Search

We use observations from the APOGEE survey to explore the relationship between stellar parameters and multiplicity. We combine high-resolution repeat spectroscopy for 41,363 dwarf and subgiant stars with abundance measurements from the APOGEE pipeline and distances and stellar parameters derived using \textit{Gaia} DR2 parallaxes from \cite{Sanders2018} to identify and characterise stellar multiples with periods below 30 years, corresponding to \drvm$\gtrsim$ 3 \kms, where \drvm\ is the maximum APOGEE-detected shift in the radial velocities. Chemical composition is responsible for most of the variation in the close binary fraction in our sample, with stellar parameters like mass and age playing a secondary role. In addition to the previously identified strong anti-correlation between the close binary fraction and \feh\, we find that high abundances of $\alpha$ elements also suppress multiplicity at most values of \feh\ sampled by APOGEE. The anti-correlation between $\alpha$ abundances and multiplicity is substantially steeper than that observed for Fe, suggesting C, O, and Si in the form of dust and ices dominate the opacity of primordial protostellar disks and their propensity for fragmentation via gravitational stability. Near \feh{} = 0 dex, the bias-corrected close binary fraction ($a, Comment: 15 pages, 10 figures, plus appendices; accepted to MNRAS

We study the effects of filaments on galaxy properties in the Sloan Digital Sky Survey (SDSS) Data Release 12 using filaments from the `Cosmic Web Reconstruction' catalogue (Chen et al. 2016), a publicly available filament catalogue for SDSS. Since filaments are tracers of medium-to-high density regions, we expect that galaxy properties associated with the environment are dependent on the distance to the nearest filament. Our analysis demonstrates that a red galaxy or a high-mass galaxy tend to reside closer to filaments than a blue or low-mass galaxy. After adjusting the effect from stellar mass, on average, early-forming galaxies or large galaxies have a shorter distance to filaments than late-forming galaxies or small galaxies. For the Main galaxy sample (MGS), all signals are very significant ($>6\sigma$). For the LOWZ and CMASS sample, the stellar mass and size are significant ($>2 \sigma$). The filament effects we observe persist until $z = 0.7$ (the edge of the CMASS sample). Comparing our results to those using the galaxy distances from redMaPPer galaxy clusters as a reference, we find a similar result between filaments and clusters. Moreover, we find that the effect of clusters on the stellar mass of nearby galaxies depends on the galaxy's filamentary environment. Our findings illustrate the strong correlation of galaxy properties with proximity to density ridges, strongly supporting the claim that density ridges are good tracers of filaments., Comment: To appear in MNRAS

To better prepare physiology students for 21st century careers, we incorporated classroom-based undergraduate research experiences and service learning/community-engaged learning (SLCE) into a college-level physiology laboratory course. The interventions were incorporated over 4 yr and assessed using validated surveys of student-reported learning gains related to attitudes toward science, the scientific process, and career paths. Students reported the greatest learning gains in those years when students did novel research oriented around a common theme of water quality. The gains were greater than those of a matched cohort that participated in an apprentice-style summer undergraduate research experience. With respect to the SLCE related to youth science literacy, students provided evidence of learning related to academics, personal growth, and civic mindedness. For example, many expressed discomfort about being in a new situation, often describing the differences between themselves and the youth with whom they interacted. However, students also grew in confidence about collaborating with people who were different from them and in their role as the “scientist.” Limitations of the study include the quasi-experimental design and the incorporation of multiple interventions at the same time. Future studies should examine improvement in content acquisition and competency-based learning skills. Nonetheless, these results suggest that both novel research and SLCE increase student learning in the context of an undergraduate physiology laboratory course. Many of the learning gains observed with the SLCE are particularly important for physiology students, many of whom aspire to careers in health sciences, where they will be regularly working with nonscientists.

It is well known in astronomy that propagating non-Gaussian prediction uncertainty in photometric redshift estimates is key to reducing bias in downstream cosmological analyses. Similarly, likelihood-free inference approaches, which are beginning to emerge as a tool for cosmological analysis, require a characterization of the full uncertainty landscape of the parameters of interest given observed data. However, most machine learning (ML) or training-based methods with open-source software target point prediction or classification, and hence fall short in quantifying uncertainty in complex regression and parameter inference settings. As an alternative to methods that focus on predicting the response (or parameters) $\mathbf{y}$ from features $\mathbf{x}$, we provide nonparametric conditional density estimation (CDE) tools for approximating and validating the entire probability density function (PDF) $\mathrm{p}(\mathbf{y}|\mathbf{x})$ of $\mathbf{y}$ given (i.e., conditional on) $\mathbf{x}$. As there is no one-size-fits-all CDE method, the goal of this work is to provide a comprehensive range of statistical tools and open-source software for nonparametric CDE and method assessment which can accommodate different types of settings and be easily fit to the problem at hand. Specifically, we introduce four CDE software packages in $\texttt{Python}$ and $\texttt{R}$ based on ML prediction methods adapted and optimized for CDE: $\texttt{NNKCDE}$, $\texttt{RFCDE}$, $\texttt{FlexCode}$, and $\texttt{DeepCDE}$. Furthermore, we present the $\texttt{cdetools}$ package, which includes functions for computing a CDE loss function for tuning and assessing the quality of individual PDFs, along with diagnostic functions. We provide sample code in $\texttt{Python}$ and $\texttt{R}$ as well as examples of applications to photometric redshift estimation and likelihood-free cosmological inference via CDE., Comment: 27 pages, 7 figures, 4 tables

The shapes of cluster central galaxies are not randomly oriented, but rather exhibit coherent alignments with the shapes of their parent clusters as well as with the surrounding large-scale structures. In this work, we aim to identify the galaxy and cluster quantities that most strongly predict the central galaxy alignment phenomenon among a large parameter space with a sample of 8237 clusters and 94817 members within 0.1, Comment: matches version accepted to MNRAS; minor changes in presentation compared to v1, no changes to results

