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We present a novel multimodal neural network for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a convolutional neural network branch for learning morphological features from $r$-band images with an artificial neural network branch for extracting spectral energy distribution (SED) information. Specifically, we have used 9-band optical ($ugri$) and NIR ($ZYHJK_s$) data from the Kilo-Degree Survey (KiDS) Data Release 5. The two branches of the network are concatenated and feed into fully-connected layers for final classification. We train the network on a spectroscopically confirmed sample from the Sloan Digital Sky Survey cross-matched with KiDS. The trained model achieves 98.76\% overall accuracy on an independent testing dataset, with F1 scores exceeding 95\% for each class. Raising the output probability threshold, we obtain higher purity at the cost of a lower completeness. We have also validated the network using external catalogs cross-matched with KiDS, correctly classifying 99.74\% of a pure star sample selected from Gaia parallaxes and proper motions, and 99.74\% of an external galaxy sample from the Galaxy and Mass Assembly survey, adjusted for low-redshift contamination. We apply the trained network to 27,334,751 KiDS DR5 sources with $r \leqslant 23$ mag to generate a new classification catalog. This multimodal neural network successfully leverages both morphological and SED information to enable efficient and robust classification of stars, quasars, and galaxies in large photometric surveys., Comment: 18 pages, 12 figures, 2 tables, Submitted to ApJS

Advanced silicon photonic technologies enable integrated optical sensing and communication (IOSAC) in real time for the emerging application requirements of simultaneous sensing and communication for next-generation networks. Here, we propose and demonstrate the IOSAC system on the silicon nitride (SiN) photonics platform. The IOSAC devices based on microring resonators are capable of monitoring the variation of analytes, transmitting the information to the terminal along with the modulated optical signal in real-time, and replacing bulk optics in high-precision and high-speed applications. By directly integrating SiN ring resonators with optical communication networks, simultaneous sensing and optical communication are demonstrated by an optical signal transmission experimental system using especially filtering amplified spontaneous emission spectra. The refractive index (RI) sensing ring with a sensitivity of 172 nm/RIU, a figure of merit (FOM) of 1220, and a detection limit (DL) of 8.2*10-6 RIU is demonstrated. Simultaneously, the 1.25 Gbps optical on-off-keying (OOK) signal is transmitted at the concentration of different NaCl solutions, which indicates the bit-error-ratio (BER) decreases with the increase in concentration. The novel IOSAC technology shows the potential to realize high-performance simultaneous biosensing and communication in real time and further accelerate the development of IoT and 6G networks., Comment: 11pages, 5 figutres

Gravitational wave signal from the inspiral of stellar-mass binary black hole can be used as standard sirens to perform cosmological inference. This inspiral covers a wide range of frequency bands, from the millihertz band to the audio-band, allowing for detections by both space-borne and ground-based gravitational wave detectors. In this work, we conduct a comprehensive study on the ability to constrain the Hubble constant using the dark standard sirens, or gravitational wave events that lack electromagnetic counterparts. To acquire the redshift information, we weight the galaxies within the localization error box with photometric information from several bands and use them as a proxy for the binary black hole redshift. We discover that TianQin is expected to constrain the Hubble constant to a precision of roughly $30\%$ through detections of $10$ gravitational wave events; in the most optimistic case, the Hubble constant can be constrained to a precision of $< 10 \%$, assuming TianQin I+II. In the optimistic case, the multi-detector network of TianQin and LISA is capable of constraining the Hubble constant to within $5\%$ precision. It is worth highlighting that the multi-band network of TianQin and Einstein Telescope is capable of constraining the Hubble constant to a precision of about $1\%$. We conclude that inferring the Hubble constant without bias from photo-z galaxy catalog is achievable, and we also demonstrate self-consistency using the P$-$P plot. On the other hand, high-quality spectroscopic redshift information is crucial for improving the estimation precision of Hubble constant., Comment: 29 pages, 14 figures, update to match published version

Recently, Generative Adversarial Networks (GANs)} have been widely used for portrait image generation. However, in the latent space learned by GANs, different attributes, such as pose, shape, and texture style, are generally entangled, making the explicit control of specific attributes difficult. To address this issue, we propose a SofGAN image generator to decouple the latent space of portraits into two subspaces: a geometry space and a texture space. The latent codes sampled from the two subspaces are fed to two network branches separately, one to generate the 3D geometry of portraits with canonical pose, and the other to generate textures. The aligned 3D geometries also come with semantic part segmentation, encoded as a semantic occupancy field (SOF). The SOF allows the rendering of consistent 2D semantic segmentation maps at arbitrary views, which are then fused with the generated texture maps and stylized to a portrait photo using our semantic instance-wise (SIW) module. Through extensive experiments, we show that our system can generate high quality portrait images with independently controllable geometry and texture attributes. The method also generalizes well in various applications such as appearance-consistent facial animation and dynamic styling., Comment: Project page: https://apchenstu.github.io/sofgan/ Code: https://github.com/apchenstu/sofgan

Sculpturing desired shapes in single crystal diamond is ever more crucial in the realization of complex devices for nanophotonics, quantum computing, and quantum optics. The crystallographic orientation dependent wet etch of single crystalline silicon in potassium hydroxide (KOH) allows a range of shapes formed and has significant impacts on MEMS (microelectromechanical systems), AFM (atomic force microscopy), and microfluidics. Here, a crystal direction dependent dry etching principle in an inductively-coupled plasma reactive ion etcher is presented, which allows to selectively reveal desired crystal planes in monocrystalline diamond by controlling the etching conditions. The principle is demonstrated when the kinetic energy of incident ions on diamond surfaces is reduced below a certain threshold leading to anisotropic etching and faceting along specific crystal planes. Using the principle, monolithic diamond nanopillars for magnetometry using nitrogen vacancy centers are fabricated. In these nanopillars, a half-tapering angle up to 21{\deg} is achieved, the highest angle reported, which leads to a high photon efficiency and high mechanical strength of the nanopillar. These results represent the first demonstration of crystallographic orientation dependent reactive ion etch principle, which opens a new window for shaping specific nanostructures which is at the heart of nanotechnology. It is believed that this principle will prove to be valuable for structuring and patterning of other single crystal materials as well., Comment: This is pre-print version. Submission version for Advanced materials publication http://doi.wiley.com/10.1002/adma.201705501