Context . The study of asteroids, particularly near-Earth asteroids, is key to gaining insights into our Solar System and can help prevent dangerous collisions. Beyond finding new objects, additional observations of known asteroids will improve our knowledge of their orbit. Aims . We have developed an automated pipeline to process and search for asteroid trails in images taken with OmegaCAM, the wide- field imager mounted on the VLT Survey Telescope (VST), on the European Southern Observatory’s Cerro Paranal. The pipeline inputs a FITS image and outputs the position, length, and angle of all the asteroids trails detected. Methods . A convolutional neural network was trained on a set of synthetic asteroid trails, with trail lengths 5–120 pixels (1–25″) and S/Ns 3–20. Its performance was tested on synthetic trails and validated using real trails, chosen from the Solar System Object Image Search of the Canadian Astronomy Data Centre. Results . On the synthetic trails, the pipeline achieved a completeness of 70% for trails with length ≥15 pixels (3″), with a precision of 82%. On the real trails, the pipeline achieved a completeness of 65%, with a precision of 44%, a lower value likely due to the higher presence of contaminants and stars in the field. The pipeline was able to detect both low- and high-S/N asteroid trails. Conclusions . Our method shows a strong potential to make new discoveries and precoveries in VST data across the S/N range studied, especially in the fainter end, which remains largely unexplored.

Astronomical data reduction is usually done with processing pipelines that consist of a series of individual processing steps that can be executed stand-alone. These processing steps are then strung together into workflows and fed with data to address a particular processing goal. In this paper, we propose a data processing system that automatically derives processing workflows for different use cases from a single specification of a cascade of processing steps. The system works by using formalized descriptions of data processing pipelines that specify the input and output of each processing step. Inputs can be existing data or the output of a previous step. Rules to select the most appropriate input data are directly attached to the description. A version of the proposed system has been implemented as the ESO Data Processing System (EDPS) in the Python language. The specification of processing cascades and data organisation rules use a restrictive set of Python classes, attributes and functions. The EDPS implementation of the proposed system was used to demonstrate that it is possible to automatically derive from a single specification of a pipeline processing cascade the workflows that the European Southern Observatory uses for quality control, archive production, and specialized science reduction. The EDPS will be used to replace all data reduction systems using different workflow specifications that are currently used at the European Southern Observatory.

We applied machine learning to the entire data history of ESO's High Accuracy Radial Velocity Planet Searcher (HARPS) instrument. Our primary goal was to recover the physical properties of the observed objects, with a secondary emphasis on simulating spectra. We systematically investigated the impact of various factors on the accuracy and fidelity of the results, including the use of simulated data, the effect of varying amounts of real training data, network architectures, and learning paradigms. Our approach integrates supervised and unsupervised learning techniques within autoencoder frameworks. Our methodology leverages an existing simulation model that utilizes a library of existing stellar spectra in which the emerging flux is computed from first principles rooted in physics and a HARPS instrument model to generate simulated spectra comparable to observational data. We trained standard and variational autoencoders on HARPS data to predict spectral parameters and generate spectra. Our models excel at predicting spectral parameters and compressing real spectra, and they achieved a mean prediction error of approximately 50 K for effective temperatures, making them relevant for most astrophysical applications. Furthermore, the models predict metallicity ([M/H]) and surface gravity (log g) with an accuracy of approximately 0.03 dex and 0.04 dex, respectively, underscoring their broad applicability in astrophysical research. The models' computational efficiency, with processing times of 779.6 ms on CPU and 3.97 ms on GPU, makes them valuable for high-throughput applications like massive spectroscopic surveys and large archival studies. By achieving accuracy comparable to classical methods with significantly reduced computation time, our methodology enhances the scope and efficiency of spectroscopic analysis.

The ESO Data Processing System (EDPS) is a new software infrastructure to run the European Southern Observatory's (ESO) science data processing modules ("pipeline recipes"). These recipes are used for quality control at the telescope, unsupervised production of science and calibration products for ESO's science archive, and for interactive data reduction by external science users. EDPS can be used to address all these use cases and therefore replaces individual dedicated systems that have been in use ESO up to now.

