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With over 50 years of active research in Nigeria, Astronomy is still faced with various challenges, in particular poor funding from government and the prevailing harsh economic condition. Despite these challenges, there is a recent growing interest of the younger generation in astronomy which comes mainly through astronomy outreach programme, biennial summer schools and annual conference of the astronomical society of Nigeria. Electric power supply has remained a long-lasting problem and contributes immensely, especially in rural communities, to the hindrance faced in areas of education like astronomy which can not progress without the use of computers for data visualisation and analysis. As a matter of fact, cultural astronomy already exist and is well recognised in these poor communities. The amazing credit card size 5V DC battery-powered Raspberry Pi computers and Virtual Observatory(VO) will play a major role in doing modern astronomy in these communities despite these hindrances. We target the less privileged students in six rural secondary schools (located in 6 different states) by bringing to their doorsteps astronomy using low cost but effective tools. Various hands-on astronomy exercises were carried out with the VO tools. We discuss our experiences with the students and teachers with this pilot project which at the same time promotes not only astronomy but also Science Technology Engineering and Mathematics (STEM). We intend to expand the number of schools covered through grant from IAU and donations of the Raspberry Pi computers from astronomy enthusiasts and organisations.

In this contribution we present a progress report on two atomic and molecular databases, BEAMDB and MolD, which are web services at the Serbian virtual observatory (SerVO) and nodes within the Virtual Atomic and Molecular Data Center (VAMDC). The Belgrade Electron/Atom (Molecule) DataBase (BEAMDB) provides collisional data for electron interactions with atoms and molecules. The Photodissociation (MolD) database contains photo-dissociation cross sections for individual rovibrational states of diatomic molecular ions and rate coefficients for the chemi-ionisation/recombination processes. We also present a progress report on the major upgrade of these databases and plans for the future. As an example of how the data from the BEAMDB may be used, a review of electron scattering from methane is described.

We present frb-voe, a publicly available software package that enables radio observatories to broadcast fast radio burst (FRB) alerts to subscribers through low-latency virtual observatory events (VOEvents). We describe a use case of frb-voe by the Canadian Hydrogen Intensity Mapping Experiment Fast Radio Burst (CHIME/FRB) Collaboration, which has broadcast thousands of FRB alerts to subscribers worldwide. Using this service, observers have daily opportunities to conduct rapid multiwavelength follow-up observations of new FRB sources. Alerts are distributed as machine-readable reports and as emails containing FRB metadata, and are available to the public within approximately 13 s of detection. A sortable database and a downloadable JSON file containing FRB metadata from all broadcast alerts can be found on CHIME/FRB’s public webpage. The frb-voe service also provides users with the ability to retrieve FRB names from the Transient Name Server through the frb-voe client user interface. The frb-voe service can act as a foundation on which any observatory that detects FRBs can build its own VOEvent broadcasting service to contribute to the coordinated multiwavelength follow-up of astrophysical transients.

We present a mock stellar catalog, matching in volume, depth and data model the content of the planned Gaia early data release 3 (Gaia EDR3). We have generated our catalog (GeDR3mock) using galaxia, a tool to sample stars from an underlying Milky Way (MW) model or from N-body data. We used an updated Besançon Galactic model together with the latest PARSEC stellar evolutionary tracks, now also including white dwarfs. We added the Magellanic clouds and realistic open clusters with internal rotation. We empirically modeled uncertainties based on Gaia DR2 (GDR2) and scaled them according to the longer baseline in Gaia EDR3. The apparent magnitudes were reddened according to a new selection of 3D extinction maps. To help with the Gaia selection function we provide all-sky magnitude limit maps in G and BP for a few relevant GDR2 subsets together with the routines to produce these maps for user-defined subsets. We supplement the catalog with photometry and extinctions in non-Gaia bands. The catalog is available in the Virtual Observatory (http://dc.g-vo.org/tableinfo/gedr3mock.main) and can be queried just like the actual Gaia EDR3 will be. We highlight a few capabilities of the Astronomy Data Query Language with educative catalog queries. We use the data extracted from those queries to compare GeDR3mock to GDR2, which emphasises the importance of adding observational noise to the mock data. Since the underlying truth, e.g., stellar parameters, is know in GeDR3mock, it can be used to construct priors as well as mock data tests for parameter estimation. All code, models and data used to produce GeDR3mock are linked and contained in galaxia_wrap (https://github.com/jan-rybizki/Galaxia_wrap), a python package, representing a fast galactic forward model, able to project MW models and N-body data into realistic Gaia observables.

Astronomy has been at the forefront of the development of the techniques and methodologies of data intensive science for over a decade with large sky surveys and distributed efforts such as the Virtual Observatory. However, it faces a new data deluge with the next generation of synoptic sky surveys which are opening up the time domain for discovery and exploration. This brings both new scientific opportunities and fresh challenges, in terms of data rates from robotic telescopes and exponential complexity in linked data, but also for data mining algorithms used in classification and decision making. In this paper, we describe how an informatics-based approach—part of the so-called “fourth paradigm” of scientific discovery—is emerging to deal with these. We review our experiences with the Palomar-Quest and Catalina Real-Time Transient Sky Surveys; in particular, addressing the issue of the heterogeneity of data associated with transient astronomical events (and other sensor networks) and how to manage and analyze it.

We present Real-time Automated Photometric IDentification (RAPID), a novel time series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with gated recurrent units (GRUs), we present the first method specifically designed to provide early classifications of astronomical timeseries data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPID well suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of observed transients from the ZTF data stream. We have made RAPID available as an open-source software package8 for machine-learning-based alert brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds.

