Astronomical Catalogs Research Articles

A Pilot Study from A Course-Based Undergraduate Research Experience for Online Degree-Seeking Astronomy Students

Research-based active learning approaches are critical for the teaching and learning of undergraduate STEM majors. Course-based undergraduate research experiences (CUREs) are becoming more commonplace in traditional, in-person academic environments, but have only just started to be utilized in online education. Online education has been shown to create accessible pathways to knowledge for individuals from nontraditional student backgrounds, and increasing the diversity of STEM fields has been identified as a priority for future generations of scientists and engineers. We developed and instructed a rigorous, six-week curriculum on the topic of observational astronomy, dedicated to educating second year online astronomy students in practices and techniques for astronomical research. Throughout the course, the students learned about telescopes, the atmosphere, filter systems, adaptive optics systems, astronomical catalogs, and image viewing/processing tools. We developed a survey informed by previous research validated assessments aimed to evaluate course feedback, course impact, student self-efficacy, student science identity and community values, and student sense of belonging. The survey was administered at the conclusion of the course to all eleven students yielding eight total responses. Although preliminary, the results of our analysis indicate that students’ confidence in utilizing the tools and skills taught in the course was significant. Students also felt a great sense of belonging to the astronomy community and increased confidence in conducting astronomical research in the future.

A Comparative Study of Ultraluminous Infrared Galaxies in the IRAS and SDSS Surveys

We present a comprehensive study of ultraluminous infrared galaxies (ULIRGs), leveraging data from the Infrared Astronomical Satellite Faint Source Catalogue and the spectroscopic catalog in the Sloan Digital Sky Survey Data Release 16. Our meticulous crossmatching technique significantly enhances the reliability of ULIRG identification, resulting in the identification of 283 reliable ULIRGs, including 102 new detections, while discarding 120 previously reported false sources. Covering a redshift range of z = 0.018–0.996, with a median redshift of z¯=0.259 , our uniform sample reveals apparent interaction features in approximately 40% of ULIRGs, increasing to 92% for those with z < 0.1. Through optical spectra analysis, it is indicated that over 58% of ULIRGs host an active galactic nucleus (AGN), which is twice as high as detections based solely on infrared colors. Moreover, a pronounced excess of radio emissions associated with AGN activity results in a steeper radio–far-infrared correlation. Notably, Type I ULIRGs exhibit properties similar to those of narrow-line Seyfert 1 galaxies, with an elevated incidence rate of Mg ii broad absorption lines (16.7%), surpassing that of typical optically selected quasars by over tenfold, consistent with current evolutionary models. We anticipate that forthcoming telescopes such as the China Space Station Telescope and Leighton Chajnantor Telescope will provide deeper insights into ULIRG morphology, dust distribution, molecular gas, and AGN activity.

The digitized First Byurakan Survey database. New carbon stars confirmed

Some amount of the new carbon ( C ) stars was confirmed among the sample of the Late-Type Stars (LTSs) candidates, selected on the Digitized First Byurakan Survey (DFBS) spectral plates. The DFBS is the digitized version of the First Byurakan Survey (FBS, or Markarian survey). The FBS was the first systematic survey of the extragalactic sky. This objective-prism survey was carried out in 1965- 1980 by B. E. Markarian and colleagues using the 1 m Schmidt telescope of the Byurakan Astrophysical Observatory. In this work we present Gaia DR3 photometric data, spectra, light curves, distances, high above/below Galactic plane, radial velocities (RV), and other important physical characteristics from modern astronomical catalogues for some amount new confirmed DFBS carbon stars. The confirmation is based on Gaia DR3 BP/RP low-resolution spectral database. In previous studies of the DFBS plates, these objects were presented as LTSs candidates. Some objects are located more than 7 kpc from the Galactic plane. Four of them are N-type C stars, from which two are Mira-type variables. The remaining objects are CH-type giants at high Galactic latitudes. Most probably they present binary systems.

Self-consistent Combined HST, K-band, and Spitzer Photometric Catalogs of the BUFFALO Survey Fields

This article presents new astronomical source catalogs using data from the BUFFALO Survey. These catalogs contain detailed information for over 100,000 astronomical sources in the six BUFFALO clusters: A370, A2744, AS1063, MACS 0416, MACS 0717, and MACS 1149 spanning a total of 240 arcmin2. The catalogs include positions and forced photometry measurements of these objects in the F275W, F336W, F435W, F606W, F814W, F105W, F125W, F140W, and F160W HST bands, Keck-NIRC2/VLT-HAWKI Ks band, and IRAC Channel 1 and 2 bands. Additionally, we include photometry measurements in the F475W, F625W, and F110W bands for A370. This catalog also includes photometric redshift estimates computed via template fitting using LePhare. When comparing to a spectroscopic reference, we obtain an outlier fraction of 8.6% and scatter, normalized median absolute deviation, of 0.059. The catalogs are publicly available for their use by the community (https://archive.stsci.edu/hlsp/buffalo/).

