Searching for Carriers of the Diffuse Interstellar Bands Across Disciplines, using Natural Language Processing
This addresses the problem of synthesizing vast scientific literature for researchers in interdisciplinary fields like astrophysics, though it is incremental as it applies existing NLP methods to a new domain.
The researchers tackled the challenge of information overload in interdisciplinary science by applying natural language processing (NLP) to search for compounds that could explain Diffuse Interstellar Bands (DIBs), identifying several biological molecules with transitions at DIB wavelengths as promising candidates.
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.