Top-emitting 940-nm thin-film VCSELs transferred onto aluminum heatsinks

Thin-film vertical cavity surface emitting lasers (VCSELs) mounted onto heatsinks open up the way toward low-power consumption and high-power operation, enabling them to be widely used for energy saving high-speed optical data communication and three-dimensional sensor applications. There are two conventional VCSEL polarity structures: p-on-n and n-on-p polarity. The former is more preferably used owing to the reduced series resistance of n-type bottom distributed Bragg reflection (DBR) as well as the lower defect densities of n-type GaAs substrates. In this study, the p-on-n structures of thin-film VCSELs, including an etch stop layer and a highly n-doped GaAs ohmic layer, were epitaxially grown in upright order by using low-pressure metalorganic chemical vapor deposition (LP-MOCVD). The p-on-n structures of thin-film VCSELs were transferred onto an aluminum heatsink via a double-transfer technique, allowing the top-emitting thin-film VCSELs to keep the p-on-n polarity with the removal of the GaAs substrate. The threshold current (Ith) and voltage (Vth) of the fabricated top-emitting thin-film VCSELs were 1 mA and 2.8 V, respectively. The optical power was 7.7 mW at a rollover point of 16.1 mA.

Deep Learning for Photonic Design and Analysis: Principles and Applications

Innovative techniques play important roles in photonic structure design and complex optical data analysis. As a branch of machine learning, deep learning can automatically reveal the inherent connections behind the data by using hierarchically structured layers, which has found broad applications in photonics. In this paper, we review the recent advances of deep learning for the photonic structure design and optical data analysis, which is based on the two major learning paradigms of supervised learning and unsupervised learning. In addition, the optical neural networks with high parallelism and low energy consuming are also highlighted as novel computing architectures. The challenges and perspectives of this flourishing research field are discussed.

Information-statistical approach to inverse optical problem solution for 3D disperse systems with nano- and micro particles

Multiparameter analysis of simultaneous optical data for 3D disperse systems (consisted from nano- and/or microparticles of different nature) by information-statistical methods can help to estimate the share of different types of particles in mixtures. At the solution of inverse optical problem for unknown poly-component 3D DS, the comparison of measured parameters with the known ones from the set of mono-component 3D DS can help to identify the component content of the system under study. The approach was tested on the biomineral water mixtures of kaolin clay and bacterium coli bacillus with the help of the program based on the information-statistical theory. To solve the impurity optical recognition tasks, the Base of optical data for 3D DS is needed.

Late-time Evolution and Modeling of the Off-axis Gamma-Ray Burst Candidate FIRST J141918.9+394036

We present new radio and optical data, including very-long-baseline interferometry, as well as archival data analysis, for the luminous, decades-long radio transient FIRST J141918.9+394036. The radio data reveal a synchrotron self-absorption peak around 0.3 GHz and a radius of around 1.3 mas (0.5 pc) 26 yr post-discovery, indicating a blastwave energy ∼5 × 1050 erg. The optical spectrum shows a broad [O iii]λ4959,5007 emission line that may indicate collisional excitation in the host galaxy, but its association with the transient cannot be ruled out. The properties of the host galaxy are suggestive of a massive stellar progenitor that formed at low metallicity. Based on the radio light curve, blastwave velocity, energetics, nature of the host galaxy and transient rates, we find that the properties of J1419+3940 are most consistent with long gamma-ray burst (LGRB) afterglows. Other classes of (optically discovered) stellar explosions as well as neutron star mergers are disfavored, and invoking any exotic scenario may not be necessary. It is therefore likely that J1419+3940 is an off-axis LGRB afterglow (as suggested by Law et al. and Marcote et al.), and under this premise the inverse beaming fraction is found to be f b − 1 ≃ 280 − 200 + 700 , corresponding to an average jet half-opening angle < θ j > ≃ 5 − 2 + 4 degrees (68% confidence), consistent with previous estimates. From the volumetric rate we predict that surveys with the Very Large Array, Australian Square Kilometre Array Pathfinder, and MeerKAT will find a handful of J1419+3940-like events over the coming years.

Gamma-Ray Orbital Modulation of the Transitioning Millisecond Pulsar Binary XSS J12270–4859

We report on gamma-ray orbital modulation of the transitioning MSP binary XSS J12270–4859 detected in the Fermi Large Area Telescope (LAT) data. We use long-term optical data taken with the XMM-Newton OM and the Swift UltraViolet Optical Telescope to inspect radio timing solutions that are limited to relatively short time intervals and find that extrapolation of the solutions aligns well with the phasing of the optical data over 15 yr. The Fermi-LAT data folded on the timing solutions exhibit significant modulation (p = 5 × 10−6) with a gamma-ray minimum at the inferior conjunction of the pulsar. Intriguingly, the source seems to show similar modulation in both the low-mass X-ray binary and the MSP states, implying that mechanisms for gamma-ray emission in the two states are similar. We discuss these findings and their implications using an intrabinary shock scenario.