Scientific data collected at ESO's observatories are freely and openly accessible online through the ESO Science Archive Facility. In addition to the raw data straight out of the instruments, the ESO Science Archive also contains four million processed science files, and counting, available for use by scientists and astronomy enthusiasts worldwide. ESO subscribes to the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for scientific data management and stewardship. All data in the ESO Science Archive are distributed according to the terms of the Creative Commons Attribution 4.0 International licence (CC BY 4.0).

Scientific data collected at ESO's observatories are freely and openly accessible online through the ESO Science Archive Facility. In addition to the raw data straight out of the instruments, the ESO Science Archive also contains four million processed science files available for use by scientists and astronomy enthusiasts worldwide. ESO subscribes to the FAIR (Findable, Accessible, Interoperable, Reusable) guiding principles for scientific data management and stewardship. All data in the ESO Science Archive are distributed according to the terms of the Creative Commons Attribution 4.0 International licence (CC BY 4.0).

Context. Classical Cepheids are primary distance indicators and a crucial stepping stone in determining the present-day value of the Hubble constant H-0 to the precision and accuracy required to constrain apparent deviations from the ACDM Concordance Cosmological Model. Aims. We measured the iron and oxygen abundances of a statistically significant sample of 89 Cepheids in the Large Magellanic Cloud (LMC), one of the anchors of the local distance scale, quadrupling the prior sample and including 68 of the 70 Cepheids used to constrain H-0 by the SHOES program. The goal is to constrain the extent to which the luminosity of Cepheids is influenced by their chemical composition, which is an important contributor to the uncertainty on the determination of the Hubble constant itself and a critical factor in the internal consistency of the distance ladder. Methods. We derived stellar parameters and chemical abundances from a self-consistent spectroscopic analysis based on equivalent width of absorption lines. Results. The iron distribution of Cepheids in the LMC can be very accurately described by a single Gaussian with a mean [Fe/H] = -0.409 +/- 0.003 dex and sigma = 0.076 +/- 0.003 dex. We estimate a systematic uncertainty on the absolute mean values of 0.1 dex. The width of the distribution is fully compatible with the measurement error and supports the low dispersion of 0.069 mag seen in the near-infrared Hubble Space Telescope LMC period-luminosity relation. The uniformity of the abundance has the important consequence that the LMC Cepheids alone cannot provide any meaningful constraint on the dependence of the Cepheid period-luminosity relation on chemical composition at any wavelength. This revises a prior claim based on a small sample of 22 LMC Cepheids that there was little dependence (or uncertainty) between composition and near-infrared luminosity, a conclusion which would produce an apparent conflict between anchors of the distance ladder with different mean abundance. The chemical homogeneity of the LMC Cepheid population makes it an ideal environment in which to calibrate the metallicity dependence between the more metal-poor Small Magellanic Cloud and metal-rich Milky Way and NGC 4258.

Context. Classical Cepheids are primary distance indicators and a crucial stepping stone in determining the present-day value of the Hubble constant H 0 to the precision and accuracy required to constrain apparent deviations from the ΛCDM Concordance Cosmological Model. Aims. We measured the iron and oxygen abundances of a statistically significant sample of 89 Cepheids in the Large Magellanic Cloud (LMC), one of the anchors of the local distance scale, quadrupling the prior sample and including 68 of the 70 Cepheids used to constrain H 0 by the SH0ES program. The goal is to constrain the extent to which the luminosity of Cepheids is influenced by their chemical composition, which is an important contributor to the uncertainty on the determination of the Hubble constant itself and a critical factor in the internal consistency of the distance ladder. Methods. We derived stellar parameters and chemical abundances from a self-consistent spectroscopic analysis based on equivalent width of absorption lines. Results. The iron distribution of Cepheids in the LMC can be very accurately described by a single Gaussian with a mean [Fe/H] = −0.409 ± 0.003 dex and σ = 0.076 ± 0.003 dex. We estimate a systematic uncertainty on the absolute mean values of 0.1 dex. The width of the distribution is fully compatible with the measurement error and supports the low dispersion of 0.069 mag seen in the near-infrared Hubble Space Telescope LMC period–luminosity relation. The uniformity of the abundance has the important consequence that the LMC Cepheids alone cannot provide any meaningful constraint on the dependence of the Cepheid period–luminosity relation on chemical composition at any wavelength. This revises a prior claim based on a small sample of 22 LMC Cepheids that there was little dependence (or uncertainty) between composition and near-infrared luminosity, a conclusion which would produce an apparent conflict between anchors of the distance ladder with different mean abundance. The chemical homogeneity of the LMC Cepheid population makes it an ideal environment in which to calibrate the metallicity dependence between the more metal-poor Small Magellanic Cloud and metal-rich Milky Way and NGC 4258.

