J/AJ/165/95      Identifying Exoplanets with Deep Learning. V.      (Tey+, 2023)

Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations. Tey E., Moldovan D., Kunimoto M., Huang C.X., Shporer A., Daylan T., Muthukrishna D., Vanderburg A., Dattilo A., Ricker G.R., Seager S. <Astron. J., 165, 95 (2023)> =2023AJ....165...95T 2023AJ....165...95T
ADC_Keywords: Exoplanets; Optical; Stars, masses; Stars, diameters Keywords: Neural networks ; Transit photometry ; Exoplanet detection methods ; Exoplanet catalogs Abstract: The TESS mission produces a large amount of time series data, only a small fraction of which contain detectable exoplanetary transit signals. Deep-learning techniques such as neural networks have proved effective at differentiating promising astrophysical eclipsing candidates from other phenomena such as stellar variability and systematic instrumental effects in an efficient, unbiased, and sustainable manner. This paper presents a high-quality data set containing light curves from the Primary Mission and 1st Extended Mission full-frame images and periodic signals detected via box least-squares. The data set was curated using a thorough manual review process then used to train a neural network called Astronet-Triage-v2. On our test set, for transiting/eclipsing events, we achieve a 99.6% recall (true positives over all data with positive labels) at a precision of 75.7% (true positives over all predicted positives). Since 90% of our training data is from the Primary Mission, we also test our ability to generalize on held-out 1st Extended Mission data. Here, we find an area under the precision-recall curve of 0.965, a 4% improvement over Astronet-Triage. On the TESS object of interest (TOI) Catalog through 2022 April, a shortlist of planets and planet candidates, Astronet-Triage-v2 is able to recover 3577 out of 4140 TOIs, while Astronet-Triage only recovers 3349 targets at an equal level of precision. In other words, upgrading to Astronet-Triage-v2 helps save at least 200 planet candidates from being lost. The new model is currently used for planet candidate triage in the Quick-Look Pipeline. Description: During its Prime Mission (2018 July 25-2020 July 4), TESS collected full-frame images (FFIs) every 30 minutes for 2yr covering 70% of the entire sky. The FFI cadence was updated to 10 minutes for the 1st Extended Mission (2020 July 4-2022 September 1). QLP produces light curves from these images for all observed targets in the TESS Input Catalog (TIC) with TESS-band magnitude brighter than 13.5. File Summary: -------------------------------------------------------------------------------- FileName Lrecl Records Explanations -------------------------------------------------------------------------------- ReadMe 80 . This file table2.dat 161 24926 *Threshold-crossing events (TCE) table -------------------------------------------------------------------------------- Note on table2.dat: The values in this table, particularly in the R* and Dur columns, do not reflect the actual number of significant digits. These are the same values that were passed to Astronet during the training and evaluation. -------------------------------------------------------------------------------- See also: IV/38 : TESS Input Catalog - v8.0 (TIC-8) (Stassun+, 2019) I/352 : Distances to 1.47 billion stars in Gaia EDR3 (Bailer-Jones+, 2021) J/ApJS/224/12 : Kepler planetary candidates. VII. 48-month (Coughlin+, 2016) J/AJ/156/102 : TESS Input Catalog and Candidate Target List (Stassun+, 2018) J/ApJS/235/38 : Kepler planetary cand. VIII. DR25 reliability (Thompson+, 2018) J/AJ/157/169 : Identifying exoplanets with deep learning K2 (Dattilo+, 2019) J/AJ/158/25 : Automated triage and vetting of TESS candidates (Yu+, 2019) J/A+A/633/A53 : TESS planet candidates classification (Osborn+, 2020) J/ApJS/254/39 : Exoplanet candidates from TESS first 2yr obs (Guerrero+, 2021) Byte-by-byte Description of file: table2.dat -------------------------------------------------------------------------------- Bytes Format Units Label Explanations -------------------------------------------------------------------------------- 1- 10 I10 --- TIC TESS Input Catalog identifier 12- 22 F11.6 d Epoch [1198/2366] BLS-detected transit center in BTJD 24- 34 F11.7 d Per [0.06/167] BLS-detected period 36- 50 A15 d Dur BLS-detected duration 52- 63 I12 ppm Depth [0/396326348810] BLS-detected depth 65- 72 F8.6 Msun Mass [0.16/3.69]? Stellar mass via TIC 8.2 74- 86 F13.9 Rsun Rad [0.18/151]? Stellar radius via TIC 8.2 88-102 F15.10 Rsun eRad [0.1/1000]? Estimated stellar radius; Section 3.2 104-111 F8.5 mag Tmag [0.57/13.6] TESS-band magnitude 113-113 I1 --- Year [1/3] TESS Cycle signal was detected 115-125 F11.6 d MinT [1325/2362] Minimum BTJD used from the light curve 127-137 F11.6 d MaxT [1541/2390] Maximum BTJD used from the light curve 139-143 A5 --- Split Dataset signal was used for; test, train, or val 145-145 A1 --- CLabel Consensus label (1) 147-147 A1 --- L1 Label assigned by labeller L1 149-149 A1 --- L2 Label assigned by labeller L2 151-151 A1 --- L3 Label assigned by labeller L3 153-153 A1 --- L4 Label assigned by labeller L4 155-155 A1 --- L5 Label assigned by labeller L5 157-157 A1 --- L6 Label assigned by labeller L6 159-159 A1 --- L7 Label assigned by labeller L7 161-161 A1 --- L8 Label assigned by labeller L8 -------------------------------------------------------------------------------- Note (1): Final label used for training by unanimous vote or discussion. When absent, we used a weighted average of the individual votes for training. -------------------------------------------------------------------------------- History: From electronic version of the journal References: Shallue et al. Paper I : 2018AJ....155...94S 2018AJ....155...94S Dattilo et al. Paper II : 2019AJ....157..169D 2019AJ....157..169D Cat. J/AJ/157/169 Yu et al. Paper III: 2019AJ....158...25Y 2019AJ....158...25Y Cat. J/AJ/158/25 De Beurs et al. Paper IV : 2022AJ....164...49D 2022AJ....164...49D Cat. J/AJ/164/49
(End) Prepared by [AAS], Coralie Fix [CDS], 02-Jun-2023
The document above follows the rules of the Standard Description for Astronomical Catalogues; from this documentation it is possible to generate f77 program to load files into arrays or line by line