README

This repository contains code intended to simplify the analysis of astronomical X-ray data of thermonuclear (type-I) bursts.

Full documentation can be found at https://burst.sci.monash.edu/concord/

This code is under active development, but the v1.0.0 release is associated with a companion paper accepted by Astrophysical Journal Supplements (see Galloway et al. 2022, also available at arXiv:2210.03598). A preprint of the paper is available in the doc subdirectory of this repository

To get started, look at the Inferring burster properties jupyter notebook

What is this repository for?

• Analysis of thermonuclear X-ray bursts
• Reading in and plotting thermonuclear burst data and models
• Performing model-observation comparisons

How do I get set up?

Use the included environment.yml file to set up a conda environment with the required dependencies:

conda env create -f environment.yml

This will create an environment called concord, which you can activate with:

conda activate concord

Then add concord to the local environment with: python3 -m pip install .

You can then import the repository and use the functions. Here's a very simple example, to find the peak luminosity of a burst from 4U 0513+40 measured by RXTE, as part of the MINBAR sample. The first part calculates the isotropic luminosity, neglecting the uncertainty in both the peak flux and the distance: ```

import concord as cd import astropy.units as u Fpk, eFpk = 21.72, 0.6 # 1E-9 erg/cm^2/s bolometric; MINBAR #3443 d = (10.32, 0.24, 0.20) # asymmetric errors from Watkins et al. 2015 liso = cd.luminosity( Fpk, dist=d[0], isotropic=True ) WARNING:homogenizeparams:no bolometric correction applied print (liso) 2.767771097997098e+38 erg / s The second part takes into account both the uncertainties in the peak flux and distance (including the asymmetric errors), and also includes the model-predicted effect of the high system inclination (>80 degrees): lasym = cd.luminosity( (Fpk, eFpk), dist=d, burst=True, imin=80, imax=90, fulldist=True) WARNING:homogenizeparams:no bolometric correction applied lc = lasym['lum'].pdfpercentiles([50, 50 - cd.CONF / 2, 50 + cd.CONF / 2]) lunit = 1e38*u.erg/u.s print ('''\nIsotropic luminosity is {:.2f}e38 erg/s ... Taking into account anisotropy, ({:.2f}-{:.2f}+{:.2f})e38 erg/s'''.format(liso/lunit, ... lc[0]/lunit, ... (lc[0]-lc[1])/lunit, (lc[2]-lc[0])/lunit)) Isotropic luminosity is 2.77e38 erg/s Taking into account anisotropy, (4.85-0.43+0.51)e38 erg/s `` With thefulldist=Trueoption, the function returns a dictionary with the Monte-Carlo generated distribution of the result (keylum) and all the intermediate quantities. Check theInferring burster properties` notebook for additional demonstrations of usage

There are a number of sources for bursts to analyse: * The Multi-INstrument Burst ARchive (MINBAR) contains more than 7000 events from 85 sources, and you can download the entire dataset and a Python repository to access them from http://burst.sci.monash.edu and links therein * The reference burst sample including lightcurves and recurrence times from http://burst.sci.monash.edu/reference * Kepler-predicted model bursts from http://burst.sci.monash.edu/kepler

Or, you can define routines to read in your own model predictions, for example based on the KeplerBurst class

Who do I talk to?

• Duncan.Galloway@monash.edu

Why concord?

Because we're trying to achieve a "concordance" fit to a wide range of burst data. Plus, concord was cool