ASTROS: A multidisciplinary automated structural design tool

ASTROS (Automated Structural Optimization System) is a finite-element-based multidisciplinary structural optimization procedure developed under Air Force sponsorship to perform automated preliminary structural design. The design task is the determination of the structural sizes that provide an optimal structure while satisfying numerous constraints from many disciplines. In addition to its automated design features, ASTROS provides a general transient and frequency response capability, as well as a special feature to perform a transient analysis of a vehicle subjected to a nuclear blast. The motivation for the development of a single multidisciplinary design tool is that such a tool can provide improved structural designs in less time than is currently needed. The role of such a tool is even more apparent as modern materials come into widespread use. Balancing conflicting requirements for the structure's strength and stiffness while exploiting the benefits of material anisotropy is perhaps an impossible task without assistance from an automated design tool. Finally, the use of a single tool can bring the design task into better focus among design team members, thereby improving their insight into the overall task

Evolution in the Volumetric Type Ia Supernova Rate from the Supernova Legacy Survey

We present a measurement of the volumetric Type Ia supernova (SN Ia) rate (SNR_Ia) as a function of redshift for the first four years of data from the Canada-France-Hawaii Telescope Supernova Legacy Survey (SNLS). This analysis includes 286 spectroscopically confirmed and more than 400 additional photometrically identified SNe Ia within the redshift range 0.1 ≤ z ≤ 1.1. The volumetric SNR_Ia evolution is consistent with a rise to z ~ 1.0 that follows a power law of the form (1+z)^α, with α = 2.11 ± 0.28. This evolutionary trend in the SNLS rates is slightly shallower than that of the cosmic star formation history (SFH) over the same redshift range. We combine the SNLS rate measurements with those from other surveys that complement the SNLS redshift range, and fit various simple SN Ia delay-time distribution (DTD) models to the combined data. A simple power-law model for the DTD (i.e., ∝ t^(–β)) yields values from β = 0.98 ± 0.05 to β = 1.15 ± 0.08 depending on the parameterization of the cosmic SFH. A two-component model, where SNR_Ia is dependent on stellar mass (M_stellar) and star formation rate (SFR) as SNR_(Ia)(z) = A × M_(stellar)(z) + B × SFR(z), yields the coefficients A = (1.9 ± 0.1) × 10^(–1)4 SNe yr^(–1) M^(–1)_☉ and B = (3.3 ± 0.2) × 10^(–4) SNe yr^(–1) (M_☉ yr^(–1))^(–1). More general two-component models also fit the data well, but single Gaussian or exponential DTDs provide significantly poorer matches. Finally, we split the SNLS sample into two populations by the light-curve width (stretch), and show that the general behavior in the rates of faster-declining SNe Ia (0.8 ≤ s < 1.0) is similar, within our measurement errors, to that of the slower objects (1.0 ≤ s < 1.3) out to z ~ 0.8

Supernova Constraints and Systematic Uncertainties from the First Three Years of the Supernova Legacy Survey

We combine high-redshift Type Ia supernovae from the first three years of the Supernova Legacy Survey (SNLS) with other supernova (SN) samples, primarily at lower redshifts, to form a high-quality joint sample of 472 SNe (123 low-z, 93 SDSS, 242 SNLS, and 14 Hubble Space Telescope). SN data alone require cosmic acceleration at >99.999% confidence, including systematic effects. For the dark energy equation of state parameter (assumed constant out to at least z = 1.4) in a flat universe, we find w = –0.91^(+0.16)_(–0.20)(stat)^(+0.07)_(–0.14)(sys) from SNe only, consistent with a cosmological constant. Our fits include a correction for the recently discovered relationship between host-galaxy mass and SN absolute brightness. We pay particular attention to systematic uncertainties, characterizing them using a systematic covariance matrix that incorporates the redshift dependence of these effects, as well as the shape-luminosity and color-luminosity relationships. Unlike previous work, we include the effects of systematic terms on the empirical light-curve models. The total systematic uncertainty is dominated by calibration terms. We describe how the systematic uncertainties can be reduced with soon to be available improved nearby and intermediate-redshift samples, particularly those calibrated onto USNO/SDSS-like systems

Pan-STARRS1 Discovery of Two Ultraluminous Supernovae at z ≈ 0.9

We present the discovery of two ultraluminous supernovae (SNe) at z ≈ 0.9 with the Pan-STARRS1 Medium Deep Survey. These SNe, PS1-10ky and PS1-10awh, are among the most luminous SNe ever discovered, comparable to the unusual transients SN 2005ap and SCP 06F6. Like SN 2005ap and SCP 06F6, they show characteristic high luminosities (M_(bol) ≈ –22.5 mag), blue spectra with a few broad absorption lines, and no evidence for H or He. We have constructed a full multi-color light curve sensitive to the peak of the spectral energy distribution in the rest-frame ultraviolet, and we have obtained time series spectroscopy for these SNe. Given the similarities between the SNe, we combine their light curves to estimate a total radiated energy over the course of explosion of (0.9-1.4) × 10^(51) erg. We find photospheric velocities of 12,000-19,000 km s^(–1) with no evidence for deceleration measured across ~3 rest-frame weeks around light curve peak, consistent with the expansion of an optically thick massive shell of material. We show that, consistent with findings for other ultraluminous SNe in this class, radioactive decay is not sufficient to power PS1-10ky, and we discuss two plausible origins for these events: the initial spin-down of a newborn magnetar in a core-collapse SN, or SN shock breakout from the dense circumstellar wind surrounding a Wolf-Rayet star

