arXiv subjects

Utsav Akhaury

Publications and source records attributed to Utsav Akhaury.

5 recordsLinked to original sources

arXiv2026

The GOGREEN Survey: AI Powered Deconvolution Lifts The Veil on Outside-in Environmental Quenching at z > 1

A powerful probe of the physical processes that quench star formation in dense environments is determining where within galaxies star formation is suppressed. At high redshift, the spatial resolution of multi-band imaging limits such measurements. We use deep-learning-based deconvolution to recover spatially resolved optical and near-infrared photometry for galaxies in nine GOGREEN clusters at 1<z<1.4, using customized models trained on HST and JWST imaging. Using resolved rest-frame UVJ colors, we classify galaxies by the star-forming states of their inner and outer regions into predominantly star-forming, predominantly quiescent, inside-quenched, or outside-quenched. We find that 24% of galaxies classified as quiescent from their integrated colors retain significant star formation. The predominantly quiescent fraction increases with stellar mass and is higher in clusters than in the field while the cluster quenched fraction excess is, when limiting to predominantly quenched galaxies, approximately 20%. Contrary to previous GOGREEN studies using integrated colors, we find this excess to be independent of stellar mass, demonstrating that partially quenched galaxies can bias measurements based on integrated colors. Among galaxies retaining significant star formation, outside-quenched galaxies are substantially more common than inside-quenched galaxies and have a fraction excess of (22.8+/-5.8)% in clusters relative to the field at low masses. This provides evidence that clusters preferentially suppress star formation in the outskirts of low-mass galaxies. Our results demonstrate the importance of spatially resolved classifications for interpreting environmental quenching at z~1 and the potential of deep-learning-based deconvolution to recover such information from large ground-based imaging datasets.

astro-ph.GA↗

arXiv2025

Joint multiband deconvolution for Euclid and Vera C. Rubin images

With the advent of surveys like Euclid and Vera C. Rubin, astrophysicists will have access to both deep, high-resolution images and multiband images. However, these two types are not simultaneously available in any single dataset. It is therefore vital to devise image deconvolution algorithms that exploit the best of both worlds and that can jointly analyze datasets spanning a range of resolutions and wavelengths. In this work we introduce a novel multiband deconvolution technique aimed at improving the resolution of ground-based astronomical images by leveraging higher-resolution space-based observations. The method capitalizes on the fortunate fact that the Rubin $r$, $i$, and $z$ bands lie within the Euclid VIS band. The algorithm jointly de-convolves all the data to convert the $r$-, $i$-, and $z$-band Rubin images to the resolution of Euclid by leveraging the correlations between the different bands. We also investigate the performance of deep-learning-based denoising with DRUNet to further improve the results. We illustrate the effectiveness of our method in terms of resolution and morphology recovery, flux preservation, and generalization to different noise levels. This approach extends beyond the specific Euclid-Rubin combination, offering a versatile solution to improving the resolution of ground-based images in multiple photometric bands by jointly using any space-based images with overlapping filters.

astro-ph.IM↗

arXiv2024

Ground-based image deconvolution with Swin Transformer UNet

As ground-based all-sky astronomical surveys will gather millions of images in the coming years, a critical requirement emerges for the development of fast deconvolution algorithms capable of efficiently improving the spatial resolution of these images. By successfully recovering clean and high-resolution images from these surveys, the objective is to deepen the understanding of galaxy formation and evolution through accurate photometric measurements. We introduce a two-step deconvolution framework using a Swin Transformer architecture. Our study reveals that the deep learning-based solution introduces a bias, constraining the scope of scientific analysis. To address this limitation, we propose a novel third step relying on the active coefficients in the sparsity wavelet framework. We conducted a performance comparison between our deep learning-based method and Firedec, a classical deconvolution algorithm, based on an analysis of a subset of the EDisCS cluster samples. We demonstrate the advantage of our method in terms of resolution recovery, generalisation to different noise properties, and computational efficiency. The analysis of this cluster sample not only allowed us to assess the efficiency of our method, but it also enabled us to quantify the number of clumps within these galaxies in relation to their disc colour. This robust technique that we propose holds promise for identifying structures in the distant universe through ground-based images.

astro-ph.IM↗

arXiv2022

Deep Learning-based galaxy image deconvolution

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.

astro-ph.IM↗

arXiv2021

Machine Learning Based Forward Solver: An Automatic Framework in gprMax

General full-wave electromagnetic solvers, such as those utilizing the finite-difference time-domain (FDTD) method, are computationally demanding for simulating practical GPR problems. We explore the performance of a near-real-time, forward modeling approach for GPR that is based on a machine learning (ML) architecture. To ease the process, we have developed a framework that is capable of generating these ML-based forward solvers automatically. The framework uses an innovative training method that combines a predictive dimensionality reduction technique and a large data set of modeled GPR responses from our FDTD simulation software, gprMax. The forward solver is parameterized for a specific GPR application, but the framework can be extended in a straightforward manner to different electromagnetic problems.

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