Astrometric calibration of images with a small field of view is often inferior to the internal accuracy of the source detections due to the small number of accessible guide stars. One important experiment with such challenges is the Hubble Space Telescope (HST). A possible solution is to cross-calibrate overlapping fields instead of just relying on standard stars. Following the approach of \citet{2012ApJ...761..188B}, we use infinitesimal 3D rotations for fine-tuning the calibration but devise a better objective that is robust to a large number of false candidates in the initial set of associations. Using Bayesian statistics, we accommodate bad data by explicitly modeling the quality, which yields a formalism essentially identical to an $M$-estimation in robust statistics. Our results on simulated and real catalogs show great potentials for improving the HST calibration, and those with similar challenges.
Building on previous Bayesian approaches, we introduce a novel formulation of probabilistic cross-identification, where detections are directly associated to (hypothesized) astronomical objects in a globally optimal way. We show that this new method scales better for processing multiple catalogs than enumerating all possible candidates, especially in the limit of crowded fields, which is the most challenging observational regime for new-generation astronomy experiments such as the Rubin Observatory Legacy Survey of Space and Time (LSST). Here we study simulated catalogs where the ground-truth …
We announce the public release of the application program interface (API) for the Open Astronomy Catalogs (OACs), the OACAPI. The OACs serve near-complete collections of supernova, tidal disruption, kilonova, and fast stars data (including photometry, spectra, radio, and X-ray observations) via a user-friendly web interface that displays the data interactively and offers full data downloads. The OACAPI, by contrast, enables users to specifically download particular pieces of the OAC dataset via a flexible programmatic syntax, either via URL GET requests, or via a module within the astroquery P…
PHOTOMETRYPIPELINE (PP) is an automated pipeline that produces calibrated photometry from imaging data through image registration, aperture photometry, photometric calibration, and target identification with only minimal human interaction. PP utilizes the widely used Source Extractor software for source identification and aperture photometry; SCAMP is used for image registration. Both image registration and photometric calibration are based on matching field stars with star catalogs, requiring catalog coverage of the respective field. A number of different astrometric and photometric catalogs …
Machine learning, algorithms to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using the Gaussian mixture model and show two applications in pulsar astronomy. After reviewing the Gaussian mixture model and the related Expectation-Maximization algorithm, we present a data classification method using the Neyman-Pearson test. To demonstrate the method, we apply the algorithm to two classification problems. Firstly, it is applied to the well kno…
We have investigated a number of factors that can have significant impacts on the classification performance of $γ$-ray sources detected by Fermi Large Area Telescope (LAT) with machine learning techniques. We show that a framework of automatic feature selection can construct a simple model with a small set of features which yields better performance over previous results. Secondly, because of the small sample size of the training/test sets of certain classes in $γ$-ray, nested re-sampling and cross-validations are suggested for quantifying the statistical fluctuations of the quoted accuracy. …
The field of TeV gamma-ray astronomy has produced many exciting results over the last decade. Both the source catalogue, and the range of astrophysical questions which can be addressed, continue to expand. This article presents a topical review of the field, with a focus on the observational results of the imaging atmospheric Cherenkov telescope arrays. The results encompass pulsars and their nebulae, supernova remnants, gamma-ray binary systems, star forming regions and starburst and active galaxies.
Model-Agnostic Population Inference for Gravitational-Wave Astronomy: From LVK to LISA
State Key Laboratory of Radio Astronomy and Technology, Xinjiang Astronomical Observatory, CAS（中国科学院新疆天文台射电天文与技术重点实验室）；School of Astronomy and Space Science, University of Chinese Academy of Sciences（中国科学院大学天文与空间科学学院）；Institute of Fundamental Physics and Quantum Technology, Ningbo University（宁波大学基础物理与量子技术学院）；School of Physical Science and Technology, Ningbo University（宁波大学物理科学与技术学院）
We present new fully-automatic classification model to select extragalactic objects within astronomy photometric catalogs. Construction of the our classification model is based on the three important procedures: 1) data representation to create feature space; 2) building hypersurface in feature space to limit range of features (outliers detection); 3) building hyperplane separating extragalactic objects from the galactic ones. We trained our model with 1.7 million objects (1.4 million galaxies and quasars, 0.3 million stars). The application of the model is presented as a photometric catalog o…
ESASky is a new science-driven discovery portal for all ESA astronomical missions that gives users worldwide a simplified access to high-level science-ready products from ESA and other data providers. The tool features a sky exploration interface and a single/multiple target interface, and it requires no prior knowledge of specific details of each mission. Users can explore the sky in multiple wavelengths, quickly see the data available for their targets, and retrieve the relevant products, with just a few clicks. The first version of the tool, released in May 2016, provides access to imaging …
The Surviving Fragment of the Zij-i Malikshahi: A Preliminary Edition, Translation, and Study of the 100-Star Catalogue in BnF Arabe 5968
S. U. Umarov Physical-Technical Institute, National Academy of Sciences of Tajikistan（塔吉克斯坦科学院 S.U. 乌马罗夫物理技术研究所）；Institute of Astrophysics, National Academy of Sciences of Tajikistan（塔吉克斯坦科学院天体物理研究所）
IAU Centre for the Protection of the Dark and Quiet Sky, IAU-UAI Headquarters（国际天文学联合会暗夜与静空保护中心，国际天文学联合会-国际天体测量与天体力学联合委员会总部）；Institute of Physics, Czech Academy of Sciences（捷克科学院物理研究所）
