Single Transit Detection In Kepler With Machine Learning And Onboard
Spacecraft Diagnostics
- URL: http://arxiv.org/abs/2403.03427v1
- Date: Wed, 6 Mar 2024 03:16:47 GMT
- Title: Single Transit Detection In Kepler With Machine Learning And Onboard
Spacecraft Diagnostics
- Authors: Matthew T. Hansen and Jason A. Dittmann
- Abstract summary: Exoplanet discovery at long orbital periods requires reliably detecting individual transits without additional information about the system.
We present a novel technique using an ensemble of Convolutional Neural Networks incorporating the onboard spacecraft diagnostics of emphKepler to classify transits within a light curve.
Our neural network pipeline has the potential to discover additional planets in the emphKepler dataset, and crucially, within the $eta$-Earth regime.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Exoplanet discovery at long orbital periods requires reliably detecting
individual transits without additional information about the system. Techniques
like phase-folding of light curves and periodogram analysis of radial velocity
data are more sensitive to planets with shorter orbital periods, leaving a
dearth of planet discoveries at long periods. We present a novel technique
using an ensemble of Convolutional Neural Networks incorporating the onboard
spacecraft diagnostics of \emph{Kepler} to classify transits within a light
curve. We create a pipeline to recover the location of individual transits, and
the period of the orbiting planet, which maintains $>80\%$ transit recovery
sensitivity out to an 800-day orbital period. Our neural network pipeline has
the potential to discover additional planets in the \emph{Kepler} dataset, and
crucially, within the $\eta$-Earth regime. We report our first candidate from
this pipeline, KOI 1271.02. KOI 1271.01 is known to exhibit strong Transit
Timing Variations (TTVs), and so we jointly model the TTVs and transits of both
transiting planets to constrain the orbital configuration and planetary
parameters and conclude with a series of potential parameters for KOI 1271.02,
as there is not enough data currently to uniquely constrain the system. We
conclude that KOI 1271.02 has a radius of 5.32 $\pm$ 0.20 $R_{\oplus}$ and a
mass of $28.94^{0.23}_{-0.47}$ $M_{\oplus}$. Future constraints on the nature
of KOI 1271.02 require measuring additional TTVs of KOI 1271.01 or observing a
second transit of KOI 1271.02.
Related papers
- Panopticon: a novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves [0.0]
We develop a deep learning model, Panopticon, to detect transits in high precision photometric light curves.
We trained the model on a set of simulated PLATO light curves in which we injected, at pixel level, either planetary, eclipsing binary, or background eclipsing binary signals.
The approach is able to recover 90% of our test population, including more than 25% of the Earth-analogs, even in the unfiltered light curves.
arXiv Detail & Related papers (2024-09-05T12:21:51Z) - 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) - Computing Transiting Exoplanet Parameters with 1D Convolutional Neural
Networks [0.0]
Two 1D convolutional neural network models are presented.
One model operates on complete light curves and estimates the orbital period.
The other one operates on phase-folded light curves and estimates the semimajor axis of the orbit and the square of the planet-to-star radius ratio.
arXiv Detail & Related papers (2024-02-21T10:17:23Z) - 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) - Ultra-low Power Deep Learning-based Monocular Relative Localization
Onboard Nano-quadrotors [64.68349896377629]
This work presents a novel autonomous end-to-end system that addresses the monocular relative localization, through deep neural networks (DNNs), of two peer nano-drones.
To cope with the ultra-constrained nano-drone platform, we propose a vertically-integrated framework, including dataset augmentation, quantization, and system optimizations.
Experimental results show that our DNN can precisely localize a 10cm-size target nano-drone by employing only low-resolution monochrome images, up to 2m distance.
arXiv Detail & Related papers (2023-03-03T14:14:08Z) - Predicting the Stability of Hierarchical Triple Systems with
Convolutional Neural Networks [68.8204255655161]
We propose a convolutional neural network model to predict the stability of hierarchical triples.
All trained models are made publicly available, allowing to predict the stability of hierarchical triple systems $200$ times faster than pure $N$-body methods.
arXiv Detail & Related papers (2022-06-24T17:58:13Z) - Semi-signed neural fitting for surface reconstruction from unoriented
point clouds [53.379712818791894]
We propose SSN-Fitting to reconstruct a better signed distance field.
SSN-Fitting consists of a semi-signed supervision and a loss-based region sampling strategy.
We conduct experiments to demonstrate that SSN-Fitting achieves state-of-the-art performance under different settings.
arXiv Detail & Related papers (2022-06-14T09:40:17Z) - 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) - Alleviating the transit timing variation bias in transit surveys. I.
RIVERS: Method and detection of a pair of resonant super-Earths around
Kepler-1705 [0.0]
Transit timing variations (TTVs) can provide useful information for systems observed by transit.
They can also act as a detection bias that can prevent the detection of small planets in transit surveys.
Here we introduce a detection method that is robust to large TTVs.
arXiv Detail & Related papers (2021-11-12T17:15:52Z) - Identifying Potential Exomoon Signals with Convolutional Neural Networks [0.0]
We train an ensemble of convolutional neural networks (CNNs) to identify candidate exomoon signals in single-transit events observed by Kepler.
We find a small fraction of these transits contain moon-like signals, though we caution against strong inferences of the exomoon occurrence rate from this result.
arXiv Detail & Related papers (2021-09-22T03:37:09Z) - Towards Robust Monocular Visual Odometry for Flying Robots on Planetary
Missions [49.79068659889639]
Ingenuity, that just landed on Mars, will mark the beginning of a new era of exploration unhindered by traversability.
We present an advanced robust monocular odometry algorithm that uses efficient optical flow tracking.
We also present a novel approach to estimate the current risk of scale drift based on a principal component analysis of the relative translation information matrix.
arXiv Detail & Related papers (2021-09-12T12:52:20Z)
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.