SuperNova Artificial Inference by Lstm neural networks (SNAIL) =============================

The difficulties in acquiring spectroscopic data have been a major challenge for supernova surveys. SNAIL is developed to provide a data-driven solution. Based on an observation dataset including 3091 spectra from 361 individual SNe Ia, we trained LSTM neural networks to learn from the spectroscopic time-series data of type Ia supernovae. The model enables the construction of spectral sequences from spectroscopic observations with limited time coverage.

This repository is associated to the paper "Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks (Hu et al. 2022, ApJ)".

Installation -----------Create a separate Python enviornment (recommended) for SNAIL: :

$ conda create -y --name env4snail python=3.6.6

One can install any desired version of SNAIL from Github https://github.com/thomasvrussell/snail: :

$ (env4snail): python setup.py install

Alternatively, one can also choose PyPI to install latest stable version of SNAIL: :

$ (env4snail): pip install astro-snail

Note: To avoid package name duplicate in PyPI, we have to change PyPI package name to be astro-snail, but there is no any differnce on package content or usage.

Note: Given that the LSTM model provided in this repository was trained on a Python 3.6.6 enviornment using tensorflow, here I recommend users to setup SNAIL in a separate enviornment with Python 3.6.6 so that the model can be loaded without any potential compatibility issues.

Additional dependencies -----------

• R : In order to reduce the data dimension, we use Functional Principal Component Analysis (FPCA) to parameterize supernova spectra before feeding them into neural networks. The FPCA parameterization and FPCA reconstruction are achieved by the fpca package in R programming language. One can install them, e.g., on CentOS :

  $ yum install R
  R > install.packages("fpca")

• An Update on 2024-01-06: I noticed that the package fpca was removed from the CRAN repository, however, one can still install fpca from archival source file :

  $ wget https://cran.r-project.org/src/contrib/Archive/fpca/fpca_0.2-1.tar.gz
  R > install.packages('your/path/to/fpca_0.2-1.tar.gz', repos=NULL, type='source')

• TensorFlow : tensorflow is required to load a given LSTM model and make the spectral predictions. The default LSTM model in this repository is trained on an enviornment with tensorflow 1.14.0. To avoid potential compatibility issues casued by different tensorflow versions, we recommend users to install the same version via Conda :

  $ conda install -n env4snail -c anaconda tensorflow=1.14.0

• PYPHOT (optional) : pyphot is a portable package to compute synthetic photometry of a spectrum with given filter. In our work, the tool was used to correct the continuum component of a supernova spectrum so that its synthetic photometry could be in line with the observed light curves. One may consider to install the package if such color calibration is necessary. We recommend users to install the stable version 1.4.3 from PyPI :

  $ (env4snail): pip install pyphot==1.4.3

Download archival datasets -----------

SNAIL allows users to access to the following archival datasets

These datasets are stored on Zenodo platform, one can download the related files (~ 2GB) through the Zenodo page: https://doi.org/10.5281/zenodo.5637790.

Quick start guide -----------

We prepared several jupyter notebooks as quick tutorials to use our package in a friendly way.

Publications use our method -----------

• SN2018agk: A prototypical Type Ia Supernova with a smooth power-law rise in Kepler (K2) (Qinan Wang, et al., 2021, ApJ, see Figure 5 & 6).

• Support spectral sequence with arbitrary timesteps as input. (current model only accepts spectral pair as input.)
• Support more flexible wavelength range for input spectra. (current model is trained on spectra with uniform wavelength range from 3800 to 7200 A.)

• SNAIL2 based on PyTorch is under development! It will support spectral time-series with dynamic length as inputs! [Lei, Mar 7, 2022]
• SNAIL2 will also include the SNIFS dataset used in SNEMO (Saunders+, 2018). Note that we do not know the real SN names in this SNIFS dataset, so we simply assume they all are brand new SNe. (the compromise will be corrected when the complete SNIFS dataset becomes public). [Lei, Mar 7, 2022]
• We would like to incoporate more public spectroscopy datasets into our training set. Users are encouaged to give any useful suggestions about this (just email to us or post on Github!). [Lei, Mar 7, 2022]
• We already finished a beta version of SNAIL2 based on PyTorch! In the new version, we extended our spectral training set by incoporating the SNIFS dataset. For training LSTM model on spectral time-series of dynamic lengths, we designed a new selection process to construct the training sample set. Moreover, the way we correct the spectral color by photometry has been improved. With these modifications, SNAIL2 seems has a even better performance on predictive accuracy than the old versions. [Lei, Mar 11, 2022]

Common issues -----------

TBD

The latest source code can be obtained from https://github.com/thomasvrussell/snail.

When submitting bug reports or questions via the issue tracker, please include the following information:

• OS platform.
• Python version.
• Tensorflow version.
• Version of SNAIL.

Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks (Hu L., Chen X., Wang L., 2022, ApJ).

Arxiv link: https://arxiv.org/abs/2202.02498.

Related DOI: https://doi.org/10.3847/1538-4357/ac5c48.