STELLA

Convolutional Neural Networks for Flare Identification in TESS 2-minute Data ("stella")

Primary Investigator: Adina Feinstein

HLSP Authors: Adina Feinstein, Benjamin Montet, Megan Ansdell

Released: 2020-10-21

Updated: 2020-10-21

Primary Reference(s): Feinstein et al. 2020a, Feinstein et al. 2020b

DOI: 10.17909/t9-7f44-6r51

Citations: See ADS Statistics

Read Me

alternate — Example of light curves colored by the average "probability" as determined by 10 ensembled stella CNN models. Flares are easily distinguishable from differing spot modulation behaviors.

Overview

Previous methods of flare detection with both Kepler and TESS data have relied on light curve detrending and using outlier detection heuristics for identifying flare events. stella is a novel way to detect flares in TESS short cadence data using convolutional neural networks (CNNs). Any TESS short cadence light curve can be run through the CNN models provided, without any detrending. The models created by the team return a probability light curve (see example figure), with values between 0-1 if a given light curve event is a flare or not. It takes < 1 minute to predict flares on a single TESS sector light curve using these models.

The CNN models were created with Google's machine learning API, Tensorflow. The team has created 100 trained ensembled models to use when predicting flares in other short cadence TESS light curves. Any single model can be used on its own, however the team recommends using at least 10 models and averaging the results. The details of each model can be found in Feinstein et al. 2020. The models can be opened and explored using either Tensorflow, h5py, or any other software that can open HDF5 files. An example of opening the models can be found using the Jupyter notebook in the Data Access section.

The stella software is installable through the Python package index via pip and can also be found on GitHub: https://github.com/afeinstein20/stella. Users of the stella software or models should cite both of the Primary References linked at the top-left of this page.

Data Products

Data file naming convention:

hlsp_stella_tess_ensemblemodel_s<seed>_tess_v0.1.0_cnn.h5

where:

<seed> = the random seed used to generate that model, which is a three-digit, zero-padded integer ranging from '000' to '099'.

Data file types:

_cnn.h5	Tensorflow CNN model.

Data Access

The Tensorflow CNN models can be downloaded from the table below, each link containing the HDF5 file for a given seed. A tar bundle containing all of the HDF5 files can be downloaded here for those who want all the files (~100 MB): hlsp_stella_tess_ensemblemodel_all_tess_v0.1.0_bundle.tar.gz

A Jupyter notebook is available that demonstrates how to open and read in the models from the HDF5 files can be downloaded here: hlsp_stella_tess_ensemblemodel_any_tess_v0.1.0_demo.ipynb.

NOTE: this notebook is provided "as-is" by the HLSP team and MAST does not actively maintain this notebook.

000	001	002	003	004
005	006	007	008	009
010	011	012	013	014
015	016	017	018	019
020	021	022	023	024
025	026	027	028	029
030	031	032	033	034
035	036	037	038	039
040	041	042	043	044
045	046	047	048	049
050	051	052	053	054
055	056	057	058	059
060	061	062	063	064
065	066	067	068	069
070	071	072	073	074
075	076	077	078	079
080	081	082	083	084
085	086	087	088	089
090	091	092	093	094
095	096	097	098	099

Citations

Please remember to cite the appropriate paper(s) below and the DOI if you use these data in a published work.

Note: These HLSP data products are licensed for use under CC BY 4.0.

000	001	002	003	004
005	006	007	008	009
010	011	012	013	014
015	016	017	018	019
020	021	022	023	024
025	026	027	028	029
030	031	032	033	034
035	036	037	038	039
040	041	042	043	044
045	046	047	048	049
050	051	052	053	054
055	056	057	058	059
060	061	062	063	064
065	066	067	068	069
070	071	072	073	074
075	076	077	078	079
080	081	082	083	084
085	086	087	088	089
090	091	092	093	094
095	096	097	098	099

000	001	002	003	004
005	006	007	008	009
010	011	012	013	014
015	016	017	018	019
020	021	022	023	024
025	026	027	028	029
030	031	032	033	034
035	036	037	038	039
040	041	042	043	044
045	046	047	048	049
050	051	052	053	054
055	056	057	058	059
060	061	062	063	064
065	066	067	068	069
070	071	072	073	074
075	076	077	078	079
080	081	082	083	084
085	086	087	088	089
090	091	092	093	094
095	096	097	098	099