Innovations in the Analysis of Chandra-ACIS Observations

Accepted by the ApJ, 2010 Mar 10 (#343576) Innovations in the Analysis of Chandra-ACIS Observations Patrick S. Broos,1 Leisa K. Townsley,1 Eric D. Feigelson,1 Konstantin V. Getman,1 Franz E. Bauer,23 Gordon P. Garmire1 patb@astro.psu.edu ABSTRACT As members of the instrument team for the Advanced CCD Imaging Spectrometer (ACIS) on NASA’s Chandra X-ray Observatory and as Chandra General Observers, we have developed a wide variety of data analysis methods that we believe are useful to the Chandra community, and have constructed a significant body of publicly-available software (the ACIS Extract package) addressing important ACIS data and science analysis tasks. This paper seeks to describe these data analysis methods for two purposes: to document the data analysis work performed in our own science projects, and to help other ACIS observers judge whether these methods may be useful in their own projects (regardless of what tools and procedures they choose to implement those methods). The ACIS data analysis recommendations we offer here address much of the workflow in a typical ACIS project, including data preparation, point source detection via both wavelet decomposition and image recon- struction, masking point sources, identification of diffuse structures, event extraction for both point and diffuse sources, merging extractions from multiple observations, nonparametric broad-band photometry, analysis of low-count spectra, and automation of these tasks. Many of the innovations presented here arise from several, often interwoven, complications that are found in many Chandra projects: large numbers of point sources (hun- dreds to several thousand), faint point sources, misaligned multiple observations of an astronomical field, point source crowding, and scientifically relevant diffuse emission. Subject headings: methods: data analysis; methods: statistical; techniques: image processing; X-rays: general 1. INTRODUCTION Since its launch in 1999, the Chandra X-ray Observatory (Weisskopf et al. 2002) has revolutionized X-ray astronomy. Chandra provides remarkable angular resolution—unlikely to be matched by another X-ray observatory within the next two decades—and its most commonly used instrument, the Advanced CCD Imaging Spectrometer (ACIS), produces observations with a very low background (Garmire et al. 2003).1 These two technical capabilities allow detection of point sources with as few as ∼5 observed X-ray photons (commonly referred to as “events” or “counts”), a data analysis regime unique among X-ray observatories. Observations of Galactic star clusters and mosaics of nearby galaxies or extragalactic deep fields often produce hundreds to thousands of weak X-ray sources. Chandra’s excellent sensitivity to point sources and angular resolution also provide a unique capability for studying diffuse emission superposed onto those point sources, since they can be effectively identified and then masked. For many types of ACIS “imaging” studies,2 most observers follow a data analysis workflow that is similar to that outlined in Figure 1. Relatively raw data derived from satellite telemetry, known as “Level 1 Data Products”3 (L1), are passed through a variety of repair and cleaning operations to produce “Level 2 Data Products” (L2) that 1Department of Astronomy & Astrophysics, 525 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA 2Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, Colorado 80301, USA 3Pontificia Universidad Cat´olica de Chile, Departamento de Astronom´ıa y Astrof´ısica, Casilla 306, Santiago 22, Chile 1See also the Chandra Proposers’ Observatory Guide (http://asc.harvard.edu/proposer/POG/pog_pdf.html). 2 Our discussion here is limited to data taken in the most common ACIS configuration, called Timed Exposure Mode, at either the ACIS-I or ACIS-S aimpoint. Dispersed data from the Chandra gratings are not addressed here. 3http://cxc.harvard.edu/ciao/dictionary/levels.html arXiv:1003.2397v1 [astro-ph.HE] 11 Mar 2010 – 2 – are appropriate for analysis. A common workflow for studying point sources (solid boxes and arrows on left side of Figure 1) consists of binning the L2 event data into one or more images that are searched for sources. The events and background associated with each point source in the catalog are “extracted” and calibrated. Observed sources properties (e.g., count rates, apparent fluxes, spectra, light curves) are estimated, then combined with calibration products to estimate intrinsic astrophysical source properties. A common and similar workflow for studying diffuse emission (right side of Figure 1) consists of removing (“masking”) the point sources from the data, constructing images, identifying several regions of diffuse emission to study, and then extracting and analyzing those diffuse sources in a manner similar to that used for point sources. masked imagery diffuse source (DS) detection DS catalog DS extraction imagery point source (PS) detection PS catalog

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