In astronomy, upcoming space telescopes with wide-field optical instruments have a spatially varying point spread function (PSF). Specific scientific goals require a high-fidelity estimation of the PSF at target positions where no direct measurement of the PSF is provided. Even though observations of the PSF are available at some positions of the field of view (FOV), they are undersampled, noisy, and integrated into wavelength in the instrument's passband. PSF modeling represents a challenging ill-posed problem, as it requires building a model from these observations that can infer a super-resolved PSF at any wavelength and position in the FOV. Current data-driven PSF models can tackle spatial variations and super-resolution. However, they are not capable of capturing PSF chromatic variations. Our model, coined WaveDiff, proposes a paradigm shift in the data-driven modeling of the point spread function field of telescopes. We change the data-driven modeling space from the pixels to the wavefront by adding a differentiable optical forward model into the modeling framework. This change allows the transfer of a great deal of complexity from the instrumental response into the forward model. The proposed model relies on efficient automatic differentiation technology and modern stochastic first-order optimization techniques recently developed by the thriving machine-learning community. Our framework paves the way to building powerful, physically motivated models that do not require special calibration data. This paper demonstrates the WaveDiff model in a simplified setting of a space telescope. The proposed framework represents a performance breakthrough with respect to the existing state-of-the-art data-driven approach. The pixel reconstruction errors decrease six-fold at observation resolution and 44-fold for a 3x super-resolution. The ellipticity errors are reduced at least 20 times, and the size error is reduced more than 250 times. By only using noisy broad-band in-focus observations, we successfully capture the PSF chromatic variations due to diffraction. WaveDiff source code and examples associated with this paper are available at this link .
With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method would allow for the reconstruction of a cleaner estimation of the sky. The deconvolved images would be helpful to perform photometric measurements to help make progress in the fields of galaxy formation and evolution. We propose a new deconvolution method based on the Learnlet transform. Eventually, we investigate and compare the performance of different Unet architectures and Learnlet for image deconvolution in the astrophysical domain by following a two-step approach: a Tikhonov deconvolution with a closed-form solution, followed by post-processing with a neural network. To generate our training dataset, we extract HST cutouts from the CANDELS survey in the F606W filter (V-band) and corrupt these images to simulate their blurred-noisy versions. Our numerical results based on these simulations show a detailed comparison between the considered methods for different noise levels.
Permanent researcher position in the field of cosmological simulations in the context of Euclid and other cosmological surveys.
The Astrophysics Department at CEA Paris-Saclay, Irfu, invites applications for a permanent researcher position in the field of cosmological simulations in the context of Euclid and other cosmological surveys. The applicant is expected to conduct top-end research projects based on ambitious cosmological simulations and their analysis, in connection with the development of new numerical simulation codes for exascale supercomputers and with the exploitation of large cosmological projects including Euclid as well as other complementary surveys. While the key purpose of this position is to strengthen the scientific outcome of cosmological surveys using simulations, we also seek to strengthen the links between large-scale cosmology, structure formation, and the physics of galaxy formation and galaxy clusters, in relation with various ground- and space-based instruments such as ALMA, JWST, and the E-ELT. The group at Paris-Saclay has extensive experience in using grid-based simulation codes such as the RAMSES code and newer GPU-friendly prototypes but also considers the use of other numerical techniques, and has easy access to top-end facilities at the national and European levels (GENCI, PRACE) which are expected to be involved in the proposed research.
The applicant should hold a PhD degree in astrophysics or related fields and demonstrate a strong experience in the completion of simulation-based cosmological projects and their links with observations. Experience in numerical code development/optimization and in extragalactic astronomical observations are also welcome.
The cosmology and galaxy evolution lab in Saclay comprises experts of large scale structure and galaxy formation in observations, simulations and theory (Anzari, Arnaud, Aussel, Bournaud, Cesarsky, Codis, Cuillandre, Daddi, Elbaz, Le Floch, Lehoucq, Magnelli, Pierre, Pratt). We strongly collaborate with the CosmoStat lab, the star formation and interstellar medium lab, the astrophysical plasma modelling lab, as well as the cosmology group in the Particle Physics Department of CEA Paris-Saclay.
The deadline to apply is March, 3rd. Applicants should submit a CV, a research summary (4 pages limit) and a research project (4 pages limit), and arrange for 3 to 4 letters of recommendations to be sent to email@example.com by the same deadline.
Potential applicants are welcome to make early contact with Frederic Bournaud (firstname.lastname@example.org) and/or any member of the group listed above.
ARGOS Postdoctoral position (Heraklion, Crete): Distributing Processing, Deep Learning and Radio-Interferometry
|Position:||1 year Postdoc - can be extended.|
George Tzagkarakis at email@example.com
Stage M2: Étude de la rotation de la surface des étoiles avec les données de la mission TESS de la NASA, à l’aide de techniques de filtrage par ondelettes et d’apprentissage profond
TITAN AstroStatistics PhD position (Heraklion, Greece) : Morphology and Spatial Distribution of the Dust Emission using Deep Learning Methods
|Position:||PhD 3 years, Heraklion Crete|
|Deadline:||28/02/2023 starting before December 2023|
|Contacts:||See attached pdf|