Devising a procedure for the brightness alignment of astronomical frames background by a high frequency filtration to improve accuracy of the brightness estimation of objects

The object of this study is the background substrate of astronomical frames. To detect and compare the image of an object in a frame with its real image from astronomical catalogs, it is necessary to uniformly distribute the brightness of the background image substrate. Most often, the background alignment of astronomical frames is performed using the hardware calibration method applying the construction of service frames. However, it does not make it possible to eliminate the background from temporary stray light. Therefore, to solve this problem, a procedure has been proposed for brightness alignment of the background frame using high-pass filtering. For high-pass filtering of images, three high-pass filters were considered – an ideal filter, a Butterworth filter, and a Gaussian filter. To remove coarse-grained image components from the image, a high-pass filter was used, which attenuates low-frequency harmonics of the image spectrum while simultaneously passing high-frequency harmonics. Applying the devised procedure for brightness alignment of the background substrate of the frame has made it possible to increase the signal-to-noise ratio and reduce the dynamic range of the background substrate of the image. The study showed that when assessing brightness and identifying frames, the fitting provides better accuracy of reference to the starry sky. Also, the standard deviation of frame identification errors in this case is 5–7 times less than without using the devised procedure. The devised procedure for brightness alignment of the background frame substrate was tested in practice within the framework of the CoLiTec project. It was implemented at the stage of intra-frame processing in the Lemur software for automated detection of new objects and tracking of known objects

TT-HEALpix: A New Data Indexing Strategy for Efficient Cross-match of Large-scale Astronomical Catalogs

Cross-matching is an indispensable operation in the data preparation, analysis, and research processes of multi-band astronomy and time-domain astronomy. Multi-catalog time-series data reconstruction is an important part of time-domain astronomy. In the large-scale distributed reconstruction process, boundary problems have always affected the accuracy of time-series data. To optimize these boundary problems and improve data precision, this paper proposes a new hybrid astronomical data indexing method called Translated Transformation based HEALPix Dual Index (TT-HEALPix). Under the reasonable Healpix division level, by translation transformation, the two indexes before and after the transformation form a unique pseudo-hybrid index strategy, which not only retains the advantages of the hybrid index scheme suitable for large-scale parallel computing, but also compensates for its shortage of high omission at the block boundary position. Based on TT-HEALPix, this paper completes the multi-catalog time-series reconstruction process on the Spark platform and compares it with the HEALPix+HTM hybrid indexing strategy. The experiments demonstrate that TT-HEALPix has significant advantages over the traditional HEALPix+HTM hybrid indexing method in terms of data accuracy and cross-matching efficiency. At level 9 of the Healpix index, TT-HEALPix achieves a 6%–19% improvement in cross-matching efficiency in a distributed environment compared to HEALPix+HTM. In terms of data accuracy, for the AST3-II dataset at level 9, TT-HEALPix has 62.2% accuracy improvement over HEALPix and 45.5% improvement over HEALPix+HTM. In conclusion, the proposed novel indexing strategy, TT-HEALPix, is better suited to the efficiency and accuracy requirements of cross-match.

Ensuring Consistency between Noise and Detection in Hierarchical Bayesian Inference

Hierarchical Bayesian inference can simultaneously account for both measurement uncertainty and selection effects within astronomical catalogs. In particular, the imposed hierarchy encodes beliefs about the interdependence of the physical processes that generate the observed data. We show that several proposed approximations within the literature actually correspond to inferences that are incompatible with any physical detection process. This generically leads to biases and is associated with the assumption that detectability is independent of the observed data given the true source parameters. We show several examples of how this error can affect astrophysical inferences based on catalogs of coalescing binaries observed through gravitational waves, including misestimating the redshift evolution of the merger rate as well as incorrectly inferring that general relativity is the correct theory of gravity when it is not. In general, one cannot directly fit for the “detected distribution” and “divide out” the selection effects in post-processing. Similarly, when comparing theoretical predictions to observations, it is better to simulate detected data (including both measurement noise and selection effects) rather than comparing estimates of the detected distributions of event parameters (which include only selection effects). While the biases introduced by model misspecification from incorrect assumptions may be smaller than statistical uncertainty for moderate catalog sizes (O(100) events), they will nevertheless pose a significant barrier to precision measurements of astrophysical populations.

