A conditional variational autoencoder (cVAE) framework for producing continuous surrogate spectra for kilonova models.

This package provides the interface to predict spectra. It does not provide an interface to do the data prep for training and training itself. The currently trained and provided models are:

• M. Bulla BNS Models
• M. Bulla BHNS Models
• D.Kasen BNS Models

This work requires the use of pyphot; pyphot requires hdf5. See their installation intructions to see how to install that for your system. Then, if you install KilonovaNet via pip, dependencies (including pyphot) should install properly.

Install via pip:

pip install kilonovanet

In order to produce surrogate spectra, you will need to specify the model and torch files. These are not included in this package, you must download them separately from the KilonovaNet github from data and model folders.

After you have the files in your system, you can produce spectra with the following:

import kilonovanet
import numpy as np

metadata_file = "data/metadata_bulla_bns.json"
torch_file = "models/bulla-bns-latent-20-hidden-1000-CV-4-2021-04-21-epoch-200.pt"
times = np.array([1.2, 2.2])
physical_parameters = np.array([1.0e-2, 9.0e-2, 3.0e1, 3.0e-1])

model = kilonovanet.Model(metadata_file, torch_file)
spectra = model.predict_spectra(physical_parameters, times)

In order to produce some photometric observations, the following have to be specified:

• the model
• the corresponding parameters of the model (see their papers, repositories, etc.)
• the times post-merger to produce the observations
• the filters in which to produce the observations

I have specified some filters in the github folder filter_data, but any filter transmission curves should work properly.

After you have filter profiles, use the following to produce synthetic photometric observations:

import kilonovanet
import numpy as np
 
metadata_file = "data/metadata_bulla_bns.json"
torch_file = "models/bulla-bns-latent-20-hidden-1000-CV-4-2021-04-21-epoch-200.pt"
filter_lib = "data/filter_data"

times = np.array([1.2, 1.2, 1.2, 2.2, 2.2, 2.2, 2.2])
filters = np.array(["LSST_u", "LSST_z", "LSST_y", "LSST_u", "LSST_z", "LSST_y"])
distance = 40.0 * 10 ** 6 * 3.086e18 # 40 Mpc in cm
physical_parameters = np.array([1.0e-2, 9.0e-2, 3.0e1, 3.0e-1])

model = kilonovanet.Model(metadata_file, torch_file, filter_library_path=filter_lib)
mags = model.predict_magnitudes(physical_parameters, times=times, filters=filters,
distance=distance)

If you intend to use the same set of observations often, e.g. when doing an MCMC-based fit, you can specify all of them in an Observations object and then simply call model.predict_magnitudes(physical_parameters).