy images and spectra into a unified semantic space.
• The methodology employs a transformer-based spectrum model with InfoNCE loss, enabling robust zero-shot regression for galaxy properties.
• Results show AstroCLIP outperforms traditional photometric methods, offering scalable and precise solutions for astronomical data analysis.

An Academic Overview of "AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models"

The paper, "AstroCLIP: Cross-Modal Pre-Training for Astronomical Foundation Models," introduces a novel approach designed to streamline the construction of astronomical models that integrate and learn from different observational modalities. Leveraging cross-modal contrastive learning, the authors propose embedding both images and optical spectra of galaxies into a shared semantic space, aiming to enhance both intra-modal and cross-modal relationships and utility.

Methodological Insights

AstroCLIP utilizes a contrastive learning framework inspired by CLIP, a model known for linking images and text in computer vision tasks. This research extends the concept to astronomy, where different observational modalities, such as images and spectra, can be thought of as distinct yet complementary views of celestial objects.

The authors delineate their methodology, embedding galaxy data from the Dark Energy Spectroscopic Instrument (DESI) in two separate modalities into a joint embedding space using spectral and visual data. They employ a self-supervised contrastive loss mechanism, specifically the InfoNCE loss, to maximize semantic consistency across both data modalities. For spectrum data, a dedicated transformer-based model was developed and pre-trained in a self-supervised fashion to fill masked segments of spectra, highlighting the model's capacity to infer missing spectral information reliably.

Results and Implications

Prominent results reported include the model's adeptness in organizing galaxy data according to semantic properties such as redshift and stellar mass, validated through zero-shot regression tasks. The embeddings generated by AstroCLIP were found to be more effective than traditional photometric methods and existing pre-trained models, outperforming the latter without any further fine-tuning, especially for spectral embeddings.

The potential applications of AstroCLIP are vast, primarily due to its capability of accurately performing cross-modal similarity searches and encoding semantically rich and aligned representations. This could facilitate a range of downstream tasks, including astrophysical property predictions and anomaly detection, which are becoming increasingly critical as the volume of astronomical data grows exponentially.

Theoretical and Practical Implications

Conceptually, AstroCLIP pushes forward the understanding and potential applications of foundation models in astronomy by emphasizing a multi-modal approach. By proposing a mechanism that does not require extensive labeled datasets, AstroCLIP signals the potential for scalability essential for handling future data influx from surveys like the Vera C. Rubin Legacy Surveys of Space and Time (LSST).

Practically, the transformer-based spectrum model could redefine how spectral data is modeled in astronomical research, showcasing robustness and fidelity in prediction without heavy reliance on annotated data.

Future Directions

The researchers suggest that the integration of other data modalities, possibly extending beyond images and spectra, could be a productive exploration for future research. As the volume and diversity of astronomical data increase, embedding models that harness the synergy of various data types without substantial supervision are likely to gain importance, potentially extending beyond the field of astronomy into other sciences involving multi-modal data.

In summary, AstroCLIP represents a significant stride toward leveraging recent advancements in machine learning to enhance astronomical data analysis, promising advancements in both theoretical understanding and practical solutions for handling increasingly complex datasets. Its introduction of transformer-based models for galaxy spectra marks a potential paradigm shift, positing new benchmarks for accuracy and efficiency in the analysis of astronomical phenomena.