Efficient and Noise Robust Photon-Counting Imaging with First Signal Photon Unit Method

Efficient photon-counting imaging in low signal photon level is challenging, especially when noise is intensive. In this paper, we report a first signal photon unit (FSPU) method to rapidly reconstruct depth image from sparse signal photon counts with strong noise robustness. The method consists of acquisition strategy and reconstruction strategy. Different statistic properties of signal and noise are exploited to quickly distinguish signal unit during acquisition. Three steps, including maximum likelihood estimation (MLE), anomaly censorship and total variation (TV) regularization, are implemented to recover high quality images. Simulations demonstrate that the method performs much better than traditional photon-counting methods such as peak and cross-correlation methods, and it also has better performance than the state-of-the-art unmixing method. In addition, it could reconstruct much clearer images than the first photon imaging (FPI) method when noise is severe. An experiment with our photon-counting LIDAR system was conducted, which indicates that our method has advantages in sparse photon-counting imaging application, especially when signal to noise ratio (SNR) is low. Without the knowledge of noise distribution, our method reconstructed the clearest depth image which has the least mean square error (MSE) as 0.011, even when SNR is as low as −10.85 dB.

Electromagnetic response to high-frequency gravitational waves having additional polarization states: distinguishing and probing tensor-mode, vector-mode and scalar-mode gravitons

Gravitational waves (GWs) from extra dimensions, very early universe, and some high-energy astrophysical processes might have at most six polarization states: tensor- and nontensor-mode gravitons. The peak regions or partial peak regions (of the amplitudes or energy densities) of some of such GWs are just distributed in the GHz or higher frequency band, which would be an optimal frequency band for the electromagnetic (EM) response to such high-frequency GWs (HFGWs). In this paper we investigate the EM response to the HFGWs, and for the first time we obtain the concrete form of analytic solutions of the perturbative EM fields caused by all six possible polarizations of the HFGWs in the background stable EM fields and in the proposed three dimensional synchro-resonance system (3DSR system), respectively. It is found that all such six polarizations may in principle show separability and detectability. Moreover, the detection frequency band ($$\sim 10^{8}$$ ∼ 10 8 to $$10^{12}$$ 10 12 Hz or higher) of the signal photon fluxes by the 3DSR system and the observation frequency range ($$\sim 7\times 10^{7}$$ ∼ 7 × 10 7 to $$3\times 10^{9}$$ 3 × 10 9 Hz) of the signals by the FAST (Five-hundred-meter Aperture Spherical Telescope, China) have a certain overlapping property, and thus their coincidence experiments in the future for observations will have high complementarity.

Taming the Delayed Choice Quantum Eraser

I discuss the delayed choice quantum eraser experiment by drawing an analogy to a Bell-type measurement and giving a straightforward account in standard quantum mechanics. The delayed choice quantum eraser experiment turns out to resemble a Bell-type scenario in which the resolution of the paradox is rather trivial, and so there really is no mystery. At first glance, the experiment suggests that measurements on one part of an entangled photon pair (the idler) can be employed to control whether the measurement outcome of the other part of the photon pair (the signal) produces interference fringes at a screen after being sent through a double slit. Significantly, the choice whether there is interference or not can be made long after the signal photon encounters the screen. The results of the experiment have been alleged to invoke some sort of backwards in time influence. I argue that this issue can be eliminated by taking into proper account the role of the signal photon. Likewise, in the de Broglie–Bohm picture the trajectories of the particle can be given a well-defined description at any instant of time during the experiment. Thus, it is again clear that there is no need to resort to any kind of backwards in time influence.Quanta 2019; 8: 44-56.

Photon-Counting Lidar: An Adaptive Signal Detection Method for Different Land Cover Types in Coastal Areas

Airborne or space-borne photon-counting lidar can provide successive photon clouds of the Earth’s surface. The distribution and density of signal photons are very different because different land cover types have different surface profiles and reflectance, especially in coastal areas where the land cover types are various and complex. A new adaptive signal photon detection method is proposed to extract the signal photons for different land cover types from the raw photons captured by the MABEL (Multiple Altimeter Beam Experimental Lidar) photon-counting lidar in coastal areas. First, the surface types with 30 m resolution are obtained via matching the geographic coordinates of the MABEL trajectory with the NLCD (National Land Cover Database) datasets. Second, in each along-track segment with a specific land cover type, an improved DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm with adaptive thresholds and a JONSWAP (Joint North Sea Wave Project) wave algorithm is proposed and integrated to detect signal photons on different surface types. The result in Pamlico Sound indicates that this new method can effectively detect signal photons and successfully eliminate noise photons below the water level, whereas the MABEL result failed to extract the signal photons in vegetation segments and failed to discard the after-pulsing noise photons. In the Atlantic Ocean and Pamlico Sound, the errors of the RMS (Root Mean Square) wave height between our result and in-situ result are −0.06 m and 0.00 m, respectively. However, between the MABEL and in-situ result, the errors are −0.44 m and −0.37 m, respectively. The mean vegetation height between the East Lake and Pamlico Sound was also calculated as 15.17 m using the detecting signal photons from our method, which agrees well with the results (15.56 m) from the GFCH (Global Forest Canopy Height) dataset. Overall, for different land cover types in coastal areas, our study indicates that the proposed method can significantly improve the performance of the signal photon detection for photon-counting lidar data, and the detected signal photons can further obtain the water levels and vegetation heights. The proposed approach can also be extended for ICESat-2 (Ice, Cloud, and land Elevation Satellite-2) datasets in the future.

Towards Non-Degenerate Quantum Lithography

The photonic de Broglie wavelength of a non-degenerate entangled photon pair is measured by using a Young’s double slit interferometer, which proves that the non-degenerate entangled photon pairs have the potential to be used in quantum lithography. Experimental results show that the de Broglie wavelength of non-degenerate biphotons is well defined and its wavelength is neither the wavelength of the signal photon, nor the wavelength of the idler photon. According to the de Broglie equation, its wavelength corresponds to the momentum of the biphoton, which equals the sum of the momenta of signal and idler photons. The non-degenerate ghost interference/diffraction is also observed in these experiments.

Singular Spectrum Analysis: A Note on Data Processing for Fourier Transform Hyperspectral Imagers

Hyperspectral remote sensing is experiencing a dazzling proliferation of new sensors, platforms, systems, and applications with the introduction of novel, low-cost, low-weight sensors. Curiously, relatively little development is now occurring in the use of Fourier transform (FT) systems, which have the potential to operate at extremely high throughput without use of a slit or reductions in both spatial and spectral resolution that thin film based mosaic sensors introduce. This study introduces a new physics-based analytical framework called singular spectrum analysis (SSA) to process raw hyperspectral imagery collected with FT imagers that addresses some of the data processing issues associated with the use of the inverse FT. Synthetic interferogram data are analyzed using SSA, which adaptively decomposes the original synthetic interferogram into several independent components associated with the signal, photon and system noise, and the field illumination pattern.