Adaptive processing with signal contaminated training samples

We consider the adaptive beamforming or adaptive detection problem in the case of signal contaminated training samples, i.e., when the latter may contain a signal-like component. Since this results in a significant degradation of the signal to interference and noise ratio at the output of the adaptive filter, we investigate a scheme to jointly detect the contaminated samples and subsequently take this information into account for estimation of the disturbance covariance matrix. Towards this end, a Bayesian model is proposed, parameterized by binary variables indicating the presence/absence of signal-like components in the training samples. These variables, together with the signal amplitudes and the disturbance covariance matrix are jointly estimated using a minimum mean-square error (MMSE) approach. Two strategies are proposed to implement the MMSE estimator. First, a stochastic Markov Chain Monte Carlo method is presented based on Gibbs sampling. Then a computationally more efficient scheme based on variational Bayesian analysis is proposed. Numerical simulations attest to the improvement achieved by this method compared to conventional methods such as diagonal loading. A successful application to real radar data is also presented

Near term measurements with 21 cm intensity mapping: neutral hydrogen fraction and BAO at z<2

It is shown that 21 cm intensity mapping could be used in the near term to make cosmologically useful measurements. Large scale structure could be detected using existing radio telescopes, or using prototypes for dedicated redshift survey telescopes. This would provide a measure of the mean neutral hydrogen density, using redshift space distortions to break the degeneracy with the linear bias. We find that with only 200 hours of observing time on the Green Bank Telescope, the neutral hydrogen density could be measured to 25% precision at redshift 0.54<z<1.09. This compares favourably to current measurements, uses independent techniques, and would settle the controversy over an important parameter which impacts galaxy formation studies. In addition, a 4000 hour survey would allow for the detection of baryon acoustic oscillations, giving a cosmological distance measure at 3.5% precision. These observation time requirements could be greatly reduced with the construction of multiple pixel receivers. Similar results are possible using prototypes for dedicated cylindrical telescopes on month time scales, or SKA pathfinder aperture arrays on day time scales. Such measurements promise to improve our understanding of these quantities while beating a path for future generations of hydrogen surveys.Comment: 6 pages, 5 figures. Submitted to Phys. Rev. D. Addressed reviewer comments. Changed figure format, added more detailed technical discussion, and added forecasts for aperture arrays. Added references

Pulsar State Switching from Markov Transitions and Stochastic Resonance

Markov processes are shown to be consistent with metastable states seen in pulsar phenomena, including intensity nulling, pulse-shape mode changes, subpulse drift rates, spindown rates, and X-ray emission, based on the typically broad and monotonic distributions of state lifetimes. Markovianity implies a nonlinear magnetospheric system in which state changes occur stochastically, corresponding to transitions between local minima in an effective potential. State durations (though not transition times) are thus largely decoupled from the characteristic time scales of various magnetospheric processes. Dyadic states are common but some objects show at least four states with some transitions forbidden. Another case is the long-term intermittent pulsar B1931+24 that has binary radio-emission and torque states with wide, but non-monotonic duration distributions. It also shows a quasi-period of $38\pm5$ days in a 13-yr time sequence, suggesting stochastic resonance in a Markov system with a forcing function that could be strictly periodic or quasi-periodic. Nonlinear phenomena are associated with time-dependent activity in the acceleration region near each magnetic polar cap. The polar-cap diode is altered by feedback from the outer magnetosphere and by return currents from an equatorial disk that may also cause the neutron star to episodically charge and discharge. Orbital perturbations in the disk provide a natural periodicity for the forcing function in the stochastic resonance interpretation of B1931+24. Disk dynamics may introduce additional time scales in observed phenomena. Future work can test the Markov interpretation, identify which pulsar types have a propensity for state changes, and clarify the role of selection effects.Comment: 25 pages, 6 figures, submitted to the Astrophysical Journa

Reconstruction of lensing from the cosmic microwave background polarization

Gravitational lensing of the cosmic microwave background (CMB) polarization field has been recognized as a potentially valuable probe of the cosmological density field. We apply likelihood-based techniques to the problem of lensing of CMB polarization and show that if the B-mode polarization is mapped, then likelihood-based techniques allow significantly better lensing reconstruction than is possible using the previous quadratic estimator approach. With this method the ultimate limit to lensing reconstruction is not set by the lensed CMB power spectrum. Second-order corrections are known to produce a curl component of the lensing deflection field that cannot be described by a potential; we show that this does not significantly affect the reconstruction at noise levels greater than 0.25 microK arcmin. The reduction of the mean squared error in the lensing reconstruction relative to the quadratic method can be as much as a factor of two at noise levels of 1.4 microK arcmin to a factor of ten at 0.25 microK arcmin, depending on the angular scale of interest.Comment: matches PRD accepted version. 28 pages, 8 fig

