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We present the first release of the Calar Alto CAFOS direct imaging data, a project led by the Spanish Virtual Observatory with the goal of enhancing the use of the Calar Alto archive by the astrophysics community. Data Release 1 contains 23 903 reduced and astrometrically calibrated images taken from 2008 March to 2019 July with a median of the mean uncertainties in the astrometric calibration of 0.04 arcsec. The catalogue associated to 6132 images in the Sloan griz filters provides accurate astrometry and PSF calibrated photometry for 139 337 point-like detections corresponding to 21 985 different sources extracted from a selection of 2338 good-quality images. The mean internal astrometric and photometric accuracies are 0.05 arcsec and 0.04 mag, respectively. In this work we describe the approach followed to process and calibrate the images, and the construction of the associated catalogue, together with the validation quality tests carried out. Finally, we present three cases to prove the science capabilities of the catalogue: discovery and identification of asteroids, identification of potential transients, and identification of cool and ultracool dwarfs.

 

This study applies computationally efficient shallow neural networks to predict topographic effect multipliers directly from digital elevation data obtained from complex terrain, such as mountainous areas. Data were obtained from boundary layer wind tunnel (BLWT) modeling of surface wind flow over six regions in mainland Puerto Rico and its municipal islands. The results demonstrate an improvement over linear regression models, even for computationally efficient low neuron count and single hidden layer models. The paper proposes the development of a global BLWT data atlas to inform development of methods to predict topographic wind speedup for a diverse range of topography and surface roughness conditions. It also identifies knowledge gaps that could prevent standardization of data collected from different BLWT experimental designs. 

We present five far- and near-ultraviolet spectra of the Type II plateau supernova, SN 2022acko, obtained 5, 6, 7, 19, and 21 days after explosion, all observed with the Hubble Space Telescope/Space Telescope Imaging Spectrograph. The first three epochs are earlier than any Type II plateau supernova has been observed in the far-ultraviolet revealing unprecedented characteristics. These three spectra are dominated by strong lines, primarily from metals, which contrasts with the featureless early optical spectra. The flux decreases over the initial time series as the ejecta cool and line blanketing takes effect. We model this unique data set with the non–local thermodynamic equilibrium radiation transport codeCMFGEN, finding a good match to the explosion of a low-mass red supergiant with energyE_kin= 6 × 10⁵⁰erg. With these models we identify, for the first time, the ions that dominate the early ultraviolet spectra. We present optical photometry and spectroscopy, showing that SN 2022acko has a peak absolute magnitude ofV= − 15.4 mag and plateau length of ∼115 days. The spectra closely resemble those of SN 2005cs and SN 2012A. Using the combined optical and ultraviolet spectra, we report the fraction of flux as a function of bluest wavelength on days 5, 7, and 19. We create a spectral time-series of Type II supernovae in the ultraviolet, demonstrating the rapid decline of flux over the first few weeks of evolution. Future observations of Type II supernovae are required to map out the landscape of exploding red supergiants, with and without circumstellar material, which is best revealed in high-quality ultraviolet spectra.

 

Context. The ESA Gaia mission provides a unique time-domain survey for more than 1.6 billion sources with G ≲ 21 mag. Aims. We showcase stellar variability in the Galactic colour-absolute magnitude diagram (CaMD). We focus on pulsating, eruptive, and cataclysmic variables, as well as on stars that exhibit variability that is due to rotation and eclipses. Methods. We describe the locations of variable star classes, variable object fractions, and typical variability amplitudes throughout the CaMD and show how variability-related changes in colour and brightness induce “motions”. To do this, we use 22 months of calibrated photometric, spectro-photometric, and astrometric Gaia data of stars with a significant parallax. To ensure that a large variety of variable star classes populate the CaMD, we crossmatched Gaia sources with known variable stars. We also used the statistics and variability detection modules of the Gaia variability pipeline. Corrections for interstellar extinction are not implemented in this article. Results. Gaia enables the first investigation of Galactic variable star populations in the CaMD on a similar, if not larger, scale as was previously done in the Magellanic Clouds. Although the observed colours are not corrected for reddening, distinct regions are visible in which variable stars occur. We determine variable star fractions to within the current detection thresholds of Gaia . Finally, we report the most complete description of variability-induced motion within the CaMD to date. Conclusions. Gaia enables novel insights into variability phenomena for an unprecedented number of stars, which will benefit the understanding of stellar astrophysics. The CaMD of Galactic variable stars provides crucial information on physical origins of variability in a way that has previously only been accessible for Galactic star clusters or external galaxies. Future Gaia data releases will enable significant improvements over this preview by providing longer time series, more accurate astrometry, and additional data types (time series BP and RP spectra, RVS spectra, and radial velocities), all for much larger samples of stars.