arXiv subjects

Shiro Ikeda

Publications and source records attributed to Shiro Ikeda.

47 records · Page 3Linked to original sources

arXiv2017

Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope using Sparse Modeling

We propose a new imaging technique for radio and optical/infrared interferometry. The proposed technique reconstructs the image from the visibility amplitude and closure phase, which are standard data products of short-millimeter very long baseline interferometers such as the Event Horizon Telescope (EHT) and optical/infrared interferometers, by utilizing two regularization functions: the $\ell_1$-norm and total variation (TV) of the brightness distribution. In the proposed method, optimal regularization parameters, which represent the sparseness and effective spatial resolution of the image, are derived from data themselves using cross validation (CV). As an application of this technique, we present simulated observations of M87 with the EHT based on four physically motivated models. We confirm that $\ell_1$+TV regularization can achieve an optimal resolution of $\sim 20-30$% of the diffraction limit $λ/D_{\rm max}$, which is the nominal spatial resolution of a radio interferometer. With the proposed technique, the EHT can robustly and reasonably achieve super-resolution sufficient to clearly resolve the black hole shadow. These results make it promising for the EHT to provide an unprecedented view of the event-horizon-scale structure in the vicinity of the super-massive black hole in M87 and also the Galactic center Sgr A*.

astro-ph.IM↗

arXiv2016

Data Compression for the Tomo-e Gozen with Low-rank Matrix Approximation

Optical wide-field surveys with a high cadence are expected to create a new field of astronomy, so-called "movie astronomy," in the near future. The amount of data of the observations will be huge, and hence efficient data compression will be indispensable. Here we propose a low-rank matrix approximation with sparse matrix decomposition as a promising solution to reduce the data size effectively, while preserving sufficient scientific information. We apply one of the methods to the movie data obtained with the prototype model of the Tomo-e Gozen mounted on the 1.0-m Schmidt telescope of Kiso Observatory. Once the full-scale observation of the Tomo-e Gozen commences, it will generate ~30 TB of data per night. We demonstrate that the data are compressed by a factor of about 10 in size without losing transient events like optical short transient point-sources and meteors. The intensity of point sources can be recovered from the compressed data. The processing runs sufficiently fast, compared with the expected data-acquisition rate in the actual observing runs.

astro-ph.IM↗

arXiv2016

Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey

We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with Hyper Suprime-Cam (HSC) on the Subaru telescope. Our goal is to select real transient events accurately and in a timely manner out of a large number of false candidates, obtained with the standard difference-imaging method. We have developed the transient selector which is based on majority voting of three ML machines of AUC Boosting, Random Forest, and Deep Neural Network. We applied it to our observing runs of Subaru-HSC in 2015 May and August, and proved it to be efficient in selecting optical transients. The false positive rate was 1.0% at the true positive rate of 90% in the magnitude range of 22.0--25.0 mag for the former data. For the latter run, we successfully detected and reported ten candidates of supernovae within the same day as the observation. From these runs, we learned the following lessons: (1) the training using artificial objects is effective in filtering out false candidates, especially for faint objects, and (2) combination of ML by majority voting is advantageous.

astro-ph.IM↗

arXiv2016

Maximum-expectation matching under recourse

This paper addresses the problem of maximizing the expected size of a matching in the case of unreliable vertices and/or edges. The assumption is that upon failure, remaining vertices that have not been matched may be subject to a new assignment. This process may be repeated a given number of times, and the objective is to end with the overall maximum number of matched vertices. The origin of this problem is in kidney exchange programs, going on in several countries, where a vertex is an incompatible patient-donor pair; the objective is to match these pairs so as to maximize the number of served patients. A new scheme is proposed for matching rearrangement in case of failure, along with a prototype algorithm for computing the optimal expectation for the number of matched vertices. Computational experiments reveal the relevance and limitations of the algorithm, in general terms and for the kidney exchange application.

cs.DS↗

arXiv2016

PRECL: A new method for interferometry imaging from closure phase

For short-wavelength VLBI observations, it is difficult to measure the phase of the visibility function accurately. The closure phases are reliable measurements under this situation, though it is not sufficient to retrieve all of the phase information. We propose a new method, Phase Retrieval from Closure Phase (PRECL). PRECL estimates all the visibility phases only from the closure phases. Combining PRECL with a sparse modeling method we have already proposed, imaging process of VLBI does not rely on dirty image nor self-calibration. The proposed method is tested numerically and the results are promising.

