Related papers: Protostellar classification using supervised machi…
We present a novel method of robust probabilistic cosmic web particle classification in three dimensions using a supervised machine learning algorithm. Training data was generated using a simplified $\Lambda$CDM toy model with…
In this paper we undertake a study of the 21 square degree SMOG field, a Spitzer cryogenic mission Legacy program to map a region of the outer Milky Way towards the Perseus and Outer spiral arms with the IRAC and MIPS instruments. We…
Using photometric data of infrared surveys, young stellar object (YSO) status is verified for 141 objects selected in our previous papers in the Cassiopeia and Camelopardalis segment of the Milky Way bounded by Galactic coordinates (l, b) =…
The automatic classification of X-ray detections is a necessary step in extracting astrophysical information from compiled catalogs of astrophysical sources. Classification is useful for the study of individual objects, statistics for…
We present the full catalog of Young Stellar Objects (YSOs) identified in the 18 molecular clouds surveyed by the Spitzer Space Telescope "cores to disks" (c2d) and "Gould Belt" (GB) Legacy surveys. Using standard techniques developed by…
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…
There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…
Accurately distinguishing between quiescent and star-forming galaxies is essential for understanding galaxy evolution. Traditional methods, such as spectral energy distribution (SED) fitting, can be computationally expensive and may…
The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the…
As nearby neighbors to the Milky Way, the Large and Small Magellanic Clouds (LMC and SMC) provide a unique opportunity to study star formation in the context of their galactic ecosystems. Thousands of young stellar objects (YSOs) have been…
The rarity and deeply embedded nature of stars with masses larger than 8 solar masses has limited our understanding of their formation. Previous work has shown that complementing spectral energy distributions with interferometric and…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a…
Context. In modern astronomy, machine learning has proved to be efficient and effective to mine the big data from the newesttelescopes. Spectral surveys enable us to characterize millions of objects, while long exposure time observations…
The evolutionary classification of molecular clumps, crucial for understanding star formation, is commonly based on human-assigned categories derived from infrared (IR) emission and well-established morphological criteria. However, due to…
We present morphological classifications obtained using machine learning for objects in SDSS DR6 that have been classified by Galaxy Zoo into three classes, namely early types, spirals and point sources/artifacts. An artificial neural…
We propose a machine-learning-based technique to determine the number density of radio sources as a function of their flux density, for use in next-generation radio surveys. The method uses a convolutional neural network trained on…
Utilizing a decade-long unTimely dataset, supplemented by multi-band data from archives, we search for young stellar objects (YSOs) with variations larger than one magnitude in W1 band within a region of 110 square degrees in the Galactic…
Recently, machine learning methods presented a viable solution for automated classification of image-based data in various research fields and business applications. Scientists require a fast and reliable solution to be able to handle the…
A grid of 20 millions 3-1100$\mu$m SED models is presented for synthetic young clusters embedded in dense clumps. The models depend on four primary parameters: the clump mass M$_{clump}$ and dust temperature T$_{dust}$, the fraction of mass…