Rahul Biswas

Improved cosmological constraints from a joint analysis of the SDSS-II and SNLS supernova samples

Aims. We present cosmological constraints from a joint analysis of type Ia supernova (SN Ia) observations obtained by the SDSS-II and SNLS collaborations. The dataset includes several low-redshift samples (z< 0.1), all three seasons from the SDSS-II (0.05 <z< 0.4), and three years from SNLS (0.2 <z< 1), and it totals 740 spectroscopically confirmed type Ia supernovae with high-quality light curves. Methods. We followed the methods and assumptions of the SNLS three-year data analysis except for the following important improvements: 1) the addition of the full SDSS-II spectroscopically-confirmed SN Ia sample in both the training of the SALT2 light-curve model and in the Hubble diagram analysis (374 SNe); 2) intercalibration of the SNLS and SDSS surveys and reduced systematic uncertainties in the photometric calibration, performed blindly with respect to the cosmology analysis; and 3) a thorough investigation of systematic errors associated with the SALT2 modeling of SN Ia light curves. Results. We produce recalibrated SN Ia light curves and associated distances for the SDSS-II and SNLS samples. The large SDSS-II sample provides an effective, independent, low-z anchor for the Hubble diagram and reduces the systematic error from calibration systematics in the low-z SN sample. For a flat ΛCDM cosmology, we find Ωm =0.295 ± 0.034 (stat+sys), a value consistent with the most recent cosmic microwave background (CMB) measurement from the Planck and WMAP experiments. Our result is 1.8σ (stat+sys) different than the previously published result of SNLS three-year data. The change is due primarily to improvements in the SNLS photometric calibration. When combined with CMB constraints, we measure a constant dark-energy equation of state parameter w =−1.018 ± 0.057 (stat+sys) for a flat universe. Adding baryon acoustic oscillation distance measurements gives similar constraints: w =−1.027 ± 0.055. Our supernova measurements provide the most stringent constraints to date on the nature of dark energy.

Cosmology with photometrically classified type Ia supernovae from the SDSS-II supernova survey

We present the cosmological analysis of 752 photometrically–classified Type Ia Supernovae (SNe Ia) obtained from the full Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey, supplemented with host–galaxy spectroscopy from the SDSS-III Baryon Oscillation Spectroscopic Survey (BOSS). Our photometric–classification method is based on the SN typing technique of Sako et al. (2011), aided by host galaxy redshifts (0.05 < z < 0.55). SNANA simulations of our methodology estimate that we have a SN Ia typing efficiency of 70.8%, with only 3.9% contamination f rom core-collapse (non-Ia) SNe. We demonstrate that this level of contamination has no effect on our cosmological constraints. We quantify and correct for our selection effects (e.g., Malmquist bias) using simulations. When fitting

Voids as a precision probe of dark energy

The shapes of cosmic voids, as measured in spectroscopic galaxy redshift surveys, constitute a promising new probe of dark energy (DE). We forecast constraints on the DE equation of state and its variation from current and future surveys and find that the promise of void shape measurements compares favorably to that of standard methods such as supernovae and cluster counts even for currently available data. Owing to the complementary nature of the constraints, void shape measurements improve the Dark Energy Task Force figure of merit by 2 orders of magnitude for a future large scale experiment such as EUCLID when combined with other probes of dark energy available on a similar time scale. Modeling several observational and theoretical systematics has only moderate effects on these forecasts. We discuss additional systematics which will require further study using simulations.

The taming of closed time-like curves

We consider a 1,d/2 orbifold, where 2 acts by time and space reversal, also known as the embedding space of the elliptic de Sitter space. The background has two potentially dangerous problems: time-nonorientability and the existence of closed time-like curves. We first show that closed causal curves disappear after a proper definition of the time function. We then consider the one-loop vacuum expectation value of the stress tensor. A naive QFT analysis yields a divergent result. We then analyze the stress tensor in bosonic string theory, and find the same result as if the target space would be just the Minkowski space 1,d, suggesting a zero result for the superstring. This leads us to propose a proper reformulation of QFT, and recalculate the stress tensor. We find almost the same result as in Minkowski space, except for a potential divergence at the initial time slice of the orbifold, analogous to a spacelike Big Bang singularity. Finally, we argue that it is possible to define local S-matrices, even if the spacetime is globally time-nonorientable.