The ESO Data Processing System (EDPS) is a new software infrastructure to run the European Southern Observatory’s (ESO) science data processing modules (“pipeline recipes”) that is currently in an advanced status of development. These recipes are used for quality control purposes, unsupervised production of science and calibration products for the archive, and are embedded in an interactive data reduction system that is also offered to external science users. EDPS aims to unify all these use cases and thereby replace individual dedicated systems that are currently used at the European Southern Observatory (ESO). All pipelines are based on self-contained recipes with well-defined inputs, outputs, and parameters. They are strung together into different workflows and fed with data selected by pre-defined rules. Even though the dependencies of the recipes imply an execution cascade that is identical for all use cases, the actual workflows and data selection rules for the different use cases are significantly different. EDPS will be a Python based system that generates workflows for ESO’s science data processing use cases from a general specification for the dependencies of the recipes. By starting from a single specification for different workflows, EDPS will improve the reliability and reproducible of data processing. At the same time, EDPS is expected to drastically lower implementation and maintenance costs for the workflows.

ABSTRACT The Arecibo Ultra-Deep Survey (AUDS) is a blind H i survey aimed at detecting galaxies beyond the local Universe in the 21-cm emission line of neutral hydrogen (H i). The Arecibo L-band Feed Array (ALFA) was used to image an area of 1.35 deg2 to a redshift depth of 0.16, using a total on-source integration time of over 700 h. The long integration time and small observation area makes it one of the most sensitive H i surveys, with a noise level of ∼75 μJy per 21.4 kHz (equivalent to 4.5 km s−1 at redshift z = 0). We detect 247 galaxies in the survey, more than doubling the number already detected in AUDS60. The mass range of detected galaxies is $\log (M_{\rm H\,{\small I}}~[h_{70}^{-2}\, {\rm M}_\odot ]) = 6.32\!-\!10.76$. A modified maximum likelihood method is employed to construct an H i mass function (HIMF). The best fitting Schechter parameters are low-mass slope α = −1.37 ± 0.05, characteristic mass $\log (M^*~[h_{70}^{-2}\, {\rm M}_\odot ]) = 10.15 \pm 0.09$, and density $\Phi _* = (2.41 \pm 0.57) \times 10^{-3} h_{70}^3$ Mpc−3 dex−1. The sample was divided into low- and high-redshift bins to investigate the evolution of the HIMF. No change in low-mass slope α was measured, but an increased characteristic mass M*, was noted in the higher redshift sample. Using Sloan Digital Sky Survey data to define relative galaxy number density, the dependence of the HIMF with environment was also investigated in the two AUDS regions. We find no significant variation in α or M*. In the surveyed region, we measured a cosmic H i density $\Omega _{\rm H\,{\small I}} = (3.55 \pm 0.30) \times 10^{-4}\, h_{70}^{-1}$. There appears to be no evolutionary trend in $\Omega _{\rm H\,{\small I}}$ above 2σ significance between redshifts of 0 and 0.16.

