113,648 research outputs found

    Ionized Gas in Damped Lyman Alpha Protogalaxies: II. Comparison Between Models and the Kinematic Data

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    We test semi-analytic models for galaxy formation with accurate kinematic data of damped Lyman alpha protogalaxies (DLAs) presented in the companion paper I. The models envisage centrifugally supported exponential disks at the centers of dark matter halos which are filled with ionized gas undergoing radial infall to the disks. The halo masses are drawn from cross-section weighted mass distributions predicted by CDM cosmogonies, or by the null hypothesis (TF model) that the dark matter mass distribution has not evolved since z ~ 3. In our models, C IV absorption lines detected in DLAs arise in infalling ionized clouds while the low-ion absorption lines arise from neutral gas in the disks. Using Monte Carlo methods we find: (a) The CDM models are incompatible with the low-ion statistics at more than 99% confidence whereas some TF models cannot be excluded at more than 88% confidence. (b) Both CDM and TF models agree with the observed distribution of C IV velocity widths. (c) The CDM models generate differences between the mean velocities of C IV and low ion profiles in agreement with the data, while the TF model produces differences in the means that are too large. (d) Both CDM and TF models produce ratios of C IV to low-ion velocity widths that are too large. (e) Both CDM and TF models generate C IV versus low-ion cross-correlation functions incompatible with the data. While it is possible to select model parameters resulting in consistency with the data, the disk-halo configuration assumed in both cosmogonies still does not produce significant overlap in velocity space between C IV low-ion velocity profiles. We conjecture that including angular momentum of the infalling clouds will increase the overlap between C IV and low-ion profiles.Comment: 18 pages, 12 Figures, Accepted for publication in the Dec. 20 issue of the Astrophysical Journa

    Can the jet steepen the light curves of GRB afterglow?

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    Beaming of relativistic ejecta in GRBs has been postulated by many authors in order to reduce the total GRB energy, thus it is very important to look for the observational evidence of beaming. Rhoads (1999) has pointed out that the dynamics of the blast wave, which is formed when the beamed ejecta sweeping the external medium, will be significantly modified by the sideways expansion due to the increased swept up matter. He claimed that shortly after the bulk Lorentz factor (Γ\Gamma ) of the blast wave drops below the inverse of the initial opening angle (θ0\theta_{0}) of the beamed ejecta, there will be a sharp break in the afterglow light curves. However, some other authors have performed numerical calculations and shown that the break of the light curve is weaker and much smoother than the one analytically predicted. In this paper we reanalyse the dynamical evolution of the jet blast wave, calculate the jet emission analytically, we find that the sharp break predicted by Rhoads will actually not exist, and for most cases the afterglow light curve will almost not be affected by sideways expansion unless the beaming angle is extremely small. We demonstrate that only when θ0<0.1\theta_{0}<0.1, the afterglow light curves may be steepened by sideways expansion, and in fact there cannot be two breaks as claimed before. We have also constructed a simple numerical code to verify our conclusion.Comment: 12 pages, 2 figures, accepted by ApJ, added numerical calculation

    How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

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    Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

    Enrollment in YFV Vaccine Trial: An Evaluation of Recruitment Outcomes Associated with a Randomized Controlled Double-Blind Trial of a Live Attenuated Yellow Fever Vaccine

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    This investigation evaluated several factors associated with diverse participant enrollment of a clinical trial assessing safety, immunogenicity, and comparative viremia associated with administration of 17-D live, attenuated yellow fever vaccine given alone or in combination with human immune globulin. We obtained baseline participant information (e.g., sociodemographic, medical) and followed recruitment outcomes from 2005 to 2007. Of 355 potential Yellow Fever vaccine study participants, 231 cases were analyzed. Strong interest in study participation was observed among racial and ethnically diverse persons with 36.34% eligible following initial study screening, resulting in 18.75% enrollment. The percentage of white participants increased from 63.66% (prescreened sample) to 81.25% (enrollment group). The regression model was significant with white race as a predictor of enrollment (OR=2.744, 95% CI=1.415-5.320, p=0.003).In addition, persons were more likely to enroll via direct outreach and referral mechanisms compared to mass advertising (OR=2.433, 95% CI=1.102-5.369). The findings indicate that racially diverse populations can be recruited to vaccine clinical trials, yet actual enrollment may not reflect that diversit
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