Effective Fine-Tuning of Vision-Language Models for Accurate Galaxy Morphology Analysis

Galaxy morphology analysis involves classifying galaxies by their shapes and structures. For this task, directly training domain-specific models on large, annotated astronomical datasets is effective but costly. In contrast, fine-tuning vision foundation models on a smaller set of astronomical images is more resource-efficient but generally results in lower accuracy. To harness the benefits of both approaches and address their shortcomings, we propose GalaxAlign, a novel method that fine-tunes pre-trained foundation models to achieve high accuracy on astronomical tasks. Specifically, our method extends a contrastive learning architecture to align three types of data in fine-tuning: (1) a set of schematic symbols representing galaxy shapes and structures, (2) textual labels of these symbols, and (3) galaxy images. This way, GalaxAlign not only eliminates the need for expensive pretraining but also enhances the effectiveness of fine-tuning. Extensive experiments on galaxy classification and similarity search demonstrate that our method effectively fine-tunes general pre-trained models for astronomical tasks by incorporating domain-specific multi-modal knowledge.

VisRec: A Semi-Supervised Approach to Radio Interferometric Data Reconstruction

Radio telescopes produce visibility data about celestial objects, but these data are sparse and noisy. As a result, images created on raw visibility data are of low quality. Recent studies have used deep learning models to reconstruct visibility data to get cleaner images. However, these methods rely on a substantial amount of labeled training data, which requires significant labeling effort from radio astronomers. Addressing this challenge, we propose VisRec, a model-agnostic semi-supervised learning approach to the reconstruction of visibility data. Specifically, VisRec consists of both a supervised learning module and an unsupervised learning module. In the supervised learning module, we introduce a set of data augmentation functions to produce diverse training examples. In comparison, the unsupervised learning module in VisRec augments unlabeled data and uses reconstructions from non-augmented visibility data as pseudo-labels for training. This hybrid approach allows VisRec to effectively leverage both labeled and unlabeled data. This way, VisRec performs well even when labeled data is scarce. Our evaluation results show that VisRec outperforms all baseline methods in reconstruction quality, robustness against common observation perturbation, and generalizability to different telescope configurations.

A Transformer-Conditioned Neural Fields Pipeline with Polar Coordinate Representation for Astronomical Radio Interferometric Data Reconstruction

In radio astronomy, visibility data, which are measurements of wave signals from radio telescopes, are transformed into images for observation of distant celestial objects. However, these resultant images usually contain both real sources and artifacts, due to signal sparsity and other factors. One way to obtain cleaner images is to reconstruct samples into dense forms before imaging. Unfortunately, existing visibility reconstruction methods may miss some components of the frequency data, so blurred object edges and persistent artifacts remain in the images. Furthermore, the computation overhead is high on irregular visibility samples due to the data skew. To address these problems, we propose PolarRec, a reconstruction method for interferometric visibility data, which consists of a transformer-conditioned neural fields pipeline with a polar coordinate representation. This representation matches the way in which telescopes observe a celestial area as the Earth rotates. We further propose Radial Frequency Loss function, using radial coordinates in the polar coordinate system to correlate with the frequency information, to help reconstruct complete visibility. We also group visibility sample points by angular coordinates in the polar coordinate system, and use groups as the granularity for subsequent encoding with a Transformer encoder. Consequently, our method can capture the inherent characteristics of visibility data effectively and efficiently. Our experiments demonstrate that PolarRec markedly improves imaging results by faithfully reconstructing all frequency components in the visibility domain while significantly reducing the computation cost.

A Conditional Denoising Diffusion Probabilistic Model for Radio Interferometric Image Reconstruction

In radio astronomy, signals from radio telescopes are transformed into images of observed celestial objects, or sources. However, these images, called dirty images, contain real sources as well as artifacts due to signal sparsity and other factors. Therefore, radio interferometric image reconstruction is performed on dirty images, aiming to produce clean images in which artifacts are reduced and real sources are recovered. So far, existing methods have limited success on recovering faint sources, preserving detailed structures, and eliminating artifacts. In this paper, we present VIC-DDPM, a Visibility and Image Conditioned Denoising Diffusion Probabilistic Model. Our main idea is to use both the original visibility data in the spectral domain and dirty images in the spatial domain to guide the image generation process with DDPM. This way, we can leverage DDPM to generate fine details and eliminate noise, while utilizing visibility data to separate signals from noise and retaining spatial information in dirty images. We have conducted experiments in comparison with both traditional methods and recent deep learning based approaches. Our results show that our method significantly improves the resulting images by reducing artifacts, preserving fine details, and recovering dim sources. This advancement further facilitates radio astronomical data analysis tasks on celestial phenomena.

AMMASurv: Asymmetrical Multi-Modal Attention for Accurate Survival Analysis with Whole Slide Images and Gene Expression Data

The use of multi-modal data such as the combination of whole slide images (WSIs) and gene expression data for survival analysis can lead to more accurate survival predictions. Previous multi-modal survival models are not able to efficiently excavate the intrinsic information within each modality. Moreover, despite experimental results show that WSIs provide more effective information than gene expression data, previous methods regard the information from different modalities as similarly important so they cannot flexibly utilize the potential connection between the modalities. To address the above problems, we propose a new asymmetrical multi-modal method, termed as AMMASurv. Specifically, we design an asymmetrical multi-modal attention mechanism (AMMA) in Transformer encoder for multi-modal data to enable a more flexible multi-modal information fusion for survival prediction. Different from previous works, AMMASurv can effectively utilize the intrinsic information within every modality and flexibly adapts to the modalities of different importance. Extensive experiments are conducted to validate the effectiveness of the proposed model. Encouraging results demonstrate the superiority of our method over other state-of-the-art methods.