1. A short introduction to some basic statistical tests using SPSS : seminar, 9. 5. 2024, VipavaAhmad Hosseini, 2024, other performed works Abstract: This short 1-hour seminar aims to provide a gentle introduction to quantitative analysis using the statistical software SPSS. It is designed to assist individuals who wish to familiarize themselves with using SPSS as a tool for data manipulation and conducting some basic statistical tests. The seminar is ideal for students and practitioners who have a solid understanding of various statistical tests (such as GLM methods) and theoretical quantitative data analysis methodologies and are interested in exploring their application in SPSS. Additionally, it caters to students and researchers who have completed courses in Applied Statistics and Methodology but have not had significant exposure to statistical analysis software.
Keywords: Quantitative Analysis, Statistical Software SPSS, Statistical Tests, GLM Methods, Applied Statistics
Published in RUNG: 06.01.2025; Views: 694; Downloads: 3
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2. AutoSourceID-Classifier : star-galaxy classification using a convolutional neural network with spatial informationF. Stoppa, Saptashwa Bhattacharyya, R. Ruiz de Austri, P. Vreeswijk, S. Caron, Gabrijela Zaharijas, S. Bloemen, G. Principe, Denys Malyshev, Veronika Vodeb, 2023, original scientific article Abstract: Aims: Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification’s reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.
Methods: The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts
generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and
additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.
Results: We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor.
While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced
error propagation inherent in ASID-C’s direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
Keywords: astronomical databases, data analysis, statistics, image processing
Published in RUNG: 12.12.2023; Views: 2412; Downloads: 11
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