Abstract
In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labour intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects that, unlike current methods, uses a neural network.Neural networks, also called 'deep learning' or 'deep nets', are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms, deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 478-491 |
| Number of pages | 14 |
| Journal | Monthly Notices of the Royal Astronomical Society |
| Volume | 474 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 1 2018 |
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Keywords
- Methods: data analysis
- Planets and satellites: detection
- Techniques: photometric
ASJC Scopus subject areas
- Astronomy and Astrophysics
- Space and Planetary Science
Cite this
Searching for exoplanets using artificial intelligence. / Pearson, Kyle A.; Palafox, Leon; Griffith, Caitlin.
In: Monthly Notices of the Royal Astronomical Society, Vol. 474, No. 1, 01.02.2018, p. 478-491.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Searching for exoplanets using artificial intelligence
AU - Pearson, Kyle A.
AU - Palafox, Leon
AU - Griffith, Caitlin
PY - 2018/2/1
Y1 - 2018/2/1
N2 - In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labour intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects that, unlike current methods, uses a neural network.Neural networks, also called 'deep learning' or 'deep nets', are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms, deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.
AB - In the last decade, over a million stars were monitored to detect transiting planets. Manual interpretation of potential exoplanet candidates is labour intensive and subject to human error, the results of which are difficult to quantify. Here we present a new method of detecting exoplanet candidates in large planetary search projects that, unlike current methods, uses a neural network.Neural networks, also called 'deep learning' or 'deep nets', are designed to give a computer perception into a specific problem by training it to recognize patterns. Unlike past transit detection algorithms, deep nets learn to recognize planet features instead of relying on hand-coded metrics that humans perceive as the most representative. Our convolutional neural network is capable of detecting Earth-like exoplanets in noisy time series data with a greater accuracy than a least-squares method. Deep nets are highly generalizable allowing data to be evaluated from different time series after interpolation without compromising performance. As validated by our deep net analysis of Kepler light curves, we detect periodic transits consistent with the true period without any model fitting. Our study indicates that machine learning will facilitate the characterization of exoplanets in future analysis of large astronomy data sets.
KW - Methods: data analysis
KW - Planets and satellites: detection
KW - Techniques: photometric
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U2 - 10.1093/mnras/stx2761
DO - 10.1093/mnras/stx2761
M3 - Article
AN - SCOPUS:85045564206
VL - 474
SP - 478
EP - 491
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
SN - 0035-8711
IS - 1
ER -