nDspec: a new Python library for modelling multi-dimensional datasets in X-ray astronomy

Abstract

The current fleet of X-ray telescopes produces a wealth of multi-dimensional data, allowing us to study sources in time, photon energy, and polarisation. At the same time, it has become increasingly clear that progress in our physical understanding will only come from studying these sources in multiple dimensions simultaneously. Enabling multi-dimensional studies of X-ray sources requires new theoretical models predicting these datasets, new methods to analyse them, and, crucially, a software framework to combine data, models, and methods efficiently. However, the current ecosystem of software packages developed for X-ray data analysis does not provide the flexibility for advanced modelling of multi-dimensional datasets. In this paper, we introduce nDspec, a new python-based library designed to allow users to seamlessly model one- and multi-dimensional datasets common to X-ray astronomy. Unlike most other libraries, it is designed as a flexible, modular, and extensible framework capable of accommodating multi-dimensional data and able to connect to a range of different inference tools and algorithms. Here we focus on modelling timing and spectral-timing data as a function of both Fourier frequency and energy, in addition to limited support for time-averaged spectra. We discuss design philosophy and current features, and showcase an example use case by characterising a NICER observation of a black hole X-ray binary. We also highlight plans for extensions to other dimensions and new features, such as the inclusion of polarimetry and the improved statistical methods for Bayesian inference.