Supernova 2009kf: An Ultraviolet Bright Type IIP Supernova Discovered with Pan-STARRS 1 and GALEX

We present photometric and spectroscopic observations of a luminous Type IIP Supernova (SN) 2009kf discovered by the Pan-STARRS 1 (PS1) survey and also detected by the Galaxy Evolution Explorer. The SN shows a plateau in its optical and bolometric light curves, lasting approximately 70 days in the rest frame, with an absolute magnitude of M_V = -18.4 mag. The P-Cygni profiles of hydrogen indicate expansion velocities of 9000 km s^(-1) at 61 days after discovery which is extremely high for a Type IIP SN. SN 2009kf is also remarkably bright in the near-ultraviolet (NUV) and shows a slow evolution 10-20 days after optical discovery. The NUV and optical luminosity at these epochs can be modeled with a blackbody with a hot effective temperature (T ~ 16,000 K) and a large radius (R ~ 1 × 10^(15) cm). The bright bolometric and NUV luminosity, the light curve peak and plateau duration, the high velocities, and temperatures suggest that 2009kf is a Type IIP SN powered by a larger than normal explosion energy. Recently discovered high-z SNe (0.7 < z < 2.3) have been assumed to be IIn SNe, with the bright UV luminosities due to the interaction of SN ejecta with a dense circumstellar medium. UV-bright SNe similar to SN 2009kf could also account for these high-z events, and its absolute magnitude M_(NUV) = -21.5 ± 0.5 mag suggests such SNe could be discovered out to z ~ 2.5 in the PS1 survey

The Peculiar Velocities of Local Type Ia Supernovae and their Impact on Cosmology

We quantify the effect of supernova Type Ia peculiar velocities on the derivation of cosmological parameters. The published distant and local Ia SNe used for the Supernova Legacy Survey first-year cosmology report form the sample for this study. While previous work has assumed that the local SNe are at rest in the CMB frame (the No Flow assumption), we test this assumption by applying peculiar velocity corrections to the local SNe using three different flow models. The models are based on the IRAS PSCz galaxy redshift survey, have varying beta = Omega_m^0.6/b, and reproduce the Local Group motion in the CMB frame. These datasets are then fit for w, Omega_m, and Omega_Lambda using flatness or LambdaCDM and a BAO prior. The chi^2 statistic is used to examine the effect of the velocity corrections on the quality of the fits. The most favored model is the beta=0.5 model, which produces a fit significantly better than the No Flow assumption, consistent with previous peculiar velocity studies. By comparing the No Flow assumption with the favored models we derive the largest potential systematic error in w caused by ignoring peculiar velocities to be Delta w = +0.04. For Omega_Lambda, the potential error is Delta Omega_Lambda = -0.04 and for Omega_m, the potential error is Delta Omega_m < +0.01. The favored flow model (beta=0.5) produces the following cosmological parameters: w = -1.08 (+0.09,-0.08), Omega_m = 0.27 (+0.02,-0.02) assuming a flat cosmology, and Omega_Lambda = 0.80 (+0.08,-0.07) and Omega_m = 0.27 (+0.02,-0.02) for a w = -1 (LambdaCDM) cosmology.Comment: 4 pages, 2 figures, 1 table, accepted for publication in ApJ Letter

Hierarchical Subquery Evaluation for Active Learning on a Graph

To train good supervised and semi-supervised object classifiers, it is critical that we not waste the time of the human experts who are providing the training labels. Existing active learning strategies can have uneven performance, being efficient on some datasets but wasteful on others, or inconsistent just between runs on the same dataset. We propose perplexity based graph construction and a new hierarchical subquery evaluation algorithm to combat this variability, and to release the potential of Expected Error Reduction. Under some specific circumstances, Expected Error Reduction has been one of the strongest-performing informativeness criteria for active learning. Until now, it has also been prohibitively costly to compute for sizeable datasets. We demonstrate our highly practical algorithm, comparing it to other active learning measures on classification datasets that vary in sparsity, dimensionality, and size. Our algorithm is consistent over multiple runs and achieves high accuracy, while querying the human expert for labels at a frequency that matches their desired time budget.Comment: CVPR 201

The influence of galaxy surface brightness on the mass-metallicity relation

We study the effect of surface brightness on the mass-metallicity relation using nearby galaxies whose gas content and metallicity profiles are available. Previous studies using fiber spectra indicated that lower surface brightness galaxies have systematically lower metallicity for their stellar mass, but the results were uncertain because of aperture effect. With stellar masses and surface brightnesses measured at WISE W1 and W2 bands, we re-investigate the surface brightness dependence with spatially-resolved metallicity profiles and find the similar result. We further demonstrate that the systematical difference cannot be explained by the gas content of galaxies. For two galaxies with similar stellar and gas masses, the one with lower surface brightness tends to have lower metallicity. Using chemical evolution models, we investigate the inflow and outflow properties of galaxies of different masses and surface brightnesses. We find that, on average, high mass galaxies have lower inflow and outflow rates relative to star formation rate. On the other hand, lower surface brightness galaxies experience stronger inflow than higher surface brightness galaxies of similar mass. The surface brightness effect is more significant for low mass galaxies. We discuss implications on the different inflow properties between low and high surface brightness galaxies, including star formation efficiency, environment and mass assembly history