Short-Period Variables in TESS Full-Frame Image Light Curves Identified via Convolutional Neural Networks

TL;DR

A convolutional neural network is presented that is computationally efficient, produces accurate predictions, and minimizes the required human search effort in the search for transiting exoplanets.

Abstract

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~85% of the sky throughout its two-year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast dataset, we aim to provide an approach that is both computationally efficient, produces highly performant predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short period variables. To make a prediction for a given light curve, our network requires no prior target parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present a collection of 14156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of short-period main sequence binaries and another of Delta Scuti stars. Our neural network model and related code is additionally provided as open-source code for public use and extension.

Figures (16)

• Figure 1: The FFI light curve and folded light curve for TIC ID 149989733 sector 10. This light curve was chosen as a typical example of the light curves identified by our NN. A notable aspect is that only 2 or 3 data points exist for each period (1.326 hours), resulting in the periodicity not being clear to a human observer in the unfolded light curve. Despite no specific periodicity-detecting mechanisms being included in the NN, it learns to identify such periods in the unfolded data. The time is given in Barycentric Julian Day (BTJD) time tenenbaum2018tess. With the Julian Day in the Barycentric Dynamical Time standard (BJD), $\text{BTJD} = \text{BJD}-2457000.0$. BJD is used for its accurate time standard which accounts for many different timing corrections, including leap seconds eastman2010achieving. The flux given is median normalized flux for the . Color is based on unfolded time. This is an example from our clusters (see \ref{['subsec:partitioning_the_data']} for clusters explanation).
• Figure 2: The FFI light curve and folded light curve for TIC ID 159971257 sector 23. This is an example from our binary cluster with clearly distinct binary signal peaks (see \ref{['subsec:partitioning_the_data']} for clusters explanation). Presented the same as \ref{['fig:representative_light_curve_figure0']}. See \ref{['fig:representative_light_curve_figure0']} for details.
• Figure 3: A random set of examples of synthetic periodic signals to be injected into TESS light curves as training data. Two periods of each synthetic signal are shown.
• Figure 4: An overview of the architecture of the convolutional neural network used in this work. See olmschenk2021transit for details. All convolution/dense layers within a block use a number of filters/units equivalent to the size of the last dimension of their output tensor. A kernel size of 3 is used in all convolutional layers. For clarity of the diagram, three deviations from the diagram blocks are not shown. First, the first convolution block and the last dense block do not apply dropout or batch normalization. Second, the final convolution block uses a standard dropout instead of spatial dropout as the following layer is a dense layer. Third, pooling is only used by the first 6 convolution blocks. The remaining convolution blocks do not use pooling.
• Figure 5: The period distribution of the short-period variables as estimated by our pipeline. Note that for binary systems, this estimated period may be half of the system's full orbital period.