### Abstract

We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA is a detection and classification algorithm based on the Machine Learning technique (Random Forest). The goal of this paper is to show the power of SIDRA for quick and accurate signal detection and classification.We first diagnose the power of the method with simulated light curves and try it on a subset of the Kepler space mission catalogue.We use five classes of simulated light curves (CONSTANT, TRANSIT, VARIABLE, MLENS and EB for constant light curves, transiting exoplanet, variable, microlensing events and eclipsing binaries, respectively) to analyse the power of the method. The algorithm uses four features in order to classify the light curves. The training sample contains 5000 light curves (1000 from each class) and 50 000 random light curves for testing. The total SIDRA success ratio is ≥90 per cent. Furthermore, the success ratio reaches 95-100 per cent for the CONSTANT, VARIABLE, EB and MLENS classes and 92 per cent for the TRANSIT class with a decision probability of 60 per cent. Because the TRANSIT class is the one which fails the most, we run a simultaneous fit using SIDRA and a Box Least Square (BLS)-based algorithm for searching for transiting exoplanets. As a result, our algorithm detects 7.5 per cent more planets than a classic BLS algorithm, with better results for lower signal-to-noise light curves. SIDRA succeeds to catch 98 per cent of the planet candidates in the Kepler sample and fails for 7 per cent of the false alarms subset. SIDRA promises to be useful for developing a detection algorithm and/or classifier for large photometric surveys such as TESS and PLATO exoplanet future space missions.

Original language | English |
---|---|

Pages (from-to) | 626-633 |

Number of pages | 8 |

Journal | Monthly Notices of the Royal Astronomical Society |

Volume | 455 |

Issue number | 1 |

DOIs | |

Publication status | Published - 2016 |

### Fingerprint

### Keywords

- Detection
- Fundamental parameters
- Photometric
- Planetary systems
- Planets and satellites
- Planets and satellites
- Techniques

### ASJC Scopus subject areas

- Space and Planetary Science
- Astronomy and Astrophysics

### Cite this

*Monthly Notices of the Royal Astronomical Society*,

*455*(1), 626-633. https://doi.org/10.1093/mnras/stv2333

**SIDRA : A blind algorithm for signal detection in photometric surveys.** / Mislis, Dimitrios; Bachelet, E.; Al-Subai, Khalid; Bramich, D. M.; Parley, N.

Research output: Contribution to journal › Article

*Monthly Notices of the Royal Astronomical Society*, vol. 455, no. 1, pp. 626-633. https://doi.org/10.1093/mnras/stv2333

}

TY - JOUR

T1 - SIDRA

T2 - A blind algorithm for signal detection in photometric surveys

AU - Mislis, Dimitrios

AU - Bachelet, E.

AU - Al-Subai, Khalid

AU - Bramich, D. M.

AU - Parley, N.

PY - 2016

Y1 - 2016

N2 - We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA is a detection and classification algorithm based on the Machine Learning technique (Random Forest). The goal of this paper is to show the power of SIDRA for quick and accurate signal detection and classification.We first diagnose the power of the method with simulated light curves and try it on a subset of the Kepler space mission catalogue.We use five classes of simulated light curves (CONSTANT, TRANSIT, VARIABLE, MLENS and EB for constant light curves, transiting exoplanet, variable, microlensing events and eclipsing binaries, respectively) to analyse the power of the method. The algorithm uses four features in order to classify the light curves. The training sample contains 5000 light curves (1000 from each class) and 50 000 random light curves for testing. The total SIDRA success ratio is ≥90 per cent. Furthermore, the success ratio reaches 95-100 per cent for the CONSTANT, VARIABLE, EB and MLENS classes and 92 per cent for the TRANSIT class with a decision probability of 60 per cent. Because the TRANSIT class is the one which fails the most, we run a simultaneous fit using SIDRA and a Box Least Square (BLS)-based algorithm for searching for transiting exoplanets. As a result, our algorithm detects 7.5 per cent more planets than a classic BLS algorithm, with better results for lower signal-to-noise light curves. SIDRA succeeds to catch 98 per cent of the planet candidates in the Kepler sample and fails for 7 per cent of the false alarms subset. SIDRA promises to be useful for developing a detection algorithm and/or classifier for large photometric surveys such as TESS and PLATO exoplanet future space missions.

AB - We present the Signal Detection using Random-Forest Algorithm (SIDRA). SIDRA is a detection and classification algorithm based on the Machine Learning technique (Random Forest). The goal of this paper is to show the power of SIDRA for quick and accurate signal detection and classification.We first diagnose the power of the method with simulated light curves and try it on a subset of the Kepler space mission catalogue.We use five classes of simulated light curves (CONSTANT, TRANSIT, VARIABLE, MLENS and EB for constant light curves, transiting exoplanet, variable, microlensing events and eclipsing binaries, respectively) to analyse the power of the method. The algorithm uses four features in order to classify the light curves. The training sample contains 5000 light curves (1000 from each class) and 50 000 random light curves for testing. The total SIDRA success ratio is ≥90 per cent. Furthermore, the success ratio reaches 95-100 per cent for the CONSTANT, VARIABLE, EB and MLENS classes and 92 per cent for the TRANSIT class with a decision probability of 60 per cent. Because the TRANSIT class is the one which fails the most, we run a simultaneous fit using SIDRA and a Box Least Square (BLS)-based algorithm for searching for transiting exoplanets. As a result, our algorithm detects 7.5 per cent more planets than a classic BLS algorithm, with better results for lower signal-to-noise light curves. SIDRA succeeds to catch 98 per cent of the planet candidates in the Kepler sample and fails for 7 per cent of the false alarms subset. SIDRA promises to be useful for developing a detection algorithm and/or classifier for large photometric surveys such as TESS and PLATO exoplanet future space missions.

KW - Detection

KW - Fundamental parameters

KW - Photometric

KW - Planetary systems

KW - Planets and satellites

KW - Planets and satellites

KW - Techniques

UR - http://www.scopus.com/inward/record.url?scp=84958292246&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958292246&partnerID=8YFLogxK

U2 - 10.1093/mnras/stv2333

DO - 10.1093/mnras/stv2333

M3 - Article

VL - 455

SP - 626

EP - 633

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 1

ER -