LSTM Neural Networks for Spectroscopic Studies of Type Ia Supernovae

Overview

Package Description

The difficulties in acquiring spectroscopic data have been a major challenge for supernova surveys. snlstm 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 very limited time coverage.

This repository is associated to the paper "Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks (Hu et al. 2021, ApJ, under review)".

Installation

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

python setup.py install

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")
    
  • 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 incompatiability issues casued by different tensorflow versions, we recommend users to install the same version via Conda

    conda install -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 latest version from Github (pyphot 1.1)

    pip install git+https://github.com/mfouesneau/pyphot
    

Download archival datasets

snlstm allows users to access to the following archival datasets

[1] A spectral-observation dataset : it is comprised of 3091 observed spectra from 361 SNe Ia, largely contributed from CfA (Blondin et al. 2012), BSNIP (Silverman et al. 2012), CSP (Folatelli et al. 2013) and Supernova Polarimetry Program (Wang & Wheeler 2008; Cikota et al. 2019a; Yang et al. 2020).
[2] A spectral-template dataset : it includes 361 spectral templates, each of them (covering -15 to +33d with wavelength from 3800 to 7200 A) was generated from the available spectroscopic observations of an individual SN via a LSTM neural network model.
[3] An auxiliary photometry dataset : it provides the B & V light curves of these SNe (in total, 196 available), that were used to calibrate the synthetic B-V color of the observed spectra.

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.

[*] 1-Access_to_Archival_ObservationData.ipynb : this notebook is to show how to access to the spectral-observation dataset and the auxiliary photometry dataset.
[†] 2-Access_to_Archival_TemplateData.ipynb : one can obtain the LSTM generated spectral time sequences in the spectral-template dataset following this notebook.
[‑] 3-SpecData_Process_Example.ipynb : the notebook demonstrates the pre-processing of the spectroscopic data described in our paper, including smooth, rebinning, lines removal and color calibration, etc.
[Β§] 4-LSTM_Predictions_on_New_SN.ipynb : the notebook provides a guide for users who want apply our LSTM model on very limited spectroscopic data of newly discovered SNe Ia. In this notebook, we use SN 2016coj, a well-observed SN Ia from the latest BSNIP data release, as an example.
[ΒΆ] 5-LSTM_Estimate_Spectral_Phase.ipynb : our neural network is trained based on the spectral data with known phases, however, it is still possible to apply the model to the spectra without any prior phase knownlege. The idea is wrong given phase of input spectrum will degrade the predictive accuracy of our method, that is to say, we can find the best-fit phase of input spectrum by minimizing the accuacy of prediction for itself. This notebook is to show how to estimate spectral phase via our model. For the case of SN 2016coj in the notebook, the estimation errors are around 0.5 - 2.0d.

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).

Todo list

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

Common issues

TBD

Development

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

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

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

Cite

Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks (Hu et al. 2021, ApJ, under review).

You might also like...
Incorporating Transformer and LSTM to Kalman Filter with EM algorithm

Deep learning based state estimation: incorporating Transformer and LSTM to Kalman Filter with EM algorithm Overview Kalman Filter requires the true p

Forecasting directional movements of stock prices for intraday trading using LSTM and random forest
Forecasting directional movements of stock prices for intraday trading using LSTM and random forest

Forecasting directional movements of stock-prices for intraday trading using LSTM and random-forest https://arxiv.org/abs/2004.10178 Pushpendu Ghosh,

Deep learning based hand gesture recognition using LSTM and MediaPipie.
Deep learning based hand gesture recognition using LSTM and MediaPipie.

Hand Gesture Recognition Deep learning based hand gesture recognition using LSTM and MediaPipie. Demo video using PingPong Robot Files Pretrained mode

A3C LSTM  Atari with Pytorch plus A3G design
A3C LSTM Atari with Pytorch plus A3G design

NEWLY ADDED A3G A NEW GPU/CPU ARCHITECTURE OF A3C FOR SUBSTANTIALLY ACCELERATED TRAINING!! RL A3C Pytorch NEWLY ADDED A3G!! New implementation of A3C

Tree LSTM implementation in PyTorch

Tree-Structured Long Short-Term Memory Networks This is a PyTorch implementation of Tree-LSTM as described in the paper Improved Semantic Representati

Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi

LSTM and QRNN Language Model Toolkit for PyTorch

LSTM and QRNN Language Model Toolkit This repository contains the code used for two Salesforce Research papers: Regularizing and Optimizing LSTM Langu

Multi-layer convolutional LSTM with Pytorch

Convolution_LSTM_pytorch Thanks for your attention. I haven't got time to maintain this repo for a long time. I recommend this repo which provides an

A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi

LSTM-Time-Series-Prediction A Simple LSTM-Based Solution for "Heartbeat Signal Classification and Prediction" in Tianchi Contest. The Link of the Cont

Releases(v1.1.2)
Code release for "Self-Tuning for Data-Efficient Deep Learning" (ICML 2021)

Self-Tuning for Data-Efficient Deep Learning This repository contains the implementation code for paper: Self-Tuning for Data-Efficient Deep Learning

THUML @ Tsinghua University 101 Dec 11, 2022
MGFN: Multi-Graph Fusion Networks for Urban Region Embedding was accepted by IJCAI-2022.

