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S. Mehmood

Publications and source records attributed to S. Mehmood.

2 recordsLinked to original sources

ASKAP EMU detection of an Odd Radio Circle (ORC) candidate: J094412-751016 (Anglerfish)

We report diffuse extended radio-continuum emission spatially coinciding with the IR source WISEA J094409.17-751012.8, and a semi-variable star, V687 Carinae. We use 944 MHz radio data from the large-scale Evolutionary Map of the Universe (EMU) survey to analyse this diffuse emission (EMU J094412-751016), which we nickname "Anglerfish". We investigate if the spatially correlated infrared (IR) source, WISEA J094409.17-751012.8, is physically related to Anglerfish. The IR colours of WISEA J094409.17-751012.8 are indicative of an elliptical galaxy, raising the possibility that Anglerfish may belong to the newly-discovered class of extragalactic radio sources known as Odd Radio Circles (ORCs) with WISEA J094409.17-751012.8 as the host galaxy. We also investigate the possibility that Anglerfish is physically related to the star, V687 Carinae, and whether it may be a remnant from a previous epoch of stellar mass-loss. We determine that a physical association between the radio emission and the star is unlikely due to the emission's non-thermal nature and the star's weak stellar winds compared to the theoretical expansion velocity of the 'shell'. It is possible that Anglerfish may be a Galactic high-latitude supernova remnant (SNR); however, we find that the observed size and luminosity are not consistent with this scenario. We also investigate the ORC scenario, which we deem the most likely scenario based on the Anglerfish's observed properties such as size, brightness, lack of other frequency detections, and spectral index. We therefore propose Anglerfish as an ORC candidate, but note that additional radio and optical observations are vital to further constrain the properties and confirm this classification.

astro-ph.GA

Physics Informed Neural Network Enhanced Denoising for Atomic Resolution STEM Imaging

Atomic resolution STEM images often suffer from noise due to low electron doses and instrument imperfections, hence it is challenging to obtain critical structural details required for material analysis. To address the problem, we propose a Physics-Informed Neural Network (PINN) framework for denoising STEM images. Our method integrates spectral fidelity, total variation, and brightness/contrast consistency losses to ensure the preservation of fine structures, smooth regions, and physical signal intensities, maintaining the structural integrity of the denoised images. Our proposed method effectively balances noise reduction with the preservation of atomic resolution details and complements existing methods, seeking to enhance the utility of STEM images in material characterization and analysis.

cond-mat.mtrl-sci