642 research outputs found

    A Search for Cosmic Microwave Background Anisotropies on Arcminute Scales with Bolocam

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    We have surveyed two science fields totaling one square degree with Bolocam at 2.1 mm to search for secondary CMB anisotropies caused by the Sunyaev- Zel'dovich effect (SZE). The fields are in the Lynx and Subaru/XMM SDS1 fields. Our survey is sensitive to angular scales with an effective angular multipole of l_eff = 5700 with FWHM_l = 2800 and has an angular resolution of 60 arcseconds FWHM. Our data provide no evidence for anisotropy. We are able to constrain the level of total astronomical anisotropy, modeled as a flat bandpower in C_l, with frequentist 68%, 90%, and 95% CL upper limits of 590, 760, and 830 uKCMB^2. We statistically subtract the known contribution from primary CMB anisotropy, including cosmic variance, to obtain constraints on the SZE anisotropy contribution. Now including flux calibration uncertainty, our frequentist 68%, 90% and 95% CL upper limits on a flat bandpower in C_l are 690, 960, and 1000 uKCMB^2. When we instead employ the analytic spectrum suggested by Komatsu and Seljak (2002), and account for the non-Gaussianity of the SZE anisotropy signal, we obtain upper limits on the average amplitude of their spectrum weighted by our transfer function of 790, 1060, and 1080 uKCMB^2. We obtain a 90% CL upper limit on sigma8, which normalizes the power spectrum of density fluctuations, of 1.57. These are the first constraints on anisotropy and sigma8 from survey data at these angular scales at frequencies near 150 GHz.Comment: 68 pages, 17 figures, 2 tables, accepted for publication in Ap

    Spherical harmonic decomposition applied to spatial-temporal analysis of human high-density EEG

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    We demonstrate an application of spherical harmonic decomposition to analysis of the human electroencephalogram (EEG). We implement two methods and discuss issues specific to analysis of hemispherical, irregularly sampled data. Performance of the methods and spatial sampling requirements are quantified using simulated data. The analysis is applied to experimental EEG data, confirming earlier reports of an approximate frequency-wavenumber relationship in some bands.Comment: 12 pages, 8 figures, submitted to Phys. Rev. E, uses APS RevTeX style

    A Fluctuation Analysis of the Bolocam 1.1mm Lockman Hole Survey

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    We perform a fluctuation analysis of the 1.1mm Bolocam Lockman Hole Survey, which covers 324 square arcmin to a very uniform point source-filtered RMS noise level of 1.4 mJy/beam. The fluctuation analysis has the significant advantage of utilizing all of the available data. We constrain the number counts in the 1-10 mJy range, and derive significantly tighter constraints than in previous work: the power-law index is 2.7 (+0.18, -0.15), while the amplitude is equal to 1595 (+85,-238) sources per mJy per square degree, or N(>1 mJy) = 940 (+50,-140) sources/square degree (95% confidence). Our results agree extremely well with those derived from the extracted source number counts by Laurent et al (2005). Our derived normalization is about 2.5 times smaller than determined by MAMBO at 1.2mm by Greve et al (2004). However, the uncertainty in the normalization for both data sets is dominated by the systematic (i.e., absolute flux calibration) rather than statistical errors; within these uncertainties, our results are in agreement. We estimate that about 7% of the 1.1mm background has been resolved at 1 mJy.Comment: To appear in the Astrophysical Journal; 22 pages, 9 figure

    Using ordinal logistic regression to evaluate the performance of laser-Doppler predictions of burn-healing time

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    Background Laser-Doppler imaging (LDI) of cutaneous blood flow is beginning to be used by burn surgeons to predict the healing time of burn wounds; predicted healing time is used to determine wound treatment as either dressings or surgery. In this paper, we do a statistical analysis of the performance of the technique. Methods We used data from a study carried out by five burn centers: LDI was done once between days 2 to 5 post burn, and healing was assessed at both 14 days and 21 days post burn. Random-effects ordinal logistic regression and other models such as the continuation ratio model were used to model healing-time as a function of the LDI data, and of demographic and wound history variables. Statistical methods were also used to study the false-color palette, which enables the laser-Doppler imager to be used by clinicians as a decision-support tool. Results Overall performance is that diagnoses are over 90% correct. Related questions addressed were what was the best blood flow summary statistic and whether, given the blood flow measurements, demographic and observational variables had any additional predictive power (age, sex, race, % total body surface area burned (%TBSA), site and cause of burn, day of LDI scan, burn center). It was found that mean laser-Doppler flux over a wound area was the best statistic, and that, given the same mean flux, women recover slightly more slowly than men. Further, the likely degradation in predictive performance on moving to a patient group with larger %TBSA than those in the data sample was studied, and shown to be small. Conclusion Modeling healing time is a complex statistical problem, with random effects due to multiple burn areas per individual, and censoring caused by patients missing hospital visits and undergoing surgery. This analysis applies state-of-the art statistical methods such as the bootstrap and permutation tests to a medical problem of topical interest. New medical findings are that age and %TBSA are not important predictors of healing time when the LDI results are known, whereas gender does influence recovery time, even when blood flow is controlled for. The conclusion regarding the palette is that an optimum three-color palette can be chosen 'automatically', but the optimum choice of a 5-color palette cannot be made solely by optimizing the percentage of correct diagnoses

    Studies of atmospheric noise on Mauna Kea at 143 GHz with Bolocam

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    We report measurements of the fluctuations in atmospheric emission (atmospheric noise) above Mauna Kea recorded with Bolocam at 143 GHz. These data were collected in November and December of 2003 with Bolocam mounted on the Caltech Submillimeter Observatory (CSO), and span approximately 40 nights. Below ≃ 0.5 Hz, the data time-streams are dominated by the f-δ atmospheric noise in all observing conditions. We were able to successfully model the atmospheric fluctuations using a Kolmogorov-Taylor turbulence model for a thin wind-driven screen in approximately half of our data. Based on this modeling, we developed several algorithms to remove the atmospheric noise, and the best results were achieved when we described the fluctuations using a low-order polynomial in detector position over the 8 arcminute focal plane. However, even with these algorithms, we were not able to reach photon-background-limited instrument photometer (BLIP) performance at frequencies below ≃ 0.5 Hz in any observing conditions. Therefore, we conclude that BLIP performance is not possible from the CSO below ≃ 0.5 Hz for broadband 150 GHz receivers with subtraction of a spatial atmospheric template on scales of several arcminutes
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