ARIADNE Is a code written in python 3.7+ designed to fit broadband photometry to different stellar atmosphere models automatically using Nested Sampling algorithms.
You can install ARIADNE with pip install astroARIADNE
Otherwise you can clone this repository with
git clone https://github.com/jvines/astroARIADNE.git
cd astroARIADNE
And then run
python setupy.py install
But for the code to work, first you must install the necessary dependencies.
- Numpy (https://numpy.org/)
- Scipy (https://www.scipy.org/)
- Pandas (https://pandas.pydata.org/)
- numba (http://numba.pydata.org/)
- astropy (https://astropy.readthedocs.io/en/stable/)
- astroquery (https://astroquery.readthedocs.io/en/latest/)
- regions (https://astropy-regions.readthedocs.io/en/latest/index.html)
- PyAstronomy (https://pyastronomy.readthedocs.io/en/latest/)
- corner (https://corner.readthedocs.io/en/latest/)
- tqdm (https://tqdm.github.io/)
- matplotlib (https://matplotlib.org/)
- termcolor (https://pypi.org/project/termcolor/)
- extinction (https://extinction.readthedocs.io/en/latest/)
- pyphot (http://mfouesneau.github.io/docs/pyphot/)
- dustmaps (https://dustmaps.readthedocs.io/en/latest/)
- PyMultinest (https://johannesbuchner.github.io/PyMultiNest/) [OPTIONAL]
- dynesty (https://dynesty.readthedocs.io/en/latest/)
- isochrones (https://isochrones.readthedocs.io/en/latest/)
PyMultinest is an optional package and can be hard to install! If you're planning on doing BMA only then you can skip installing it!!
Most can be easily installed with pip or conda but some might have special instructions (like PyMultinest!!)
ARIADNE has been tested on OS X up to Catalina and Linux. It does NOT run on Windows because healpy, a dependency of dustmaps isn't available for Windows (see healpy/healpy#25 (comment))
But note that plotting the SED model is optional. You can run the code without them!
| Model | Link |
|---|---|
| Phoenix v2 | ftp://phoenix.astro.physik.uni-goettingen.de/HiResFITS/PHOENIX-ACES-AGSS-COND-2011/ |
| Phoenix v2 wavelength file | ftp://phoenix.astro.physik.uni-goettingen.de/HiResFITS/WAVE_PHOENIX-ACES-AGSS-COND-2011.fits |
| BT-Models | http://osubdd.ens-lyon.fr/phoenix/ |
| Castelli & Kurucz | http://ssb.stsci.edu/cdbs/tarfiles/synphot3.tar.gz |
| Kurucz 1993 | http://ssb.stsci.edu/cdbs/tarfiles/synphot4.tar.gz |
The wavelength file for the Phoenix model has to be placed in the root folder of the PHOENIXv2 models.
For the code to find these models, you have to place them somewhere in your computer as follows:
Models_Dir
│
└───BTCond
│ │
│ └───CIFIST2011
│
└───BTNextGen
│ │
│ └───AGSS2009
│
└───BTSettl
│ │
│ └───AGSS2009
│
└───Castelli_Kurucz
│ │
│ └───ckm05
│ │
│ └───ckm10
│ │
│ └───ckm15
│ │
│ └───ckm20
│ │
│ └───ckm25
│ │
│ └───ckp00
│ │
│ └───ckp02
│ │
│ └───ckp05
│
└───Kurucz
│ │
│ └───km01
│ │
│ └───km02
│ │
│ └───km03
│ │
│ └───km05
│ │
│ └───km10
│ │
│ └───km15
│ │
│ └───km20
│ │
│ └───km25
│ │
│ └───kp00
│ │
│ └───kp01
│ │
│ └───kp02
│ │
│ └───kp03
│ │
│ └───kp05
│ │
│ └───kp10
│
└───PHOENIXv2
│
└─── WAVE_PHOENIX-ACES-AGSS-COND-2011.fits
└───Z-0.0
│
└───Z-0.5
│
└───Z-1.0
│
└───Z-1.5
│
└───Z-2.0
│
└───Z+0.5
│
└───Z+1.0
- The Phoenix v2 models with alpha enhancements are unused
- BT-models are BT-Settl, BT-Cond, and BT-NextGen
To use ARIADNE start by setting up the stellar information, this is done by importing the Star module.
from astroARIADNE.star import StarAfter importing, a star has to be defined.
