Peter E. Freeman

Sherpa : a mission-independent data analysis application

The ever-increasing quality and complexity of astronomical data underscores the need for new and powerful data analysis applications. This need has led to the development of Sherpa, a modeling and fitting program in the CIAO software package that enables the analysis of multi-dimensional, multi-wavelength data. In this paper, we present an overview of Sherpas features, which include: support for a wide variety of input and output data formats, including the new Model Descriptor List (MDL) format; a model language which permits the construction of arbitrarily complex model expressions, including ones representing instrument characteristics; a wide variety of fit statistics and methods of optimization, model comparison, and parameter estimation; multi-dimensional visualization, provided by ChIPS; and new interactive analysis capabilities provided by embedding the S-Lang interpreted scripting language. We conclude by showing example Sherpa analysis sessions.

Is RX J1856.5?3754 a Quark Star?

Deep Chandra Low Energy Transmission Grating and High Resolution Camera spectroscopic observations of the isolated neutron star candidate RX J1856.5-3754 have been analyzed to search for metallic and resonance cyclotron spectral features and for pulsation behavior. As found from earlier observations, the X-ray spectrum is well represented by an ~60 eV (7 × 105 K) blackbody. No unequivocal evidence of spectral line or edge features has been found, arguing against metal-dominated models. The data contain no evidence for pulsation, and we place a 99% confidence upper limit of 2.7% on the unaccelerated pulse fraction over a wide frequency range from 10-4 to 100 Hz. We argue that the derived interstellar medium neutral hydrogen column density of 8 × 1019 cm-2 ≤ NH ≤ 1.1 × 1020 cm-2 favors the larger distance from two recent Hubble Space Telescope parallax analyses, placing RX J1856.5-3754 at ~140 pc instead of ~60 pc and in the outskirts of the R CrA dark molecular cloud. That such a comparatively rare region of high interstellar matter (ISM) density is precisely where an isolated neutron star reheated by accretion of ISM would be expected is either entirely coincidental or current theoretical arguments excluding this scenario for RX J1856.5-3754 are premature. Taken at face value, the combined observational evidence—a lack of spectral and temporal features and an implied radius of R∞ = 3.8-8.2 km that is too small for current neutron star models—points to a more compact object, such as allowed for quark matter equations of state.

Chandra multiwavelength project. II. First results of X-ray source properties

The Chandra Multiwavelength Project (ChaMP) is a wide-area (~14 deg2) survey of serendipitous Chandra X-ray sources, aiming to establish fair statistical samples covering a wide range of characteristics (such as absorbed active galactic nuclei [AGNs] and high-z clusters of galaxies) at flux levels (fX ~ 10-15 to 10-14 ergs s-1 cm-2) intermediate between the Chandra Deep Field surveys and previous missions. We present the first results of X-ray source properties obtained from the initial sample of 62 observations. The data have been uniformly reduced and analyzed with techniques specifically developed for the ChaMP and then validated by visual examination. Utilizing only near-on-axis X-ray-bright sources (to avoid problems caused by incompleteness and the Eddington bias), we derive the log N- log S relation in soft (0.5-2 keV) and hard (2-8 keV) energy bands. The ChaMP data are consistent with previous results of ROSAT, ASCA, and Chandra Deep Field surveys. In particular, our data nicely fill in the flux gap in the hard band between the Chandra Deep Field data and the previous ASCA data. We check whether there is any systematic difference in the source density between cluster and noncluster fields and also search for field-to-field variation, both of which have been previously reported. We found no significant field-to-field cosmic variation in either test within the statistics (~1 σ) across the flux levels included in our sample. In the X-ray color-color plot, most sources fall in the location characterized by photon index = 1.5-2 and NH = a few × 1020 cm2, suggesting that they are typical broadline AGNs. There also exist a considerable number of sources with peculiar X-ray colors (e.g., highly absorbed, very hard, very soft). We confirm a trend that on average the X-ray color hardens as the count rate decreases. Since the hardening is confined to the softest energy band (0.3-0.9 keV), we conclude that it is most likely due to absorption. We cross-correlate the X-ray sources with other catalogs and describe their properties in terms of optical color, X-ray-to-optical luminosity ratio, and X-ray colors.

