Bayesian inferences of galaxy formation from the K-band luminosity and HI mass functions of galaxies: constraining star formation and feedback

We infer mechanisms of galaxy formation for a broad family of semi-analytic models (SAMs) constrained by the K-band luminosity function and HI mass function of local galaxies using tools of Bayesian analysis. Even with a broad search in parameter space the whole model family fails to match to constraining data. In the best fitting models, the star formation and feedback parameters in low-mass haloes are tightly constrained by the two data sets, and the analysis reveals several generic failures of models that similarly apply to other existing SAMs. First, based on the assumption that baryon accretion follows the dark matter accretion, large mass-loading factors are required for haloes with circular velocities lower than 200 km/s, and most of the wind mass must be expelled from the haloes. Second, assuming that the feedback is powered by Type-II supernovae with a Chabrier IMF, the outflow requires more than 25% of the available SN kinetic energy. Finally, the posterior predictive distributions for the star formation history are dramatically inconsistent with observations for masses similar to or smaller than the Milky-Way mass. The inferences suggest that the current model family is still missing some key physical processes that regulate the gas accretion and star formation in galaxies with masses below that of the Milky Way.Comment: 17 pages, 9 figures, 1 table, accepted for publication in MNRA

Bits from Photons: Oversampled Image Acquisition Using Binary Poisson Statistics

We study a new image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measurement of the local light intensity. To analyze its performance, we formulate the oversampled binary sensing scheme as a parameter estimation problem based on quantized Poisson statistics. We show that, with a single-photon quantization threshold and large oversampling factors, the Cram\'er-Rao lower bound (CRLB) of the estimation variance approaches that of an ideal unquantized sensor, that is, as if there were no quantization in the sensor measurements. Furthermore, the CRLB is shown to be asymptotically achievable by the maximum likelihood estimator (MLE). By showing that the log-likelihood function of our problem is concave, we guarantee the global optimality of iterative algorithms in finding the MLE. Numerical results on both synthetic data and images taken by a prototype sensor verify our theoretical analysis and demonstrate the effectiveness of our image reconstruction algorithm. They also suggest the potential application of the oversampled binary sensing scheme in high dynamic range photography

Phase Retrieval for Sparse Signals: Uniqueness Conditions

In a variety of fields, in particular those involving imaging and optics, we often measure signals whose phase is missing or has been irremediably distorted. Phase retrieval attempts the recovery of the phase information of a signal from the magnitude of its Fourier transform to enable the reconstruction of the original signal. A fundamental question then is: "Under which conditions can we uniquely recover the signal of interest from its measured magnitudes?" In this paper, we assume the measured signal to be sparse. This is a natural assumption in many applications, such as X-ray crystallography, speckle imaging and blind channel estimation. In this work, we derive a sufficient condition for the uniqueness of the solution of the phase retrieval (PR) problem for both discrete and continuous domains, and for one and multi-dimensional domains. More precisely, we show that there is a strong connection between PR and the turnpike problem, a classic combinatorial problem. We also prove that the existence of collisions in the autocorrelation function of the signal may preclude the uniqueness of the solution of PR. Then, assuming the absence of collisions, we prove that the solution is almost surely unique on 1-dimensional domains. Finally, we extend this result to multi-dimensional signals by solving a set of 1-dimensional problems. We show that the solution of the multi-dimensional problem is unique when the autocorrelation function has no collisions, significantly improving upon a previously known result.Comment: submitted to IEEE TI

On the origin of cold dark matter halo density profiles

N-body simulations predict that CDM halo-assembly occurs in two phases: 1) a fast accretion phase with a rapidly deepening potential well; and 2) a slow accretion phase characterised by a gentle addition of mass to the outer halo with little change in the inner potential well. We demonstrate, using one-dimensional simulations, that this two-phase accretion leads to CDM halos of the NFW form and provides physical insight into the properties of the mass accretion history that influence the final profile. Assuming that the velocities of CDM particles are effectively isotropised by fluctuations in the gravitational potential during the fast accretion phase, we show that gravitational collapse in this phase leads to an inner profile rho(r) ~ r^{-1}. Slow accretion onto an established potential well leads to an outer profile with rho(r) ~ r^{-3}. The concentration of a halo is determined by the fraction of mass that is accreted during the fast accretion phase. Using an ensemble of realistic mass accretion histories, we show that the model predictions of the dependence of halo concentration on halo formation time, and hence the dependence of halo concentration on halo mass, and the distribution of halo concentrations all match those found in cosmological N-body simulations. Using a simple analytic model that captures much of the important physics we show that the inner r^{-1} profile of CDM halos is a natural result of hierarchical mass assembly with a initial phase of rapid accretion.Comment: Accepted for publication in MNRAS, references added, 11 pages, 8 figure