XMM-Newton Video Tutorials

This video is a beginner-friendly introduction to the XMM-Newton X-ray Observatory and its data analysis process. It covers the mission's instruments (EPIC, RGS, OM), the structure of X-ray data, and how to work with ODF and PPS files. Viewers learn how to download, calibrate, and filter data using SAS and pySAS, including imaging and spectral extraction. Timing mode and high-resolution RGS spectroscopy are also introduced. Resources and platforms like SciServer, Datalabs, and support from ESA/NASA are provided for further learning.

SciServer: Getting Started

This video explains the key steps involved in setting up a data analysis workflow using SciServer. It walks through container creation, selecting the correct compute image, and accessing XMM-Newton tutorial notebooks in JupyterLab.

Be sure to save any changes to your persistent directory, and refer to the intro notebook for more information about the file structure and data volumes.

Intro to pySAS: Images & Filters

This section introduces the structure and functionality of the XMM-Newton observatory, detailing the roles of the EPIC, RGS, and Optical Monitor instruments. It explores the foundational concept of photon counting in X-ray astronomy, emphasizing the importance of event files for scientific analysis. The session explains how to perform image calibration and background filtering, and introduces pySAS, a Python interface for SAS, used within Jupyter notebooks to streamline the data processing workflow.

Intro to SAS in SciServer

The focus here is on utilizing the SciServer platform for remote XMM-Newton data analysis. It outlines how users interact with persistent and temporary storage, configure Docker-based containers, and employ JS9 for interactive visualization of FITS images. Pre-configured Jupyter notebooks are demonstrated as tools for filtering flaring particle backgrounds, extracting event files, and performing spectral and timing analyses within a collaborative cloud-based environment.

Intro to SAS in Datalabs

This part presents ESA Datalabs as an alternative e-science platform for conducting XMM-Newton analysis. It explains the process of launching a SAS-ready environment from a public catalog, utilizing pre-mounted data volumes for calibration and analysis threads, and working within a Jupyter Lab interface. Emphasis is placed on interactivity, with tools such as JS9 and Lcviz enhancing the user experience, and on reproducibility through shared environments and custom analysis workflows.

Standard Spectral Extraction in Datalabs

A complete example of spectral data analysis is provided, using ESA Datalabs to process an observation of an ultra-luminous X-ray pulsar. The walkthrough covers data acquisition, preparation of calibration files, execution of the epproc task, and the application of filtering to remove background noise. It proceeds to demonstrate the use of barycentric correction, image generation, region selection, light curve extraction, and spectral analysis, culminating in the application of timing tools such as Stingray.

Intro to RGS extraction and fitting

The final segment focuses on high-resolution X-ray spectroscopy using the Reflection Grating Spectrometer. It describes the physical principles underlying RGS dispersion, the format of the resulting data products, and the use of rgsproc for automated processing. Attention is given to interpreting RGS spectra, identifying spectral lines, and applying fitting techniques to study complex astrophysical sources such as ultrafast outflows and Compton-thick active galactic nuclei.

Timing Mode in ESA Datalabs w/ Pileup

This segment covers EPIC-pn timing mode analysis using ESA Datalabs, focusing on high time-resolution data processing, light curve extraction, and barycentric corrections. It explores pile-up mitigation, region optimization, and interpretation of photon arrival times. Workflows for cleaning and visualizing time series in notebooks are demonstrated to support compact object science such as pulsars and X-ray binaries.

ESAS Intro in SciServer

This session introduces the Extended Source Analysis Software (ESAS) in SciServer for analyzing diffuse X-ray emission. Topics include separating particle backgrounds, masking point sources, and generating images and spectra from MOS data. The complexities of modeling soft X-ray backgrounds and the need for careful data filtering are emphasized. Users are guided through ESAS tasks in Jupyter notebooks, highlighting both its flexibility and common pitfalls.

OM Intro and Science

An overview of the Optical Monitor (OM) outlines its UV and optical capabilities and role in multiwavelength studies. Photometric data extraction, calibration, and correlation with X-ray data are discussed. Astrophysical use cases show how OM contributes to understanding accretion processes, flares, and long-term variability in X-ray sources.

Source Detection and Pipeline

This talk presents the XMM-Newton source detection pipeline and its SAS-based processing chain. It reviews event filtering, background modeling, and multi-band detection strategies. The session highlights tasks like eboxdetect and emldetect, explains statistical confidence in detections, and shows how to verify and use outputs for further analysis.

XMM Synergies with XRISM

The final segment explores scientific synergies between XMM-Newton and XRISM. XRISM’s high-resolution spectroscopy complements XMM’s broad-band sensitivity, enhancing diagnostics of hot astrophysical plasmas. Applications include galaxy clusters, AGN feedback, and supernova remnants. Observation planning, calibration alignment, and joint spectral modeling are also discussed.