LSTM Neural Networks for Spectroscopic Studies of Type Ia Supernovae

Related tags

Deep Learningsupernovaetime-series-analysisspectrosopylstm-neural-network
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.
[§] 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.
[¶] 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

zshicode 57 Dec 27, 2022
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,

Pushpendu Ghosh 270 Dec 24, 2022
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

Brad 24 Nov 11, 2022
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

David Griffis 532 Jan 2, 2023
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

Riddhiman Dasgupta 529 Dec 10, 2022
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

Rishikesh S 15 Aug 20, 2022
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

Salesforce 1.9k Jan 8, 2023
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

Zijie Zhuang 733 Dec 30, 2022
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

KevinCHEN 1 Jun 13, 2022
Releases(v1.1.2)
Owner
GitHub Repository
PyTorch implementation of the ExORL: Exploratory Data for Offline Reinforcement Learning

ExORL: Exploratory Data for Offline Reinforcement Learning This is an original PyTorch implementation of the ExORL framework from Don't Change the Alg

Denis Yarats 52 Jan 01, 2023
Low-code/No-code approach for deep learning inference on devices

EzEdgeAI A concept project that uses a low-code/no-code approach to implement deep learning inference on devices. It provides a componentized framewor

On-Device AI Co., Ltd. 7 Apr 05, 2022
Weighing Counts: Sequential Crowd Counting by Reinforcement Learning

LibraNet This repository includes the official implementation of LibraNet for crowd counting, presented in our paper: Weighing Counts: Sequential Crow

Hao Lu 18 Nov 05, 2022
[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Fudan Zhang Vision Group 897 Jan 05, 2023
A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset.

A repository for the updated version of CoinRun used to collect MUGEN, a multimodal video-audio-text dataset. This repo contains scripts to train RL agents to navigate the closed world and collect vi

MUGEN 11 Oct 22, 2022
My tensorflow implementation of "A neural conversational model", a Deep learning based chatbot

Deep Q&A Table of Contents Presentation Installation Running Chatbot Web interface Results Pretrained model Improvements Upgrade Presentation This wor

Conchylicultor 2.9k Dec 28, 2022
The project is an official implementation of our CVPR2019 paper "Deep High-Resolution Representation Learning for Human Pose Estimation"

Deep High-Resolution Representation Learning for Human Pose Estimation (CVPR 2019) News [2020/07/05] A very nice blog from Towards Data Science introd

Leo Xiao 3.9k Jan 05, 2023
Orchestrating Distributed Materials Acceleration Platform Tutorial

Orchestrating Distributed Materials Acceleration Platform Tutorial This tutorial for orchestrating distributed materials acceleration platform was pre

BIG-MAP 1 Jan 25, 2022
A faster pytorch implementation of faster r-cnn

A Faster Pytorch Implementation of Faster R-CNN Write at the beginning [05/29/2020] This repo was initaited about two years ago, developed as the firs

Jianwei Yang 7.1k Jan 01, 2023
Source Code for DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances (https://arxiv.org/pdf/2012.01775.pdf)

DialogBERT This is a PyTorch implementation of the DialogBERT model described in DialogBERT: Neural Response Generation via Hierarchical BERT with Dis

Xiaodong Gu 67 Jan 06, 2023
《Lerning n Intrinsic Grment Spce for Interctive Authoring of Grment Animtion》

Learning an Intrinsic Garment Space for Interactive Authoring of Garment Animation Overview This is the demo code for training a motion invariant enco

YuanBo 213 Dec 14, 2022
Tutorial materials for Part of NSU Intro to Deep Learning with PyTorch.

Intro to Deep Learning Materials are part of North South University (NSU) Intro to Deep Learning with PyTorch workshop series. (Slides) Related materi

Hasib Zunair 9 Jun 08, 2022
GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration Stefan Abi-Karam*, Yuqi He*, Rishov Sarkar*, Lakshmi Sathidevi, Zihang Qiao, Co

Sharc-Lab 19 Dec 15, 2022
(ICCV 2021) ProHMR - Probabilistic Modeling for Human Mesh Recovery

ProHMR - Probabilistic Modeling for Human Mesh Recovery Code repository for the paper: Probabilistic Modeling for Human Mesh Recovery Nikos Kolotouros

Nikos Kolotouros 209 Dec 13, 2022
ML course - EPFL Machine Learning Course, Fall 2021

EPFL Machine Learning Course CS-433 Machine Learning Course, Fall 2021 Repository for all lecture notes, labs and projects - resources, code templates

EPFL Machine Learning and Optimization Laboratory 1k Jan 04, 2023
Shōgun

The SHOGUN machine learning toolbox Unified and efficient Machine Learning since 1999. Latest release: Cite Shogun: Develop branch build status: Donat

Shōgun ML 2.9k Jan 04, 2023
Scalable machine learning based time series forecasting

mlforecast Scalable machine learning based time series forecasting. Install PyPI pip install mlforecast Optional dependencies If you want more functio

Nixtla 145 Dec 24, 2022
SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

SubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification

Sayed Hashim 3 Nov 15, 2022
A PyTorch Implementation of Single Shot Scale-invariant Face Detector.

S³FD: Single Shot Scale-invariant Face Detector A PyTorch Implementation of Single Shot Scale-invariant Face Detector. Eval python wider_eval_pytorch.

carwin 235 Jan 07, 2023
Official code repository of the paper Learning Associative Inference Using Fast Weight Memory by Schlag et al.

Learning Associative Inference Using Fast Weight Memory This repository contains the offical code for the paper Learning Associative Inference Using F

Imanol Schlag 18 Oct 12, 2022