Astronomy and Computing

Machine and Deep Learning morphological classification for 670,560 galaxies from Sloan Digital Sky Survey Data Release 7 (SDSS-DR7). Classifications are provided for 2 classes problem (0: elliptical; or, 1: spiral galaxy) and 3 classes problem (0: elliptical, 1: non-barred spiral, or 2: barred spiral galaxy). ML2classes classification is obtained by Traditional Machine Learning Approach, using Morphological non-parametric parameters and Decision Tree. Classifications using Deep Learning are obtained using a Convolutional Neural Network (CNN). Morphological non-parametric parameters are provided as well: Concentration (C), Asymmetry (A), Smoothness (S), Gradient Pattern Analysis (G2) parameter and Entropy (H). We also provide the Error from CyMorph processing. All error flags are mapped as follows: Error = 0: success (no errors); Error = 1: many objects of significant brightness inside 2 Rp of the galaxy; Error = 2: not possible to calculate the galaxy's Rp; Error = 3: problem calculating GPA; Error = 4: problem calculating H; Error = 5: problem calculating C; Error = 6: problem calculating A; Error = 7: problem calculating S.

Run times of the LOFAR prefactor pipeline obtained by scaling the Number of CPUs, Data size and skymodel size. The prefactor version used for this data was https://github.com/apmechev/prefactor/commit/da4ac885bce9b24e604c9aac6bf649992065326f

Code and self-downloading data of the paper "Unsupervised Learning of Structure in Spectroscopic Cubes" in Jupyter notebooks format.

MAPLE worksheets. edge.mw: contains the full derivation of the Edgeworth decomposition for the false alarm probability of a sample wavelet transform qij.mw: contains only the final expressions for the coefficients of the normality test

This dataset contains an archive with experimental pictures for testing star tracker algorithms. Camera calibration parameters are also included. The dataset is used for obtaining the results presented in: Louis Jannin, Leonard Felicetti, "Calibration and testing strategies to correct atmospheric effects on star tracking algorithms", Astronomy and Computing, 2024

This dataset contains an archive with experimental pictures for testing star tracker algorithms. Camera calibration parameters are also included. The dataset is used for obtaining the results presented in: Louis Jannin, Leonard Felicetti, "Calibration and testing strategies to correct atmospheric effects on star tracking algorithms", Astronomy and Computing, 2024

The "dr16q_superset_redshift.csv" file provides a catalogue of spectroscopic redshift predictions for spectra from the 16th data release of the Sloan Digital Sky Survey (SDSS) quasar superset catalogue (Lyke et al., 2020). Redshifts are predicted by a Bayesian convolutional neural network named Bayesian SZNet with associated predictive uncertainties in the form of predictive variances. The catalogue is released in the CSV format with the following columns: plate: spectroscopic plate number; mjd: modified Julian day of the spectroscopic observation; fiberid: fiber identification number; z_pred: redshift from Bayesian SZNet; variance: predictive variance associated with redshift from Bayesian SZNet; z: primary redshift; source_z: origin of the reported redshift in z; is_qso_final: flag indicating quasars included in the DR16Q (Lyke et al., 2020); z_vi: redshift from visual inspection; z_pipe: redshift from the SDSS pipeline; zwarning: quality flag on the redshift from the SDSS pipeline; z_dr12q: redshift from the DR12Q catalogue (Pâris et al., 2017); z_dr7q_sch: redshift from the DR7Q catalogue (Schneider et al., 2010); z_dr6q_hw: redshift from the DR6 catalogue (Hewett and Wild, 2010); z_10k: redshift from the random visual inspection of 10000 spectra in the DR16Q superset; z_pca: redshift from the redvsblue algorithm; z_qn: redshift from QuasarNET (Busca and Balland, 2018); z_pred_1 to z_pred_256: sampled redshifts from Bayesian SZNet; where columns z, source_z, is_qso_final, z_vi, z_pipe, zwarning, z_dr12q, z_dr7q_sch, z_dr6q_hw, z_10k, z_pca, and z_qn are taken from the 16th data release of the SDSS quasar superset catalogue.

This dataset includes primary simulations of gravitational microlensing events used in the paper "Modelling the projected separation of microlensing events using systematic time-series feature engineering" by Alex Kennedy, Gemma Nash, Nicholas Rattenbury and Andreas W. Kempa-Liehr. See the README.md file for a complete description. The 'simulated' dataset contains 3,000 simulated light curves used for training and testing models. The 'corrupted' dataset includes 27,300 simulated light curves which were then distorted by data outages and observational error, as described in the attached article.