pySAS is a python wrapper for the Science Analysis Software (SAS) used for analyzing XMM-Newton data. This repository is maintained by the XMM-Newton Guest Observer Facility (GOF) at Goddard Space Flight Center for testing new pySAS functionality.
Deep Learning-Based Super-Resolution and De-Noising for XMM-Newton Images.
EXOD is a Bayesian algorithm designed for detecting rapid transient X-ray sources in XMM-Newton data. See: https://nx1.info/science/EXOD.pdf
Simulation code for XMM-Newton EPIC-pn data using SIXTE and SIMPUT, designed to create training data for deep learning based super-resolution and de-noising.
Python pipeline designed to obtain energy resolved pulse profiles of X-ray pulsar with XMM-Newton and NuSTAR observations.
Code and data analysis notebook for the X-ray binary Vela X-1 presented in the paper linked below.
A data science analysis of the 4XMM-DR14 X-ray source catalog from the XMM-Newton space observatory, featuring original dimensionality reduction technique and astronomical data preprocessing.
Scripts intended to assist in XMM-Newton data reduction and analysis
Implementation of YOLOv8 (ultralytics) in the analysis of XMM-Newton astronomical images
NASA Archive Installer
Unsupervised anomaly detection in XMM-Newton X-ray multiband time series. Transformer Autoencoder with Masked Denoising achieves AUC-ROC 0.918 on blind test set. Includes LSTM-AE & Isolation Forest baselines, DeLong tests, bootstrap CIs. TFG · UC3M 2025.ifically interesting anomalies.
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