We carry out astrophysical inference for compact binary merger events in LIGO-Virgo's first gravitational-wave transient catalog (GWTC-1) using a physically motivated calibration model. We demonstrate that importance sampling can be used to reduce the cost of what would otherwise be a computationally challenging analysis. We show that including the physical estimate for the calibration error distribution has negligible impact on the inference of parameters for the events in GWTC-1. Studying a simulated signal with matched filter signal-to-noise ratio $\text{SNR}=200$, we project that a calibra…
Principal Component Analysis (PCA) is being extensively used in Astronomy but not yet exhaustively exploited for variability search. The aim of this work is to investigate the effectiveness of using the PCA as a method to search for variable stars in large photometric data sets. We apply PCA to variability indices computed for light curves of 18152 stars in three fields in M 31 extracted from the Hubble Source Catalogue. The projection of the data into the principal components is used as a stellar variability detection and classification tool, capable of distinguishing between RR Lyrae stars, …
We apply a combination of a Genetic Algorithms (GA) and Support Vector Machines (SVM) machine learning algorithm to solve two important problems faced by the astronomical community: star/galaxy separation, and photometric redshift estimation of galaxies in survey catalogs. We use the GA to select the relevant features in the first step, followed by optimization of SVM parameters in the second step to obtain an optimal set of parameters to classify or regress, in process of which we avoid over-fitting. We apply our method to star/galaxy separation in Pan-STARRS1 data. We show that our method co…
Much of the progress made in time-domain astronomy is accomplished by relating observational multi-wavelength time series data to models derived from our understanding of physical laws. This goal is typically accomplished by dividing the task in two: collecting data (observing), and constructing models to represent that data (theorizing). Owing to the natural tendency for specialization, a disconnect can develop between the best available theories and the best available data, potentially delaying advances in our understanding new classes of transients. We introduce MOSFiT: the Modular Open-Sou…
The first detections of gravitational waves from binary neutron star mergers with advanced LIGO and Virgo observatories are anticipated in the next five years. These detections could pave the way for multi-messenger gravitational-wave (GW) and electromagnetic (EM) astronomy if GW triggers are successfully followed up with targeted EM observations. However, GW sky localization is relatively poor, with expected localization areas of ~10--100 square degrees; this presents a challenge for following up GW signals from compact binary mergers. Prioritizing wide-field follow-ups based on the probabili…
The UBVRI broad band photometric system is widely used in CCD astronomy. There are a lot of sets of standard stars for this photometric system, the Landolt's and Stetson's catalogues being the most precise and reliable. Another photometric system, recently considerably spread in CCD observations is ugriz, which originates from the Sloan Digital Sky Survey (SDSS) and has now many variations based on its 5 broad-band filters. One of the photometric systems based on it is The Panoramic Survey Telescope and Rapid Response System (Pan-STARRS). In this paper we compare the BVRI magnitudes in the Ste…
The past four years have seen a scientific revolution through the birth of a new field: gravitational-wave astronomy. The first detection of gravitational waves---recognised by the 2017 Nobel Prize in Physics---provided unprecedented tests of general relativity while unveiling a previously unknown class of massive black holes, thirty times more massive than the Sun. The subsequent detection of gravitational waves from a merging binary neutron star confirmed the hypothesised connection between binary neutron stars and short gamma-ray bursts while providing an independent measurement of the expa…
Mixture models combine multiple components into a single probability density function. They are a natural statistical model for many situations in astronomy, such as surveys containing multiple types of objects, cluster analysis in various data spaces, and complicated distribution functions. This chapter in the CRC Handbook of Mixture Analysis is concerned with astronomical applications of mixture models for cluster analysis, classification, and semi-parametric density estimation. We present several classification examples from the literature, including identification of a new class, analysis …
Work in time-domain astronomy necessitates robust, automated data processing pipelines that operate in real time. We present the BANZAI pipeline which processes the thousands of science images produced across the Las Cumbres Observatory Global Telescope (LCOGT) network of robotic telescopes each night. BANZAI is designed to perform near real-time preview and end-of-night final processing for four types of optical CCD imagers on the three LCOGT telescope classes. It performs instrumental signature removal (bad pixel masking, bias and dark removal, flat-field correction), astrometric fitting and…
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 and wide surveysconstrain their strides from millions to billions. Aims.In this study, we construct a supervised machine learning algorithm, to classify the objects in the Javalambre Photometric LocalUniverse Survey first data release (J-PLUS DR1). Methods.The sample set is featured with 12-waveband photometry, and magnitudes are labeled with spectrum-based ca…
Swift is a multi-wavelength observatory specifically designed for gamma-ray burst (GRB) astronomy that is operational since 2004. Swift is also a very flexible multi-purpose facility that supports a wide range of scientific fields such as active galactic nuclei, supernovae, cataclysmic variables, Galactic transients, active stars and comets. The Swift X-ray Telescope (XRT) has collected more than 150 Ms of observations in its first seven years of operations. We present the list of all the X-ray point sources detected in XRT imaging data taken in photon counting mode during the first seven year…
PhoPS: An automated photometric pipeline for survey-era astronomy
Türkiye National Observatories, TUG（土耳其国家天文台）；The Scientific and Technological Research Council of Türkiye (TÜBİTAK)（土耳其科学技术研究委员会）；Instituto de Astrofísica de Andalucía (IAA-CSIC)（安达卢西亚天体物理研究所）；LIRA, CNRS UMR8254, Observatoire de Paris（巴黎天文台）
