The 2nd European ALMA school kicked off on Monday 26th January in Leiden. With almost 70 participants from 22 countries - and 20 tutors and lecturers from all the European ARC nodes - this is the largest ALMA-specific training event in Europe to date. Unusually for the Netherlands, Day 1 started with a low pwv and abundant sunshine. Over the next five days, we will cover a wide range of topics, from introduction to CASA to advanced data-processing techniques. 

Many thanks to our sponsors: Dutch Research Council, LKBF, NOVA, and the Leiden University Funds.

ALMA has made information available to assist with early proposal planning for Cycle 13 (see Read More). Full details will be published in the Cycle 13 Call for Proposals.

The results from the recently completed ESO ALMA development study on the development of sub-micron sized Aluminium Nitride barrier Superconductor Insulator Superconductor junctions have now been published in an article in the Journal of Superconductor Science and Technology . The full study report has now also been made public on the ESO ALMA development pages.

Progress in engineering at millimetre wavelengths and advances in semiconductor fabrication techniques offer low noise amplifier (LNA) based radioastronomy receivers which operate at higher temperatures and wider operational bandwidths, with reduced noise, higher pixel density, and more. The University of Manchester and STFC Rutherford Appleton Laboratory have recently completed ESO's ALMA development study, Towards ALMA System-on-Chip European Receivers (TASER). This two year project developed key technologies for compact integrated front end receivers for ALMA. The benefits of LNA receiver integration and miniaturisation are, among others, lower operational costs, reduced manufacturing and testing labour needs, and the potential for high density focal plane array/phased array feed technology. The results of the TASER study are important steps towards implementing the ALMA Development Roadmap, and beyond, with the aim of enhancing ALMA's status as a globally leading scientific tool.

The ALMA Science Archive (ASA) now allows you to visually search for images that are morphologically similar to a given ASA image (currently 259,126 continuum images and 196,322 peak-flux images of data-cubes) with the aim to help you find such images extremely rapidly within the vast ASA holdings. A description of the state-of-the-art deep learning method used to determine similar images - self-supervised contrastive affine-transformation-independent representation learning with a deep neural network - and the interface we have developed can be found in this ESO Messenger article.