The XMM Cluster Survey: X‐ray analysis methodology

The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we describe the data processing methodology applied to the 5776 XMM observations used to construct the current XCS source catalogue. A total of 3675 > 4σ cluster candidates with >50 background-subtracted X-ray counts are extracted from a total non-overlapping area suitable for cluster searching of 410 deg2. Of these, 993 candidates are detected with >300 background-subtracted X-ray photon counts, and we demonstrate that robust temperature measurements can be obtained down to this count limit. We describe in detail the automated pipelines used to perform the spectral and surface brightness fitting for these candidates, as well as to estimate redshifts from the X-ray data alone. A total of 587 (122) X-ray temperatures to a typical accuracy of <40 (<10) per cent have been measured to date. We also present the methodology adopted for determining the selection function of the survey, and show that the extended source detection algorithm is robust to a range of cluster morphologies by inserting mock clusters derived from hydrodynamical simulations into real XMMimages. These tests show that the simple isothermal β-profiles is sufficient to capture the essential details of the cluster population detected in the archival XMM observations. The redshift follow-up of the XCS cluster sample is presented in a companion paper, together with a first data release of 503 optically confirmed clusters.

The XMM Cluster Survey

The XMM Cluster Survey (XCS) is a serendipitous search for galaxy clusters using all publicly available data in the XMM-Newton Science Archive. Its main aims are to measure cosmological parameters and trace the evolution of X-ray scaling relations. In this paper we describe the data processing methodology applied to the 5776 XMM observations used to construct the current XCS source catalogue. A total of 3675 > 4σ cluster candidates with >50 background-subtracted X-ray counts are extracted from a total non-overlapping area suitable for cluster searching of 410 deg2. Of these, 993 candidates are detected with >300 background-subtracted X-ray photon counts, and we demonstrate that robust temperature measurements can be obtained down to this count limit. We describe in detail the automated pipelines used to perform the spectral and surface brightness fitting for these candidates, as well as to estimate redshifts from the X-ray data alone. A total of 587 (122) X-ray temperatures to a typical accuracy of <40 (<10) per cent have been measured to date. We also present the methodology adopted for determining the selection function of the survey, and show that the extended source detection algorithm is robust to a range of cluster morphologies by inserting mock clusters derived from hydrodynamical simulations into real XMMimages. These tests show that the simple isothermal β-profiles is sufficient to capture the essential details of the cluster population detected in the archival XMM observations. The redshift follow-up of the XCS cluster sample is presented in a companion paper, together with a first data release of 503 optically confirmed clusters.

On Computing Upper Limits to Source Intensities

A common problem in astrophysics is determining how bright a source could be and still not be detected in an observation. Despite the simplicity with which the problem can be stated, the solution involves complicated statistical issues that require careful analysis. In contrast to the more familiar confidence bound, this concept has never been formally analyzed, leading to a great variety of often ad hoc solutions. Here we formulate and describe the problem in a self-consistent manner. Detection significance is usually defined by the acceptable proportion of false positives (background fluctuations that are claimed as detections, or Type I error), and we invoke the complementary concept of false negatives (real sources that go undetected, or Type II error), based on the statistical power of a test, to compute an upper limit to the detectable source intensity. To determine the minimum intensity that a source must have for it to be detected, we first define a detection threshold and then compute the probabilities of detecting sources of various intensities at the given threshold. The intensity that corresponds to the specified Type II error probability defines that minimum intensity and is identified as the upper limit. Thus, an upper limit is a characteristic of the detection procedure rather than the strength of any particular source. It should not be confused with confidence intervals or other estimates of source intensity. This is particularly important given the large number of catalogs that are being generated from increasingly sensitive surveys. We discuss, with examples, the differences between these upper limits and confidence bounds. Both measures are useful quantities that should be reported in order to extract the most science from catalogs, though they answer different statistical questions: an upper bound describes an inference range on the source intensity, while an upper limit calibrates the detection process. We provide a recipe for computing upper limits that applies to all detection algorithms.