Uniting and APOGEE to unveil the cosmic chemistry of the Milky Way disc

The spatial distribution of Galactic stars with different chemical abundances encodes information on the processes that drove the formation and evolution of the Milky Way. Survey selection functions are indispensable for analysing astronomical catalogues produced by large-scale surveys. The use of these selection functions in data modelling is more complex when data from different surveys are to be modelled simultaneously. We introduce a procedure for constructing the selection function of a sample of red clump stars that have parallaxes and elemental abundances from the mission. We separately constructed the selection function of the APOGEE DR17 red clump stars, which depends on very different observables and has a very different spatial coverage. We combined the two surveys and accounted for their joint selection function to provide strong constraints on the radial and vertical density distribution of mono-abundance populations, with offering a dense coverage of the solar neighbourhood, while APOGEE reaches larger distances near the Galactic plane. We confirm that the radial density profile steepens with increasing metallicity. The combined sample also indicates a metallicity-dependent flaring of the alpha -poor disc. We provide the code for constructing the selection function we used in this study through the GaiaUnlimited Python package.

METHODS OF TEACHING WORK WITH OBSERVATIONAL ASTRONOMICAL DATA AND CATALOGS

A significant increase in the amount of observational and experimental data has been observed in many fields of science since the middle of the last century, and especially since the beginning of this century. The problem is especially evident in the field of astronomy and astrophysics due to the large amount of accumulated observational data. In this regard, there is a global urgent problem of methodology of teaching modern methods of working with astronomical data in astronomy classes in schools, as well as in the first years of university studies. The article deals with the part of the course on working with observational astronomical data, devoted to the principles of working with astronomical catalogs. The peculiarity of this course is the presence of practical programming tasks, which are the closest to modern scientific methods and discoveries in astronomy.

Experience deploying an analysis facility for the Rubin Observatory’s Legacy Survey of Space and Time (LSST) data

The Vera C. Rubin Observatory is preparing for the execution of the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently in its final phase of construction in the Andes mountains in Chile and due to start operations in 2025 for 10 years, its 8.4-meter telescope will nightly scan the southern sky and collect images of the entire visible sky every 4 nights using a 3.2 Gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties.In this paper, we briefly present the infrastructure deployed at the French Rubin data facility (operated by IN2P3 computing center, CC-IN2P3) to deploy the Rubin Science Platform, a set of web-based services to provide effective and convenient access to LSST data for scientific analysis. We describe the main services of the platform, the components that provide those services and our deployment model. We also present the Kubernetes-based infrastructure we are experimenting with for hosting the LSST astronomical catalog, a petabyte-scale relational database developed for the specific needs of the project.

Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time

Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time

A Cross-matching Service for Data Center of Xinjiang Astronomical Observatory

Cross-matching is a key technique to achieve fusion of multi-band astronomical catalogs. Due to different equipment such as various astronomical telescopes, the existence of measurement errors, and proper motions of the celestial bodies, the same celestial object will have different positions in different catalogs, making it difficult to integrate multi-band or full-band astronomical data. In this study, we propose an online cross-matching method based on pseudo-spherical indexing techniques and develop a service combining with high performance computing system (Taurus) to improve cross-matching efficiency, which is designed for the Data Center of Xinjiang Astronomical Observatory. Specifically, we use Quad Tree Cube to divide the spherical blocks of the celestial object and map the 2D space composed of R.A. and decl. to 1D space and achieve correspondence between real celestial objects and spherical patches. Finally, we verify the performance of the service using Gaia 3 and PPMXL catalogs. Meanwhile, we send the matching results to VO tools-Topcat and Aladin respectively to get visual results. The experimental results show that the service effectively solves the speed bottleneck problem of cross-matching caused by frequent I/O, and significantly improves the retrieval and matching speed of massive astronomical data.

Improving the accuracy of identifying objects in digital frames using a procedure of full identification of measurements

The variability of shooting conditions affects the quality of images of Solar System objects in a series of frames. Identification of a frame with the corresponding part of the sky becomes difficult if the quality is poor. Because of this fact, the detection quality indicators and estimation of the position of Solar System objects are significantly reduced when using already known methods and international astronomical catalogs. To solve this problem, the procedure of full identification of measurements of objects on digital frames was devised. This procedure is based on the formation of triplets (triangles) of primary identification from the side of the digital frame and the astronomical catalog. Positional coordinates on the frame and ideal tangential coordinates from the catalog were used. Owing to this, a comparison of the primary identification triplets was carried out by comparing the calculated angles of the triangle vertices. The identity of the hypothesis was determined by comparison with the acceptable deviation. The use of the developed full identification procedure makes it possible to reduce the number of false detections and improve identification with reference astronomical objects. The study showed that when identifying frames, astrometry has better accuracy of reference to the starry sky. In addition, the standard deviation of frame identification errors in this case is 6–9 times less than without using the devised procedure. The procedure developed for complete identification was practically tested within the framework of the CoLiTec project. It was implemented in the Lemur software for automated detection of new and tracking of known objects. Owing to the use of Lemur software and the proposed computational procedure implemented in it, more than 700,000 measurements of various astronomical objects under study were successfully identified.

