Laboratoire d’Études du Rayonnement et de la Matière en Astrophysique et Atmosphères


Instrumentation for NenuFAR and SKA1

par Mickaël Coriat - publié le , mis à jour le

This service task of the SNO SKATE focuses on the development of instrumentation for NenuFAR and SKA1.

For NenuFAR :

  • Finalise the development of the control software, the pulsars and dynamic spectra instrumentation, and the imaging mode.
  • Calibrate the instrument (fine calibration of the beam, flux, and polarisation).

The OSUs involved are the OBSPM (LESIA, USN, GEPI), the OSUC (LPC2E), with a contribution from the OSUPS (AIM).

For SKA, For SKA, it concerns the participation in the ongoing construction of the two SKAO telescopes (SKA-MID and SKA-LOW). Today, the official French contributions to SKAO are :

  • Contribution to the design and realisation of the SKA-mid band 4 and 5 receivers. The OSU involved is OASU
  • Contribution to co-design activities (software/hardware) in line with sub-exascale data centres needs (SPC/SDHP-HW) whose innovative character will rely on both unprecedented capacities and environmental performance. OSUs involved today : OP, OCA (and INRIA).
    - Detailed description of the tasks currently identified in the co-design activities :
    • System level (instrument scientist) :
      • Operating plan : typology and temporal distribution of observation types, calibration strategy, use of the SDP
      • Performance budgets and numerical errors (e.g. artefacts, photometry, angular resolution, spectral resolution)
      • Contribution to testing (e.g. observation scenarios, representative data, validation of results, quality assurance metrics during processing)
    • Opération level :
      • Optimisation of workflows for data volume reduction (on-the-fly/batch processing, task organisation, data management) and hardware resource usage (e.g. sharing between workflows, reducing power consumption)
      • SAdaptive strategies to defer some processing without degrading the final quality of the results (e.g. intermediate products, compression of residual information)
      • Strategies for pooling treatments between different scientific objectives (e.g. point source and diffuse emissions)
      • Generation of intermediate products (e.g. real time requirements ; calibration ; RFI processing)
    • Algorithmic level :
      • Optimised algorithmic formulations for scaling up (e.g. problem decomposition, distribution of computations and data, use of heterogeneous computing architectures)
      • Algorithmic optimisations to reduce bottlenecks (e.g. computing, memory, disk, synchronisation)
      • Use of reduced precision, numerical analysis
      • Coding efficiency and characterisation (e.g. time, memory, IO, energy, computing resources)