Numerical counterparts of GRB host galaxies

We explore galaxy properties in general and properties of host galaxies of gamma-ray bursts (GRBs) in particular, using N-body/Eulerian hydrodynamic simulations and the stellar population synthesis model, Starburst99, to infer observable properties. We identify simulated galaxies that have optical star formation rate (SFR) and SFR-to-luminosity ratio similar to those observed in a well-defined sample of ten host galaxies. Each of the numerical counterparts are found in catalogs at the same redshifts as the observed hosts. The counterparts are found to be low-mass galaxies, with low mass-to-light ratio, recent epoch of formation, and high ratio between the SFR and the average of the SFR. When compared to the overall galaxy population, they have colors much bluer than the high-mass star-forming galaxy population. Although their SFRs span a range of values, the specific rates of the numerical counterparts are equal to or higher than the median values estimated at the different redshifts. We also emphasize the strong relationships between the specific star formation rate (SFR) and quantities known to reflect the star formation history of galaxies, i.e. color and mass-to-light ratio: At intermediate redshift, the faintest and bluest galaxies are also the objects with the highest specific rates. These results suggest that GRB host galaxies are likely to be drawn from the high specific SFR sub-population of galaxies, rather than the high SFR galaxy population. Finally, as indicated by our catalogs, in an extended sample, the majority of GRB host galaxies is expected to have specific SFRs higher than found in the magnitude-limited sample studied here.Comment: 11 pages, 11 figures. Accepted for publication in MNRA

Afterglow Light Curves and Broken Power Laws: A Statistical Study

In gamma-ray burst research it is quite common to fit the afterglow light curves with a broken power law to interpret the data. We apply this method to a computer simulated population of afterglows and find systematic differences between the known model parameters of the population and the ones derived from the power law fits. In general, the slope of the electron energy distribution is overestimated from the pre-break light curve slope while being underestimated from the post-break slope. We also find that the jet opening angle derived from the fits is overestimated in narrow jets and underestimated in wider ones. Results from fitting afterglow light curves with broken power laws must therefore be interpreted with caution since the uncertainties in the derived parameters might be larger than estimated from the fit. This may have implications for Hubble diagrams constructed using gamma-ray burst data.Comment: 4 pages, 5 figures, accepted for publication in ApJ Letter

Energy Injection in GRB Afterglow Models

We extend the standard fireball model, widely used to interpret gamma-ray burst (GRB) afterglow light curves, to include energy injections, and apply the model to the afterglow light curves of GRB 990510, GRB 000301C and GRB 010222. We show that discrete energy injections can cause temporal variations in the optical light curves and present fits to the light curves of GRB 000301C as an example. A continuous injection may be required to interpret other bursts such as GRB 010222. The extended model accounts reasonably well for the observations in all bands ranging from X-rays to radio wavelengths. In some cases, the radio light curves indicate that additional model ingredients may be needed.Comment: Accepted for publication in the Astrophysical Journa

Host Galaxies of Gamma-Ray Bursts and their Cosmological Evolution

We use numerical simulations of large scale structure formation to explore the cosmological properties of Gamma-Ray Burst (GRB) host galaxies. Among the different sub-populations found in the simulations, we identify the host galaxies as the most efficient star-forming objects, i.e. galaxies with high specific star formation rates. We find that the host candidates are low-mass, young galaxies with low to moderate star formation rate. These properties are consistent with those observed in GRB hosts, most of which are sub-luminous, blue galaxies. Assuming that host candidates are galaxies with high star formation rates would have given conclusions inconsistent with the observations. The specific star formation rate, given a galaxy mass, is shown to increase as the redshift increases. The low mass of the putative hosts makes them difficult to detect with present day telescopes and the probability density function of the specific star formation rate is predicted to change depending on whether or not these galaxies are observed.Comment: 11 pages, 10 figures. Accepted for publication in MNRA

Common and Rare Sequence Variants Influencing Tumor Biomarkers in Blood.

To access publisher's full text version of this article click on the hyperlink belowBackground: Alpha-fetoprotein (AFP), cancer antigens 15.3, 19.9, and 125, carcinoembryonic antigen, and alkaline phosphatase (ALP) are widely measured in attempts to detect cancer and to monitor treatment response. However, due to lack of sensitivity and specificity, their utility is debated. The serum levels of these markers are affected by a number of nonmalignant factors, including genotype. Thus, it may be possible to improve both sensitivity and specificity by adjusting test results for genetic effects. Methods: We performed genome-wide association studies of serum levels of AFP (N = 22,686), carcinoembryonic antigen (N = 22,309), cancer antigens 15.3 (N = 7,107), 19.9 (N = 9,945), and 125 (N = 9,824), and ALP (N = 162,774). We also examined the correlations between levels of these biomarkers and the presence of cancer, using data from a nationwide cancer registry. Results: We report a total of 84 associations of 79 sequence variants with levels of the six biomarkers, explaining between 2.3% and 42.3% of the phenotypic variance. Among the 79 variants, 22 are cis (in- or near the gene encoding the biomarker), 18 have minor allele frequency less than 1%, 31 are coding variants, and 7 are associated with gene expression in whole blood. We also find multiple conditions associated with higher biomarker levels. Conclusions: Our results provide insights into the genetic contribution to diversity in concentration of tumor biomarkers in blood. Impact: Genetic correction of biomarker values could improve prediction algorithms and decision-making based on these biomarkers.United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Institute of Dental & Craniofacial Research (NIDCR