Cosmological Parameter Uncertainties from SALT-II Type Ia Supernova Light Curve Models

We use simulated type Ia supernova (SN Ia) samples, including both photometry and spectra, to perform the first direct validation of cosmology analysis using the SALT-II light curve model. This validation includes residuals from the light curve training process, systematic biases in SN Ia distance measurements, and a bias on the dark energy equation of state parameter w. Using the SN-analysis package SNANA, we simulate and analyze realistic samples corresponding to the data samples used in the SNLS3 analysis: ∼120 low-redshift (z < 0.1) SNe Ia, ∼255 Sloan Digital Sky Survey SNe Ia (z < 0.4), and ∼290 SNLS SNe Ia (z ≤ 1). To probe systematic uncertainties in detail, we vary the input spectral model, the model of intrinsic scatter, and the smoothing (i.e., regularization) parameters used during the SALT-II model training. Using realistic intrinsic scatter models results in a slight bias in the ultraviolet portion of the trained SALT-II model, and w biases (w {sub input} – w {sub recovered}) ranging from –0.005 ± 0.012 to –0.024 ± 0.010. These biases are indistinguishable from each other within the uncertainty; the average bias on w is –0.014 ± 0.007.

Large-scale structure formation with massive neutrinos and dynamical dark energy

Over the next decade, cosmological measurements of the large-scale structure of the Universe will be sensitive to the combined effects of dynamical dark energy and massive neutrinos. The matter power spectrum is a key repository of this information. We extend higher-order perturbative methods for computing the power spectrum to investigate these effects over quasilinear scales. Through comparison with N-body simulations, we establish the regime of validity of a time-renormalization group perturbative treatment that includes dynamical dark energy and massive neutrinos. We also quantify the accuracy of standard, renormalized and Lagrangian resummation (LPT) perturbation theories without massive neutrinos. We find that an approximation that neglects neutrino clustering as a source for nonlinear matter clustering predicts the baryon acoustic oscillation (BAO) peak position to 0.25% accuracy for redshifts

The reliability of the Akaike information criterion method in cosmological model selection

1\ensuremath{\le}z\ensuremath{\le}3

The reliability of the AIC method in Cosmological Model Selection

, justifying the use of LPT for BAO reconstruction in upcoming surveys. We release a modified version of the public Copter code which includes the additional physics discussed in the paper.

THE MIRA–TITAN UNIVERSE: PRECISION PREDICTIONS FOR DARK ENERGY SURVEYS

The Akaike information criterion (AIC) has been used as a statistical criterion to compare the appropriateness of different dark energy candidate models underlying a particular data set. Under suitable conditions, the AIC is an indirect estimate of the Kullback-Leibler divergence D(T//A) of a candidate model A with respect to the truth T. Thus, a dark energy model with a smaller AIC is ranked as a better model, since it has a smaller Kullback-Leibler discrepancy with T. In this paper, we explore the impact of statistical errors in estimating the AIC during model comparison. Using a parametric bootstrap technique, we study the distribution of AIC differences between a set of candidate models due to different realizations of noise in the data and show that the shape and spread of this distribution can be quite varied. We also study the rate of success of the AIC procedure for different values of a threshold parameter popularly used in the literature. For plausible choices of true dark energy models, our studies suggest that investigating such distributions of AIC differences in addition to the threshold is useful in correctly interpreting comparisons of dark energy models using the AIC technique.

Type Ia supernovae selection and forecast of cosmology constraints for the Dark Energy Survey

The Akaike information criterion (AIC) has been used as a statistical criterion to compare the appropriateness of different dark energy candidate models underlying a particular data set. Under suitable conditions, the AIC is an indirect estimate of the Kullback-Leibler divergence D(T//A) of a candidate model A with respect to the truth T. Thus, a dark energy model with a smaller AIC is ranked as a better model, since it has a smaller Kullback-Leibler discrepancy with T. In this paper, we explore the impact of statistical errors in estimating the AIC during model comparison. Using a parametric bootstrap technique, we study the distribution of AIC differences between a set of candidate models due to different realizations of noise in the data and show that the shape and spread of this distribution can be quite varied. We also study the rate of success of the AIC procedure for different values of a threshold parameter popularly used in the literature. For plausible choices of true dark energy models, our studies suggest that investigating such distributions of AIC differences in addition to the threshold is useful in correctly interpreting comparisons of dark energy models using the AIC technique.