the SKA Era ! Antoine Marchal - CRCN CNRS @LPENS GT-ICRÑFeb 2026 Processing the GASKAP-HI full-survey w/ the GASKAP-HI collaboration imaging team Hiep Nguyen, David McConnell, James Dempsey, Naomi McClure-GrifÞths, Nick Pingel, Callum Lynn, Jackie Ma, Jo Dawson
Multi-scale CLEAN vs MEM ? Multi-scale CLEAN Maximum entropy method For HI, see Stanimirović et al 2002 Cornwell et al 2008 See review by Narayan & Nityananda 1986 1986 ÒParametricÓ multi-Gaussian model Multi-scale CLEAN vs MEM ?
Multi-scale CLEAN vs MEM ? For HI, see Stanimirović et al 2002 Cornwell et al 2008 See review by Narayan & Nityananda 1986 1986 Multi-scale CLEAN Maximum entropy method ÒParametricÓ multi-Gaussian model Multi-scale CLEAN vs MEM ?
Multi-scale CLEAN vs MEM ? || ˜ − ||2 Σ − 1 ζ ∑ i Ii ln ( Ii Mi ) For HI, see Stanimirović et al 2002 Cornwell et al 2008 See review by Narayan & Nityananda 1986 1986 Multi-scale CLEAN Maximum entropy method ÒParametricÓ multi-Gaussian model Non -parametric model Multi-scale CLEAN vs MEM ?
Multi-scale CLEAN vs MEM ? || ˜ − ||2 Σ − 1 ζ ∑ i Ii ln ( Ii Mi ) For HI, see Stanimirović et al 2002 Cornwell et al 2008 See review by Narayan & Nityananda 1986 1986 1- Positivity 2- Smoothness ÒParametricÓ multi-Gaussian model Non -parametric model Multi-scale CLEAN Maximum entropy method
brief (& biased) history of HI surveys (in the local Universe) Australian Square Kilometer PathÞnder ASKAP antennas PAF - 36 antennas (12m diam. each) - Freq. range 700 MHz - 1.8 GHz - Bandwidth 300 MHz - FoV ~30 square degrees - Baseline up to 6 km - 8 Survey Science Projects A B C ASKAP: a survey machine
brief (& biased) history of HI surveys (in the local Universe) ASKAP: a survey machine GASKAP-HI survey with the ASKAP 4000h of observations 50h / field 30h / field 200h / field
are the challenges ? 2- Image quality and fidelity 1- Deep integration ! 3- Green and Scalable ? A brief (& biased) history of HI surveys (in the local Universe)
are the challenges ? 2- Image quality and fidelity 1- Deep integration ! 3- Green and Scalable ? A brief (& biased) history of HI surveys (in the local Universe)
the wheel: A (not so new) non-linear optimization approach to join deconvolution Parkes telescope An open source code Ñ written in PyTorch framework (with ReadtheDocs, notebooks, É)
the wheel: A (not so new) non-linear optimization approach to join deconvolution 108 beams Projected sky image I(r) ˜ k (I′ k ; u, v) ≃ I′ k (ℓ, m) Beams
the wheel: A (not so new) non-linear optimization approach to join deconvolution 108 beams Projected sky image I(r) ˜ k (I′ k ; u, v) ≃ I′ k (ℓ, m) × Ak (ℓ, m) Primary beam Beams
the wheel: A (not so new) non-linear optimization approach to join deconvolution 108 beams Projected sky image I(r) ˜ k (I′ k ; u, v) ≃ ℱ[ I′ k (ℓ, m) × Ak (ℓ, m)] Primary beam NuFFT (GPU) Beams similar to MPol (by Ian Czekala, see Zawadzki et al. 2023) Beams
the wheel: A (not so new) non-linear optimization approach to join deconvolution L1,k (I′ k ) = ˜ k (I′ k ) − k Jk (I′ k ) = 1 2 ∑ u,v ( L1,k(I′ k) Σ1,k ) 2 108 residual Q(I′ k ) = 108 ∑ k Jk (I′ k ) + R(I(r)) Spatial regularisation Joint deconvolution with spatial regularisation One cost function 108 chi square
the wheel: A (not so new) non-linear optimization approach to join deconvolution Visualising A PPV cube GASKAP-HI survey with the ASKAP 4000h of observations
the wheel: A (not so new) non-linear optimization approach to join deconvolution GASKAP-HI survey with the ASKAP 4000h of observations Visualising A PPV cube
the wheel: A (not so new) non-linear optimization approach to join deconvolution GASKAP-HI survey with the ASKAP 4000h of observations Visualising A PPV cube
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