astro-ph.IM↗

arXiv2015

Variable Selection for Modeling the Absolute Magnitude at Maximum of Type Ia Supernovae

We discuss what is an appropriate set of explanatory variables in order to predict the absolute magnitude at the maximum of Type Ia supernovae. In order to have a good prediction, the error for future data, which is called the "generalization error," should be small. We use cross-validation in order to control the generalization error and LASSO-type estimator in order to choose the set of variables. This approach can be used even in the case that the number of samples is smaller than the number of candidate variables. We studied the Berkeley supernova database with our approach. Candidates of the explanatory variables include normalized spectral data, variables about lines, and previously proposed flux-ratios, as well as the color and light-curve widths. As a result, we confirmed the past understanding about Type Ia supernova: i) The absolute magnitude at maximum depends on the color and light-curve width. ii) The light-curve width depends on the strength of Si II. Recent studies have suggested to add more variables in order to explain the absolute magnitude. However, our analysis does not support to add any other variables in order to have a better generalization error.

astro-ph.SR↗

arXiv2014

Super-resolution imaging with radio interferometer using sparse modeling

We propose a new technique to obtain super-resolution images with radio interferometer using sparse modeling. In standard radio interferometry, sampling of ($u$, $v$) is quite often incomplete and thus obtaining an image from observed visibilities becomes an underdetermined problem, and a technique so-called "zero-padding" is often used to fill up unsampled grids in ($u$, $v$) plane, resulting in image degradation by finite beam size as well as numerous side-lobes. In this paper we show that directly solving such an underdetermined problem based on sparse modeling (in this paper LASSO) avoids the above problems introduced by zero-padding, leading to super-resolution images in which structure finer than the standard beam size (diffraction limit) can be reproduced. We present results of one-dimensional and two-dimensional simulations of interferometric imaging, and discuss its implications to super-resolution imaging, particularly focusing on imaging of black hole shadows with millimeter VLBI.

astro-ph.IM↗

arXiv2014

Bin Mode Estimation Methods for Compton Camera Imaging

We study the image reconstruction problem of a Compton camera which consists of semiconductor detectors. The image reconstruction is formulated as a statistical estimation problem. We employ a bin-mode estimation (BME) and extend an existing framework to a Compton camera with multiple scatterers and absorbers. Two estimation algorithms are proposed: an accelerated EM algorithm for the maximum likelihood estimation (MLE) and a modified EM algorithm for the maximum a posteriori (MAP) estimation. Numerical simulations demonstrate the potential of the proposed methods.

astro-ph.IM↗

arXiv2011

Phase retrieval from single biomolecule diffraction pattern

In this paper, we propose the SPR (sparse phase retrieval) method, which is a new phase retrieval method for coherent x-ray diffraction imaging (CXDI). Conventional phase retrieval methods effectively solve the problem for high signal-to-noise ratio measurements, but would not be sufficient for single biomolecular imaging which is expected to be realized with femto-second x-ray free electron laser pulses. The SPR method is based on the Bayesian statistics. It does not need to set the object boundary constraint that is required by the commonly used hybrid input-output (HIO) method, instead a prior distribution is defined with an exponential distribution and used for the estimation. Simulation results demonstrate that the proposed method reconstructs the electron density under a noisy condition even some central pixels are masked.

physics.data-an↗

arXiv2011

An Introductory Review of Information Theory in the Context of Computational Neuroscience

This paper introduces several fundamental concepts in information theory from the perspective of their origins in engineering. Understanding such concepts is important in neuroscience for two reasons. Simply applying formulae from information theory without understanding the assumptions behind their definitions can lead to erroneous results and conclusions. Furthermore, this century will see a convergence of information theory and neuroscience; information theory will expand its foundations to incorporate more comprehensively biological processes thereby helping reveal how neuronal networks achieve their remarkable information processing abilities.

cs.IT↗

arXiv2011

Capacity and Modulations with Peak Power Constraint

A practical communication channel often suffers from constraints on input other than the average power, such as the peak power constraint. In order to compare achievable rates with different constellations as well as the channel capacity under such constraints, it is crucial to take these constraints into consideration properly. In this paper, we propose a direct approach to compare the achievable rates of practical input constellations and the capacity under such constraints. As an example, we study the discrete-time complex-valued additive white Gaussian noise (AWGN) channel and compare the capacity under the peak power constraint with the achievable rates of phase shift keying (PSK) and quadrature amplitude modulation (QAM) input constellations.

cs.IT↗