For submillimeter spectroscopy with ground-based single-dish telescopes, removing noise contribution from the Earths atmosphere and the instrument is essential. For this purpose, here we propose a new method based on a data-scientific approach. The k
ey technique is statistical matrix decomposition that automatically separates the signals of astronomical emission lines from the drift noise components in the fast-sampled (1--10 Hz) time-series spectra obtained by a position-switching (PSW) observation. Because the proposed method does not apply subtraction between two sets of noisy data (i.e., on-source and off-source spectra), it improves the observation sensitivity by a factor of $sqrt{2}$. It also reduces artificial signals such as baseline ripples on a spectrum, which may also help to improve the effective sensitivity. We demonstrate this improvement by using the spectroscopic data of emission lines toward a high-redshift galaxy observed with a 2-mm receiver on the 50-m Large Millimeter Telescope (LMT). Since the proposed method is carried out offline and no additional measurements are required, it offers an instant improvement on the spectra reduced so far with the conventional method. It also enables efficient deep spectroscopy driven by the future 50-m class large submillimeter single-dish telescopes, where fast PSW observations by mechanical antenna or mirror drive are difficult to achieve.
Simultaneous X-ray and optical observations of black hole X-ray binaries have shown that the light curves contain multiple correlated and anti-correlated variation components when the objects are in the hard state. In the case of the black hole X-ray
binary, GX 339-4, the cross correlation function (CCF) of the light curves suggests a positive correlation with an optical lag of 0.15 s and anti-correlations with an optical lag of 1 s and X-ray lag of 4 s. This indicates the two light curves have some common signal components with different delays. In this study, we extracted and reconstructed those signal components from the data for GX 339-4. The results confirmed that correlation and anti-correlation with the optical lag are two common components. However, we found that the reconstructed light curve for the anti-correlated component indicates a positively correlated variation with an X-ray lag of ~ +1 s. In addition, the CCF for this signal component shows anti-correlations not only with the optical lag, but also with the X-ray lag, which is consistent with the CCF for the data. Therefore, our results suggest that the combination of the two positively correlated components, that is, the X-ray preceding signal with the 0.15-s optical lag and the optical preceding signal with the 1-s X-ray lag, can make the observed CCF without anti-correlated signals. The optical preceding signal may be caused by synchrotron emission in a magnetically dominated accretion flow or in a jet, while further study is required to understand the mechanism of the X-ray time lag.
We propose an image reconstruction method for an X-ray telescope system with an angular resolution booster proposed by Maeda et al.(2018). The system consists of double multi-grid masks in front of an X-ray mirror and an off-focused two-dimensional i
mager. Because the obtained image is off-focused, additional image reconstruction process is assumed to be included. Our image reconstruction method is an extension of the traditional Richardson-Lucy algorithm with two regularization terms, one for sparseness and the other for smoothness. Such a combination is desirable for astronomical imaging because astronomical objects have variety in shape from point sources, diffuse sources to mixtures of them. The performance of the system is demonstrated with simulated data for point sources and diffused X-ray sources such as Cas A and Crab Nebula. The image resolution is improved from a few arcmin of focused image without the booster to a few arcsec with the booster. Through the demonstration, the angular resolution booster with the image reconstruction method is shown to be feasible.
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 informatio
n. 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.
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 framewor
k 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.
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.
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.
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 c
onstraints, 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.
