Related papers: APOGEE Net: Improving the derived spectral paramet…
Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying…
In this experiment, we created a Multiple-Input Neural Network, consisting of Convolutional and Multi-layer Neural Networks. With this setup the selected highest-performing neural network was able to distinguish variable stars based on the…
Automated method of full spectrum fitting gives reliable estimates of stellar atmospheric parameters (Teff, logg and [Fe/H]) for late A, F, G and early K type stars. Recently, the technique was further improved in the cooler regime and the…
Characterizing the fundamental parameters of stars from observations is crucial for studying the stars themselves, their planets, and the galaxy as a whole. Stellar evolution theory predicting the properties of stars as a function of…
In the current panorama of large surveys, the vast amount of data obtained with different methods, data types, formats, and stellar samples, is making an efficient use of the available information difficult. The Survey of Surveys is a…
This work investigates the spectrum parameterization problem using deep neural networks (DNNs). The proposed scheme consists of the following procedures: first, the configuration of a DNN is initialized using a series of autoencoder neural…
Identification of metal-poor stars among field stars is extremely useful for studying the structure and evolution of the Galaxy and of external galaxies. We search for metal-poor stars using the artificial neural network (ANN) and extend…
Supervised machine learning models are increasingly being used for solving the problem of stellar classification of spectroscopic data. However, training such models requires a large number of labelled instances, the collection of which is…
In this work we explore the possibility of applying machine learning methods designed for one-dimensional problems to the task of galaxy image classification. The algorithms used for image classification typically rely on multiple costly…
Integrated light spectroscopy from galaxies can be used to study the stellar populations that cannot be resolved into individual stars. This analysis relies on stellar population synthesis (SPS) techniques to study the formation history and…
In this paper, I present a new set of synthetic spectral energy distributions (SEDs) for young stellar objects (YSOs) spanning a wide range of evolutionary stages, from the youngest deeply embedded protostars to pre-main-sequence stars with…
Deep neural networks have gained great success due to the increasing amounts of data, and diverse effective neural network designs. However, it also brings a heavy computing burden as the amount of training data is proportional to the…
We present a semi-empirical spectral classification scheme for normal B-type stars using near-infrared spectra (1.5-1.7 $\mu$m) from the SDSS APOGEE2-N DR14 database. The main motivation for working with B-type stars is their importance in…
In Astronomy, a huge amount of image data is generated daily by photometric surveys, which scan the sky to collect data from stars, galaxies and other celestial objects. In this paper, we propose a technique to leverage unlabeled…
Innovation in the ground and space-based instruments has taken us into a new age of spectroscopy, in which a large amount of stellar content is becoming available. So, automatic classification of stellar spectra became subjective in recent…
With the plentiful information available in the Gaia BP/RP spectra, there is significant scope for applying discriminative models to extract stellar atmospheric parameters and abundances. We describe an approach to leverage an `Uncertain…
Existing and upcoming instrumentation is collecting large amounts of astrophysical data, which require efficient and fast analysis techniques. We present a deep neural network architecture to analyze high-resolution stellar spectra and…
It is a challenging problem to detect and recognize targets on complex large-scene Synthetic Aperture Radar (SAR) images. Recently developed deep learning algorithms can automatically learn the intrinsic features of SAR images, but still…
The emergence of novel pathogens and zoonotic diseases like the SARS-CoV-2 have underlined the need for developing novel diagnosis and intervention pipelines that can learn rapidly from small amounts of labeled data. Combined with…
APOGEE-2 is a high-resolution, near-infrared spectroscopic survey observing roughly 300,000 stars across the entire sky. It is the successor to APOGEE and is part of the Sloan Digital Sky Survey IV (SDSS-IV). APOGEE-2 is expanding upon…