Light curves from Polzin et al. (2023). The tables in the appendix list the original provenance of these data.
Data are organized into folders by class of transient. Each folder has its own README file that lists details of the included files (as a rule, unless it is specifically stated otherwise, included light curves are not corrected for absorption or corrected to the rest frame) and will have a copy of the table that offers citations (including references therein) for these data.
If you are going to use data found here, please cite both the original source(s) of the data listed in the relevant table and Polzin et al. (2023). There is also a Zenodo reference to the dataset and package (link above) that we appreciate you citing.
Click for BibTeX
@ARTICLE{2023ApJ...959...75P,
author = {{Polzin}, Ava and
{Margutti}, Raffaella and
{Coppejans}, Deanne L. and
{Auchettl}, Katie and
{Page}, Kim L. and
{Vasilopoulos}, Georgios and
{Bright}, Joe S. and
{Esposito}, Paolo and
{Williams}, Peter K.~G. and
{Mukai}, Koji and
{Berger}, Edo},
title = {The Luminosity Phase Space of Galactic and Extragalactic X-Ray Transients Out to Intermediate Redshifts},
journal = {ApJ},
keywords = {X-ray astronomy, X-ray telescopes, X-ray transient sources, High energy astrophysics, Transient sources, Time domain astronomy, 1810, 1825, 1852, 739, 1851, 2109, Astrophysics - High Energy Astrophysical Phenomena},
year = 2023,
month = dec,
volume = {959},
number = {2},
eid = {75},
pages = {75},
doi = {10.3847/1538-4357/acf765},
archivePrefix = {arXiv},
eprint = {2211.01232},
primaryClass = {astro-ph.HE},
adsurl = {https://ui.adsabs.harvard.edu/abs/2023ApJ...959...75P},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
@misc{polzin_2023_8319602,
author = {Polzin, Ava and
Margutti, Rafaella and
Coppejans, Deanne L. and
Auchettl, Katie and
Page, Kim L. and
Vasilopoulos, Georgios and
Bright, Joe S. and
Esposito, Paolo and
Williams, Peter K. G. and
Mukai, Koji and
Berger, Edo},
title = {X-rayLCs: Data and code from Polzin et al. (2023)},
month = sep,
year = 2023,
publisher = {Zenodo},
version = {v1.0},
doi = {10.5281/zenodo.8319602},
url = {https://doi.org/10.5281/zenodo.8319602}
}(If for some reason any of the data that should be here are not or do not open, feel free to reach out via email or open an issue. Please note that a handful of light curves from the paper -- namely the superluminous SN 2018bsz, LMXRB PSR J1023+0038, and the two FXTs XRT 110103 and XRT 120830 -- are not available here.)
Interactive plots facilitating exploration of the data/DLPS are available here.
(Within /xraydlps/xraydlps/tools there is a .pkl file (lcs.pkl), which stores light curves for the major classes of transient in a dataframe. Pre-made plotting functions for this dataframe are in xraydlps and are used for our interactive plots, but we urge people interested in using the data for further analysis to look at the more complete data stored in the broader repository and consult the original sources for additional details and considerations.)
We also include some additional versions of figures in the paper that may be edifying in extra_figures.
Additionally, we offer xraydlps, a small python package to help with plotting/classification of X-ray light curves in the DLPS. If you use this package, please cite Polzin et al. (2023) and the package itself (via Zenodo above).
Full code documentation: xraydlps.readthedocs.io
To download and install,
cd ~
git clone git@github.com/avapolzin/X-rayLCs.git
cd X-rayLCs/xraydlps
sudo python3 setup.py installOR
pip install xraydlpsThe above includes download of all of the raw lightcurves in this repository, as well. If you are interested only in the plotting/classification functionality, you can just download the xraydlps directory above. Once you have a local copy, you can install the package with:
cd ~/xraydlps
sudo python3 setup.py installWithin xraydlps there are three modules, plot, classify, and tools. We walk through a handful of use cases with some example code below.
from xraydlps import plotPlotting functions are designed to be modular and allow for rapid/flexible plotting of the duration-luminosity phase space.
