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Augmenting representations with scientific papers

Nicolò Oreste Pinciroli Vago, Rocco Di Tella, Carolina Cuesta-Lázaro, Michael J. Smith, Cecilia Garraffo, Rafael Martínez-Galarza

TL;DR

This work introduces a contrastive learning framework designed to align X-ray spectra with domain knowledge extracted from scientific literature, facilitating the development of shared multimodal representations and indicates that a Mixture of Experts (MoE) strategy, which leverages both unimodal and shared representations, yields superior performance.

Abstract

Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical sources. Still, these data sources are rarely systematically integrated. This work introduces a contrastive learning framework designed to align X-ray spectra with domain knowledge extracted from scientific literature, facilitating the development of shared multimodal representations. Establishing this connection is inherently complex, as scientific texts encompass a broader and more diverse physical context than spectra. We propose a contrastive pipeline that achieves a 20% Recall@1% when retrieving texts from spectra, proving that a meaningful alignment between these modalities is not only possible but capable of accelerating the interpretation of rare or poorly understood sources. Furthermore, the resulting shared latent space effectively encodes physically significant information. By fusing spectral and textual data, we improve the estimation of 20 physical variables by 16-18% over unimodal spectral baselines. Our results indicate that a Mixture of Experts (MoE) strategy, which leverages both unimodal and shared representations, yields superior performance. Finally, outlier analysis within the multimodal latent space identifies high-priority targets for follow-up investigation, including a candidate pulsating ULX (PULX) and a gravitational lens system. Importantly, this framework can be extended to other scientific domains where aligning observational data with existing literature is possible.

Augmenting representations with scientific papers

TL;DR

This work introduces a contrastive learning framework designed to align X-ray spectra with domain knowledge extracted from scientific literature, facilitating the development of shared multimodal representations and indicates that a Mixture of Experts (MoE) strategy, which leverages both unimodal and shared representations, yields superior performance.

Abstract

Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical sources. Still, these data sources are rarely systematically integrated. This work introduces a contrastive learning framework designed to align X-ray spectra with domain knowledge extracted from scientific literature, facilitating the development of shared multimodal representations. Establishing this connection is inherently complex, as scientific texts encompass a broader and more diverse physical context than spectra. We propose a contrastive pipeline that achieves a 20% Recall@1% when retrieving texts from spectra, proving that a meaningful alignment between these modalities is not only possible but capable of accelerating the interpretation of rare or poorly understood sources. Furthermore, the resulting shared latent space effectively encodes physically significant information. By fusing spectral and textual data, we improve the estimation of 20 physical variables by 16-18% over unimodal spectral baselines. Our results indicate that a Mixture of Experts (MoE) strategy, which leverages both unimodal and shared representations, yields superior performance. Finally, outlier analysis within the multimodal latent space identifies high-priority targets for follow-up investigation, including a candidate pulsating ULX (PULX) and a gravitational lens system. Importantly, this framework can be extended to other scientific domains where aligning observational data with existing literature is possible.
Paper Structure (11 sections, 1 equation, 3 figures, 3 tables)

This paper contains 11 sections, 1 equation, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Dataset
  4. Architecture
  5. Training and evaluation
  6. Results
  7. Cross-modal retrieval
  8. Physical interpretation
  9. Outlier detection
  10. Discussion
  11. Conclusions

Figures (3)

  • Figure 1: Pipeline overview. Spectra are encoded via a transformer-based autoencoder. Scientific papers are summarized using GPT-4o-mini, and the summaries are embedded from OpenAI's Ada-002. Contrastive learning aligns modalities into a shared latent space for downstream tasks.
  • Figure 2: Recall@k% as a function of $k$, expressed as a percentage of the test set, for the ensemble model.
  • Figure 3: tSNE plots for a subset of the physical variables