We construct a catalogue for filaments using a novel approach called SCMS (subspace constrained mean shift; Ozertem & Erdogmus 2011; Chen et al. 2015). SCMS is a gradient-based method that detects filaments through density ridges (smooth curves tracing high-density regions). A great advantage of SCMS is its uncertainty measure, which allows an evaluation of the errors for the detected filaments. To detect filaments, we use data from the Sloan Digital Sky Survey, which consist of three galaxy samples: the NYU main galaxy sample (MGS), the LOWZ sample and the CMASS sample. Each of the three dataset covers different redshift regions so that the combined sample allows detection of filaments up to z = 0.7. Our filament catalogue consists of a sequence of two-dimensional filament maps at different redshifts that provide several useful statistics on the evolution cosmic web. To construct the maps, we select spectroscopically confirmed galaxies within 0.050 < z < 0.700 and partition them into 130 bins. For each bin, we ignore the redshift, treating the galaxy observations as a 2-D data and detect filaments using SCMS. The filament catalogue consists of 130 individual 2-D filament maps, and each map comprises points on the detected filaments that describe the filamentary structures at a particular redshift. We also apply our filament catalogue to investigate galaxy luminosity and its relation with distance to filament. Using a volume-limited sample, we find strong evidence (6.1$��$ - 12.3$��$) that galaxies close to filaments are generally brighter than those at significant distance from filaments., 14 pages, 12 figures, 4 tables

Important but rare and subtle processes driving galaxy morphology and star-formation may be missed by traditional spiral, elliptical, irregular or S\'ersic bulge/disk classifications. To overcome this limitation, we use a principal component analysis of non-parametric morphological indicators (concentration, asymmetry, Gini coefficient, $M_{20}$, multi-mode, intensity and deviation) measured at rest-frame $B$-band (corresponding to HST/WFC3 F125W at 1.4 $< z 10^{10} M_{\odot}$) galaxy morphologies. Principal component analysis (PCA) quantifies the correlations between these morphological indicators and determines the relative importance of each. The first three principal components (PCs) capture $\sim$75 per cent of the variance inherent to our sample. We interpret the first principal component (PC) as bulge strength, the second PC as dominated by concentration and the third PC as dominated by asymmetry. Both PC1 and PC2 correlate with the visual appearance of a central bulge and predict galaxy quiescence. PC1 is a better predictor of quenching than stellar mass, as as good as other structural indicators (S\'ersic-n or compactness). We divide the PCA results into groups using an agglomerative hierarchical clustering method. Unlike S\'ersic, this classification scheme separates compact galaxies from larger, smooth proto-elliptical systems, and star-forming disk-dominated clumpy galaxies from star-forming bulge-dominated asymmetric galaxies. Distinguishing between these galaxy structural types in a quantitative manner is an important step towards understanding the connections between morphology, galaxy assembly and star-formation., Comment: 31 pages, 24 figures, accepted for publication in MNRAS

We present image-based evolution of galaxy mergers from the Illustris cosmological simulation at 12 time-steps over 0.5 < z < 5. To do so, we created approximately one million synthetic deep Hubble Space Telescope and James Webb Space Telescope images and measured common morphological indicators. Using the merger tree, we assess methods to observationally select mergers with stellar mass ratios as low as 10:1 completing within +/- 250 Myr of the mock observation. We confirm that common one- or two-dimensional statistics select mergers so defined with low purity and completeness, leading to high statistical errors. As an alternative, we train redshift-dependent random forests (RFs) based on 5-10 inputs. Cross-validation shows the RFs yield superior, yet still imperfect, measurements of the late-stage merger fraction, and they select more mergers in bulge-dominated galaxies. When applied to CANDELS morphology catalogs, the RFs estimate a merger rate increasing to at least z = 3, albeit two times higher than expected by theory. This suggests possible mismatches in the feedback-determined morphologies, but affirms the basic understanding of galaxy merger evolution. The RFs achieve completeness of roughly 70% at 0.5 < z < 3, and purity increasing from 10% at z = 0.5 to 60% at z = 3. At earlier times, the training sets are insufficient, motivating larger simulations and smaller time sampling. By blending large surveys and large simulations, such machine learning techniques offer a promising opportunity to teach us the strengths and weaknesses of inferences about galaxy evolution., 20 pages, 16 figures, MNRAS accepted version

We use the multi-epoch radial velocities acquired by the APOGEE survey to perform a large scale statistical study of stellar multiplicity for field stars in the Milky Way, spanning the evolutionary phases between the main sequence and the red clump. We show that the distribution of maximum radial velocity shifts (\drvm) for APOGEE targets is a strong function of \logg, with main sequence stars showing \drvm\ as high as $\sim$300 \kms, and steadily dropping down to $\sim$30 \kms\ for \logg$\sim$0, as stars climb up the Red Giant Branch (RGB). Red clump stars show a distribution of \drvm\ values comparable to that of stars at the tip of the RGB, implying they have similar multiplicity characteristics. The observed attrition of high \drvm\ systems in the RGB is consistent with a lognormal period distribution in the main sequence and a multiplicity fraction of 0.35, which is truncated at an increasing period as stars become physically larger and undergo mass transfer after Roche Lobe Overflow during H shell burning. The \drvm\ distributions also show that the multiplicity characteristics of field stars are metallicity dependent, with metal-poor ([Fe/H]$\lesssim-0.5$) stars having a multiplicity fraction a factor 2-3 higher than metal-rich ([Fe/H]$\gtrsim0.0$) stars. This has profound implications for the formation rates of interacting binaries observed by astronomical transient surveys and gravitational wave detectors, as well as the habitability of circumbinary planets., 13 pages, 13 figures, replaced with version accepted by ApJ