We include here quick plotting functions for the general DLPS, the schematic DLPS, the DFPS, and our Lpk vs. t1/2 and Eiso and tdur.
Each of the functions allows you to specify which classes (and with the exception of the schematic DLPS, which subclasses) to show, it is useful to have a list of working keywords. To list available classes:
plot.list_classes()If you would like to list the subclasses, too, the command becomes:
plot.list_classes(subs = True)Similarly, colors = True will return the default colors for the (sub)classes.
To directly recreate figures from Polzin et al. (2023), you can use our built in style functions + our general DLPS plotting function:
from xraydlps.plot import set_mpldefaults, dlps_axes, dlps_legend, obs_dlps
import matplotlib.pyplot as plt
set_mpldefaults() #sets general matplotlib style parameters for all plots
fig = plt.figure(figsize = (27, 25))
obs_dlps() #plots light curves, can specify which classes to include + line color/appearance
dlps_axes() #generates axes, including minor ticks + axis labels (fully customizable)
dlps_legend() #creates legend and adds it to the plot, specify labels, colors, and appearanceFor whatever reason, you may have to run set_mpldefaults twice before the font changes propagate. Additionally, we recommend changing the font size in the legend with the fontsize key word argument to be 0.5 - 0.8 times the selected fontsize -- this is purely aesthetic, though, so we don't enforce this with hardcoding. Toggling subson = True in obs_dlps shows individual (specified) subclasses of transient. Classes are specified as a list, subclasses should be specified as a nested list, where classes sans subclasses or where subclasses should be turned off should be input as None.
And, to generate a schematic DLPS plot:
from xraydlps.plot import schematic_dlps
import matplotlib.pyplot as plt
set_mpldefaults()
fig = plt.figure(figsize = (27, 25))
schematic_dlps() #plots general dlps regions, can specify which classes to include + line color/appearance
dlps_axes()
dlps_legend()Similarly, to plot the DFPS:
from xraydlps.plot import obs_dfps
import numpy as np
set_mpldefaults()
fig = plt.figure(figsize = (27, 25))
obs_dfps() #plots dfps, can specify which classes to include + line color/appearance
dlps_axes(xlabel = r'Observed Time Since Identification (days)',
ylabel = r'0.3 - 10 keV X-ray Flux (erg s$^{-1}$ cm$^{-2}$)',
add_minoryticks = [10.**i for i in np.arange(2, 16)],
ylim = [1e-17, 3e-2]) #can specify new y-limit and label since default is for dlps + new minor tick positions
dlps_legend()We also have quick plotting functions for the Lpk vs. t1/2 and Eiso vs. tdur phase space. As with the other classes, these functions are flexible and you can alter the included (sub)classes among other features.
from xraydlps.plot import lpk_thalf
set_mpldefaults()
fig = plt.figure(figsize = (27, 25))
lpk_thalf() #plots Lpk vs. thalf, can specify which classes to include + appearance
dlps_axes(xlabel = r'Rest Frame Time Above $\frac{1}{2}$ Peak L$_x$ (days)',
add_minorxticks = [10.**i for i in np.arange(-7, 4)], xlim = [1e-7, 5e4],
ylabel = r'Peak L$_x$ (0.3 - 10 keV, erg s$^{-1}$)',
add_minoryticks = [10.**i for i in np.arange(26, 51)],
ylim = [1e26, 1e51]) #can specify new limits, labels, + minor tick positions
dlps_legend(style = 'scatter')from xraydlps.plot import iso_energy
set_mpldefaults()
fig = plt.figure(figsize = (27, 25))
iso_energy() #plots Eiso vs. tdur, can specify which classes to include + appearance
dlps_axes(xlabel = 'Rest Frame Duration (days)',
add_minorxticks = [10.**i for i in np.arange(-6, 4)], xlim = [1e-6, 5e4],
ylabel = r'Isotropic Equivalent Energy (0.3 - 10 keV, ergs)',
add_minoryticks = [10.**i for i in np.arange(32, 56)],
ylim = [1e32, 2e55]) #can specify new limits, labels, + minor tick positions
dlps_legend(style = 'scatter')from xraydlps import classifyWithin xraydlps.classify we offer some preliminary trained KNN models for classifying both light curves (using a dynamic time warping metric) and more general characteristics (Lpk, t1/2, Eiso, and tdur). We also offer a means of breaking up the existing light curves into training/test sets that default to including especially sparsely populated classes (i.e., SBOs, FRBs, ...) only in the training set, which is consistent with the models included here.
