相关论文: Deep Learning Classification in Asteroseismology
Although red clump stars function as reliable standard candles, their surface characteristics (i.e. $T_\text{eff}$, $\log g$, and [Fe/H]) overlap with those of red giant branch stars, which are not standard candles. Recent results have…
In spite of the huge advances in exoplanet research provided by the NASA Kepler Mission, there remain only a small number of transit detections around evolved stars. Here we present a reformulation of the noise properties of red-giant…
The measurement of oscillations excited by surface convection is a powerful method to study the structure and evolution of cool stars. CoRoT and Kepler have initiated a revolution in asteroseismology by detecting oscillations in thousands…
The CoRoT 5-month long observation runs give us the opportunity to analyze a large variety of red-giant stars and to derive fundamental stellar parameters from their asteroseismic properties. We perform an analysis of more than 4\,600 CoRoT…
In the coming years, next-generation space-based infrared observatories will significantly increase our samples of rare massive stars, representing a tremendous opportunity to leverage modern statistical tools and methods to test massive…
With the success of the Kepler and CoRoT missions, the number of stars with detected solar-like oscillations has increased by several orders of magnitude, for the first time we are able to perform large-scale ensemble asteroseismology of…
Asteroseismology has proven to be an excellent tool to determine not only the global stellar properties with a good precision but also to infer stellar structure, dynamics, and evolution for a large sample of Kepler stars. Prior to the…
Deep Learning (DL) is a machine learning procedure for artificial intelligence that analyzes the input data in detail by increasing neuron sizes and number of the hidden layers. DL has a popularity with the common improvements on the…
The efficiency of the transport of angular momentum and chemical elements inside intermediate-mass stars lacks proper calibration, thereby introducing uncertainties on a star's evolutionary pathway. Improvements require better estimation of…
Stellar oscillations and granulation in red giants are both powered by convection. Studying the wavelength dependence of their amplitudes can provide useful insights on the driving mechanism. It is also important for plans to carry out…
We propose a novel approach to enhance the discriminability of Convolutional Neural Networks (CNN). The key idea is to build a tree structure that could progressively learn fine-grained features to distinguish a subset of classes, by…
Deconvolution of large survey images with millions of galaxies requires to develop a new generation of methods which can take into account a space variant Point Spread Function (PSF) and have to be at the same time accurate and fast. We…
Modern convolutional neural networks (CNNs) are able to achieve human-level object classification accuracy on specific tasks, and currently outperform competing models in explaining complex human visual representations. However, the…
Asteroseismology, the study of stellar oscillations, and stellar modeling both offer profound insights into the fundamental properties and evolution of stars. With pySYD, a new open-source Python package, we were able to constrain the…
Large sky spectroscopic surveys have reached the scale of photometric surveys in terms of sample sizes and data complexity. These huge datasets require efficient, accurate, and flexible automated tools for data analysis and science…
Machine learning is a field that has been growing in importance since the early 2010s due to the increasing accuracy of classification models and hardware advances that have enabled faster training on large datasets. In the field of…
We present an image classification algorithm using deep learning convolutional neural network architecture, which classifies the morphologies of eclipsing binary systems based on their light curves. The algorithm trains the machine with…
Asteroseismology with the space-borne missions CoRoT and Kepler provides a powerful mean of testing the modeling of transport processes in stars. Rotational splittings are currently measured for a large number of red giant stars and can…
We aim to describe the oscillation power excess observed in Kepler red giants, and to investigate empirical scaling relations governing these parameters. From these scalings relations, we derive new physical properties of red giant…
In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152, Inception v3,…