We investigate how damped Lyman-$\alpha$ absorbers (DLAs) at z ~ 2-3, detected in large optical spectroscopic surveys of quasars, trace the population of star-forming galaxies. Building on previous results, we construct a model based on observed and physically motivated scaling relations in order to reproduce the bivariate distributions of metallicity, Z, and HI column density, N(HI). Furthermore, the observed impact parameters for galaxies associated to DLAs are in agreement with the model predictions. The model strongly favours a metallicity gradient, which scales with the luminosity of the host galaxy, with a value of $\gamma$* = -0.019 $\pm$ 0.008 dex kpc$^{-1}$ for L* galaxies that gets steeper for fainter galaxies. We find that DLAs trace galaxies over a wide range of galaxy luminosities, however, the bulk of the DLA cross-section arises in galaxies with L ~ 0.1 L* at z ~ 2.5 broadly consistent with numerical simulations.

The presence of strong absorption lines in the atmospheric transmission spectrum affects spectroscopic observations, in particular those in the nearand mid-infrared. Therefore, there is the need to correct scientific observations for this effect, a process known as telluric correction. The use of a detailed model of the atmospheric transmission spectrum brings several advantages over the method of empirically deriving corrections using observations of a telluric standard star. In this paper, we discuss and compare the two methods applied to K-band Multi-Object Spectrograph (KMOS) observations and show the improvements in the quality of the final products obtained by implementing the modelling technique offered by the ESO molecfit sky tool.

We present a comprehensive study of the relations between gas kinematics, metallicity and stellar mass in a sample of 82 gamma-ray burst (GRB)-selected galaxies using absorption and emission methods. We find the velocity widths of both emission and absorption profiles to be a proxy of stellar mass. We also investigate the velocity-metallicity correlation and its evolution with redshift. Using 33 GRB hosts with measured stellar mass and metallicity, we study the mass-metallicity relation for GRB host galaxies in a stellarmass range of 10(8.2)-10(11.1) M-circle dot and a redshift range of z similar to 0.3-3.4. TheGRB-selected galaxies appear to track themass-metallicity relation of star-forming galaxies but with an offset of 0.15 towards lower metallicities. This offset is comparable with the average error bar on the metallicity measurements of the GRB sample and also the scatter on the mass-metallicity relation of the general population. It is hard to decide whether this relatively small offset is due to systematic effects or the intrinsic nature of GRB hosts. We also investigate the possibility of using absorption-line metallicity measurements of GRB hosts to study the mass-metallicity relation at high redshifts. Our analysis shows that the metallicity measurements from absorption methods can significantly differ from emission metallicities and assuming identical measurements from the two methods may result in erroneous conclusions.

The European Southern Observatory has a long history of providing specialized data processing algorithms, called recipes, for most of its instruments. These recipes are used for both operational purposes at the observatory sites, and for data reduction by the scientists at their home institutions. The two applications require substantially different environments for running and controlling the recipes. In this papers, we describe the ESOReflex environment that is used for running recipes on the users' desktops. ESOReflex is a workflow driven data reduction environment. It allows intuitive representation, execution and modification of the data reduction workflow, and has facilities for inspection of and interaction with the data. It includes fully automatic data organization and visualization, interaction with recipes, and the exploration of the provenance tree of intermediate and final data products. ESOReflex uses a number of innovative concepts that have been described in Ref. 1. In October 2015, the complete system was released to the public. ESOReflex allows highly efficient data reduction, using its internal bookkeeping database to recognize and skip previously completed steps during repeated processing of the same or similar data sets. It has been widely adopted by the science community for the reduction of VLT data.

For many years the FORS2 instrument has suffered from artefacts in the flat fields caused by surface inhomogeneities on the longitudinal atmospheric dispersion corrector (LADC) that affected high-precision photometric and spectroscopic measurements. Recently, the FORS LADC prisms were exchanged, and our analysis of a large number of flat fields shows that this exchange has resulted in a significant decrease in the level of small-scale artefacts.