Redshift is a key quantity for inferring cosmological model parameters. In photometric redshift estimation, cosmologists use the coarse data collected from the vast majority of galaxies to predict the redshift of individual galaxies. To properly quantify the uncertainty in the predictions, however, one needs to go beyond standard regression and instead estimate the full conditional density $f(z|\mathbf{x})$ of a galaxy’s redshift $z$ given its photometric covariates $\mathbf{x}$. The problem is further complicated by selection bias: usually only the rarest and brightest galaxies have known redshifts, and these galaxies have characteristics and measured covariates that do not necessarily match those of more numerous and dimmer galaxies of unknown redshift. Unfortunately, there is not much research on how to best estimate complex multivariate densities in such settings. ¶ Here we describe a general framework for properly constructing and assessing nonparametric conditional density estimators under selection bias, and for combining two or more estimators for optimal performance. We propose new improved photo-$z$ estimators and illustrate our methods on data from the Sloan Data Sky Survey and an application to galaxy–galaxy lensing. Although our main application is photo-$z$ estimation, our methods are relevant to any high-dimensional regression setting with complicated asymmetric and multimodal distributions in the response variable.

We study the orientations of satellite galaxies in redMaPPer clusters constructed from the Sloan Digital Sky Survey at $0.1, Comment: 25 pages, 16 figures, 7 tables, accepted to MNRAS. Main statistical analysis tool changed, with the results remain similar

Modern surveys have provided the astronomical community with a flood of high-dimensional data, but analyses of these data often occur after their projection to lower-dimensional spaces. In this work, we introduce a local two-sample hypothesis test framework that an analyst may directly apply to data in their native space. In this framework, the analyst defines two classes based on a response variable of interest (e.g. higher-mass galaxies versus lower-mass galaxies) and determines at arbitrary points in predictor space whether the local proportions of objects that belong to the two classes significantly differs from the global proportion. Our framework has a potential myriad of uses throughout astronomy; here, we demonstrate its efficacy by applying it to a sample of 2487 i-band-selected galaxies observed by the HST ACS in four of the CANDELS program fields. For each galaxy, we have seven morphological summary statistics along with an estimated stellar mass and star-formation rate. We perform two studies: one in which we determine regions of the seven-dimensional space of morphological statistics where high-mass galaxies are significantly more numerous than low-mass galaxies, and vice-versa, and another study where we use SFR in place of mass. We find that we are able to identify such regions, and show how high-mass/low-SFR regions are associated with concentrated and undisturbed galaxies while galaxies in low-mass/high-SFR regions appear more extended and/or disturbed than their high-mass/low-SFR counterparts., Comment: 11 pages, 9 figures; accepted to MNRAS

Visualizing the high-redshift Universe is difficult due to the dearth of available data; however, the Lyman-alpha forest provides a means to map the intergalactic medium at redshifts not accessible to large galaxy surveys. Large-scale structure surveys, such as the Baryon Oscillation Spectroscopic Survey (BOSS), have collected quasar (QSO) spectra that enable the reconstruction of H I density fluctuations. The data fall on a collection of lines defined by the lines of sight (LOS) of the QSO, and a major issue with producing a 3D reconstruction is determining how to model the regions between the LOS. We present a method that produces a 3D map of this relatively uncharted portion of the Universe by employing local polynomial smoothing, a non-parametric methodology. The performance of the method is analysed on simulated data that mimics the varying number of LOS expected in real data, and then is applied to a sample region selected from BOSS. Evaluation of the reconstruction is assessed by considering various features of the predicted 3D maps including visual comparison of slices, probability density functions (PDFs), counts of local minima and maxima, and standardized correlation functions. This 3D reconstruction allows for an initial investigation of the topology of this portion of the Universe using persistent homology.

In vertebrates, many responses to stress as well as homeostatic maintenance of basal metabolism are regulated by plasma glucocorticoid hormones (GCs). Despite having crucial functions, levels of GCs are typically variable among individuals. We examined the contribution of several physiological factors to individual variation in plasma corticosterone (CORT) and the number of corticotropin-releasing hormone (CRH) neurons in the magnocellular preoptic area of the brain in free-living Allegheny Mountain dusky salamanders. We addressed three hypotheses: the current-condition hypothesis, the facilitation hypothesis and the trade-off hypothesis. Differential white blood cell count was identified as a strong contributor to individual variation in baseline CORT, stress-induced CORT and the number of CRH neurons. In contrast, we found no relationship between CORT (or CRH) and body condition, energy stores or reproductive investment, providing no support for the current-condition hypothesis or the trade-off hypothesis involving reproduction. Because of the difficulties of interpreting the functional consequences of variation in differential white blood cell counts, we were unable to distinguish between the facilitation hypothesis or the trade-off hypothesis related to immune function. However, the strong association between differential white blood cell count and hypothalamic-pituitary-adrenal/interrenal (HPA/I) activation suggests that a more thorough examination of immune profiles is critical to understanding variation in HPA/I activation.

Photometric redshift estimation is an indispensable tool of precision cosmology. One problem that plagues the use of this tool in the era of large-scale sky surveys is that the bright galaxies that are selected for spectroscopic observation do not have properties that match those of (far more numerous) dimmer galaxies; thus, ill-designed empirical methods that produce accurate and precise redshift estimates for the former generally will not produce good estimates for the latter. In this paper, we provide a principled framework for generating conditional density estimates (i.e. photometric redshift PDFs) that takes into account selection bias and the covariate shift that this bias induces. We base our approach on the assumption that the probability that astronomers label a galaxy (i.e. determine its spectroscopic redshift) depends only on its measured (photometric and perhaps other) properties x and not on its true redshift. With this assumption, we can explicitly write down risk functions that allow us to both tune and compare methods for estimating importance weights (i.e. the ratio of densities of unlabeled and labeled galaxies for different values of x) and conditional densities. We also provide a method for combining multiple conditional density estimates for the same galaxy into a single estimate with better properties. We apply our risk functions to an analysis of approximately one million galaxies, mostly observed by SDSS, and demonstrate through multiple diagnostic tests that our method achieves good conditional density estimates for the unlabeled galaxies., 11 pages; accepted by MNRAS

