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 fr
amework 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.
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 g
alaxies). 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.
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.
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 th
e 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 observers 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.
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 s
paced 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.
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 pulsa
r 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.
