相关论文: Multiple machine-learning as a powerful tool for t…

Recent breakthroughs in machine learning especially artificial intelligence shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research…

Classification of young stellar objects (YSOs) into different evolutionary stages helps us to understand the formation process of new stars and planetary systems. Such classification has traditionally been based on spectral energy…

In modern astrophysics, the machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We describe an application of the supervised…

We present our new, fully-automated method to detect and measure the ages of star clusters in nearby galaxies, where individual stars can be resolved. The method relies purely on statistical analysis of observations and Monte-Carlo…

Observations at various wavelengths of late B-type stars exhibiting strong overabundances of the chemical elements Hg and Mn in their atmospheres indicate that these stars are frequently found in binary and multiple systems. We intend to…

In this work, we propose an original method for aggregating multiple clustering coming from different sources of information. Each partition is encoded by a co-membership matrix between observations. Our approach uses a mixture of…

The physical properties of the intracluster medium (ICM) reflect signatures of the underlying gravitational potential, mergers and strong interactions with other halos and satellite galaxies, as well as galactic feedback from supernovae and…

We compare the observed colour-magnitude diagrams (CMD) and the main sequence luminosity functions (LFs) of four Large Magellanic Cloud (LMC) star clusters namely, NGC 1711, NGC 2004, NGC 2164 and NGC 2214 with the synthetic ones derived…

Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from…

This paper presents machine learning experiments performed over results of galaxy classification into elliptical (E) and spiral (S) with morphological parameters: concetration (CN), assimetry metrics (A3), smoothness metrics (S3), entropy…

A significant degree of misclassification of variable stars through the application of machine learning methods to survey data motivates a search for more reliable and accurate machine learning procedures, especially in light of the very…

The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…

Thanks to the recent wide-area photometric surveys, the number of star cluster candidates have risen exponentially in the last few years. Most detections, however, are based only on the presence of an overdensity of stars in a given region,…

CMB lensing is a promising, novel way to measure galaxy cluster masses that can be used, e.g., for mass calibration in galaxy cluster counts analyses. Understanding the statistics of the galaxy cluster mass observable obtained with such…

Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…

High-resolution cosmological hydrodynamic simulations are currently limited to relatively small volumes due to their computational expense. However, much larger volumes are required to probe rare, overdense environments, and measure…

Numerical N-body simulations are commonly used to explore stability regions around exoplanets, offering insights into the possible existence of satellites and ring systems. This study aims to utilize Machine Learning (ML) techniques to…

Pulsar candidate sifting is an essential process for discovering new pulsars. It aims to search for the most promising pulsar candidates from an all-sky survey, such as High Time Resolution Universe (HTRU), Green Bank Northern Celestial Cap…

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…

The hot intra-cluster medium (ICM) surrounding the heart of galaxy clusters is a complex medium comprised of various emitting components. Although previous studies of nearby galaxy clusters, such as the Perseus, the Coma, or the Virgo…