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Innovations in the Analysis of Chandra-Acis Observations The Astrophysical Journal, 714:1582–1605, 2010 May 10 doi:10.1088/0004-637X/714/2/1582 C 2010. The American Astronomical Society. All rights reserved. Printed in the U.S.A. INNOVATIONS IN THE ANALYSIS OF CHANDRA-ACIS OBSERVATIONS Patrick S. Broos1, Leisa K. Townsley1, Eric D. Feigelson1, Konstantin V. Getman1, Franz E. Bauer2,3,and Gordon P. Garmire1 1 Department of Astronomy & Astrophysics, 525 Davey Laboratory, Pennsylvania State University, University Park, PA 16802, USA; [email protected] 2 Space Science Institute, 4750 Walnut Street, Suite 205, Boulder, CO 80301, USA 3 Pontificia Universidad Catolica´ de Chile, Departamento de Astronom´ıa y Astrof´ısica, Casilla 306, Santiago 22, Chile Received 2010 January 11; accepted 2010 March 10; published 2010 April 21 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 reconstruction, masking point sources, identification of diffuse structures, event extraction for both point and diffuse sources, merging extractions from multiple observations, nonparametric broadband 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 (hundreds to several thousand), faint point sources, misaligned multiple observations of an astronomical field, point source crowding, and scientifically relevant diffuse emission. Key words: methods: data analysis – methods: statistical – techniques: image processing – X-rays: general Online-only material: color figures 1. INTRODUCTION arrows on the left side of Figure 1) consists of binning the L2 event data into one or more images that are searched for Since its launch in 1999, the Chandra X-ray Observatory sources. The events and background associated with each point (Weisskopf et al. 2002) has revolutionized X-ray astronomy. source in the catalog are “extracted” and calibrated. Observed Chandra provides remarkable angular resolution—unlikely to sources properties (e.g., count rates, apparent fluxes, spectra, be matched by another X-ray observatory within the next two light curves) are estimated, then combined with calibration decades—and its most commonly used instrument, the Ad- products to estimate intrinsic astrophysical source properties. vanced CCD Imaging Spectrometer (ACIS), produces obser- A common and similar workflow for studying diffuse emission vations with a very low background (Garmire et al. 2003).4 (right side of Figure 1) consists of removing (“masking”) the These two technical capabilities allow detection of point sources point sources from the data, constructing images, identifying with as few as ∼5 observed X-ray photons (commonly re- several regions of diffuse emission to study, and then extracting ferred to as “events” or “counts”), a data analysis regime unique and analyzing those diffuse sources in a manner similar to that among X-ray observatories. Observations of Galactic star clus- used for point sources. ters and mosaics of nearby galaxies or extragalactic deep fields Many Chandra studies exhibit one or more of five character- often produce hundreds to thousands of weak X-ray sources. istics that significantly complicate this familiar workflow. Chandra’s excellent sensitivity to point sources and angular resolution also provide a unique capability for studying diffuse 1. In many studies hundreds to thousands of point sources can emission superposed onto those point sources, since they can be be readily identified; executing the workflow is intractable effectively identified and then masked. without significant automation. For many types of ACIS “imaging” studies,5 most observers 2. Numerous sources with very few detected counts are follow a data analysis workflow that is similar to that outlined identified in most Chandra studies; common statistical in Figure 1. Relatively raw data derived from satellite telemetry, methods based on large-N assumptions break down at known as “Level 1 Data Products”6 (L1), are passed through several points in the workflow. a variety of repair and cleaning operations to produce “Level 2 Data Products” (L2) that are appropriate for analysis. A 3. Many studies require covering a large field of view with common workflow for studying point sources (solid boxes and multiple Chandra pointings (e.g., Figure 2). For many reasons, such mosaicked pointings usually overlap signifi- 4 See also the Chandra Proposers’ Observatory Guide cantly and/or are observed at a variety of roll angles. Thus, (http://asc.harvard.edu/proposer/POG/pog_pdf.html). many sources are observed multiple times at very different 5 Our discussion here