AbstractWe use synthetic model spectra to investigate the potential of near-ultraviolet (3000-4050 Å) observations of massive O-type stars. We highlight the He I $$\lambda $$ λ 3188 and He II $$\lambda $$ λ 3203 pair as a potential temperature diagnostic in this range, supported by estimates of gravity using the high Balmer series lines. The near-ultraviolet also contains important metallic lines for determinations of chemical abundances (oxygen in particular) and estimates of projected rotational velocities for O-type spectra. Using the model spectra we present performance estimates for observations of extragalactic massive stars with the Cassegrain U-Band Efficient Spectrograph (CUBES) now in construction for the Very Large Telescope. The high efficiency of CUBES will open-up exciting new possibilities in the study of massive stars in external galaxies. For instance, CUBES will provide new insights into the physical properties of O-type stars, including oxygen abundances, in metal-poor irregular galaxies at $$\sim $$ ∼ 1 Mpc from integrations of just 2-3 hrs. Moreover, CUBES will bring quantitative spectroscopy of more distant targets within reach for the first time, such as the O-type star (V $$\sim $$ ∼  21.5 mag) in Leo P (at 1.6 Mpc) in only half a night of observations.

AbstractThe Ariel Space Mission aims to observe a diverse sample of exoplanet atmospheres across a wide wavelength range of 0.5 to 7.8 microns. The observations are organized into four Tiers, with Tier 1 being a reconnaissance survey. This Tier is designed to achieve a sufficient signal-to-noise ratio (S/N) at low spectral resolution in order to identify featureless spectra or detect key molecular species without necessarily constraining their abundances with high confidence. We introduce a P-statistic that uses the abundance posteriors from a spectral retrieval to infer the probability of a molecule’s presence in a given planet’s atmosphere in Tier 1. We find that this method predicts probabilities that correlate well with the input abundances, indicating considerable predictive power when retrieval models have comparable or higher complexity compared to the data. However, we also demonstrate that the P-statistic loses representativity when the retrieval model has lower complexity, expressed as the inclusion of fewer than the expected molecules. The reliability and predictive power of the P-statistic are assessed on a simulated population of exoplanets with H$$_2$$ 2 -He dominated atmospheres, and forecasting biases are studied and found not to adversely affect the classification of the survey.

Synthesis models of the diffuse Cosmic X-ray Background (CXB) suggest that it can be resolved into discrete sources, primarily Active Galactic Nuclei (AGNs). Measuring the CXB accurately offers a unique probe to study the AGN population in the nearby Universe. Current hard X-ray instruments suffer from the time-dependent background and cross-calibration issues. As a result, their measurements of the CXB normalization have an uncertainty of the order of sim 15%. In this paper, we present the concept and simulated performances of a CXB detector, which could be operated on different platforms. With a 16-Unit CubeSat mission running for more than two years in space, such a detector could measure the CXB normalization with sim 1% uncertainty.

AbstractThe LOw Frequency ARray (LOFAR) is a European radio telescope operating since 2010 in the frequency bands 10 - 80 MHz and 110 - 250 MHz. This article provides an analysis of the energy consumption and the carbon footprint of LOFAR. The approach used is a Life Cycle Analysis (LCA). We find that one year of LOFAR operations requires 3,627 MWh of electricity, 48,714 m3 gas and 135,497 liters of fuel. The associated carbon emission is 1,867 tCO2e/year. Results include the footprint stemming from operations of all LOFAR stations and central processing, but exclude scientific post-processing and activities. The electrical energy required for scientific processing is assessed separately. It ranges from 1% (standard imaging and time-domain), to 40% (wide field long baseline imaging) of the energy consumption for the observation. The outcome provides a transparent baseline in making LOFAR more sustainable and can serve as a blueprint for the analysis of other research infrastructures.