Supervised Learning Detection of Sixty Non-Transiting Hot Jupiter Candidates

Supervised Learning Detection of Sixty Non-Transiting Hot Jupiter Candidates

Authors:

Millholland et al

Abstract:
The optical, full-phase photometric variations of a short-period planet provide a unique view of the planet's atmospheric composition and dynamics. The number of planets with optical phase curve detections, however, is currently too small to study them as an aggregate population, motivating an extension of the search to non-transiting planets. Here we present an algorithm for the detection of non-transiting, short-period giant planets in the Kepler field. The procedure uses the phase curves themselves as evidence for the planets' existence. We employ a supervised learning algorithm to recognize the salient time-dependent properties of synthetic phase curves; we then search for detections of signals that match these properties. After demonstrating the algorithm's capabilities, we classify 142,630 FGK Kepler stars without confirmed planets or KOIs and, for each one, assign a probability of a phase curve of a non-transiting planet being present. We identify 60 high-probability non-transiting hot Jupiter candidates. We also derive constraints on the candidates' albedos and offsets of the phase curve maxima. These targets are strong candidates for follow-up radial velocity confirmation and characterization. Once confirmed, the atmospheric information content in the phase curves may be studied in yet greater detail.

Systematic Discusses Machine Learning & Detecting Hot Jupiters

There’s no denying the fundamentally alien climates on the hot Jupiters. It’s not clear, however, how hot Jupiters form, and it’s not clear why so many of them are badly distended. Moreover, it’s only vaguely clear what the weather patterns on one would look like up close. (One thing that is clear is that the flights would all be canceled).

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Using Machine Learning for Real-time Detection of Transiets in OGLE-IV

Real-time detection of transients in OGLE-IV with application of machine learning

Authors:

Klencki et al

Abstract:

The current bottleneck of transient detection in most surveys is the problem of rejecting numerous artifacts from detected candidates. We present a triple-stage hierarchical machine learning system for automated artifact filtering in difference imaging, based on self-organizing maps. The classifier, when tested on the OGLE-IV Transient Detection System, accepts ~ 97 % of real transients while removing up to ~ 97.5 % of artifacts.

Using Machine Learning to ID Exoplanet Transit

A Machine Learning Technique to Identify Transit Shaped Signals

Authors:

Thompson et al

Abstract:

We describe a new metric that uses machine learning to determine if a periodic signal found in a photometric time series appears to be shaped like the signature of a transiting exoplanet. This metric uses dimensionality reduction and k-nearest neighbors to determine whether a given signal is sufficiently similar to known transits in the same data set. This metric is being used by the Kepler Robovetter to determine which signals should be part of the Q1-Q17 DR24 catalog of planetary candidates. The Kepler Mission reports roughly 20,000 potential transiting signals with each run of its pipeline, yet only a few thousand appear sufficiently transit shaped to be part of the catalog. The other signals tend to be variable stars and instrumental noise. With this metric we are able to remove more than 90% of the non-transiting signals while retaining more than 99% of the known planet candidates. When tested with injected transits, less than 1% are lost. This metric will enable the Kepler mission and future missions looking for transiting planets to rapidly and consistently find the best planetary candidates for follow-up and cataloging.