is limited to data taken in the most common ACIS configuration, called Timed Exposure Mode, at either th e ACIS-I or ACIS-S locations on the ACIS detector. Analysis of such sources aim point. Dispersed data from the Chandra gratings are not addressed here. can be very complex, because the Chandra point-spread 6 http://cxc.harvard.edu/ciao/dictionary/levels.html function (PSF) exhibits large variations in size and shape 1582 No. 2, 2010 INNOVATIONS IN THE ANALYSIS OF CHANDRA-ACIS OBSERVATIONS 1583 L1 event data repair; mild cleaning L2 event data aggressive cleaning masking masked masked imagery L2 event imagery data point source (PS) detection (DS) detection prune/reposition PS catalog sources DS catalog PS extraction astrophysical DS extraction observed properties; analysis observed properties; calibration products (e.g. spectral modeling) calibration products intrinsic properties tables for publication Figure 1. Data analysis workflow described here for a single ACIS field containing multiple point sources (left branch) and diffuse sources (DS, right branch). 7 across the focal plane. Analysis of diffuse emission is also -59:00:00.0 complicated by multiple pointings, since a single diffuse region may be only partially covered by a particular ACIS observation. 10:00.0 4. Some fields (such as deep exposures of rich star clusters, the Galactic Center, or nearby galaxies) are crowded, with 20:00.0 adjacent PSFs in close proximity. In such conditions, simple approaches to source extraction and background estimation 30:00.0 are not adequate. 5. Scientifically relevant diffuse emission must often be ex- tracted and studied while avoiding contamination from 40:00.0 point sources (e.g., in star-forming regions, the Galactic 50:00.0 Center, and galaxy clusters in deep fields). Declination (J2000) Since our Chandra studies often exhibit all five of these issues, we have developed a set of data analysis methods that -60:00:00.0 incorporate a variety of enhancements to standard techniques. Publicly available software implementing these methods—the 10:00.0 ACIS Extract (AE) package—has been cited in publications for at least 50 Chandra targets. 20:00.0 This paper seeks to describe these methods at a moderate level of detail. Our primary purpose is to encourage other ACIS ob- servers to consider whether the various departures from standard 30:00.0 techniques described here may be useful in their own projects 50:00.0 48:00.0 46:00.0 44:00.0 42:00.0 10:40:00.0 (regardless of what tools and procedures those observers use to Right Ascension (J2000) implement those methods). Our secondary purpose is to doc- Figure 2. Exposure map for the Chandra Carina Complex Project (L. K. ument the data analysis methods used in our own Chandra Townsley et al. 2010, in preparation) study of the Carina Nebula comprised of studies. In many cases, the only documentation available for 22 ACIS-I pointings (38 observations), with point sources masked (Section 5.4) in preparation for extraction of diffuse emission (Section 9). All point sources software and data analysis methods is online; thus this paper is are masked in the individual, high-resolution, exposure maps; at this scale only liberally footnoted with relevant URLs. We acknowledge that the larger masks (on mostly off-axis sources) are visible. URLs are more ephemeral than journal citations but we believe that they are better than no documentation at all. The high-level structure of our data analysis workflow differs from standard practice in two ways. First, for technical reasons, analysis. Second, since the point source extraction process the optimal data cleaning steps for point sources and diffuse generates estimates of source position (Section 7.1) and source sources differ for ACIS data (Section 3); thus Figure 1 shows validity (Section 4.3) that are expected to be better than the separate “cleaning” operations on those two branches of the estimates made by typical source detection procedures, we choose to adopt an iterative workflow (Section 4.1 and dashed 7 See Figures 8 and 10 in the HRMA User’s Guide boxes and lines in Figure 1) in which the point source detection (http://cxc.harvard.edu/cal/Hrma/users_guide/). process merely nominates candidates that are then repositioned 1584 BROOS ET AL. Vol. 714 and possibly discarded as likely noise peaks after extraction Several dozen studies by several independent groups (includ- results are in hand. ing at least nine Large and Very Large projects) have employed Most of this paper describes the individual data analysis AE.
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