# Searching for Carriers of the Diffuse Interstellar Bands Across Disciplines, using Natural Language Processing

arXiv: https://arxiv.org/abs/2211.08513 · HTML: https://mlsanity.com/paper/2211.08513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 69 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Building astroBERT, a language model for Astronomy & Astrophysics](https://mlsanity.com/paper/2112.00590/) (2021)
- [Understand Functionality and Dimensionality of Vector Embeddings: the Distributional Hypothesis, the Pairwise Inner Product Loss and Its Bias-Variance Trade-off](https://mlsanity.com/paper/1803.00502/) (2018)
- [Enhancing Domain Word Embedding via Latent Semantic Imputation](https://mlsanity.com/paper/1905.08900/) (2019)
- [Knowledge discovery through text-based similarity searches for astronomy literature](https://mlsanity.com/paper/1705.05840/) (2017)
- [Determining Research Priorities for Astronomy Using Machine Learning](https://mlsanity.com/paper/2203.00713/) (2022)

## Abstract (arXiv)

The explosion of scientific publications overloads researchers with information. This is even more dramatic for interdisciplinary studies, where several fields need to be explored. A tool to help researchers overcome this is Natural Language Processing (NLP): a machine-learning (ML) technique that allows scientists to automatically synthesize information from many articles. As a practical example, we have used NLP to conduct an interdisciplinary search for compounds that could be carriers for Diffuse Interstellar Bands (DIBs), a long-standing open question in astrophysics. We have trained a NLP model on a corpus of 1.5 million cross-domain articles in open access, and fine-tuned this model with a corpus of astrophysical publications about DIBs. Our analysis points us toward several molecules, studied primarily in biology, having transitions at the wavelengths of several DIBs and composed of abundant interstellar atoms. Several of these molecules contain chromophores, small molecular groups responsible for the molecule's colour, that could be promising candidate carriers. Identifying viable carriers demonstrates the value of using NLP to tackle open scientific questions, in an interdisciplinary manner.