Stars are defined in ARIADNE by their RA and DEC in degrees, a name, and optionally the Gaia DR3 source id, for example:
ra = 75.795
dec = -30.399
starname = 'NGTS-6'
gaia_id = 4875693023844840448
s = Star(starname, ra, dec, g_id=gaia_id)The starname is used purely for user identification later on, and the
gaia_id is provided to make sure the automatic photometry retrieval collects
the correct magnitudes, otherwise ARIADNE will try and get the gaia_id by
itself using a cone search centered around the RA and DEC.
Executing the previous block will start the photometry and stellar parameter retrieval routine. ARIADNE will query Gaia DR2 for an estimate on the temperature, radius, parallax and luminosity for display as preliminar information, as it's not used during the fit, and prints them along with its TIC, KIC IDs if any of those exist, its Gaia DR3 ID, and maximum line-of-sight extinction Av:
Gaia DR2 ID : 4875693023844840448
TIC : 1528696
Effective temperature : 4975.000 +/- 104.390
Stellar radius : 0.656 +/- 0.141
Stellar Luminosity : 0.238 +/- 0.003
Parallax : 3.297 +/- 0.036
Maximum Av : 0.030
If you already know any of those values, you can override the search for them by providing them in the Star constructor with their respective uncerainties. Likewise if you already have the magnitudes and wish to override the on-line search, you can provide a dictionary where the keys are the filters and values are the mag, mag_err tuples.
If you want to check the retrieved magnitudes you can call the print_mags
method from Star:
s.print_mags()This will print the filters used, magnitudes and uncertainties. For NGTS-6 this would look like this:
Filter Magnitude Uncertainty
---------------- --------- -----------
2MASS_H 11.7670 0.0380
2MASS_J 12.2220 0.0330
2MASS_Ks 11.6500 0.0320
GROUND_JOHNSON_V 14.0870 0.0210
GROUND_JOHNSON_B 15.1710 0.0140
GaiaDR2v2_G 13.8175 0.0006
GaiaDR2v2_RP 13.1127 0.0015
GaiaDR2v2_BP 14.4012 0.0027
SDSS_g 14.6390 0.0580
SDSS_i 13.3780 0.0570
SDSS_r 13.7030 0.0320
WISE_RSR_W1 11.5550 0.0270
WISE_RSR_W2 11.6360 0.0270
GALEX_NUV 21.9520 0.4090
TESS 13.1686 0.0062
Note: ARIADNE automatically prints and saves the used magnitudes and filters to a file.
The way the photometry retrieval works is that Gaia DR2 crossmatch catalogs are
queried for the Gaia ID, these crossmatch catalogs exist for ALL-WISE, APASS,
Pan-STARRS1, SDSS, 2MASS and Tycho-2, so finding photometry relies on these
crossmatches. In the case of NGTS-6, there are also Pan-STARRS1 photometry which
ARIADNE couldn't find due to the Pan-STARRS1 source not being identified in
the Gaia DR2 crossmatch, in this case if you wanted to add that photometry
manually, you can do so by using the add_mag method from Star, for example, if
you wanted to add the PS1_r mag to our Star object you would do:
s.add_mag(13.751, 0.032, 'PS1_r')If for whatever reason ARIADNE found a bad photometry point and you needed
to remove it, you can invoke the remove_mag method. For example you wanted
to remove the TESS magnitude due to it being from a blended source, you can just
run
s.remove_mag('NGTS')A list of allowed filters can be found here
ARIADNE has an incorporated prior for the interstellar extinction in the
Visual band,
- SFD (2011)
- Planck Collaboration (2013)
- Planck Collaboration (2016; GNILC)
- Lenz, Hensley & Doré (2017)
- Bayestar (2019)
These maps are all implemented through the dustmaps package and need to be downloaded. Instructions to download the dustmaps can be found in its documentation.