Combining Sentinel-1 and Landsat 8 Does Not Improve Classification Accuracy of Tropical Selective Logging

Tropical forests play a key role in the global carbon and hydrological cycles, maintaining biological diversity, slowing climate change, and supporting the global economy and local livelihoods. Yet, rapidly growing populations are driving continued degradation of tropical forests to supply wood products. The United Nations (UN) has developed the Reducing Emissions from Deforestation and Forest Degradation (REDD+) programme to mitigate climate impacts and biodiversity losses through improved forest management. Consistent and reliable systems are still needed to monitor tropical forests at large scales, however, degradation has largely been left out of most REDD+ reporting given the lack of effective monitoring and countries mainly focus on deforestation. Recent advances in combining optical data and Synthetic Aperture Radar (SAR) data have shown promise for improved ability to monitor forest losses, but it remains unclear if similar improvements could be made in detecting and mapping forest degradation. We used detailed selective logging records from three lowland tropical forest regions in the Brazilian Amazon to test the effectiveness of combining Landsat 8 and Sentinel-1 for selective logging detection. We built Random Forest models to classify pixel-based differences in logged and unlogged regions to understand if combining optical and SAR improved the detection capabilities over optical data alone. We found that the classification accuracy of models utilizing optical data from Landsat 8 alone were slightly higher than models that combined Sentinel-1 and Landsat 8. In general, detection of selective logging was high with both optical only and optical-SAR combined models, but our results show that the optical data was dominating the predictive performance and adding SAR data introduced noise, lowering the detection of selective logging. While we have shown limited capabilities with C-band SAR, the anticipated opening of the ALOS-PALSAR archives and the anticipated launch of NISAR and BIOMASS in 2023 should stimulate research investigating similar methods to understand if longer wavelength SAR might improve classification of areas affected by selective logging when combined with optical data.

Examining the Integration of Landsat Operational Land Imager with Sentinel-1 and Vegetation Indices in Mapping Southern Yellow Pines (Loblolly, Shortleaf, and Virginia Pines)

The mapping of southern yellow pines (loblolly, shortleaf, and Virginia pines) is important to supporting forest inventory and the management of forest resources. The overall aim of this study was to examine the integration of Landsat Operational Land Imager (OLI ) optical data with Sentinel-1 microwave C-band satellite data and vegetation indices in mapping the canopy cover of southern yellow pines. Specifically, this study assessed the overall mapping accuracies of the canopy cover classification of southern yellow pines derived using four data-integration scenarios: Landsat OLI alone; Landsat OLI and Sentinel-1; Landsat OLI with vegetation indices derived from satellite data—normalized difference vegetation index, soil-adjusted vegetation index, modified soil-adjusted vegetation index, transformed soil-adjusted vegetation index, and infrared percentage vegetation index; and 4) Landsat OLI with Sentinel-1 and vegetation indices. The results showed that the integration of Landsat OLI reflectance bands with Sentinel-1 backscattering coefficients and vegetation indices yielded the best overall classification accuracy, about 77%, and standalone Landsat OLI the weakest accuracy, approximately 67%. The findings in this study demonstrate that the addition of backscattering coefficients from Sentinel-1 and vegetation indices positively contributed to the mapping of southern yellow pines.

Monitoring of Spectral Signatures of Maize Crop using Temporal SAR and Optical Remote Sensing data

A study was carried out using the temporal Sentinel-1B microwave data (June to November at 12 days interval) and Sentinel-2A/2B optical data (June to November) to discriminate the maize crop from other competing crops rice and cotton in Siddipet district, Telangana state, India during kharif, 2019 (June to November). The study utilized the data from multiple sources such as Multi-temporal VH backscatter intensity from Sentinel-1B SAR and NDVI values from Sentinel-2A/2B in combination with field data to discriminate the maize crop. Synchronous to satellite pass, ground truth data on crop parameters viz., crop stage, crop vigour, biomass, plant height, plant density, soil moisture, LAI and chlorophyll content were collected. Multi-temporal VH backscatter intensity and Normalized Difference Vegetation Index (NDVI) data were used to characterize backscatter and greenness behaviour of the maize crop. The backscatter intensity (dB) for maize crop ranged from -21.83 (the lowest backscatter values) at planting to -12.52 (the highest backscatter values) at peak growth stage. The NDVI values during vegetative and reproductive stages (August and September) were >0.6 and during senescence to harvesting the values were less than or equal to 0.52. The increase in backscatter intensity values from initial vegetative stage to peak stage was due to increased volume scattering of the maize crop canopy and a continuous decline in backscatter intensity values of VH band at maturity stage, was due to decrease in greenness and moisture content in leaves of the maize crop helped in maize crop discrimination from other dominant kharif crops in the study area.