Storage optimisation and distributed architecture for time series reconstruction of massive astronomical catalogues

Storage optimisation and distributed architecture for time series reconstruction of massive astronomical catalogues

3DT-CM: A Low-complexity Cross-matching Algorithm for Large Astronomical Catalogues Using 3d-tree Approach

Location-based cross-matching is a preprocessing step in astronomy that aims to identify records belonging to the same celestial body based on the angular distance formula. The traditional approach involves comparing each record in one catalog with every record in the other catalog, resulting in a one-to-one comparison with high computational complexity. To reduce the computational time, index partitioning methods are used to divide the sky into regions and perform local cross-matching. In addition, cross-matching algorithms have been adopted on high-performance architectures to improve their efficiency. But the index partitioning methods and computation architectures only increase the degree of parallelism, and cannot decrease the complexity of pairwise-based cross-matching algorithm itself. A better algorithm is needed to further improve the performance of cross-matching algorithm. In this paper, we propose a 3d-tree-based cross-matching algorithm that converts the angular distance formula into an equivalent 3d Euclidean distance and uses 3d-tree method to reduce the overall computational complexity and to avoid boundary issues. Furthermore, we demonstrate the superiority of the 3d-tree approach over the 2d-tree method and implement it using a multi-threading technique during both the construction and querying phases. We have experimentally evaluated the proposed 3d-tree cross-matching algorithm using publicly available catalog data. The results show that our algorithm applied on two 32-core CPUs achieves equivalent performance than previous experiments conducted on a six-node CPU-GPU cluster.

Estimating the selection function of Gaia DR3 subsamples

Context. Understanding the intricacies behind the presence and absence of sources in an astronomical catalogue is crucial for the accurate interpretation of astronomical data. In particular, for the multi-dimensional Gaia data, filters and cuts on different parameters or measurements introduce a selection function that may unintentionally alter scientific conclusions in subtle ways. Aims. We aim to develop a methodology to estimate the selection function for different subsamples of stars in the Gaia catalogue. Methods. Comparing the number of stars in a given subsample to that in the overall Gaia catalogue provides an estimate of the subsample membership probability as a function of sky position, magnitude, and colour. The method used to make this estimate must differentiate the stochastic absence of subsample stars from selection effects. When multiplied with the overall Gaia catalogue selection function, this provides the total selection function of the subsample. Results. We present our new method for estimating the selection function by applying it to the sources in Gaia DR3 with heliocentric radial velocity measurements. We also compute the selection function for the stars in the Gaia-Sausage/Enceladus sample, confirming that the apparent asymmetry of its debris across the sky is merely caused by selection effects. Conclusions. The method we have developed estimates the selection function of the stars present in a subsample of Gaia data, given that the subsample is completely contained in the Gaia parent catalogue (for which the selection function is known). This tool is made available in a GaiaUnlimited Python package.

GaiaData Release 3

Context.Since July 2014, theGaiaspace mission has been continuously scanning the sky and observing the extragalactic Universe with unprecedented spatial resolution in the optical domain (∼180 mas by the end of the mission).Gaiaprovides an opportunity to study the morphology of the galaxies of the local Universe (z &lt; 0.45) with much higher resolution than has ever been attained from the ground. It also allows us to provide the first morphological all-sky space catalogue of nearby galaxies and galaxies that host quasars in the visible spectrum.Aims.We present the Data Processing and Analysis Consortium CU4-Surface Brightness Profile fitting pipeline, which aims to recover the light profile of nearby galaxies and galaxies hosting quasars.Methods.The pipeline uses a direct model based on the Radon transform to measure the two-dimensional surface brightness profile of the extended sources. It simulates a large set of 2D light profiles and iteratively looks for the one that best reproduces the 1D observations by means of a Bayesian exploration of the parameters space. We also present our method for setting up the input lists of galaxies and quasars to be processed.Results.We successfully analysed 1 103 691 known quasars and detected a host galaxy around 64 498 of them (∼6%). We publish the surface brightness profiles of the host for a subset of 15 867 quasars with robust solutions. The distribution of the Sérsic index describing the light profile of the host galaxies peaks at ∼0.8 with a mean value of ∼1.9, indicating that these galaxies hosting a quasar are consistent with disc-like galaxies. The pipeline also analysed 940 887 galaxies with both a Sérsicand a de Vaucouleurs profile and derived robust solutions for 914 837 of them. The distribution of the Sérsic indices confirms thatGaiamostly detects elliptical galaxies and that very few discs are measured.