To change the dustmap you need to provide the dustmap parameter to the Star constructor, for example:
ra = 75.795
dec = -30.399
starname = 'NGTS-6'
gaia_id = 4875693023844840448
s = Star(starname, ra, dec, g_id=gaia_id, dustmap='Bayestar')This concludes the stellar setup and now we're ready to set up the parameters for the fitting routine.
In this section we'll detail how to set up the fitter for the Bayesian Model Averaging (BMA) mode of ARIADNE. For single models the procedure is very similar.
First, import the fitter from ARIADNE
from astroARIADNE.fitter import FitterThere are several configuration parameters we have to setup, the first one is the output folder where we want ARIADNE to output the fitting files and results, next we have to select the fitting engine (for BMA only dynesty is supported), number of live points to use, evidence tolerance threshold, and the following only apply for dynesty: bounding method, sampling method, threads, dynamic nested sampler. After selecting all of those, we need to select the models we want to use and finally, we feed them all to the fitter:
out_folder = 'your folder here'
engine = 'dynesty'
nlive = 500
dlogz = 0.5
bound = 'multi'
sample = 'rwalk'
threads = 4
dynamic = False
setup = [engine, nlive, dlogz, bound, sample, threads, dynamic]
# Feel free to uncomment any unneeded/unwanted models
models = [
'phoenix',
'btsettl',
'btnextgen',
'btcond',
'kurucz',
'ck04'
]
f = Fitter()
f.star = s
f.setup = setup
f.av_law = 'fitzpatrick'
f.out_folder = out_folder
f.bma = True
f.models = models
f.n_samples = 100000Note: While you can always select all 6 models, ARIADNE has an internal filter put in place in order to avoid having the user unintentionally bias the results. For stars with Teff > 4000 K BT-Settl, BT-NextGen and BT-Cond are identical and thus only BT-Settl is used, even if the three are selected. On the other hand, Kurucz and Castelli & Kurucz are known to work poorly on stars with Teff < 4000 K, thus they aren't used in that regime.
We allow the use of four different extinction laws:
- fitzpatrick
- cardelli
- odonnell
- calzetti
The next step is setting up the priors to use:
f.prior_setup = {
'teff': ('default'),
'logg': ('default'),
'z': ('default'),
'dist': ('default'),
'rad': ('default'),
'Av': ('default')
}A quick explanation on the priors:
The default prior for Teff is an empirical prior drawn from the RAVE survey
temperatures distribution, the distance prior is drawn from the
Bailer-Jones
distance estimate from Gaia EDR3, and the radius has a flat prior ranging from
0.5 to 20 R$_\odot$. The default prior for the metallicity z and log g are
also their respective distributions from the RAVE survey, the default prior for
Av is a flat prior that ranges from 0 to the maximum of line-of-sight as per the
SFD map, finally the excess noise parameters all have gaussian priors centered
around their respective uncertainties.
We offer customization on the priors as well, those are listed in the following table.
| Prior | Hyperparameters |
|---|---|
| Fixed | value |
| Normal | mean, std |
| TruncNorm | mean, std, lower_lim, uppern_lim |
| Uniform | ini, end |
| RAVE (log g only) | --- |
| Default | --- |
So if you knew (from a spectroscopic analysis, for example) that the effective temperature is 5600 +/- 100 and the metallicity is [Fe/H] = 0.09 +/- 0.05 and you wanted to use them as priors, and the star is nearby (< 70 pc), so you wanted to fix Av to 0, your prior dictionary should look like this:
f.prior_setup = {
'teff': ('normal', 5600, 100),
'logg': ('default'),
'z': ('normal', 0.09, 0.05),
'dist': ('default'),
'rad': ('default'),
'Av': ('fixed', 0)
}After having set up everything we can finally initialize the fitter and start fitting
f.initialize()
f.fit_bma()Now we wait for our results!
