Square Kilometre Array Science Data Challenge 1: analysis and results

Abstract

As the largest radio telescope in the world, the Square Kilometre Array (SKA) will lead the next generation of radio astronomy. The feats of engineering required to construct the telescope array will be matched only by the techniques developed to exploit the rich scientific value of the data. To drive forward the development of efficient and accurate analysis methods, we are designing a series of data challenges that will provide the scientific community with high-quality datasets for testing and evaluating new techniques. In this paper we present a description and results from the first such Science Data Challenge (SDC1). Based on SKA MID continuum simulated observations and covering three frequencies (560 MHz, 1400MHz and 9200 MHz) at three depths (8 h, 100 h and 1000 h), SDC1 asked participants to apply source detection, characterization and classification methods to simulated data. The challenge opened in November 2018, with nine teams submitting results by the deadline of April 2019. In this work we analyse the results for 8 of those teams, showcasing the variety of approaches that can be successfully used to find, characterise and classify sources in a deep, crowded field. The results also demonstrate the importance of building domain knowledge and expertise on this kind of analysis to obtain the best performance. As high-resolution observations begin revealing the true complexity of the sky, one of the outstanding challenges emerging from this analysis is the ability to deal with highly resolved and complex sources as effectively as the unresolved source population.

Deep Radio Interferometric Imaging with POLISH: DSA-2000 and weak lensing

“…Figure 1 visually compares the performance of POLISH against the baseline CLEAN algorithm on synthetic DSA-2000 observations, using an SKA Data Challenge image as I sky (Bonaldi et al 2021). The input dirty image, corresponding to a 1300 MHz bandwidth PSF, is shown on the left.…”

Toward a Spanish SKA Regional Centre fully engaged with open science

“…It is worth highlighting that the use of SKA precursors and pathfinder telescopes 1 is key for the community to get prepared for exploiting SKA and to optimize the scientific analysis of the SKAO data. In addition, a series of science data challenges are being released to the community by the SKAO 19 since November 2018, and this is planned to continue until the end of 2024. The challenges entail analysis of simulated SKA data products that resemble the type of data that the SKAO will produce, with each separate challenge exploring a particular aspect of the SKA data products.…”

A co-design framework of neural networks and quantum circuits towards quantum advantage

“…When the neural networks are applied to a specific field (e.g., AI in medical or AI in astronomy), the highresolution input images bring new challenges. For example, one 3D-MRI image contains 224 × 224 × 10 ≈ 5 × 10 6 pixels 18 while one Square Kilometer Array (SKA) science data contains 32,768 × 32,768 ≈ 1 × 10 9 pixels 19,20 . The large inputs greatly increase the computation in neural network 21 , which gradually becomes the performance bottleneck, even consider the advance classical computing hardware 22,23 .…”