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

TL;DR

nDspec is a Python library that enables modeling and analysis of multi-dimensional X-ray astronomy data, facilitating studies across time, energy, and polarization dimensions to advance physical understanding.

Contribution

It introduces the alpha version of nDspec, a novel Python framework for modeling multi-dimensional datasets in X-ray astronomy, with focus on time and spectral-timing data.

Findings

Successfully modeled NICER black hole data

Demonstrated multi-dimensional data analysis capabilities

Outlined plans for extending to additional data dimensions

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 polarization. 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 data sets, new methods to analyse them and a software framework to combine data, models and methods efficiently. In this paper, we introduce the alpha release of nDspec, a new python-based library designed to allow users to model one- and multi-dimensional datasets common to X-ray astronomy. In the alpha release, we focus on modelling time-averaged data as well as Fourier spectral-timing mode, but highlight how additional dimensions can be added. We discuss design philosophy and current features, and showcase an example use case by characterizing a NICER observation of a black hole X-ray binary. We also highlight current plans for extensions to other dimensions and new features.