相关论文: Galaxy mergers classification using CNNs trained o…

Classifying galaxies is an essential step for studying their structures and dynamics. Using GalaxyZoo2 (GZ2) fractions thresholds, we collect 545 and 11,735 samples in non-galaxy and galaxy classes, respectively. We compute the Zernike…

Traditional breast cancer image classification methods require manual extraction of features from medical images, which not only require professional medical knowledge, but also have problems such as time-consuming and labor-intensive and…

We conduct an in-depth exploration of different strategies for doing event detection in videos using convolutional neural networks (CNNs) trained for image classification. We study different ways of performing spatial and temporal pooling,…

The global 21 cm HI emission-line profile of a galaxy encodes valuable information on the spatial distribution and kinematics of the neutral atomic gas. Galaxy interactions significantly influence the HI disk and imprint observable features…

Classifying the various shapes and attributes of a glioma cell nucleus is crucial for diagnosis and understanding the disease. We investigate automated classification of glioma nuclear shapes and visual attributes using Convolutional Neural…

Context. Convolutional neural networks (CNNs) have been proven to perform fast classification and detection on natural images and have potential to infer astrophysical parameters on the exponentially increasing amount of sky survey imaging…

The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To enhance the UML step, we employed a dual-encoder architecture…

A fundamental prediction of the LambdaCDM cosmology is the hierarchical build-up of structure and therefore the successive merging of galaxies into more massive ones. As one can only observe galaxies at one specific time in cosmic history,…

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to…

We propose a random forest (RF) machine learning approach to determine the accreted stellar mass fractions ($f_\mathrm{acc}$) of central galaxies, based on various dark matter halo and galaxy features. The RF is trained and tested using…

Galaxy merging is the late time manifestation of the galaxy formation process and likely significantly effects $z<1$ galaxies. A ``maximum reasonable rate'' model for merging finds a $\sim2$ mag K band increase in the luminosities of dwarf…

Galaxy mergers have long been proposed as a mechanism for funneling gas toward galactic centres, potentially triggering accretion onto supermassive black holes (SMBHs) and igniting active galactic nuclei (AGN). While simulations often…

The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy evolution. With the upcoming Large-scale Imaging Surveys, billions of galaxy images challenge astronomers to…

To read the final version please go to IEEE TGRS on IEEE Xplore. Convolutional neural networks (CNNs) have been attracting increasing attention in hyperspectral (HS) image classification, owing to their ability to capture spatial-spectral…

In many applications, Neural Nets (NNs) have classification performance on par or even exceeding human capacity. Moreover, it is likely that NNs leverage underlying features that might differ from those humans perceive to classify. Can we…

Numerical simulations of galaxy mergers are a powerful tool to study these fundamental events in the hierarchical built-up of galaxies. Recent progress have been made owing to improved modeling, increased resolution and large statistical…

Major mergers of disk galaxies are thought to be a substantial driver in galaxy evolution. To trace the fraction and the rate galaxies are in mergers over cosmic times, several observational techniques, including morphological selection…

(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…

We have used the 28'x 28' HST image mosaic from the GEMS (Galaxy Evolution from Morphology and SEDs) survey in conjunction with the COMBO-17 photometric redshift survey to constrain the incidence of major mergers between spheroid-dominated…

Galaxy merging is a fundamental aspect of the standard hierarchical galaxy formation paradigm. Recently, the Galaxy Zoo project has compiled a large, homogeneous catalogue of 3373 mergers, through direct visual inspection of the entire SDSS…