Related papers: Automatic unsupervised classification of all SDSS/…
We have compiled a large sample of low-redshift active galactic nuclei (AGN) identified via their emission line characteristics from the spectroscopic data of the Sloan Digital Sky Survey. Since emission lines are often contaminated by…
We present the result of a spectroscopic campaign targeting Active Galactic Nucleus (AGN) candidates selected using a novel unsupervised machine-learning (ML) algorithm trained on optical and mid-infrared (mid-IR) photometry. AGN candidates…
We present a study on the environments of the SDSS galaxies divided into fine classes based on their morphology, colour and spectral features. The SDSS galaxies are classified into early-type and late-type; red and blue; passive, HII,…
Spectral clustering (SC) and graph-based semi-supervised learning (SSL) algorithms are sensitive to how graphs are constructed from data. In particular if the data has proximal and unbalanced clusters these algorithms can lead to poor…
What do we see when we look at a nearby, well-resolved galaxy? Thousands of individual sources are detected in multiband imaging observations of even a fraction of a nearby galaxy, and characterizing those sources is a complex process. This…
While emission-line flux ratio diagnostics are the most common technique for identifying active galactic nuclei (AGNs) in optical spectra, applying this approach to single fiber spectra of galaxies can omit entire subpopulations of AGNs.…
We present the spectrophotometric properties of a sample of 141 emission-line galaxies at redshifts in the range $0.2<z<1.0$ with a peak around $z\in[0.2,0.4]$. The analysis is based on medium resolution ($R\_{\mathrm{s}}=500-600$), optical…
Differences in clustering properties between galaxy subpopulations complicate the cosmological interpretation of the galaxy power spectrum, but can also provide insights about the physics underlying galaxy formation. To study the nature of…
We use the W_Ha versus [NII]/Ha (WHAN) diagram to provide a comprehensive emission-line classification of SDSS galaxies. This classification is able to cope with the large population of weak line galaxies that do not appear in traditional…
Techniques to classify galaxies solely based on photometry will be necessary for future large cosmology missions, such as Euclid or LSST. However, the precision of classification is always lower in photometric surveys and can be…
The Sloan Digital Sky Survey (SDSS) and Two Micron All Sky Survey (2MASS) are rich resources for studying stellar astrophysics and the structure and formation history of the Galaxy. As new surveys and instruments adopt similar filter sets,…
(Abridged) We present an unbiassed near-IR selected AGN sample, covering 12.56 square degrees down to K ~ 15.5, selected from the Two Micron All Sky Survey (2MASS). Our only selection effect is a moderate color cut (J-K>1.2) designed to…
The WISE satellite has detected hundreds of millions sources over the entire sky. Classifying them reliably is however a challenging task due to degeneracies in WISE multicolour space and low levels of detection in its two…
Using an eigenspectrum decomposition technique, we separate the host galaxy from the broad line active galactic nucleus (AGN) in a set of 4666 spectra from the Sloan Digital Sky Survey (SDSS), from redshifts near zero up to about 0.75. The…
Automated spectral classification is an active research area in astronomy at the age of data explosion. While new generation of sky survey telescopes (e.g. LAMOST and SDSS) produce huge amount of spectra, automated spectral classification…
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…
We present an atlas of 129 spectral energy distributions for nearby galaxies, with wavelength coverage spanning from the UV to the mid-infrared. Our atlas spans a broad range of galaxy types, including ellipticals, spirals, merging…
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…
We present a new database of absorption and emission-line measurements based on the entire spectral atlas from the Sloan Digital Sky Survey (SDSS) 7th data release of galaxies within a redshift of 0.2. Our work makes use of the publicly…
We describe the target selection and resulting properties of a spectroscopic sample of luminous, red galaxies (LRG) from the imaging data of the Sloan Digital Sky Survey (SDSS). These galaxies are selected on the basis of color and…