Machine learning-based approach for ballistic coefficient estimation of resident space objects in LEO

The increasing number of Resident Space Objects poses a serious threat to the safe operation of satellites. Alongside with mitigation policies, it is fundamental to predict the trajectories of such objects which requires the accurate estimation of the physical characteristics that influence their orbits. An important role is played by the area-to-mass ratio, i.e., the ratio between the area exposed to the atmosphere/the Sun and the mass of the space object for the LEO/MEO and GEO region. Current literature proposes several approaches for the estimation of the area-to-mass ratio, ranging from semi-analytical to numerical methods. As regards the latter category, recent studies have focused on classification or regression algorithms for specific types of orbits (e.g., sun-synchronous, geostationary).In this context, this paper proposes a machine learning-based regression approach for the estimation of the ballistic coefficient (i.e., the product between the drag coefficient and the area-to-mass ratio) in Low Earth Orbit, covering a wide set of orbital parameters. Using a synthetic space catalogue, the performance of different types of machine learning techniques is evaluated and compared. A sensitivity analysis is carried out to analyse the effect on performance of the number of trainings, the propagation time, the number of objects of the training data set, and the frequency of the measurements. The applicability of the presented approach is tested and discussed using both real (i.e., based on publicly available catalogues of Two-Line Elements) and synthetic datasets.

HLC2: a highly efficient cross-matching framework for large astronomical catalogues on heterogeneous computing environments

ABSTRACT Cross-matching operation, which is to find corresponding data for the same celestial object or region from multiple catalogues, is indispensable to astronomical data analysis and research. Due to the large amount of astronomical catalogues generated by the ongoing and next-generation large-scale sky surveys, the time complexity of the cross-matching is increasing dramatically. Heterogeneous computing environments provide a theoretical possibility to accelerate the cross-matching, but the performance advantages of heterogeneous computing resources have not been fully utilized. To meet the challenge of cross-matching for substantial increasing amount of astronomical observation data, this paper proposes Heterogeneous-computing-enabled Large Catalogue Cross-matcher (HLC2), a high-performance cross-matching framework based on spherical position deviation on CPU-GPU heterogeneous computing platforms. It supports scalable and flexible cross-matching and can be directly applied to the fusion of large astronomical catalogues from survey missions and astronomical data centres. A performance estimation model is proposed to locate the performance bottlenecks and guide the optimizations. A two-level partitioning strategy is designed to generate an optimized data placement according to the positions of celestial objects to increase throughput. To make HLC2 a more adaptive solution, the architecture-aware task splitting, thread parallelization, and concurrent scheduling strategies are designed and integrated. Moreover, a novel quad-direction strategy is proposed for the boundary problem to effectively balance performance and completeness. We have experimentally evaluated HLC2 using public released catalogue data. Experiments demonstrate that HLC2 scales well on different sizes of catalogues and the cross-matching speed is significantly improved compared to the state-of-the-art cross-matchers.

An empirical model of theGaiaDR3 selection function

Interpreting and modelling astronomical catalogues requires an understanding of the catalogues’ completeness or selection function: what properties determine an object’s probability of being including in the catalogue? Here we set out to empirically quantify the completeness of the overall catalogue ofGaia’s third data release (DR3). This task is not straightforward becauseGaiais the all-sky optical survey with the highest angular resolution to date and no consistent ground truth exists to allow direct comparisons. However, well-characterised deeper imaging enables an empirical assessment ofGaia’sG-band completeness across parts of the sky. On this basis, we devised a simple analytical completeness model ofGaiaas a function of the observedGmagnitude and position over the sky, which accounts for both the effects of crowding and the complexGaiascanning law. Our model only depends on a single quantity: the median magnitudeM10in a patch of the sky of catalogued sources withastrometric_matched_transits≤10. We note thatM10reflects elementary completeness decisions in theGaiapipeline and is computable from theGaiaDR3 catalogue itself and therefore applicable across the whole sky. We calibrated our model using the Dark Energy Camera Plane Survey (DECaPS) and tested its predictions againstHubbleSpace Telescope observations of globular clusters. We found that our model predictsGaia’s completeness values to a few per cent (RMS) across the sky. We make the model available as a part of thegaiaunlimitedPython package built and maintained by the GaiaUnlimited project.