Add to ORKGMy presentation highlights the compatibility of remote sensing and astronomy methods. Here I discuss the classification of galaxy components in UGC 2885, a massive spiral galaxy, by machine learning; in particular, I compare the traditional method of maximum likelihood with the more powerful models random forest and support vector machine
We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy A...
Context. Machine learning methods are effective tools in astronomical tasks for classifying objects ...
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (...
Automating classification of galaxy components is important for understanding the formation and evol...
International audienceClassification of galaxies is traditionally associated with their morphologies...
An emerging issue in the field of astronomy is the integration, management and utilization of databa...
The numerous strategies for the automated morphological categorization of galaxies, which uses a var...
Machine learning techniques are found to be increasingly useful in analyzing data from large galaxy ...
Abstract. In the last decades more and more all-sky surveys created an enormous amount of data which...
Context. The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, have been rec...
Galaxies have complex formations of components such as stars, dust, and gas, whose spatial and tempo...
In this work, I investigate the possibility of finding a data-driven solution to the problem of auto...
International audienceWe demonstrate that highly accurate joint redshift–stellar mass probability di...
We apply four statistical learning methods to a sample of 7941 galaxies (z <0.06) from the Galaxy An...
We present a machine learning analysis of five labelled galaxy catalogues from the Galaxy And Mass A...
We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy A...
Context. Machine learning methods are effective tools in astronomical tasks for classifying objects ...
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (...
Automating classification of galaxy components is important for understanding the formation and evol...
International audienceClassification of galaxies is traditionally associated with their morphologies...
An emerging issue in the field of astronomy is the integration, management and utilization of databa...
The numerous strategies for the automated morphological categorization of galaxies, which uses a var...
Machine learning techniques are found to be increasingly useful in analyzing data from large galaxy ...
Abstract. In the last decades more and more all-sky surveys created an enormous amount of data which...
Context. The two currently largest all-sky photometric datasets, WISE and SuperCOSMOS, have been rec...
Galaxies have complex formations of components such as stars, dust, and gas, whose spatial and tempo...
In this work, I investigate the possibility of finding a data-driven solution to the problem of auto...
International audienceWe demonstrate that highly accurate joint redshift–stellar mass probability di...
We apply four statistical learning methods to a sample of 7941 galaxies (z <0.06) from the Galaxy An...
We present a machine learning analysis of five labelled galaxy catalogues from the Galaxy And Mass A...
We apply four statistical learning methods to a sample of 7941 galaxies (z < 0.06) from the Galaxy A...
Context. Machine learning methods are effective tools in astronomical tasks for classifying objects ...
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (...