We present an update to the PanSTARRS-1 Point Source Catalog (PS1 PSC), which provides morphological classifications of PS1 sources. The original PS1 PSC adopted stringent detection criteria that excluded hundreds of millions of PS1 sources from the PSC. Here, we adapt the supervised machine learning methods used to create the PS1 PSC and apply them to different photometric measurements that are more widely available, allowing us to add $\sim$144 million new classifications while expanding the the total number of sources in PS1 PSC by $\sim$10%. We find that the new methodology, which utilizes PS1 forced photometry, performs $\sim$6-8% worse than the original method. This slight degradation in performance is offset by the overall increase in the size of the catalog. The PS1 PSC is used by time-domain surveys to filter transient alert streams by removing candidates coincident with point sources that are likely to be Galactic in origin. The addition of $\sim$144 million new classifications to the PS1 PSC will improve the efficiency with which transients are discovered.
In the era of large photometric surveys, the importance of automated and accurate classification is rapidly increasing. Specifically, the separation of resolved and unresolved sources in astronomical imaging is a critical initial step for a wide array of studies, ranging from Galactic science to large scale structure and cosmology. Here, we present our method to construct a large, deep catalog of point sources utilizing Pan-STARRS1 (PS1) 3$π$ survey data, which consists of $\sim$3$\times10^9$ sources with $m\lesssim23.5\,$mag. We develop a supervised machine-learning methodology, using the ran…
We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17) and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final data release of the MaNGA survey, which is part of the SDSS Data Release 17 (DR17). The MPP-VAC-DR17 provides photometric parameters from Sèrsic and Sèrsic+Exponential fits to the 2D surface brightness profiles of the MaNGA DR17 galaxy sample in the $g$, $r$, and $i$ bands (e.g. total fluxes, half light radii, bulge-disk fractions, ellipticities, position angles, etc.). The MDLM-VAC-DR17 provides Deep Learning-based morphological classificati…
Detection of point sources in images is a fundamental operation in astrophysics, and is crucial for constraining population models of the underlying point sources or characterizing the background emission. Standard techniques fall short in the crowded-field limit, losing sensitivity to faint sources and failing to track their covariance with close neighbors. We construct a Bayesian framework to perform inference of faint or overlapping point sources. The method involves probabilistic cataloging, where samples are taken from the posterior probability distribution of catalogs consistent with an …
We present MORDOR (MORphological DecOmposeR, a new algorithm for structural decomposition of simulated galaxies based on stellar kinematics. The code measures the properties of up to five structural components (a thin/cold and a thick/warm disc, a classical and a secular bulge, and a spherical stellar halo), and determines the properties of a stellar bar (if present). A comparison with other algorithms presented in the literature yields overall good agreement, with MORDOR displaying a higher flexibility in correctly decomposing systems and identifying bars in crowded environments (e.g. with on…
An algorithm to calculate the relative orbit, ephemeris, and individual masses of unresolved astrometric binaries. Example of application on the newest Gaia DR3 binaries: the ESMORGA catalog
The H.E.S.S. Galactic Plane Survey (HGPS) represents one of the most sensitive surveys of the Galactic Plane at very high energies (VHE, 0.1-100 TeV). However the source detection algorithm of the HGPS pipeline is not well-suited for complex regions, including sources with shell-like morphologies. As an alternative and complementary approach, we have investigated blind search methods for VHE gamma-ray source detection based on well-known and widely used image processing and pattern recognition techniques. Our goal is to build in a short amount of computational time a list of potentially valuab…
We present AstroVaDEr, a variational autoencoder designed to perform unsupervised clustering and synthetic image generation using astronomical imaging catalogues. The model is a convolutional neural network that learns to embed images into a low dimensional latent space, and simultaneously optimises a Gaussian Mixture Model (GMM) on the embedded vectors to cluster the training data. By utilising variational inference, we are able to use the learned GMM as a statistical prior on the latent space to facilitate random sampling and generation of synthetic images. We demonstrate AstroVaDEr's capabi…
Revisiting the Hubble sequence in the SDSS DR7 spectroscopic sample: a publicly available bayesian automated classification
我们提出了一个自动形态分类的4种类型（E，S 0，Sab，Scd）的~700.000星系SDSS DR 7光谱样本的基础上支持向量机。分类的主要新属性是，我们将每个星系关联到四个形态类中的概率，而不是分配一个单一的类。因此，分类更好地适应自然，我们期望不同形态类型之间的连续过渡。该算法使用视觉分类进行训练，然后与几个独立的视觉分类进行比较，包括Galaxy Zoo首次发布的目录。我们发现自动分类和经典的视觉之间有很好的相关性。编译的目录旨在用于不同的应用程序，可以在http://gepicom04.obspm.fr/sdss_morphology/Morphology_2010.html下载，很快就可以从CasJobs数据库下载。
Transformer-Based Source Detection and Morphological Classification in LOFAR Deep-Field Continuum Images
University of Oxford（牛津大学）；Instituto de Astrofísica de Canarias（加那利天体物理研究所）；Universidad de La Laguna（拉古纳大学）；University of Malta（马耳他大学）；Anqing Normal University（安庆师范大学）；University of Science and Technology of China（中国科学技术大学）
We present a data-driven method to estimate absolute magnitudes for O- and B-type stars from the LAMOST spectra, which we combine with {\it Gaia} parallaxes to infer distance and binarity. The method applies a neural network model trained on stars with precise {\it Gaia} parallax to the spectra and predicts $K_{\rm s}$-band absolute magnitudes $M_{Ks}$ with a precision of 0.25\,mag, which corresponds to a precision of 12\% in spectroscopic distance. For distant stars (e.g. $>5$\,kpc), the inclusion of constraints from spectroscopic $M_{Ks}$ significantly improves the distance estimates compare…
Next-generation radio surveys will yield an unprecedented amount of data, warranting analysis by use of machine learning techniques. Convolutional neural networks are the deep learning technique that has proven to be the most successful in classifying image data. Capsule networks are a more recently developed technique that use capsules comprised of groups of neurons, that describe properties of an image including the relative spatial locations of features. The current work explores the performance of different capsule network architectures against simpler convolutional neural network architec…
We present a catalog of compact sources of radio emission at 1420 MHz in the northern Galactic plane from the Canadian Galactic Plane Survey. The catalog contains 72,787 compact sources with angular size less than 3$'$ within the Galactic longitude range $52^{\circ} < \ell < 192^{\circ}$ down to a 5$σ$ detection level of $\sim$1.2 mJy. Linear polarization properties are included for 12,368 sources with signals greater than 4$σ_{QU}$ in the CGPS Stokes $Q$ and $U$ images at the position of the total intensity peak. We compare CGPS flux densities with cataloged flux densities in the Northern VLA…