We present a semi-supervised method for photometric supernova typing. Our approach is to first use the non-linear dimension reduction technique diffusion map to detect structure in a data base of supernova light curves and subsequently employ random forest classification on a spectroscopically confirmed training set to learn a model that can predict the type of each newly observed supernova. We demonstrate that this is an effective method for supernova typing. As supernova numbers increase, our semi-supervised method efficiently utilizes this information to improve classification, a property not enjoyed by template-based methods. Applied to supernova data simulated by Kessler et al. to mimic those of the Dark Energy Survey, our methods achieve (cross-validated) 95 per cent Type Ia purity and 87 per cent Type Ia efficiency on the spectroscopic sample, but only 50 per cent Type Ia purity and 50 per cent efficiency on the photometric sample due to their spectroscopic follow-up strategy. To improve the performance on the photometric sample, we search for better spectroscopic follow-up procedures by studying the sensitivity of our machine-learned supernova classification on the specific strategy used to obtain training sets. With a fixed amount of spectroscopic follow-up time, we find that, despite collecting data on a smaller number of supernovae, deeper magnitude-limited spectroscopic surveys are better for producing training sets. For supernova Ia (II-P) typing, we obtain a 44 per cent (1 per cent) increase in purity to 72 per cent (87 per cent) and 30 per cent (162 per cent) increase in efficiency to 65 per cent (84 per cent) of the sample using a 25th (24.5th) magnitude-limited survey instead of the shallower spectroscopic sample used in the original simulations. When redshift information is available, we incorporate it into our analysis using a novel method of altering the diffusion map representation of the supernovae. Incorporating host redshifts leads to a 5 per cent improvement in Type Ia purity and 13 per cent improvement in Type Ia efficiency.

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 5,776 XMM observations used to construct the current XCS source catalogue. A total of 3,675 > 4-sigma cluster candidates with > 50 background-subtracted X-ray counts are extracted from a total non-overlapping area suitable for cluster searching of 410 deg^2. 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 XMM images. These tests show that the simple isothermal beta-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.

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2015 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved, From mock Hubble Space Telescope images, we quantify non-parametric statistics of galaxy morphology, thereby predicting the emergence of relationships among stellar mass, star formation, and observed rest-frame optical structure at 1 < z < 3. We measure automated diagnostics of galaxy morphology in cosmological simulations of the formation of 22 central galaxies with 9.3 < log10 M_*/M_sun < 10.7. These high-spatial-resolution zoom-in calculations enable accurate modeling of the rest-frame UV and optical morphology. Even with small numbers of galaxies, we find that structural evolution is neither universal nor monotonic: galaxy interactions can trigger either bulge or disc formation, and optically bulge-dominated galaxies at this mass may not remain so forever. Simulated galaxies with M_* > 10^10 M_sun contain relatively more disc-dominated light profiles than those with lower mass, reflecting significant disc brightening in some haloes at 1 < z < 2. By this epoch, simulated galaxies with specific star formation rates below 10^-9.7 yr^-1 are more likely than normal star-formers to have a broader mix of structural types, especially at M_* > 10^10 M_sun. We analyze a cosmological major merger at z ~ 1.5 and find that the newly proposed MID morphology diagnostics trace later merger stages while G-M20 trace earlier ones. MID is sensitive also to clumpy star-forming discs. The observability time of typical MID-enhanced events in our simulation sample is less than 100 Myr. A larger sample of cosmological assembly histories may be required to calibrate such diagnostics in the face of their sensitivity to viewing angle, segmentation algorithm, and various phenomena such as clumpy star formation and minor mergers, This work was partially supported by MINECO-AYA2012-31101. DC is a Juan de la Cierva fellow. This research has been partly supported by ISF grant 24/12, by NSF grant AST-1010033, and by the I-CORE Program of the PBC and the ISF grant 1829/12. GS and JL appreciate support from the HST grants program, number HST-AR-12856:01-A. Support for program #12856 (PI J. Lotz) was provided by NASA through a grant from the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555

The detection and characterization of filamentary structures in the cosmic web allows cosmologists to constrain parameters that dictates the evolution of the Universe. While many filament estimators have been proposed, they generally lack estimates of uncertainty, reducing their inferential power. In this paper, we demonstrate how one may apply the Subspace Constrained Mean Shift (SCMS) algorithm (Ozertem and Erdogmus (2011); Genovese et al. (2012)) to uncover filamentary structure in galaxy data. The SCMS algorithm is a gradient ascent method that models filaments as density ridges, one-dimensional smooth curves that trace high-density regions within the point cloud. We also demonstrate how augmenting the SCMS algorithm with bootstrap-based methods of uncertainty estimation allows one to place uncertainty bands around putative filaments. We apply the SCMS method to datasets sampled from the P3M N-body simulation, with galaxy number densities consistent with SDSS and WFIRST-AFTA and to LOWZ and CMASS data from the Baryon Oscillation Spectroscopic Survey (BOSS). To further assess the efficacy of SCMS, we compare the relative locations of BOSS filaments with galaxy clusters in the redMaPPer catalog, and find that redMaPPer clusters are significantly closer (with p-values $< 10^{-9}$) to SCMS-detected filaments than to randomly selected galaxies., To appear in MNRAS. 18 pages, 19 figures, 1 table

