One-dimensional Convolutional Neural Networks for Detecting Transiting Exoplanets

• URL: http://arxiv.org/abs/2312.07161v1
• Date: Tue, 12 Dec 2023 10:56:27 GMT
• Title: One-dimensional Convolutional Neural Networks for Detecting Transiting Exoplanets
• Authors: Santiago Iglesias \'Alvarez, Enrique D\'iez Alonso, Mar\'ia Luisa S\'anchez, Javier Rodr\'iguez Rodr\'iguez, Fernando S\'anchez Lasheras and Francisco Javier de Cos Juez
• Abstract summary: We develop an artificial neural network model that is able to detect transits in light curves obtained from different telescopes and surveys. We created artificial light curves with and without transits to try to mimic those expected for the extended mission of the Kepler telescope (K2) in order to train and validate a 1D convolutional neural network model.
• Score: 39.58317527488534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The transit method is one of the most relevant exoplanet detection techniques, which consists of detecting periodic eclipses in the light curves of stars. This is not always easy due to the presence of noise in the light curves, which is induced, for example, by the response of a telescope to stellar flux. For this reason, we aimed to develop an artificial neural network model that is able to detect these transits in light curves obtained from different telescopes and surveys. We created artificial light curves with and without transits to try to mimic those expected for the extended mission of the Kepler telescope (K2) in order to train and validate a 1D convolutional neural network model, which was later tested, obtaining an accuracy of 99.02 % and an estimated error (loss function) of 0.03. These results, among others, helped to confirm that the 1D CNN is a good choice for working with non-phased-folded Mandel and Agol light curves with transits. It also reduces the number of light curves that have to be visually inspected to decide if they present transit-like signals and decreases the time needed for analyzing each (with respect to traditional analysis).

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)
• A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients [0.0]
  Real-time anomaly detection is essential for identifying rare transients in the era of large-scale astronomical surveys. Currently, most anomaly detection algorithms for astronomical transients rely on hand-crafted features extracted from light curves. We introduce an alternative approach to detecting anomalies: using the penultimate layer of a neural network classifier as the latent space for anomaly detection.
  arXiv  Detail & Related papers  (2024-03-21T18:00:00Z)
• 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)
• 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)
• Cosmology from Galaxy Redshift Surveys with PointNet [65.89809800010927]
  In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. We employ a textitPointNet-like neural network to regress the values of the cosmological parameters directly from point cloud data. Our implementation of PointNets can analyse inputs of $mathcalO(104) - mathcalO(105)$ galaxies at a time, which improves upon earlier work for this application by roughly two orders of magnitude.
  arXiv  Detail & Related papers  (2022-11-22T15:35:05Z)
• Supernova Light Curves Approximation based on Neural Network Models [53.180678723280145]
  Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. We study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve.
  arXiv  Detail & Related papers  (2022-06-27T13:46:51Z)
• Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data [0.0]
  Astronomical transients are stellar objects that become temporarily brighter on various timescales. New astronomical sky surveys are observing unprecedented numbers of multi-wavelength transients. We present two novel methods that aim to quickly and automatically detect anomalous transient light curves in real-time.
  arXiv  Detail & Related papers  (2021-12-15T19:02:54Z)
• Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks [1.2583362454189522]
  Transiting Exoplanet Survey Satellite measured light from stars in 75% of the sky throughout its two year primary mission. Millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives.
  arXiv  Detail & Related papers  (2021-01-26T16:40:51Z)
• DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [70.80563014913676]
  We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images. We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
  arXiv  Detail & Related papers  (2020-11-24T22:51:08Z)

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.