First upper limit analysis and results from LIGO science data: stochastic background

I describe analysis of correlations in the outputs of the three LIGO interferometers from LIGO's first science run, held over 17 days in August and September of 2002, and the resulting upper limit set on a stochastic background of gravitational waves. By searching for cross-correlations between the LIGO detectors in Livingston, LA and Hanford, WA, we are able to set a 90% confidence level upper limit of h_{100}^2 Omega_0 < 23 +/- 4.6.Comment: 7 pages; 1 eps figures; proceeding from 2003 Edoardo Amaldi Meeting on Gravitational Wave

Measuring the Hausdorff Dimension of Quantum Mechanical Paths

We measure the propagator length in imaginary time quantum mechanics by Monte Carlo simulation on a lattice and extract the Hausdorff dimension $d_{H}$. We find that all local potentials fall into the same universality class giving $d_{H}=2$ like the free motion. A velocity dependent action ($S \propto \int dt \mid \vec{v} \mid^{\alpha}$) in the path integral (e.g. electrons moving in solids, or Brueckner's theory of nuclear matter) yields $d_{H}=\frac{\alpha }{\alpha - 1}$ if $\alpha > 2$ and $d_{H}=2$ if $\alpha \leq 2$. We discuss the relevance of fractal pathes in solid state physics and in $QFT$, in particular for the Wilson loop in $QCD$.Comment: uuencoded and compressed shell archive file. 8 pages with 7 figure

Training Custom Light Curve Models of SN Ia Sub-Populations Selected According to Host Galaxy Properties

Type Ia supernova (SN Ia) cosmology analyses include a luminosity step function in their distance standardization process to account for an observed yet unexplained difference in the post-standardization luminosities of SNe Ia originating from different host galaxy populations (e.g., high-mass ($M \gtrsim 10^{10} M_{\odot}$) versus low-mass galaxies). We present a novel method for including host-mass correlations in the SALT3 light curve model used for standardising SN Ia distances. We split the SALT3 training sample according to host-mass, training independent models for the low- and high-host-mass samples. Our models indicate that there are different average Si II spectral feature strengths between the two populations, and that the average SED of SNe from low-mass galaxies is bluer than the high-mass counterpart. We then use our trained models to perform a SN cosmology analysis on the 3-year spectroscopically confirmed Dark Energy Survey SN sample, treating SNe from low- and high-mass host galaxies as separate populations throughout. We find that our mass-split models reduce the Hubble residual scatter in the sample, albeit at a low statistical significance. We do find a reduction in the mass-correlated luminosity step but conclude that this arises from the model-dependent re-definition of the fiducial SN absolute magnitude rather than the models themselves. Our results stress the importance of adopting a standard definition of the SN parameters ($x_0, x_1, c$) in order to extract the most value out of the light curve modelling tools that are currently available and to correctly interpret results that are fit with different models.Comment: 15 pages, accepted by MNRA