BERTground: A Transformer-Based Model of Background Spectra on the ISS-Based NICER Space Telescope

Abstract

The Neutron star Interior Composition Explorer (NICER) is an International Space Station (ISS)-based Space Telescope developed by NASA and devoted to the study of high-energy X-Ray sources in the universe, including but not limited to neutron stars, pulsars, and black holes in stellar systems and active galactic nuclei (AGN). One prominent problem with NICER observations is the highly variable background spectra, obscuring actual signals of astrophysical sources and negatively affecting scientific analysis of the targets. Therefore, obtaining accurate estimations of the background spectra is crucial to filter the noise and facilitate better scientific discoveries of new astronomical objects. In this paper, we propose the very first Deep Neural Network architecture to model the NICER background spectra variation using information about the spacecraft and telescope associated with each observation. In particular, we develop a BERT-based architecture with tokenizers applied to different groups of features in our tabular dataset. We also introduce an adapted Tabular Deep Residual Network architecture as the predictor following the Transformer modules in our network. We show that our model outperforms the current state-of-the-art background model developed by the NICER team in most evaluation metrics. Finally, we discuss pathways and future work for the deployment of this model on NASA’s next versions of HEASARC Software packages.