The intrinsic scatter in the ellipticities of galaxies about the mean shape, known as "shape noise," is the most important source of noise in weak lensing shear measurements. Several approaches to reducing shape noise have recently been put forward, using information beyond photometry, such as radio polarization and optical spectroscopy. Here we investigate how well the intrinsic ellipticities of galaxies can be predicted using other, exclusively photometric parameters. These parameters (such as galaxy colours) are already available in the data and do not necessitate additional, often expensive observations. We apply two regression techniques, generalized additive models (GAM) and projection pursuit regression (PPR) to the publicly released data catalog of galaxy properties from CFHTLenS. In our simple analysis we find that the individual galaxy ellipticities can indeed be predicted from other photometric parameters to better precision than the scatter about the mean ellipticity. This means that without additional observations beyond photometry the ellipticity contribution to the shear can be measured to higher precision, comparable to using a larger sample of galaxies. Our best-fit model, achieved using PPR, yields a gain equivalent to having 114.3% more galaxies. Using only parameters unaffected by lensing (e.g.~surface brightness, colour), the gain is only ~12%., Comment: 6 pages, 3 figures. Submitted to MNRAS on Aug. 19

In this paper, we study the filamentary structures and the galaxy alignment along filaments at redshift $z=0.06$ in the MassiveBlack-II simulation, a state-of-the-art, high-resolution hydrodynamical cosmological simulation which includes stellar and AGN feedback in a volume of (100 Mpc$/h$)$^3$. The filaments are constructed using the subspace constrained mean shift (SCMS; Ozertem & Erdogmus (2011) and Chen et al. (2015a)). First, we show that reconstructed filaments using galaxies and reconstructed filaments using dark matter particles are similar to each other; over $50\%$ of the points on the galaxy filaments have a corresponding point on the dark matter filaments within distance $0.13$ Mpc$/h$ (and vice versa) and this distance is even smaller at high-density regions. Second, we observe the alignment of the major principal axis of a galaxy with respect to the orientation of its nearest filament and detect a $2.5$ Mpc$/h$ critical radius for filament's influence on the alignment when the subhalo mass of this galaxy is between $10^9M_\odot/h$ and $10^{12}M_\odot/h$. Moreover, we find the alignment signal to increase significantly with the subhalo mass. Third, when a galaxy is close to filaments (less than $0.25$ Mpc$/h$), the galaxy alignment toward the nearest galaxy group depends on the galaxy subhalo mass. Finally, we find that galaxies close to filaments or groups tend to be rounder than those away from filaments or groups., 11 pages, 10 figures

The Chandra Multi-wavelength Project (ChaMP) is a wide-area (~14 deg^2) survey of serendipitous Chandra X-ray sources, aiming to establish fair statistical samples covering a wide range of characteristics (such as absorbed AGNs, high z clusters of galaxies) at flux levels (fX ~ 10^-15 - 10^-14 erg sec-1 cm-2) intermediate between the Chandra Deep surveys and previous missions. We present the first ChaMP catalog, which consists of 991 near on-axis, bright X-ray sources 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. To assess source reliability and positional uncertainty, we perform a series of simulations and also use Chandra data to complement the simulation study. The false source detection rate is found to be as good as or better than expected for a given limiting threshold. On the other hand, the chance of missing a real source is rather complex, depending on the source counts, off-axis distance (or PSF), and background rate. The positional error (95% confidence level) is usually 8'). We have also developed new methods to find spatially extended or temporary variable sources and those sources are listed in the catalog.

Deep Chandra LETG+HRC-S observations of the isolated neutron star candidate RX J1856.5-3754 have been analysed to search for metallic and resonance cyclotron spectral features and for pulsation behaviour. As found from earlier observations, the X-ray spectrum is well-represented by a ~ 60 eV (7e5 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 1e-4 to 100 Hz. We argue that the derived interstellar medium neutral hydrogen column density of 8e19

The incredible variety of galaxy shapes cannot be summarized by human defined discrete classes of shapes without causing a possibly large loss of information. Dictionary learning and sparse coding allow us to reduce the high dimensional space of shapes into a manageable low dimensional continuous vector space. Statistical inference can be done in the reduced space via probability distribution estimation and manifold estimation., Comment: 4 pages, 3 figures, Statistical Challenges in 21st Century Cosmology, Proceedings IAU Symposium No. 306, 2014

Our analysis of Chandra X-Ray Observatory data for the open cluster NGC 2516, sometimes referred to as "the southern Pleiades," has yielded over 150 X-ray detections in both High-Resolution Camera and Advanced CCD Imaging Spectrometer images of the central region of the cluster. We identify some of the new X-ray sources with photometric cluster members and compare these new Chandra results with those of ROSAT. To date, 82 detected X-ray sources (42% of surveyed cluster members) are tentatively identified as cluster members. We also discuss the X-ray properties of late-type members in comparison with those of corresponding stellar types in the more metal-rich, approximately coeval Pleiades Cluster.

The Ginga data for the gamma-ray burst GRB870303 exhibit low-energy dips in two temporally distinct spectra, denoted S1 and S2. S1, spanning 4 s, exhibits a single line candidate at ~ 20 keV, while S2, spanning 9 s, exhibits apparently harmonically spaced line candidates at ~ 20 and 40 keV. We evaluate the statistical evidence for these lines, using phenomenological continuum and line models which in their details are independent of the distance scale to gamma-ray bursts. We employ the methodologies based on both frequentist and Bayesian statistical inference that we develop in Freeman et al. (1999b). These methodologies utilize the information present in the data to select the simplest model that adequately describes the data from among a wide range of continuum and continuum-plus-line(s) models. This ensures that the chosen model does not include free parameters that the data deem unnecessary and that would act to reduce the frequentist significance and Bayesian odds of the continuum-plus-line(s) model. We calculate the significance of the continuum-plus-line(s) models using the Chi-Square Maximum Likelihood Ratio test. We describe a parametrization of the exponentiated Gaussian absorption line shape that makes the probability surface in parameter space better-behaved, allowing us to estimate analytically the Bayesian odds. The significance of the continuum-plus-line models requested by the S1 and S2 data are 3.6 x 10^-5 and 1.7 x 10^-4 respectively, with the odds favoring them being 114:1 and 7:1. We also apply our methodology to the combined (S1+S2) data. The significance of the continuum-plus-lines model requested by the combined data is 4.2 x 10^-8, with the odds favoring it being 40,300:1., Comment: LaTeX2e (aastex.cls included); 41 pages text, 10 figures (on 11 pages); accepted by ApJ (to be published 1 Nov 1999, v. 525)

