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: 2023-06-15
Primary Reference(s): Claytor et al. 2022
DOI: 10.17909/davg-m919
Citations: See ADS Metrics
Slideshow
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. 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.
Update 06/15/23: The original delivery of SMARTS included a corrupted file for simulation #408. This file has been fixed and is now included in hlsp_smarts_tess_ffi_001_tess_v1.0_sim.tar.gz.
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).