The XMM Cluster Survey: forecasting cosmological and cluster scaling‐relation parameter constraints

We forecast the constraints on the values of s8, Om and cluster scaling-relation parameters which we expect to obtain from the XMM Cluster Survey (XCS). We assume a flat cold dark matter Universe and perform a Monte Carlo Markov Chain analysis of the evolution of the number density of galaxy clusters that takes into account a detailed simulated selection function. Comparing our current observed number of clusters shows good agreement with predictions. We determine the expected degradation of the constraints as a result of self-calibrating the luminositytemperature relation (with scatter), including temperature measurement errors, and relying on photometric methods for the estimation of galaxy cluster redshifts. We examine the effects of systematic errors in scaling relation and measurement error assumptions. Using only (T, z) self-calibration, we expect to measure Om to 0.03 (and O to the same accuracy assuming flatness), and s8 to 0.05, also constraining the normalization and slope of the luminositytemperature relation to 6 and 13 per cent (at 1s), respectively, in the process. Self-calibration fails to jointly constrain the scatter and redshift evolution of the luminositytemperature relation significantly. Additional archival and/or follow-up data will improve on this. We do not expect measurement errors or imperfect knowledge of their distribution to degrade constraints significantly. Scaling-relation systematics can easily lead to cosmological constraints 2s or more away from the fiducial model. Our treatment is the first exact treatment to this level of detail, and introduces a new `smoothed ML (Maximum Likelihood) estimate of expected constraints.

CHANDRA OBSERVATIONS OF MBM 12 AND MODELS OF THE LOCAL BUBBLE

Chandra observations toward the nearby molecular cloud MBM 12 show unexpectedly strong and nearly equal foreground O VIII and O VII emission. As the observed portion of MBM 12 is optically thick at these energies, the emission lines must be formed nearby, coming from either the Local Bubble (LB) or charge exchange with ions from the Sun. Equilibrium models for the LB predict stronger O VII than O VIII, so these results suggest that the LB is far from equilibrium or that a substantial portion of O VIII is from another source, such as charge exchange within the solar system. Despite the likely contamination, we can combine our results with other EUV and X-ray observations to reject LB models that posit a cool recombining plasma as the source of LB X-rays.

Photometric redshift estimation using spectral connectivity analysis

The development of fast and accurate methods of photometric redshift estimation is a vital step towards being able to fully utilize the data of next-generation surveys within precision cosmology. In this paper we apply a specific approach to spectral connectivity analysis (SCA; Lee & Wasserman 2009) called diffusion map. SCA is a class of non-linear techniques for transforming observed data (e.g., photometric colours for each galaxy, where the data lie on a complex subset of p-dimensional space) to a simpler, more natural coordinate system wherein we apply regression to make redshift predictions. As SCA relies upon eigen-decomposition, our training set size is limited to ~ 10,000 galaxies; we use the Nystrom extension to quickly estimate diffusion coordinates for objects not in the training set. We apply our method to 350,738 SDSS main sample galaxies, 29,816 SDSS luminous red galaxies, and 5,223 galaxies from DEEP2 with CFHTLS ugriz photometry. For all three datasets, we achieve prediction accuracies on par with previous analyses, and find that use of the Nystrom extension leads to a negligible loss of prediction accuracy relative to that achieved with the training sets. As in some previous analyses (e.g., Collister & Lahav 2004, Ball et al. 2008), we observe that our predictions are generally too high (low) in the low (high) redshift regimes. We demonstrate that this is a manifestation of attenuation bias, wherein measurement error (i.e., uncertainty in diffusion coordinates due to uncertainty in the measured fluxes/magnitudes) reduces the slope of the best-fit regression line. Mitigation of this bias is necessary if we are to use photometric redshift estimates produced by computationally efficient empirical methods in precision cosmology.

EXPLOITING LOW-DIMENSIONAL STRUCTURE IN ASTRONOMICAL SPECTRA

Dimension-reduction techniques can greatly improve statistical inference in astronomy. A standard approach is to use Principal Components Analysis (PCA). In this work, we apply a recently developed technique, diffusion maps, to astronomical spectra for data parameterization and dimensionality reduction, and develop a robust, eigenmode-based framework for regression. We show how our framework provides a computationally efficient means by which to predict redshifts of galaxies, and thus could inform more expensive redshift estimators such as template cross-correlation. It also provides a natural means by which to identify outliers (e.g., misclassified spectra, spectra with anomalous features). We analyze 3835 Sloan Digital Sky Survey spectra and show how our framework yields a more than 95% reduction in dimensionality. Finally, we show that the prediction error of the diffusion-map-based regression approach is markedly smaller than that of a similar approach based on PCA, clearly demonstrating the superiority of diffusion maps over PCA for this regression task.