Rotating Neutron Star Modelling

Overview

I began this independent research project following my internship at IFJ PAN in Kraków. It aims to investigate rotating neutron stars using two numerical relativity frameworks: RNS and LORENE, varying properties such as mass, radius, rotation frequency, and central density, and analysing the results.

Methods

A C++ program was created to vary the mass and radius of a neutron star between set limits using a predetermined equation of state (EoS) in both LORENE and RNS, exporting each result to a separate data file. A Python program was then developed to process this modelled data and visualise it using both two-dimensional and three-dimensional plots.

Results

2D relationships between RNS parameters — Two-dimensional relationships between RNS parameters showing dependencies between mass, radius, frequency, and central density.

The six-panel figure summarises two-dimensional parameter relationships extracted from the RNS output. Clear correlations appear between mass, radius, and central density, demonstrating the expected relativistic behaviour of compact stars under rapid rotation.

3D relationships between RNS parameters — Three-dimensional relationship between RNS parameters — frequency versus radius versus mass, coloured by central density.

The three-dimensional projection highlights how increasing rotation frequency leads to larger equatorial radii and reduced central densities at fixed mass, consistent with centrifugal flattening.

Summary

This project demonstrates a fully automated workflow for relativistic stellar modelling using RNS and LORENE, combining compiled simulation pipelines in C++ with Python-based post-processing. The resulting visualisations provide an effective framework for exploring how stellar rotation influences equilibrium structure and form a foundation for future work, including equation-of-state variations and gravitational-wave–relevant properties.

Using HEASoft to Analyse Archived NICER Data of tMSPs

Overview

This independent project focuses on analysing a compact binary system using archival X-ray observations from the NICER mission via the HEASoft database. The objective is to develop a fully automated and reproducible workflow that extracts, processes, and analyses X-ray light curves from these systems, enabling time-domain studies of variability across multiple observing epochs.

Methods

A Python-based pipeline was developed that integrates HEASoft tools in Bash via scripted workflows. This pipeline automated data retrieval from the HEASARC database, applied variable filtering parameters and calibration, and used XSELECT in batch mode to extract X-ray light curves across multiple ObsIDs, which were then visualised in Python.

A simple flare-identification model was implemented to flag transient increases in count rate above a baseline threshold. This component is treated as a toy model, serving as a proof of concept for automated variability detection rather than a statistically complete flare-analysis framework.

Results

Example NICER X-ray light curve — Example NICER light curve of 1RXS J154439.5−112820 (ObsID 1030120108), showing count rate as a function of time.

Combined NICER X-ray light curve with flare filtering — Combined NICER light curve of 1RXS J154439.5−112820 over a selected epoch. Points marked in red are identified as candidate flares.

Summary

This project demonstrates a complete workflow for NICER X-ray data analysis, from importing archival data to filtering, processing, and analysing it using HEASoft tools in Python. While the current analysis is deliberately simplified, the pipeline provides a solid foundation for future improvements incorporating more statistically robust timing and variability analyses.