Quantifying and containing the curse of high resolution coronal imaging

Future missions such as Solar Orbiter (SO), InterHelioprobe, or Solar Probe aim at approaching the Sun closer than ever before, with on board some high resolution imagers (HRI) having a subsecond cadence and a pixel area of about $(80km)^2$ at the Sun during perihelion. In order to guarantee their scientific success, it is necessary to evaluate if the photon counts available at these resolution and cadence will provide a sufficient signal-to-noise ratio (SNR). We perform a first step in this direction by analyzing and characterizing the spatial intermittency of Quiet Sun images thanks to a multifractal analysis. We identify the parameters that specify the scale-invariance behavior. This identification allows next to select a family of multifractal processes, namely the Compound Poisson Cascades, that can synthesize artificial images having some of the scale-invariance properties observed on the recorded images. The prevalence of self-similarity in Quiet Sun coronal images makes it relevant to study the ratio between the SNR present at SoHO/EIT images and in coarsened images. SoHO/EIT images thus play the role of 'high resolution' images, whereas the 'low-resolution' coarsened images are rebinned so as to simulate a smaller angular resolution and/or a larger distance to the Sun. For a fixed difference in angular resolution and in Spacecraft-Sun distance, we determine the proportion of pixels having a SNR preserved at high resolution given a particular increase in effective area. If scale-invariance continues to prevail at smaller scales, the conclusion reached with SoHO/EIT images can be transposed to the situation where the resolution is increased from SoHO/EIT to SO/HRI resolution at perihelion.Comment: 25 pages, 1 table, 7 figure

The Farthest Known Supernova: Support for an Accelerating Universe and a Glimpse of the Epoch of Deceleration

We present photometric observations of an apparent Type Ia supernova (SN Ia) at a redshift of ~1.7, the farthest SN observed to date. SN 1997ff, was discovered in a repeat observation by the HST of the HDF-), and serendipitously monitored with NICMOS on HST throughout the GTO campaign. The SN type can be determined from the host galaxy type:an evolved, red elliptical lacking enough recent star formation to provide a significant population of core-collapse SNe. The class- ification is further supported by diagnostics available from the observed colors and temporal behavior of the SN, both of which match a typical SN Ia. The photo- metric record of the SN includes a dozen flux measurements in the I, J, and H bands spanning 35 days in the observed frame. The redshift derived from the SN photometry, z=1.7+/-0.1, is in excellent agreement with the redshift estimate of z=1.65+/-0.15 derived from the U_300,B_450,V_606,I_814,J_110,J_125,H_160, H_165,K_s photometry of the galaxy. Optical and near-infrared spectra of the host provide a very tentative spectroscopic redshift of 1.755. Fits to observations of the SN provide constraints for the redshift-distance relation of SNe~Ia and a powerful test of the current accelerating Universe hypothesis. The apparent SN brightness is consistent with that expected in the decelerating phase of the preferred cosmological model, Omega_M~1/3, Omega_Lambda~2/3. It is inconsistent with grey dust or simple luminosity evolution, candidate astro- physical effects which could mimic past evidence for an accelerating Universe from SNe Ia at z~0.5.We consider several sources of possible systematic error including lensing, SN misclassification, selection bias, and calibration errors. Currently, none of these effects appears likely to challenge our conclusions.Comment: Accepted to the Astrophysical Journal 38 pages, 15 figures, Pretty version available at http://icarus.stsci.edu/~stefano/ariess.tar.g

Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates

This paper presents a novel approach for indoor acoustic source localization using microphone arrays and based on a Convolutional Neural Network (CNN). The proposed solution is, to the best of our knowledge, the first published work in which the CNN is designed to directly estimate the three dimensional position of an acoustic source, using the raw audio signal as the input information avoiding the use of hand crafted audio features. Given the limited amount of available localization data, we propose in this paper a training strategy based on two steps. We first train our network using semi-synthetic data, generated from close talk speech recordings, and where we simulate the time delays and distortion suffered in the signal that propagates from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results show that this strategy is able to produce networks that significantly improve existing localization methods based on \textit{SRP-PHAT} strategies. In addition, our experiments show that our CNN method exhibits better resistance against varying gender of the speaker and different window sizes compared with the other methods.Comment: 18 pages, 3 figures, 8 table