Multi-Graph Fusion Networks for Urban Region Embedding (IJCAI-22) This is the implementation of Multi-Graph Fusion Networks for Urban Region Embedding

202 Nov 18, 2022
PyBrain - Another Python Machine Learning Library.

PyBrain -- the Python Machine Learning Library =============================================== INSTALLATION ------------ Quick answer: make sure you

2.8k Dec 31, 2022
Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis (CVPR2022)

Multi-View Consistent Generative Adversarial Networks for 3D-aware Image Synthesis Multi-View Consistent Generative Adversarial Networks for 3D-aware

Xuanmeng Zhang 78 Dec 10, 2022
Code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge.

Open Sesame This repository contains the code for the paper Open Sesame: Getting Inside BERT's Linguistic Knowledge. Credits We built the project on t

9 Jul 24, 2022
S2s2net - Sentinel-2 Super-Resolution Segmentation Network

S2S2Net Sentinel-2 Super-Resolution Segmentation Network Getting started Install

Wei Ji 10 Nov 10, 2022
A collection of Jupyter notebooks to play with NVIDIA's StyleGAN3 and OpenAI's CLIP for a text-based guided image generation.

StyleGAN3 CLIP-based guidance StyleGAN3 + CLIP StyleGAN3 + inversion + CLIP This repo is a collection of Jupyter notebooks made to easily play with St

Eugenio Herrera 176 Dec 30, 2022
Code for ICLR 2021 Paper, "Anytime Sampling for Autoregressive Models via Ordered Autoencoding"

Anytime Autoregressive Model Anytime Sampling for Autoregressive Models via Ordered Autoencoding , ICLR 21 Yilun Xu, Yang Song, Sahaj Gara, Linyuan Go

Yilun Xu 22 Sep 08, 2022
Comp445 project - Data Communications & Computer Networks

COMP-445 Data Communications & Computer Networks Change Python version in Conda

Peng Zhao 2 Oct 03, 2022
SegNet including indices pooling for Semantic Segmentation with tensorflow and keras

SegNet SegNet is a model of semantic segmentation based on Fully Comvolutional Network. This repository contains the implementation of learning and te

Yuta Kamikawa 172 Dec 23, 2022
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks

SalGAN: Visual Saliency Prediction with Adversarial Networks Junting Pan Cristian Canton Ferrer Kevin McGuinness Noel O'Connor Jordi Torres Elisa Sayr

Image Processing Group - BarcelonaTECH - UPC 347 Nov 22, 2022
πŸ”₯ Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization πŸ”₯

πŸ”₯ Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization πŸ”₯

Rishik Mourya 48 Dec 20, 2022
Job-Recommend-Competition - Vectorwise Interpretable Attentions for Multimodal Tabular Data

SiD - Simple Deep Model Vectorwise Interpretable Attentions for Multimodal Tabul

Jungwoo Park 40 Dec 22, 2022
we propose a novel deep network, named feature aggregation and refinement network (FARNet), for the automatic detection of anatomical landmarks.

Feature Aggregation and Refinement Network for 2D Anatomical Landmark Detection Overview Localization of anatomical landmarks is essential for clinica

aoyueyuan 0 Aug 28, 2022
Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020).

SentiBERT Code for SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics (ACL'2020). https://arxiv.org/abs/20

Da Yin 66 Aug 13, 2022
πŸ”Ž Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Image Super-Resolution (ISR) The goal of this project is to upscale and improve the quality of low resolution images. This project contains Keras impl

idealo 4k Jan 08, 2023
Quantify the difference between two arbitrary curves in space

similaritymeasures Quantify the difference between two arbitrary curves Curves in this case are: discretized by inidviudal data points ordered from a

Charles Jekel 175 Jan 08, 2023
Machine Learning Toolkit for Kubernetes

Kubeflow the cloud-native platform for machine learning operations - pipelines, training and deployment. Documentation Please refer to the official do

Kubeflow 12.1k Jan 03, 2023
Cleaned test data list of DukeMTMC-reID, ICCV2021

Cleaned DukeMTMC-reID Cleaned data list of DukeMTMC-reID released with our paper accepted by ICCV 2021: Learning Instance-level Spatial-Temporal Patte

14 Feb 19, 2022
Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021

LoFTR: Detector-Free Local Feature Matching with Transformers Project Page | Paper LoFTR: Detector-Free Local Feature Matching with Transformers Jiami

ZJU3DV 1.4k Jan 04, 2023