After the fitting has finished, we need to visualize our results. ARIADNE includes a plotter object to do just that! We first star by importing the plotter:
from astroARIADNE.plotter import SEDPlotterThe setup for the plotter is already made for you, but if you really want to change them, instructions on how to change it can be found here
Before we plot the SEDs we need to tell ARIADNE where to find our models.
This step isn't necessary if you don't want or need SED plots and are happy with
the HR diagram, histograms, cornerplot and RAW SED. This is done with an
environmental variable called ARIADNE_MODELS, to set it up you just need to run
export ARIADNE_MODELS='/path/to/Models_Dir/' in your terminal. You can also
add that instruction to your .bash_profile or .bashrc and the run
source ~/.bash_profile so you don't have to export everytime.
Now that ARIADNE knows where to find the models we only need to specify the results file location and the output folder for the plots!
in_file = out_folder + 'BMA_out.pkl'
plots_out_folder = 'your plots folder here'Now we instantiate the plotter and call the desired plotting methods! We offer 5 different plots:
- A RAW SED plot
- A SED plot with the model and synthetic photometry
- A corner plot
- An HR diagram taken from MIST isochrones
- Histograms showing the parameter distributions for each model.
artist = SEDPlotter(in_file, plots_out_folder)
artist.plot_SED_no_model()
artist.plot_SED()
artist.plot_bma_hist()
artist.plot_bma_HR(10)
artist.plot_corner()The number given to plot_bma_HR is the number of extra tracks you want to
plot, drawn randomly from the posterior distribution.
If you're iterating through lots of stars you can call the SEDPlotter clean
method to clear opened figures with artist.clean()
If you don't have the models in your computer, then the plot_SED method will
fail, as it needs the complete model grid.
An example usage file is provided in the repository called test_bma.py for the
BMA approach and test.py for single model fitting.
After ARIADNE has finished running, it will output a series of files and plots showing the results of the fit and other information.
The most important file is the best_fit.dat which contains the best fiting
parameters with the 1 sigma error bars and the 3 sigma confidence interval.
Then there are pickle files for each of the used models plus a last one for the
BMA, these contain raw information about the results. There is a prior.dat
file that shows the priors used and a mags.dat file with the used magnitudes
and filters.
Another important output are the plots. Inside the plots folder you can find
CORNER.png/pdf with the cornerplot (the plot showing the distribution of the
parameters agains eachother), HR_diagram.png/pdf only for the BMA, with the HR
diagram showing the position of the star, SED_no_model.png/pdf with the RAW
SED showing each photometry point color coded to their respective filter, and
SED.png/pdf with the SED with the catalog photometry plus synthetic
photometry. If BMA was done, there's also a histograms folder inside the plot
folder with various histograms of the fitted parameters and their distribution
per model, highlighting the benefits of BMA.
Examples of those figures:
As of version 1.0, ARIADNE now allows for Infrared Excess visualization!
To visualize infrared excess you just need to add the relevant photometric
observations to the Star object with the add_mag() method. After the fitting
is done, you then need to initiate the Plotter object with the ir_excess
parameter set to True:
artist = SEDPlotter(in_file, plots_out_folder, pdf=True, ir_excess=True)Finally after plotting, you should get an SED figure with your manually added photometry!
Allowed filters for infrared excess plots are WISE W3, WISE W4, HERSCHEL PACS BLUE, GREEN and RED, names for these filters can be found in the filters page.
For a more in depth look on the inner workings of ARIADNE consider reading the paper!
Additionally, you can find how to cite ARIADNE and its dependencies here