Both NASA’s Solar Dynamics Observatory (SDO) and the JAXA/NASA Hinode mission include spectropolarimetric instruments designed to measure the photospheric magnetic field. SDO’s Helioseismic and Magnetic Imager (HMI) emphasizes full-disk, high-cadence, and good-spatial-resolution data acquisition while Hinode’s Solar Optical Telescope Spectro-Polarimeter (SOT-SP) focuses on high spatial resolution and spectral sampling at the cost of a limited field of view and slower temporal cadence. This work introduces a deep-learning system, named the Synthetic Inversion Approximation (SynthIA), that can enhance both missions by capturing the best of each instrument’s characteristics. We use SynthIA to produce a new magnetogram data product, the Synthetic Hinode Pipeline (SynodeP), that mimics magnetograms from the higher-spectral-resolution Hinode/SOT-SP pipeline, but is derived from full-disk, high-cadence, and lower-spectral-resolution SDO/HMI Stokes observations. Results on held-out data show that SynodeP has good agreement with the Hinode/SOT-SP pipeline inversions, including magnetic fill fraction, which is not provided by the current SDO/HMI pipeline. SynodeP further shows a reduction in the magnitude of the 24 hr oscillations present in the SDO/HMI data. To demonstrate SynthIA’s generality, we show the use of SDO/Atmospheric Imaging Assembly data and subsets of the HMI data as inputs, which enables trade-offs between fidelity to the Hinode/SOT-SP inversions, number of observations used, and temporal artifacts. We discuss possible generalizations of SynthIA and its implications for space-weather modeling. This work is part of the NASA Heliophysics DRIVE Science Center at the University of Michigan under grant NASA 80NSSC20K0600E, and will be open-sourced.

White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia’s sample of WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as a self-organizing map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to naturally separate WDMS binaries from single WDs from the detection of subtle red flux excesses in the XP spectra that are indicative of low-mass MS companions. We validate our approach using confirmed WDMS binaries from the Sloan Digital Sky Survey and LAMOST surveys, achieving a precision of ∼90%. We demonstrated that the luminosity of the faint companions in the missed systems is ∼50 times lower than that of their WD primaries. Applying our SOM to 90,667 sources, we identify 993 WDMS candidates, 506 of which have not been previously reported in the literature. If confirmed, our sample will increase the known WDMS binaries by 20%. Additionally, we use the Virtual Observatory Spectral Energy Distribution Analyzer tool to refine and parameterize a “golden sample” of 136 WDMS binaries through multiwavelength photometry and a two-body spectral energy distribution fitting. These high-confidence WDMS binaries are composed of low-mass WDs (∼0.42M⊙), with cool MS companions (∼2800 K). Finally, 13 systems exhibit periodic variability consistent with eclipsing binaries, making them prime targets for further follow-up observations.

We design an uncertainty-aware cost-sensitive neural network (UA-CSNet) to estimate metallicities from dereddened and corrected Gaia BP/RP (XP) spectra for giant stars. This method accounts for both stochastic errors in the input spectra and the imbalanced density distribution in [Fe/H] values. With a specialized architecture and training strategy, the UA-CSNet improves the precision of the predicted metallicities, especially for very metal-poor (VMP; [Fe/H] ≤ −2.0) stars. With the PASTEL catalog as the training sample, our model can estimate metallicities down to [Fe/H] ∼ −4. We compare our estimates with a number of external catalogs and conduct tests using star clusters, finding overall good agreement. We also confirm that our estimates for VMP stars are unaffected by carbon enhancement. Applying the UA-CSNet, we obtain reliable and precise metallicity estimates for approximately 20 million giant stars, including 360,000 VMP stars and 50,000 extremely metal-poor ([Fe/H] ≤ −3.0) stars. The resulting catalog is publicly available via the Chinese Virtual Observatory at doi: 10.12149/101604. This work highlights the potential of low-resolution spectra for metallicity estimation and provides a valuable data set for studying the formation and chemodynamical evolution of our Galaxy.

Masses for 664 single-lined hot subdwarf stars identified in LAMOST were calculated by comparing synthetic fluxes from spectral energy distribution with observed fluxes from a Virtual Observatory service. Three groups of hot subdwarf stars were selected from the whole sample according to their parallax precision to study the mass distributions. We found that He-poor sdB/sdOB stars present a wide mass distribution from 0.1 to 1.0 M ⊙ with a sharp mass peak at around 0.46 M ⊙, which is consistent with canonical binary model prediction. He-rich sdB/sdOB/sdO stars present a much flatter mass distribution than He-poor sdB/sdOB stars and with a mass peak at around 0.42 M ⊙. By comparing the observed mass distributions to the predictions of different formation scenarios, we concluded that the binary merger channel, including two helium white dwarfs (He-WDs) and He-WD + main-sequence mergers, cannot be the only main formation channel for He-rich hot subdwarfs, and other formation channels, such as the surviving companions from Type Ia supernovae, could also make impacts on producing this special population, especially for He-rich hot subdwarfs with masses less than 0.44 M ⊙. He-poor sdO stars also present a flatter mass distribution with an inconspicuous peak mass at 0.18 M ⊙. The similar mass– ΔRVmax distribution between He-poor sdB/sdOB and sdO stars supports the scenario that He-poor sdO stars could be the subsequent evolution stage of He-poor sdB/sdOB stars.