Related papers: Galaxy Structural Parameters in Source Extractor
Image decomposition of galaxies is now routinely used to estimate the structural parameters of galactic components. In this work, I address questions on the reliability of this technique. In particular, do bars and AGN need to be taken into…
Structural disturbances, such as galaxy mergers or instabilities, are key candidates for driving galaxy evolution, so it is important to detect and quantify galaxies hosting these disturbances spanning a range of masses, environments, and…
We present the Cosmic Evolution Early Release Science Survey (CEERS) catalog, including space-based photometry, photometric redshifts, and physical parameters for more than 80,000 galaxies. The imaging used for this catalog comes from the…
Using the ESO-Sculptor galaxy redshift survey data (ESS), we have extensively tested the Principal Components Analysis (PCA) method to perform the spectral classification of galaxies with $z \la$ 0.5. This method allows us to classify all…
This paper proposes a spatial feature extraction method based on energy of the features for classification of the hyperspectral data. A proposed orthogonal filter set extracts spatial features with maximum energy from the principal…
A major problem in extragalactic astronomy is the inability to distinguish in a robust, physical, and model independent way how galaxy populations are related to each other and to their formation histories. A similar, but distinct, and also…
Extended stellar clusters with effective radii larger than 10 pc have been found in various environments. Objects with masses comparable to globular clusters (GCs) are called extended clusters (ECs), while objects with masses in the dwarf…
The Planet Hunting and Asteroseismology Explorer Spectrophotometer, PHASES, is a concept for a space-borne instrument to obtain flux calibrated spectra and measure micro-magnitude photometric variations of nearby stars. The science drivers…
Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in galaxy star formation histories and dust attenuation curves.…
We develop a novel statistical strong lensing approach to probe the cosmological parameters by exploiting multiple redshift image systems behind galaxies or galaxy clusters. The method relies on free-form mass inversion of strong lenses and…
Evolutionary studies that compare galaxy structure as a function of redshift are complicated by the fact that any particular galaxy's appearance depends in part on the rest-frame wavelength of the observation. This leads to the necessity…
Continuous gravitational-wave (CW) signals such as emitted by spinning neutron stars are an important target class for current detectors. However, the enormous computational demand prohibits fully coherent broadband all-sky searches for…
We present global structural parameter measurements of 109,533 unique, H_F160W-selected objects from the CANDELS multi-cycle treasury program. Sersic model fits for these objects are produced with GALFIT in all available near-infrared…
A new generation of spectral synthesis models has been developed in the recent years, but there is no matching -- in terms of quality and resolution -- set of template galaxy spectra for testing and refining the new models. Our main goal is…
We investigate how observations of strong lensing can be used to infer cosmological parameters, in particular the equation of state of dark energy. We focus on the growth of the critical lines of lensing clusters with the source redshift as…
We present the spectral atlas of sources observed in low resolution with the Infrared Spectrograph on board the Spitzer Space Telescope. More than 11,000 distinct sources were extracted using a dedicated algorithm based on the SMART…
Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to…
Sample selection improves the efficiency and effectiveness of machine learning models by providing informative and representative samples. Typically, samples can be modeled as a sample graph, where nodes are samples and edges represent…
Fine-grained visual categorization (FGVC), which aims at classifying objects with small inter-class variances, has been significantly advanced in recent years. However, ultra-fine-grained visual categorization (ultra-FGVC), which targets at…
We derive physical parameters of galaxies from their observed spectrum, using MOPED, the optimized data compression algorithm of Heavens, Jimenez & Lahav 2000. Here we concentrate on parametrising galaxy properties, and apply the method to…