41,101 research outputs found

    The Halo Occupation Distribution of SDSS Quasars

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    We present an estimate of the projected two-point correlation function (2PCF) of quasars in the Sloan Digital Sky Survey (SDSS) over the full range of one- and two-halo scales, 0.02-120 Mpc/h. This was achieved by combining data from SDSS DR7 on large scales and Hennawi et al. (2006; with appropriate statistical corrections) on small scales. Our combined clustering sample is the largest spectroscopic quasar clustering sample to date, containing ~48,000 quasars in the redshift range 0.4<z<2.5 with median redshift 1.4. We interpret these precise 2PCF measurements within the halo occupation distribution (HOD) framework and constrain the occupation functions of central and satellite quasars in dark matter halos. In order to explain the small-scale clustering, the HOD modeling requires that a small fraction of z~1.4 quasars, fsat=(7.4+/-1.4) 10^(-4), be satellites in dark matter halos. At z~1.4, the median masses of the host halos of central and satellite quasars are constrained to be Mcen=(4.1+0.3/-0.4) 10^12 Msun/h and Msat=(3.6+0.8/-1.0) 10^14 Msun/h, respectively. To investigate the redshift evolution of the quasar-halo relationship, we also perform HOD modeling of the projected 2PCF measured by Shen et al. (2007) for SDSS quasars with median redshift 3.2. We find tentative evidence for an increase in the mass scale of quasar host halos---the inferred median mass of halos hosting central quasars at z~3.2 is Mcen=(14.1+5.8/-6.9) 10^12 Msun/h. The cutoff profiles of the mean occupation functions of central quasars reveal that quasar luminosity is more tightly correlated with halo mass at higher redshifts. The average quasar duty cycle around the median host halo mass is inferred to be fq=(7.3+0.6/-1.5) 10^(-4) at z~1.4 and fq=(8.6+20.4/-7.2) 10^(-2) at z~3.2. We discuss the implications of our results for quasar evolution and quasar-galaxy co-evolution.Comment: matches the ApJ published versio

    Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control

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    Constrained optimization of high-dimensional numerical problems plays an important role in many scientific and industrial applications. Function evaluations in many industrial applications are severely limited and no analytical information about objective function and constraint functions is available. For such expensive black-box optimization tasks, the constraint optimization algorithm COBRA was proposed, making use of RBF surrogate modeling for both the objective and the constraint functions. COBRA has shown remarkable success in solving reliably complex benchmark problems in less than 500 function evaluations. Unfortunately, COBRA requires careful adjustment of parameters in order to do so. In this work we present a new self-adjusting algorithm SACOBRA, which is based on COBRA and capable to achieve high-quality results with very few function evaluations and no parameter tuning. It is shown with the help of performance profiles on a set of benchmark problems (G-problems, MOPTA08) that SACOBRA consistently outperforms any COBRA algorithm with fixed parameter setting. We analyze the importance of the several new elements in SACOBRA and find that each element of SACOBRA plays a role to boost up the overall optimization performance. We discuss the reasons behind and get in this way a better understanding of high-quality RBF surrogate modeling

    Imaging starspot evolution on Kepler target KIC 5110407 using light curve inversion

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    The Kepler target KIC 5110407, a K-type star, shows strong quasi-periodic light curve fluctuations likely arising from the formation and decay of spots on the stellar surface rotating with a period of 3.4693 days. Using an established light-curve inversion algorithm, we study the evolution of the surface features based on Kepler space telescope light curves over a period of two years (with a gap of .25 years). At virtually all epochs, we detect at least one large spot group on the surface causing a 1-10% flux modulation in the Kepler passband. By identifying and tracking spot groups over a range of inferred latitudes, we measured the surface differential rotation to be much smaller than that found for the Sun. We also searched for a correlation between the seventeen stellar flares that occurred during our observations and the orientation of the dominant surface spot at the time of each flare. No statistically-significant correlation was found except perhaps for the very brightest flares, suggesting most flares are associated with regions devoid of spots or spots too small to be clearly discerned using our reconstruction technique. While we may see hints of long-term changes in the spot characteristics and flare statistics within our current dataset, a longer baseline of observation will be needed to detect the existence of a magnetic cycle in KIC 5110407.Comment: 32 pages, 15 figures, accepted to Ap

    Experimental design trade-offs for gene regulatory network inference: an in silico study of the yeast Saccharomyces cerevisiae cell cycle

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    Time-series of high throughput gene sequencing data intended for gene regulatory network (GRN) inference are often short due to the high costs of sampling cell systems. Moreover, experimentalists lack a set of quantitative guidelines that prescribe the minimal number of samples required to infer a reliable GRN model. We study the temporal resolution of data vs quality of GRN inference in order to ultimately overcome this deficit. The evolution of a Markovian jump process model for the Ras/cAMP/PKA pathway of proteins and metabolites in the G1 phase of the Saccharomyces cerevisiae cell cycle is sampled at a number of different rates. For each time-series we infer a linear regression model of the GRN using the LASSO method. The inferred network topology is evaluated in terms of the area under the precision-recall curve AUPR. By plotting the AUPR against the number of samples, we show that the trade-off has a, roughly speaking, sigmoid shape. An optimal number of samples corresponds to values on the ridge of the sigmoid
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