We ensure that classes with only one observation (i.e., SBOs, FRBs, magnetar flares) are included in the training set, so we urge caution in using the results of the KNN classification as anything other than preliminary guidance. (The pre-trained classifiers we provide are at best ~60-70% accurate based on testing.) The results of visual comparison to the DLPS are likely comparable.
To use a pre-trained model:
from xraydlps.classify import lc_class, sum_class, dtw_dist
labels, probabilities = lc_class(time, lum) #to classify a light curve
labels, probabilities = sum_class(time, lum) #to classify light curve summary statisticsYou will have to import dtw_dist so that the classifier can access it.
If you are starting from a light curve that is not in the 0.3-10 keV band or does not use default units, you can specify arguments in lc_class or in converting to the summary statistics with xraydlps.tools.convert (see below) to easily rectify this:
labels, probabilities = lc_class(time, lum, tunits = u.s, k = 2.132)
labels, probabilities = sum_class(time, lum, tunits = u.s, k = 2.132)There are some other arguments that are meant to allow further customization of the pre-trained models -- verbose = True (the default) has the classifier output all possible classes + assigned probabilities (skip_zero = True to only show classes with non-zero probabilities) and full = True will use the classifier trained on all of the available light curves rather than a subset.
For those who want to try other hyperparameters, metrics, or datasets with the KNeighborsClassifier, we include a couple of convenience functions to facilitate training -- xraydlps.classify.train_lcmodel and xraydlps.classify.train_summodel. We encourage users who are interested in using the classifier for more than preliminary guidance to train and validate their own models this way.
Our own implementation of dynamic time warping is available as dtw_dist (xraydlps.classify.dtw_dist) and can be used by a classifier by setting metric = dtw_dist or equivalent.
from xraydlps import toolsThe two front-facing functions in tools are xraydlps.tools.n_obs and xraydlps.tools.convert.
To determine the anticipated number of observations of a class of transients per year (within z ≤ 1) for a given instrument, n_obs takes the peak X-ray luminosity of that class and its volumetric event rate. It also takes the field-of-view and the flux limit of that instrument.
from xraydlps.tools import n_obs
n_obs(lpk, rate, sens, fov)The default units for each of these inputs are erg/s, Gpc-3/yr, deg2, and erg/s/cm-2. To change the units, simply add the arguments lunits, runits, fovunits, and funits, specifying your input with astropy.units:
import astropy.units as u
n_obs(lpk, rate, sens, fov, lunits = u.J/u.s, runits = u.Mpc**-3/u.yr,
funits = u.J/u.s/u.m**2, fovunits = u.arcmin**2)You can also change the output units -- the default is Nobs/yr -- with e.g. perunits = u.d, which will return Nobs/day.
convert takes an input light curve and converts it to peak Lx, thalf, Eiso, and duration points for use in plotting/classification.
from xraydlps.tools import convert
lpk, thalf, eiso, dur = convert(time, lum)If the input light curve is not in the 0.3 - 10 keV band, the argument k sets a k-correction. Similarly, if the input light curve is not in the default units of days and erg/s, you can specify tunits and lunits with astropy units. The output will be in erg/s, days, erg, days.
- numpy
- matplotlib
- pandas
- scikit-learn
- astropy
- pickle
- scipy