Context. High redshift star-forming galaxies are discovered routinely because of a flux excess in narrowband filters caused by an emission line. In most cases, the width of such filters is broader than typical line widths, and the throughput of the filters varies substantially within the bandpass. This leads to substantial uncertainties in redshifts and fluxes that are derived from the observations with one specific narrowband filter.Aims. The uncertainty in measured line parameters can be sharply reduced by using repeated observations of the same target field with filters that have overlapping passbands but differ slightly in central wavelength or wavelength dependence of the effective filter curve. Such data are routinely collected with some large field imaging cameras that use multiple detectors and a separate filter for each of the detectors. An example is the European Southern Observatory's VISTA InfraRed CAMera (VIRCAM).Methods. We developed a method of determining more accurate redshift and line flux estimates from the ratio of apparent fluxes measured from observations in different narrowband filters and several matching broadband filters. A parameterized model of the line and continuum flux is used to predict the flux ratios as a function of redshift based on the known filter curves. These model predictions are then used to determine the most likely redshift and line flux.Results. We tested the obtainable quality of parameter estimation for the example of H alpha in the VIRCAM NB118 filters both on simulated and actual observations where the latter were based on the UltraVISTA DR2 data set. We combined the narrowband data with deep broadband data in Y, J, and H. We find that with this method, the errors in the measured lines fluxes can be reduced up to almost an order of magnitude.Conclusions. We conclude that existing narrowband data can be used to derive accurate line fluxes if the observations include images taken with sufficiently different filter curves. For the UltraVISTA survey, the best-suited narrowband filter combinations allow an accuracy in wavelength of better than 1 nm and in flux of better than 15% at any redshift within the bandpass of the filters. In contrast, analyzing the data without exploiting the difference in filter curves leads to an uncertainty in wavelength of 10 nm and up to an order of magnitude errors in line flux estimates.

AbstractWe analyze a sample of 20 absorption systems intrinsic to long duration GRB host galaxies at z > 2 forwhich the metallicities are known. We compare the relation between the metallicity and cold gas velocity width for thissample to that of the Damped Lyman-alpha systems (DLAs) in the sight-lines of quasars (QSOs), and find completeagreement. We then compare the redshift evolution of the velocity-metallicity relation of our sample to that of QSODLAsand find that also GRB hosts favour a late onset of this evolution, around a redshift of ~2.6. We compute predicted stellar masses for the GRB host galaxies using the prescription determined from QSO-DLAsamples and compare the measured stellar masses for the four hosts where stellar masses have been determinedfrom SED fits. We find excellent agreement and conclude that, on basis of all available data and tests, long durationGRB-DLA hosts and intervening QSO-DLAs are consistent with being drawn from the same underlying population.

The Arecibo Ultra-Deep Survey (AUDS) combines the unique sensitivity of the telescope with the wide field of the Arecibo L-band Feed Array (ALFA) to directly detect 21 cm HI emission from galaxies at distances beyond the local Universe bounded by the lower frequency limit of ALFA (z = 0.16). AUDS has collected 700 h of integration time in two fields with a combined area of 1.35 deg(2). In this paper, we present data from 60 per cent of the total survey, corresponding to a sensitivity level of 80 mu Jy. We discuss the data reduction, the search for galaxies, parametrization, optical identification and completeness. We detect 102 galaxies in the mass range of log(M-HI/M) -2 log h = 5.6-10.3. We compute the HI mass function (HIMF) at the highest redshifts so far measured. A fit of a Schechter function results in alpha = -1.37 +/- 0.03, Phi* = (7.72 +/- 1.4) x 10(-3) h(3) Mpc(-3) and log (M-HI*/M-circle dot) = (9.75 +/- 0.041) + 2 log h. Using the measured HIMF, we find a cosmic HI density of Omega(HI) = (2.33 +/- 0.07) x 10(-4) h(-1) for the sample (z = 0.065). We discuss further uncertainties arising from cosmic variance. Because of its depth, AUDS is the first survey that can determine parameters for the HIMF in independent redshift bins from a single homogeneous data set. The results indicate little evolution of the comoving mass function and Omega(HI) within this redshift range. We calculate a weighted average for Omega(HI) in the range 0 < z < 0.2, combining the results from AUDS as well as results from other 21 cm surveys and stacking, finding a best combined estimate of Omega(HI) = (2.63 +/- 0.10) x 10(-4) h(-1).