相关论文: Quadtree features for machine learning on CMDs

This work presents an approach (fitCMD) designed to obtain a comprehensive set of astrophysical parameters from colour-magnitude diagrams (CMDs) of star clusters. Based on initial mass function (IMF) properties taken from isochrones, fitCMD…

With the unprecedented increase of known star clusters, quick and modern tools are needed for their analysis. In this work, we develop an artificial neural network trained on synthetic clusters to estimate the age, metallicity, extinction,…

We present the analysis of a deep colour-magnitude diagram (CMD) of NGC 1831, a rich star cluster in the LMC. The data were obtained with HST/WFPC2 in the F555W (~V) and F814W (~I) filters, reaching m_555 ~ 25. We discuss and apply a method…

We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…

We present a new tool for colour-magnitude diagram (CMD) studies, $Powerful~CMD$. This tool is built on the basis of the advanced stellar population synthesis (ASPS) model, in which single stars, binary stars, rotating stars, and star…

Context. Machine-Learning (ML) solves problems by learning patterns from data, with limited or no human guidance. In Astronomy, it is mainly applied to large observational datasets, e.g. for morphological galaxy classification. Aims. We…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…

We present the first part of the first large and homogeneous CCD color-magnitude diagram (CMD) data base, comprising 52 nearby Galactic globular clusters (GGC) imaged in the V and I bands using only two telescopes (one for each hemisphere).…

Machine learning techniques can reveal hidden structure in large data amounts and can potentially extent or even replace analytical scientific methods. In nanophotonics, modes can increase the light yield from emitters located inside the…

Upcoming deep optical surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will scan the sky to unprecedented depths and detect billions of galaxies. This amount of detections will however cause the apparent…

Forthcoming astronomical surveys are expected to detect new sources in such large numbers that measuring their spectroscopic redshift measurements will be not be practical. Thus, there is much interest in using machine learning to yield the…

The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical descriptors designed by astronomers called features. These…

Reproducing color-magnitude diagrams (CMDs) of star-resolved galaxies is one of the most precise methods for measuring the star formation history (SFH) of nearby galaxies back to the earliest time. The upcoming big data era poses challenges…

We present detailed comparisons between high quality observational colour-magnitude diagrams (CMDs) of open star clusters and synthetic CMDs based on MonteCarlo numerical simulations. The comparisons account for all of the main parameters…

We present a simple approach for obtaining robust values of astrophysical parameters from the observed colour-magnitude diagrams (CMDs) of star clusters. The basic inputs are the Hess diagram built with the photometric measurements of a…

We present a deep machine learning (ML) approach to constraining cosmological parameters with multi-wavelength observations of galaxy clusters. The ML approach has two components: an encoder that builds a compressed representation of each…

In this paper we present the second and final part of a large and photometrically homogeneous CCD color-magnitude diagram (CMD) data base, comprising 52 nearby Galactic globular clusters (GGC) imaged in the V and I bands. The catalog has…

Most Machine Learning (ML) methods, from clustering to classification, rely on a distance function to describe relationships between datapoints. For complex datasets it is hard to avoid making some arbitrary choices when defining a distance…

Machine learning can provide powerful tools to detect patterns in multi-dimensional parameter space. We use K-means -a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabeled data- to study a sample of…

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…