Discovery of Small Ultra-short-period Planets Orbiting KG Dwarfs in Kepler Survey Using GPU Phase Folding and Deep Learning Detection System
- URL: http://arxiv.org/abs/2312.17382v3
- Date: Sat, 14 Sep 2024 07:18:34 GMT
- Title: Discovery of Small Ultra-short-period Planets Orbiting KG Dwarfs in Kepler Survey Using GPU Phase Folding and Deep Learning Detection System
- Authors: Kaitlyn Wang, Jian Ge, Kevin Willis, Kevin Wang, Yinan Zhao, Quanquan Hu,
- Abstract summary: We employ the GPFC method, a novel GPU Phase Folding algorithm combined with a Convolutional Neural Network, on Kepler photometry data.
To date, we have identified five new ultra-short-period planets (USPs)
Kepler-158d, Kepler-963c, Kepler-879c, Kepler-1489c, and KOI-4978.02 are among the smallest planets that are closest to their host stars.
- Score: 3.766418873729154
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Of over 5,000 exoplanets identified so far, only a few hundred possess sub-Earth radii. The formation processes of these sub-Earths remain elusive, and acquiring additional samples is essential for investigating this unique population. In our study, we employ the GPFC method, a novel GPU Phase Folding algorithm combined with a Convolutional Neural Network, on Kepler photometry data. This method enhances the transit search speed significantly over the traditional Box-fitting Least Squares method, allowing a complete search of the known Kepler KOI data within days using a commercial GPU card. To date, we have identified five new ultra-short-period planets (USPs): Kepler-158d, Kepler-963c, Kepler-879c, Kepler-1489c, and KOI-4978.02. Kepler-879c with a radius of $0.4 R_\oplus$ completes its orbit around a G dwarf in 0.646716 days. Kepler-158d with a radius of $0.43 R_\oplus$ orbits a K dwarf star every 0.645088 days. Kepler-1489c with a radius of $0.51 R_\oplus$ orbits a G dwarf in 0.680741 days. Kepler-963c with a radius of $0.6 R_\oplus$ revolves around a G dwarf in 0.919783 days, and KOI-4978.02 with a radius of $0.7 R_\oplus$ circles a G dwarf in 0.941967 days. Among our findings, Kepler-879c, Kepler-158d and Kepler-963c rank as the first, the third, the fourth smallest USPs identified to date. Notably, Kepler-158d stands as the smallest USP found orbiting K dwarfs while Kepler-963c, Kepler-879c, Kepler-1489c, and KOI-4978.02 are the smallest USPs found orbiting G dwarfs. Kepler-879c, Kepler-158d, Kepler-1489c, and KOI-4978.02 are among the smallest planets that are closest to their host stars, with orbits within 5 stellar radii. In addition, these discoveries highlight GPFC's promising capability in identifying small, new transiting exoplanets within photometry data from Kepler, TESS, and upcoming space transit missions, PLATO and ET.
Related papers
- Real-time gravitational-wave inference for binary neutron stars using machine learning [71.29593576787549]
We present a machine learning framework that performs complete BNS inference in just one second without making any approximations.
Our approach enhances multi-messenger observations by providing (i) accurate localization even before the merger; (ii) improved localization precision by $sim30%$ compared to approximate low-latency methods; and (iii) detailed information on luminosity distance, inclination, and masses.
arXiv Detail & Related papers (2024-07-12T18:00:02Z) - Machine learning-based identification of Gaia astrometric exoplanet orbits [0.0]
Third Gaia data release (DR3) contains $sim$170 000 astrometric orbit solutions of two-body systems located within $sim$500 pc of the Sun.
Several DR3 two-body systems with exoplanet, brown-dwarf, stellar, and black-hole components have been confirmed in this way.
We developed an alternative machine learning approach that uses only the DR3 orbital solutions with the aim of identifying the best candidates for exoplanets and brown-dwarf companions.
arXiv Detail & Related papers (2024-04-14T20:17:14Z) - Exoplanets Prediction in Multi-Planetary Systems and Determining the
Correlation Between the Parameters of Planets and Host Stars Using Artificial
Intelligence [0.0]
We search for additional exoplanets in 229 multi-planetary systems that house at least three or more confirmed planets.
We employ efficient machine learning approaches to analyze a dataset comprising 762 confirmed exoplanets and eight Solar System planets.
For giant planets, we observe a strong correlation between planetary radius and the mass of their host stars, which might provide intriguing insights into the relationship between giant planet formation and stellar characteristics.
arXiv Detail & Related papers (2024-02-27T21:28:08Z) - DBNets: A publicly available deep learning tool to measure the masses of
young planets in dusty protoplanetary discs [49.1574468325115]
We develop DBNets, a tool to quickly infer the mass of allegedly embedded planets from protoplanetary discs.
We extensively tested our tool on out-of-distribution data.
DBNets can identify inputs strongly outside its training scope returning an uncertainty above a specific threshold.
It can be reliably applied only on discs observed with inclinations below approximately 60deg, in the optically thin regime.
arXiv Detail & Related papers (2024-02-19T19:00:09Z) - Machine learning methods for the search for L&T brown dwarfs in the data
of modern sky surveys [67.17190225886465]
Brown dwarfs (BD) should account for up to 25 percent of all objects in the Galaxy.
Due to their weakness, spectral studies of brown dwarfs are rather laborious.
Numerous attempts have been made to search for and create a set of brown dwarfs using their colours as a decision rule.
arXiv Detail & Related papers (2023-08-06T08:14:35Z) - Deep-learning based measurement of planetary radial velocities in the
presence of stellar variability [70.4007464488724]
We use neural networks to reduce stellar RV jitter in three years of HARPS-N sun-as-a-star spectra.
We find that the multi-line CNN is able to recover planets with 0.2 m/s semi-amplitude, 50 day period, with 8.8% error in the amplitude and 0.7% in the period.
arXiv Detail & Related papers (2023-04-10T18:33:36Z) - Fast Rates for Maximum Entropy Exploration [52.946307632704645]
We address the challenge of exploration in reinforcement learning (RL) when the agent operates in an unknown environment with sparse or no rewards.
We study the maximum entropy exploration problem two different types.
For visitation entropy, we propose a game-theoretic algorithm that has $widetildemathcalO(H3S2A/varepsilon2)$ sample complexity.
For the trajectory entropy, we propose a simple algorithm that has a sample of complexity of order $widetildemathcalO(mathrmpoly(S,
arXiv Detail & Related papers (2023-03-14T16:51:14Z) - Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds [53.110934987571355]
We propose Geodesic Sinkhorn -- based on a heat kernel on a manifold graph.
We apply our method to the computation of barycenters of several distributions of high dimensional single cell data from patient samples undergoing chemotherapy.
arXiv Detail & Related papers (2022-11-02T00:51:35Z) - Identifying Exoplanets with Machine Learning Methods: A Preliminary
Study [1.553390835237685]
We propose the idea of using machine learning methods to identify exoplanets.
We used the Kepler dataset collected by NASA from the Kepler Space Observatory to conduct supervised learning.
We also conducted unsupervised learning, which divides confirmed exoplanets into different clusters, using k-means clustering.
arXiv Detail & Related papers (2022-04-01T23:48:26Z) - Alleviating the Transit Timing Variations bias in transit surveys. II.
RIVERS: Twin resonant Earth-sized planets around Kepler-1972 recovered from
Kepler's false positive [0.0]
We show that Kepler-1972 c, initially the "not transit-like" false positive KOI-3184.02, is an Earth-sized planet whose orbit is perturbed by Kepler-1972 b.
Despite the faintness of the signals, we recovered the transits of the planets using the RIVERS method.
Alleviating this bias is essential for an unbiased view of Kepler systems, some of the TESS stars, and the upcoming PLATO mission.
arXiv Detail & Related papers (2022-01-27T11:53:13Z) - Exoplanet Detection using Machine Learning [0.0]
We introduce a new machine learning based technique to detect exoplanets using the transit method.
For Kepler data, the method is able to predict a planet with an AUC of 0.948, so that 94.8 per cent of the true planet signals are ranked higher than non-planet signals.
For the Transiting Exoplanet Survey Satellite (TESS) data, we found our method can classify light curves with an accuracy of 0.98, and is able to identify planets with a recall of 0.82 at a precision of 0.63.
arXiv Detail & Related papers (2020-11-28T14:06:39Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.