Related papers: Protostellar classification using supervised machi…
This paper follows series of our works on the applicability of various machine learning methods to the morphological galaxy classification (Vavilova et al., 2021, 2022). We exploited the sample of 315776 SDSS DR9 galaxies with absolute…
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift…
[Abridged] Although galaxies are found to follow a tight relation between their star formation rate and stellar mass, they are expected to exhibit complex star formation histories (SFH), with short-term fluctuations. The goal of this pilot…
In this work we have carried out an in-depth analysis of the young stellar content in the W3 GMC. The YSO population was identified and classified in the IRAC/MIPS color-magnitude space according to the `Class' scheme and compared to other…
The scientific study of the Solar System's minor bodies ultimately starts with a search for those bodies. This chapter presents a review of the use of machine learning techniques to find moving objects, both natural and artificial, in…
Young Stellar Objects (YSOs) and in particular protostars are known to show a variety of high-energy processes. Observations in the X-ray and centimetric radio wavelength ranges are thought to constrain some of these processes, e.g.,…
We provide a library of some 7000 SEDs for starbursts and ultra luminous galaxies (http://www.eso.org/~rsiebenm/sb_models). Its purpose is to quickly obtain estimates of the basic parameters, such as luminosity, size and dust or gas mass…
X-rays from very young stars are powerful probes to investigate the mechanisms at work in the very first stages of the star formation and the origin of X-ray emission in very young stars. We present results from a 500 ks long observation of…
With the Wide-Field Infrared Survey Explorer (WISE), we searched for young stellar objects (YSOs) in a 100 deg^2 region centered on the lightly studied Canis Major star forming region. Applying stringent magnitude cuts to exclude the…
Efficient identification and follow-up of astronomical transients is hindered by the need for humans to manually select promising candidates from data streams that contain many false positives. These artefacts arise in the difference images…
In recent decades, large-scale sky surveys such as Sloan Digital Sky Survey (SDSS) have resulted in generation of tremendous amount of data. The classification of this enormous amount of data by astronomers is time consuming. To simplify…
We aim to develop a model-driven deep learning approach to age determination, by training neural networks on stellar evolutionary grids. Contrary to the usual data-driven deep learning approach of using prior age estimates as training data,…
Machine learning-based techniques open up many opportunities and improvements to derive deeper and more practical insights from data that can help businesses make informed decisions. However, the majority of these techniques focus on the…
The Serpens Molecular Cloud is one of the most active sites of ongoing star formation at a distance of about 300 pc, and hence is very well-suited for studies of young low-mass stars and sub-stellar objects. In this paper, for the Serpens…
Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constellation for scene…
The ESA Euclid mission will survey more than 14,000 deg$^2$ of the sky in visible and near-infrared wavelengths, mapping the extra-galactic sky to constrain our cosmological model of the Universe. Although the survey focusses on regions…
We develop a novel method based on machine learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-D parameter space. A deep neural network is trained on a precomputed…
Fine-grained open-set recognition (FineOSR) aims to recognize images belonging to classes with subtle appearance differences while rejecting images of unknown classes. A recent trend in OSR shows the benefit of generative models to…
We search for evidence of a relation between properties of young stellar objects (YSOs) and their parent molecular clouds to understand the initial conditions of high-mass star formation. A sample of 135 sources was selected from the…
The Serpens Molecular Cloud is one of the most active star-forming regions within 500 pc, with over one thousand of YSOs at different evolutionary stages. The ages of the member stars inform us about the star formation history of the cloud.…