Testing theories of hierarchical structure formation requires estimating the distribution of galaxy morphologies and its change with redshift. One aspect of this investigation involves identifying galaxies with disturbed morphologies (e.g., merging galaxies). This is often done by summarizing galaxy images using, e.g., the CAS and Gini-M20 statistics of Conselice (2003) and Lotz et al. (2004), respectively, and associating particular statistic values with disturbance. We introduce three statistics that enhance detection of disturbed morphologies at high-redshift (z ~ 2): the multi-mode (M), intensity (I), and deviation (D) statistics. We show their effectiveness by training a machine-learning classifier, random forest, using 1,639 galaxies observed in the H band by the Hubble Space Telescope WFC3, galaxies that had been previously classified by eye by the CANDELS collaboration (Grogin et al. 2011, Koekemoer et al. 2011). We find that the MID statistics (and the A statistic of Conselice 2003) are the most useful for identifying disturbed morphologies. We also explore whether human annotators are useful for identifying disturbed morphologies. We demonstrate that they show limited ability to detect disturbance at high redshift, and that increasing their number beyond approximately 10 does not provably yield better classification performance. We propose a simulation-based model-fitting algorithm that mitigates these issues by bypassing annotation., Comment: 15 pages, 14 figures, accepted for publication in MNRAS

Parameter estimation in astrophysics often requires the use of complex physical models. In this paper we study the problem of estimating the parameters that describe star formation history (SFH) in galaxies. Here, high-dimensional spectral data from galaxies are appropriately modeled as linear combinations of physical components, called simple stellar populations (SSPs), plus some nonlinear distortions. Theoretical data for each SSP is produced for a fixed parameter vector via computer modeling. Though the parameters that define each SSP are continuous, optimizing the signal model over a large set of SSPs on a fine parameter grid is computationally infeasible and inefficient. The goal of this study is to estimate the set of parameters that describes the SFH of each galaxy. These target parameters, such as the average ages and chemical compositions of the galaxy's stellar populations, are derived from the SSP parameters and the component weights in the signal model. Here, we introduce a principled approach of choosing a small basis of SSP prototypes for SFH parameter estimation. The basic idea is to quantize the vector space and effective support of the model components. In addition to greater computational efficiency, we achieve better estimates of the SFH target parameters. In simulations, our proposed quantization method obtains a substantial improvement in estimating the target parameters over the common method of employing a parameter grid. Sparse coding techniques are not appropriate for this problem without proper constraints, while constrained sparse coding methods perform poorly for parameter estimation because their objective is signal reconstruction, not estimation of the target parameters., Comment: Published in at http://dx.doi.org/10.1214/11-AOAS500 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

Statistical inference of cosmological quantities of interest is complicated by significant observational limitations, including heteroscedastic measurement error and irregular selection effects. These observational difficulties exacerbate challenges posed by the often-complex relationship between estimands and the distribution of observables; indeed, in some situations it is only possible to simulate realizations of observations under various assumed cosmological theories. When faced with these challenges, one is naturally led to consider utilizing repeated simulations of the full data generation process, and then comparing observed and simulated data sets to constrain the parameters. In such a scenario, one would not have a likelihood function relating the parameters to the observable data. This paper will present an overview of methods that allow a likelihood-free approach to inference, with emphasis on approximate Bayesian computation, a class of procedures originally motivated by similar inference problems in population genetics.

We introduce a new feature of galaxy images, maxRatio, and demonstrate its effectiveness at detecting merging galaxies at high redshift.

A common problem in astrophysics is determining how bright a source could be and still not be detected. Despite the simplicity with which the problem can be stated, the solution involves complex statistical issues that require careful analysis. In contrast to the 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 (the TypeI error), and we invoke the complementary concept of false negatives (the TypeII 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 TypeII 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 and 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 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., 30 pages, 12 figures, accepted in ApJ

We forecast the constraints on the values of sigma_8, Omega_m, and cluster scaling relation parameters which we expect to obtain from the XMM Cluster Survey (XCS). We assume a flat Lambda-CDM 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 luminosity-temperature 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 Omega_m to +-0.03 (and Omega_Lambda to the same accuracy assuming flatness), and sigma_8 to +-0.05, also constraining the normalization and slope of the luminosity-temperature relation to +-6 and +-13 per cent (at 1sigma) respectively in the process. Self-calibration fails to jointly constrain the scatter and redshift evolution of the luminosity-temperature 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 2sigma 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' estimate of expected constraints., 28 pages, 17 figures. Revised version, as accepted for publication in MNRAS. High-resolution figures available at http://xcs-home.org (under "Publications")

To further our knowledge of the complex physical process of galaxy formation, it is essential that we characterize the formation and evolution of large databases of galaxies. The spectral synthesis STARLIGHT code of Cid Fernandes et al. (2004) was designed for this purpose. Results of STARLIGHT are highly dependent on the choice of input basis of simple stellar population (SSP) spectra. Speed of the code, which uses random walks through the parameter space, scales as the square of the number of basis spectra, making it computationally necessary to choose a small number of SSPs that are coarsely sampled in age and metallicity. In this paper, we develop methods based on diffusion map (Lafon & Lee, 2006) that, for the first time, choose appropriate bases of prototype SSP spectra from a large set of SSP spectra designed to approximate the continuous grid of age and metallicity of SSPs of which galaxies are truly composed. We show that our techniques achieve better accuracy of physical parameter estimation for simulated galaxies. Specifically, we show that our methods significantly decrease the age-metallicity degeneracy that is common in galaxy population synthesis methods. We analyze a sample of 3046 galaxies in SDSS DR6 and compare the parameter estimates obtained from different basis choices., Resubmitted to MNRAS; 16 pages, 15 figures

