Observations of the Crab Nebula and Pulsar with the Large-Sized Telescope Prototype of the Cherenkov Telescope Array

CTA (Cherenkov Telescope Array) is the next generation ground-based observatory for gamma-ray astronomy at very-high energies. The Large-Sized Telescope prototype (\LST{}) is located at the Northern site of CTA, on the Canary Island of La Palma. LSTs are designed to provide optimal performance in the lowest part of the energy range covered by CTA, down to $\simeq 20$ GeV. \LST{} started performing astronomical observations in November 2019, during its commissioning phase, and it has been taking data since then. We present the first \LST{} observations of the Crab Nebula, the standard candle of very-high energy gamma-ray astronomy, and use them, together with simulations, to assess the basic performance parameters of the telescope. The data sample consists of around 36 hours of observations at low zenith angles collected between November 2020 and March 2022. \LST{} has reached the expected performance during its commissioning period - only a minor adjustment of the preexisting simulations was needed to match the telescope behavior. The energy threshold at trigger level is estimated to be around 20 GeV, rising to $\simeq 30$ GeV after data analysis. Performance parameters depend strongly on energy, and on the strength of the gamma-ray selection cuts in the analysis: angular resolution ranges from 0.12 to 0.40 degrees, and energy resolution from 15 to 50\%. Flux sensitivity is around 1.1\% of the Crab Nebula flux above 250 GeV for a 50-h observation (12\% for 30 minutes). The spectral energy distribution (in the 0.03 - 30 TeV range) and the light curve obtained for the Crab Nebula agree with previous measurements, considering statistical and systematic uncertainties. A clear periodic signal is also detected from the pulsar at the center of the Nebula.Comment: Submitted to Ap

Deep learning-based imaging in radio interferometry

Context. The sparse layouts of radio interferometers result in an incomplete sampling of the sky in Fourier space which leads to artifacts in the reconstructed images. Cleaning these systematic effects is essential for the scientific use of radiointerferometric images. Aims. Established reconstruction methods are often time-consuming, require expert knowledge, and suffer from a lack of reproducibility. We have developed a prototype deep learning-based method that generates reproducible images in an expedient fashion. Methods. To this end, we take advantage of the efficiency of convolutional neural networks to reconstruct image data from incomplete information in Fourier space. The neural network architecture is inspired by super-resolution models that utilize residual blocks. Using simulated data of radio galaxies that are composed of Gaussian components, we trained deep learning models whose reconstruction capability is quantified using various measures. Results. The reconstruction performance is evaluated on clean and noisy input data by comparing the resulting predictions with the true source images. We find that source angles and sizes are well reproduced, while the recovered fluxes show substantial scatter, albeit not worse than existing methods without fine-tuning. Finally, we propose more advanced approaches using deep learning that include uncertainty estimates and a concept to analyze larger images

Anaerobic Alcohol Conversion to Carbonyl Compounds over Nanoscaled Rh-Doped SrTiO3 under Visible Light

Photocatalytic oxidation of organic compounds on semiconductors provides a mild approach for organic synthesis and solar energy utilization. Herein, we identify the key points for the photocatalytic oxidation over Pt-loaded Rh-doped strontium titanate allowing the conversion of alcohols efficiently and selectively to aldehydes and ketones under anaerobic conditions and visible light with an apparent quantum efficiency of pure benzyl alcohol oxidation at 420 nm of <= 49.5%. Mechanistic investigations suggest that thermodynamically the controlled valence band edge position via Rh doping provides a suitable oxidation ability of photogenerated holes, avoiding the powerful hydroxyl radical intermediates prone to overoxidation resulting in high selectivity. Kinetically, oxygen vacancies induced by Rh3+ substitution in the SrTiO3 lattice not only favor the dissociative adsorption of alcohols yielding alkoxy species but also induce the weakening of the alpha-C-H bond facilitating its cleavage by the photogenerated holes. Pt nanoparticles deposited as a cocatalyst contribute to the final hydrogen evolution