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papers:rg:ndungu2023 [2023/10/23 02:35] – asad | papers:rg:ndungu2023 [2023/10/24 06:27] (current) – asad | ||
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+ | - SKA will generate datasets on the scale of exabytes. SKA-LOW data rate 10 TB/s, SKA-MID 19 TB/s. | ||
+ | - MeerKAT raw data rate 2.2 TB/s. MWA 300 GB/s, LOFAR 13 TB/s. | ||
+ | - EMU will map 70M radio sources, SKA 500M. | ||
+ | - Need for classification: | ||
+ | - Yatawatta 2021 gives a smart calibration package based on deep reinforcement learning. | ||
+ | - LOFAR sensitivity 100 $\mu$Jy, resolution $6'' | ||
+ | - FR0 are 5 times more numerous than FR-I-II combined. The gradient in Fig.3 is nice. | ||
+ | - Is it right that WAT-NAT come from both FR I-II??? | ||
+ | - IVOA (int virt obs alliance) gives FAIR (findable accessible interoperable reusable) data. | ||
+ | - Besides classification, | ||
+ | - Table 2 dos not have a dataset created from Sasmal 23. | ||
+ | - Most works depend on deep and shallow CNN (ConvNet, mimicks human visual cortex). The deeper (more layers) the better. | ||
+ | - Regularization techniques (dropout of weakly connected neurons) used to solve overfitting. | ||
+ | - Bowles 21 uses attention-gate layers to suppress irrelevant info. | ||
+ | - Tang 22 uses multidomain multibranch CNN to take multiple inputs. | ||
+ | - Brand 23 aligned the PC of all galaxies with the axis of the coordinate system via PCA. | ||
+ | - Sadeghi 21 uses Zernike polynomials (SVM) to extract image moments. Ntwaetsile 21 uses Haralick features for texture. | ||
+ | - Tang 19 successfully used FIRST-trained model on NVSS, but not the reverse. | ||
+ | - Wang 21 uses SKnet module for attention. Zhang 22 uses SE Net. | ||
+ | - Tiramisu (Pino 21) works best for source extraction, detection, localization and then classification. | ||
+ | - Main drawback of AI is the lack of explainability. | ||
+ | - Another drawback is that we are reducing 4D data cubes into 2D. | ||
papers/rg/ndungu2023.1698050127.txt.gz · Last modified: 2023/10/23 02:35 by asad