The surprising discovery of an accelerating universe led cosmologists to posit the existence of "dark energy"--a mysterious energy field that permeates the universe. Understanding dark energy has become the central problem of modern cosmology. After describing the scientific background in depth, we formulate the task as a nonlinear inverse problem that expresses the comoving distance function in terms of the dark-energy equation of state. We present two classes of methods for making sharp statistical inferences about the equation of state from observations of Type Ia Supernovae (SNe). First, we derive a technique for testing hypotheses about the equation of state that requires no assumptions about its form and can distinguish among competing theories. Second, we present a framework for computing parametric and nonparametric estimators of the equation of state, with an associated assessment of uncertainty. Using our approach, we evaluate the strength of statistical evidence for various competing models of dark energy. Consistent with current studies, we find that with the available Type Ia SNe data, it is not possible to distinguish statistically among popular dark-energy models, and that, in particular, there is no support in the data for rejecting a cosmological constant. With much more supernova data likely to be available in coming years (e.g., from the DOE/NASA Joint Dark Energy Mission), we address the more interesting question of whether future data sets will have sufficient resolution to distinguish among competing theories., Published in at http://dx.doi.org/10.1214/08-AOAS229 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

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., Resubmitted to MNRAS (11 pages, 8 figures)

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 SDSS 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., Comment: 24 pages, 8 figures

In recent years, there has been an avalanche of new data in observational high-energy astrophysics. Recently launched or soon-to-be launched space-based telescopes that are designed to detect and map ultra-violet, X-ray, and $\gamma$-ray electromagnetic emission are opening a whole new window to study the cosmos. Because the production of high-energy electromagnetic emission requires temperatures of millions of degrees and is an indication of the release of vast quantities of stored energy, these instruments give a completely new perspective on the hot and turbulent regions of the universe. The new instrumentation allows for very high resolution imaging, spectral analysis, and time series analysis; the Chandra X-ray Observatory, for example, produces images at least thirty times sharper than any previous X-ray telescope. The complexity of the instruments, of the astronomical sources, and of the scientific questions leads to a subtle inference problem that requires sophisticated statistical tools. For example, data are subject to non-uniform stochastic censoring, heteroscedastic errors in measurement, and background contamination. Astronomical sources exhibit complex and irregular spatial structure. Scientists wish to draw conclusions as to the physical environment and structure of the source, the processes and laws which govern the birth and death of planets, stars, and galaxies, and ultimately the structure and evolution of the universe. ¶ The California-Harvard Astrostatistics Collaboration is a group of astrophysicists and statisticians working together to develop statistical methods, computational techniques, and freely available software to address outstanding inferential problems in high-energy astrophysics. We emphasize fully model-based statistical inference; we explicitly model the complexities of both astronomical sources and the data generation mechanisms inherent in new high-tech instruments, and fully utilize the resulting highly structured models in learning about the underlying astronomical and physical processes. Using these models requires sophisticated scientific computation, advanced methods for statistical inference, and careful model checking procedures. ¶ Here we discuss the broad scientific goals of observation high-energy astrophysics, the specifics of the data collection mechanism involved with the Chandra X-ray Observatory, current statistical methods, and the Bayesian models and methods that we propose. We illustrate our statistical strategy in the context of several applied examples, including the estimation of hardness ratios, spectral analysis, multiscale image analysis, and reconstruction of the distribution of the temperature of hot plasma in a stellar corona. This paper was presented at the Case Studies in Bayesian Statistics Workshop 7 held at Carnegie Mellon University in September 2003.

Wavelet functions are proving extremely useful for detecting sources in binned, two-dimensional photon counts images. In this chapter, we describe the mission-independent source detection algorithm WAAVDETEET, part of the Chandra Interactive Analysis of Observations (CIAO) software package, and discuss the statistical challenges we have faced in its development, such as: what is the best way to estimate the local background in each pixel, if it is a priori unknown? What is the best way to eliminate false detections caused by instrumental variations? And what is the significance of a detected source?

We analyze first-year data of WMAP to determine the significance of asymmetry in summed power between arbitrarily defined opposite hemispheres, using maps that we create ourselves with software developed independently of the WMAP team. We find that over the multipole range l=[2,64], the significance of asymmetry is ~ 10^-4, a value insensitive to both frequency and power spectrum. We determine the smallest multipole ranges exhibiting significant asymmetry, and find twelve, including l=[2,3] and [6,7], for which the significance -> 0. In these ranges there is an improbable association between the direction of maximum significance and the ecliptic plane (p ~ 0.01). Also, contours of least significance follow great circles inclined relative to the ecliptic at the largest scales. The great circle for l=[2,3] passes over previously reported preferred axes and is insensitive to frequency, while the great circle for l=[6,7] is aligned with the ecliptic poles. We examine how changing map-making parameters affects asymmetry, and find that at large scales, it is rendered insignificant if the magnitude of the WMAP dipole vector is increased by approximately 1-3 sigma (or 2-6 km/s). While confirmation of this result would require data recalibration, such a systematic change would be consistent with observations of frequency-independent asymmetry. We conclude that the use of an incorrect dipole vector, in combination with a systematic or foreground process associated with the ecliptic, may help to explain the observed asymmetry., 45 pages, 16 figures (21 figure files), high-resolution versions of Figures 1-3 at http://www.stat.cmu.edu/~pfreeman, accepted for publication in ApJ

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 Sherpa's 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.

