Macroscopic dark matter refers to a variety of dark matter candidates that would be expected to (... more Macroscopic dark matter refers to a variety of dark matter candidates that would be expected to (elastically) scatter off of ordinary matter with a large geometric cross-section. A wide range of macro masses M_X and cross-sections σ_X remain unprobed. We show that over a wide region within the unexplored parameter space, collisions of a macro with a human body would result in serious injury or death. We use the absence of such unexplained impacts with a well-monitored subset of the human population to exclude a region bounded by σ_X ≥ 10^-8 - 10^-7 cm^2 and M_X < 50 kg. Our results open a new window on dark matter: the human body as a dark matter detector.
While the use of numerical general relativity for modeling astrophysical phenomena and compact ob... more While the use of numerical general relativity for modeling astrophysical phenomena and compact objects is commonplace, the application to cosmological scenarios is only just beginning. Here, we examine the expansion of a spacetime using the Baumgarte-Shapiro-Shibata-Nakamura formalism of numerical relativity in synchronous gauge. This work represents the first numerical cosmological study that is fully relativistic, nonlinear, and without symmetry. The universe that emerges exhibits an average Friedmann-Lemaître-Robertson-Walker (FLRW) behavior; however, this universe also exhibits locally inhomogeneous expansion beyond that expected in linear perturbation theory around a FLRW background.
Astrostatistical Challenges for the New Astronomy, 2012
We present a Bayesian hierarchical model for inferring the cosmological parameters from the super... more We present a Bayesian hierarchical model for inferring the cosmological parameters from the supernovae type Ia fitted with the SALT-II lightcurve fitter. We demonstrate with simulated data sets that our method delivers tighter statistical constraints on the cosmological parameters over 90% of the time, that it reduces statistical bias typically by a factor ~2–3 and that it has better coverage properties than the usual χ 2 approach. As a further benefit, a full posterior probability distribution for the dispersion of the intrinsic magnitude of SNe is obtained. We apply this method to recent SNIa data, and by combining them with CMB and BAO data we obtain Ωm = 0:28 ± 0:02, ΩΛ = 0:73 ± 0:01 (assuming ω = −1) and Ω m = 0:28 ± 0:01, ω = −0:90 ± 0:05 (assuming flatness; statistical uncertainties only). We constrain the intrinsic dispersion of the B-band magnitude of the SNIa population, obtaining \(\sigma _\mu ^{\text{int}} \) = 0:13 ± 0:01 [mag].
Macroscopic dark matter refers to a variety of dark matter candidates that would be expected to (... more Macroscopic dark matter refers to a variety of dark matter candidates that would be expected to (elastically) scatter off of ordinary matter with a large geometric cross-section. A wide range of macro masses M_X and cross-sections σ_X remain unprobed. We show that over a wide region within the unexplored parameter space, collisions of a macro with a human body would result in serious injury or death. We use the absence of such unexplained impacts with a well-monitored subset of the human population to exclude a region bounded by σ_X ≥ 10^-8 - 10^-7 cm^2 and M_X < 50 kg. Our results open a new window on dark matter: the human body as a dark matter detector.
While the use of numerical general relativity for modeling astrophysical phenomena and compact ob... more While the use of numerical general relativity for modeling astrophysical phenomena and compact objects is commonplace, the application to cosmological scenarios is only just beginning. Here, we examine the expansion of a spacetime using the Baumgarte-Shapiro-Shibata-Nakamura formalism of numerical relativity in synchronous gauge. This work represents the first numerical cosmological study that is fully relativistic, nonlinear, and without symmetry. The universe that emerges exhibits an average Friedmann-Lemaître-Robertson-Walker (FLRW) behavior; however, this universe also exhibits locally inhomogeneous expansion beyond that expected in linear perturbation theory around a FLRW background.
Astrostatistical Challenges for the New Astronomy, 2012
We present a Bayesian hierarchical model for inferring the cosmological parameters from the super... more We present a Bayesian hierarchical model for inferring the cosmological parameters from the supernovae type Ia fitted with the SALT-II lightcurve fitter. We demonstrate with simulated data sets that our method delivers tighter statistical constraints on the cosmological parameters over 90% of the time, that it reduces statistical bias typically by a factor ~2–3 and that it has better coverage properties than the usual χ 2 approach. As a further benefit, a full posterior probability distribution for the dispersion of the intrinsic magnitude of SNe is obtained. We apply this method to recent SNIa data, and by combining them with CMB and BAO data we obtain Ωm = 0:28 ± 0:02, ΩΛ = 0:73 ± 0:01 (assuming ω = −1) and Ω m = 0:28 ± 0:01, ω = −0:90 ± 0:05 (assuming flatness; statistical uncertainties only). We constrain the intrinsic dispersion of the B-band magnitude of the SNIa population, obtaining \(\sigma _\mu ^{\text{int}} \) = 0:13 ± 0:01 [mag].
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