4.6 Article

Deep learning for a space-variant deconvolution in galaxy surveys

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ASTRONOMY & ASTROPHYSICS
卷 641, 期 -, 页码 -

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EDP SCIENCES S A
DOI: 10.1051/0004-6361/201937039

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methods: statistical; methods: data analysis; methods: numerical

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The deconvolution of large survey images with millions of galaxies requires developing a new generation of methods that can take a space-variant point spread function into account. These methods have also to be accurate and fast. We investigate how deep learning might be used to perform this task. We employed a U-net deep neural network architecture to learn parameters that were adapted for galaxy image processing in a supervised setting and studied two deconvolution strategies. The first approach is a post-processing of a mere Tikhonov deconvolution with closed-form solution, and the second approach is an iterative deconvolution framework based on the alternating direction method of multipliers (ADMM). Our numerical results based on GREAT3 simulations with realistic galaxy images and point spread functions show that our two approaches outperform standard techniques that are based on convex optimization, whether assessed in galaxy image reconstruction or shape recovery. The approach based on a Tikhonov deconvolution leads to the most accurate results, except for ellipticity errors at high signal-to-noise ratio. The ADMM approach performs slightly better in this case. Considering that the Tikhonov approach is also more computation-time efficient in processing a large number of galaxies, we recommend this approach in this scenario.

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Euclid: Calibrating photometric redshifts with spectroscopic cross-correlations

K. Naidoo, H. Johnston, B. Joachimi, J. L. van den Busch, H. Hildebrandt, O. Ilbert, O. Lahav, N. Aghanim, B. Altieri, A. Amara, M. Baldi, R. Bender, C. Bodendorf, E. Branchini, M. Brescia, J. Brinchmann, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, M. Castellano, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, C. J. Conselice, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, J. Dinis, F. Dubath, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, P. Fosalba, M. Frailis, E. Franceschi, P. Franzetti, M. Fumana, S. Galeotta, B. Garilli, W. Gillard, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S. V. H. Haugan, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Kuemmel, A. Kiessling, M. Kilbinger, T. Kitching, R. Kohley, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, E. Maiorano, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, S. Maurogordato, M. Meneghetti, E. Merlin, G. Meylan, M. Moresco, L. Moscardini, E. Munari, R. Nakajima, S. M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W. J. Percival, V. Pettorino, S. Pires, G. Polenta, M. Poncet, L. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, C. Rosset, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, J. -l. Starck, C. Surace, P. Tallada-Crespi, A. N. Taylor, I. Tereno, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, E. A. Valentijn, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, M. Wetzstein, A. Zacchei, G. Zamorani, J. Zoubian, S. Andreon, D. Maino, V. Scottez, A. H. Wright

Summary: Cosmological constraints from the Euclid imaging survey rely on accurate determination of tomographic redshift bins' true redshift distributions, n(z). We investigate the calibration of mean redshift, < z >, of ten Euclid tomographic redshift bins using cross-correlation with spectroscopic samples. Our results show that clustering redshifts can attain uncertainties exceeding the Euclid requirement by at least a factor of three, but systematic biases limit the accuracy. Future work includes extending the method to higher redshifts with quasar reference samples.

ASTRONOMY & ASTROPHYSICS (2023)

Article Astronomy & Astrophysics

Probabilistic mass-mapping with neural score estimation

B. Remy, F. Lanusse, N. Jeffrey, J. Liu, J. -l. Starck, K. Osato, T. Schrabback

Summary: This study introduces a novel methodology that efficiently samples the high-dimensional Bayesian posterior of weak lensing mass-mapping problem, using a non-Gaussian prior based on simulations. The results demonstrate that the proposed method outperforms previous algorithms in terms of root-mean-square error and Pearson correlation, and successfully reconstructs the highest-quality convergence map of the HST/ACS COSMOS field.

ASTRONOMY & ASTROPHYSICS (2023)

Article Mathematics, Applied

Rethinking data-driven point spread function modeling with a differentiable optical model

Tobias Liaudat, Jean-Luc Starck, Martin Kilbinger, Pierre-Antoine Frugier

Summary: Upcoming space telescopes with wide-field optical instruments have a spatially varying point spread function (PSF). This paper presents a new data-driven model called WaveDiff, which shifts the modeling space from pixels to the wavefront, allowing for the capture of chromatic variations in the PSF. The proposed model achieves significant performance improvements in pixel reconstruction errors and ellipticity errors compared to existing approaches.

INVERSE PROBLEMS (2023)

Article Astronomy & Astrophysics

Starlet higher order statistics for galaxy clustering and weak lensing

Virginia Ajani, Joachim Harnois-Deraps, Valeria Pettorino, Jean-Luc Starck

Summary: We introduce a wavelet-based multi-scale summary statistics for photometric galaxy clustering and weak lensing, including local maxima count and integral(1)-norm. We use cosmo-SLICS simulations to compute wavelet-based non-Gaussian statistics for weak-lensing convergence maps and galaxy maps. Forecasts on important cosmological parameters are obtained, and the starlet peaks and integral-norm are found to be useful summary statistics that improve constraints compared to the power spectrum, even when combining the two probes.

ASTRONOMY & ASTROPHYSICS (2023)

Proceedings Paper Computer Science, Artificial Intelligence

A BREGMAN MAJORIZATION-MINIMIZATION FRAMEWORK FOR PET IMAGE RECONSTRUCTION

Claire Rossignol, Florent Sureau, Emilie Chouzenoux, Claude Comtat, Jean-Christophe Pesquet

Summary: This study introduces the concept of Bregman majorization to provide a unified view of MM-based methods for image reconstruction in the presence of Poisson noise. Three algorithmic solutions are presented and compared for computational efficiency.

2022 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, ICIP (2022)

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