Mission Overview

Simulated TESS Light Curves for Measuring Rotation with Deep Learning ("SMARTS")

 

Primary Investigator: Zachary R. Claytor

HLSP Authors: Zachary R. Claytor, Jennifer L. van Saders, Joe Llama, Peter Sadowski, Brandon Quach, Ellis Avallone

Released: 2022-03-03

Updated: 2022-03-03

Primary Reference(s): Claytor et al. 2022

DOI: 10.17909/davg-m919

Citations: See ADS Metrics

Read Me

SMARTS-TESS light curve simulation
SMARTS-TESS light curve simulation

Example of a SMARTS-TESS light curve simulation. A pure-simulation light curve is joined with a real TESS galaxy light curve to produce the final product: a simulated, stellar rotational light curve with real TESS noise and systematics.

Example of a SMARTS-TESS wavelet transform
Example of a SMARTS-TESS wavelet transform

Example of a SMARTS-TESS wavelet transform. Wavelet power spectra are binned to 64x64 pixels to serve as input to machine learning models as in Claytor et al. (2022).

Overview

Conventional methods of detecting stellar rotation from TESS light curves have struggled to obtain periods longer than 13.7 days due to complicated systematics related to the telescope's orbit. Machine learning has been shown to see beyond TESS's systematics and obtain long periods, but it requires large training sets with known rotation periods. "SMARTS" (Stellar Magnetism, Activity, and Rotation with Time Series) is a training set of synthetic light curves and binned wavelet transforms designed to mimic the full-frame image light curves of the TESS continuous viewing zones. The light curves were generated using the physically realistic spot evolution models in butterpy and include rotation, varying activity levels, magnetic cycles, spot emergence and decay, and latitudinal differential rotation. They are combined with real TESS galaxy light curves and stitched sector-to-sector to emulate TESS's systematics and noise.

The SMARTS data and butterpy are fully described by Claytor et al. (2022). This HLSP contains 1 million simulations spanning rotation periods of 0.1—180 days. The butterpy package can interact with the SMARTS dataset and is installable through the Python package index via pip or on GitHub. Users of the SMARTS data set should cite the Primary Reference linked at the top-left of this page. 

Data Products

The SMARTS data files are ordered by rotation period in increments of 1 day, and bundled into groups with the following naming convention:

hlsp_smarts_tess_ffi_<mmm>_tess_v1.0_sim.tar.gz

where:

  • <mmm> = the maximum rotation period in the group in days. e.g., "002" = periods of 1-2 days, "065" = periods of 64-65 days. 

Within the bundles, each file is named according to:

smarts-tess-v1.0-<simid>.fits

where:

  • <simid> = the 6-digit, zero-padded simulation ID.

Data file types:

.tar.gz

Bundle of light curves organized by rotation period in increments of 1 day.

.fits

Metadata, light curve, and wavelet transform corresponding to a single simulation.

Data Access

Files can be downloaded directly from https://archive.stsci.edu/hlsps/smarts. The following table includes cURL scripts for downloading groups of SMARTS files in 10-day increments of simulated rotation period. Each group contains 10 files comprising ~5 GB of data, and is named for the simulated rotation periods included in it (e.g., "011-020" = bundles of light curves with periods 11-20 days). A cURL script is also provided for downloading all files ("All", downloads ~99 GB of data). 

cURL Scripts for Downloading Groups of Simulated Light Curves

cURL Script for All Files

000-010 011-020 021-030 031-040 041-050

051-060

061-070 071-080 081-090 091-100 101-110

111-120

121-130

131-140 141-150 151-160 161-170

171-180

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.

References