The results show that ANNs can lead to systematic biases in the annotation of the data. These biases are difficult to detect and profile, and can behave in a ...
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Neural Network Bias in Analysis of Galaxy ... The purpose of this study is to test possible biases when using neural network analysis of photometry data.
The results show that ANNs can lead to systematic biases in the annotation of the data. These biases are difficult to detect and profile, and can behave in a ...
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In this work, we investigate two main forms of biases: class-dependent residuals, and mode collapse. We do this in a case study, in which we estimate ...
Jan 10, 2022 · Here we demonstrate that the training of a DCNN is sensitive to the context of the training data such as the location of the objects in the sky.
Feb 21, 2022 · Photometric Redshift Estimation with Convolutional Neural Networks and Galaxy Images: A Case Study of Resolving Biases in Data-Driven Methods.
Neural. Networks are typically used to replace existing algorithms with a faster and more efficient solution, hence more suited for large data volumes. In ...
Sep 20, 2024 · Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods.
We argue that the emission lines are features that GaSNet-II associates to ClusB galaxies and not RedGAL, where they are not dominant.
Jun 14, 2022 · Photometric redshift estimation with convolutional neural networks and galaxy images: Case study of resolving biases in data-driven methods.