Wavelets are scaleable, oscillatory functions that deviate from zero only within a limited spatial regime and have average value zero. In addition to their use as source characterizers, wavelet functions are rapidly gaining currency within the source detection field. Wavelet-based source detection involves the correlation of scaled wavelet functions with binned, two-dimensional image data. If the chosen wavelet function exhibits the property of vanishing moments, significantly non-zero correlation coefficients will be observed only where there are high-order variations in the data; e.g., they will be observed in the vicinity of sources. In this paper, we describe the mission-independent, wavelet-based source detection algorithm WAVDETECT, part of the CIAO software package. Aspects of our algorithm include: (1) the computation of local, exposure-corrected normalized (i.e. flat-fielded) background maps; (2) the correction for exposure variations within the field-of-view; (3) its applicability within the low-counts regime, as it does not require a minimum number of background counts per pixel for the accurate computation of source detection thresholds; (4) the generation of a source list in a manner that does not depend upon a detailed knowledge of the point spread function (PSF) shape; and (5) error analysis. These features make our algorithm considerably more general than previous methods developed for the analysis of X-ray image data, especially in the low count regime. We demonstrate the algorithm's robustness by applying it to various images., Comment: Accepted for publication in Ap. J. Supp. (v. 138 Jan. 2002). 61 pages, 23 figures, expands to 3.8 Mb. Abstract abridged for astro-ph submission

We demonstrate that models of resonant cyclotron radiation transfer in a strong field (i.e. cyclotron scattering) can account for spectral lines seen at two epochs, denoted S1 and S2, in the Ginga data for GRB870303. Using a generalized version of the Monte Carlo code of Wang et al. (1988,1989b), we model line formation by injecting continuum photons into a static plane-parallel slab of electrons threaded by a strong neutron star magnetic field (~ 10^12 G) which may be oriented at an arbitrary angle relative to the slab normal. We examine two source geometries, which we denote "1-0" and "1-1," with the numbers representing the relative electron column densities above and below the continuum photon source plane. We compare azimuthally symmetric models, i.e. models in which the magnetic field is parallel to the slab normal, with models having more general magnetic field orientations. If the bursting source has a simple dipole field, these two model classes represent line formation at the magnetic pole, or elsewhere on the stellar surface. We find that the data of S1 and S2, considered individually, are consistent with both geometries, and with all magnetic field orientations, with the exception that the S1 data clearly favor line formation away from a polar cap in the 1-1 geometry, with the best-fit model placing the line-forming region at the magnetic equator. Within both geometries, fits to the combined (S1+S2) data marginally favor models which feature equatorial line formation, and in which the observer's orientation with respect to the slab changes between the two epochs. We interpret this change as being due to neutron star rotation, and we place limits on the rotation period., LaTeX2e (aastex.cls included); 45 pages text, 17 figures (on 21 pages); accepted by ApJ (to be published 1 Nov 1999, v. 525)

The high quality of the AXAF X-ray data provides new challenges for the X-ray data analysis. It is clear that an “old” approach is not enough to fully exploit the capabilities of the AXAF instruments. We describe a few of the statistical and computational problems that we have so far identified. Some of them appear to be theoretically solvable but computationally challenging, while others state problems for theoretical statistics which, so far as we know, are unsolved.The problems divide, from an astronomical point of view, into: Modeling the Data (e.g. nonlinear parameter estimation, uncertainties in the model, weighting the data, correlated residuals), Source Detection (events in N-space, use of wavelets, significance of detected structures) and Instrument Related Issues (pile-up in AXAF ACIS, overlapping orders in grating spectra).

We demonstrate that the cyclotron resonant scattering model can account for the spectral lines seen at two epochs, S1 and S2, in the Ginga data for GB870303. Using a Monte Carlo code, we model line formation by injecting continuum photons into a static plane‐parallel slab of electrons threaded by a superstrong magnetic field (B∼1012 G) oriented at an angle Ψ relative to the slab normal. We apply rigorous statistical inference to compare azimuthally‐symmetric models (Ψ=0°) with models having a more general magnetic field orientation. If the burst source is a neutron star with a simple dipole field, these two classes of magnetic field orientations are equivalent to models in which the line forming region lies at the magnetic pole, or elsewhere on the stellar surface. We present the results of separate fits to the spectra for epochs S1 and S2, as well as joint fits to both spectra. The joint fits marginally favor a line forming region with Ψ=90°; i.e., a line forming region at the magnetic equator, assuming ...

The cyclotron line in the spectrum of the accretion-powered pulsar Her X-1 offers an opportunity to assess the ability of the BATSE Spectroscopy Detectors (SDs) to detect lines like those seen in some GRBs. Preliminary analysis of an initial SD pulsar mode observation of Her X-1 indicated a cyclotron line at an energy of approximately 44 keV, rather than at the expected energy of approximately 36 keV. Our analysis of four SD pulsar mode observations of Her X-1 made during high-states of its 35 day cycle confirms this result. We consider a number of phenomenological models for the continuum spectrum and the cyclotron line. This ensures that we use the simplest models that adequately describe the data, and that our results are robust. We find modest evidence (significance Q ~ 10^-4-10^-2) for a line at approximately 44 keV in the data of the first observation. Joint fits to the four observations provide stronger evidence (Q ~ 10^-7-10^-4) for the line. Such a shift in the cyclotron line energy of an accretion-powered pulsar is unprecedented., Comment: 5 pages, LaTeX (style files aipbook.sty, aps.sty, aps10.sty, prabib.sty, psfig.sty, and revtex.sty included with PAPER.tex), 2 embedded PostScript figures (mongo1.ps, mongo2.ps)

We provide an approximate prescription for determining the Bayesian odds favoring spectral models with lines over models without lines, using a new line parametrization which has several advantages over previous parametrizations. we use an exponentiated Gaussian line model, parameterized in terms of the equivalent width, WE, and the full width at half maximum, W1/2, of the line itself (not the full width of the Gaussian). Unlike other, equivalent, parameterizations, this parametrization yields Bayesian posterior probability distributions (and frequentist χ2 surfaces) which are approximately Gaussian near the maximum likelihood parameter values, and allows the formulation of prior probability distributions for the continuum and line parameters which are independent of each other. It has the additional advantage that it easily treats ‘‘saturated’’ lines, in which WE=W1/2. We use Bayesian inference and this parameterization to determine whether the Burst and Transient Source Experiment (BATSE) Spectroscopy D...