High Spectral Resolution Research Articles

Design and First Tests From Room Temperature to 200 mK of a 16-to-1 CMOS Multiplexing ASIC for High Impedance NbSi TESs

AbstractAchieving high spectral and spatial resolution of wide astrophysical objects in the X-ray band will be the main focus of the future X-ray space telescopes. We explore a new technological solution based on high impedance NbSi TES detectors ($$\sim $$ ∼ 2 M$$\Omega $$ Ω ) enabling the transfer of the pre-amplification stage to higher temperatures (4 K) and the use of a 50 mK CMOS time-division multiplexer to reduce power dissipation at 50 mK. We present the design and first tests, down to 200 mK, of this CMOS ASIC eventually able to work down to 50 mK and multiplexing 16 high impedance NbSi TES detectors to one 4 K amplifier with a total power budget under 2 $$\mu $$ μ W. In parallel of this development we fabricated 4-by-4 NbSi pixel matrices to build a complete demonstrator (comprising the detector array, presented in another paper to be published, the multiplexing ASIC and the 4 K amplification stage). We aim at a multiplexing frame time of 48 $$\mu $$ μ s leaving 3 $$\mu $$ μ s for reading-out each high impedance pixel. The multiplexing ASIC embeds parasitic capacity compensation techniques.

Fine-structure changing collisions in 87Rb upon D2 excitation in the hyperfine Paschen-Back regime

Abstract We investigate fine structure changing collisions in 87Rb vapour upon D2 excitation in a thermal vapour at 75 ∘C; the atoms are placed in a 0.6 T axial magnetic field in order to gain access to the hyperfine Pashen-Back regime. Following optical excitation on the D2 line, the exothermic transfer 5P 3 / 2 → 5P 1 / 2 occurs as a consequence of buffer-gas collisions; the 87Rb subsequently emits a photon on the D1 transition. We employ single-photon counting apparatus to monitor the D1 fluorescence, with an etalon filter to provide high spectral resolution. By studying the D1 fluorescence when the D2 excitation laser is scanned, we see that during the collisional transfer process the m J ′ quantum number of the atom changes, but the nuclear spin projection quantum number, m I ′ , is conserved. A simple kinematic model incorporating a coefficient of restitution in the collision accounted for the change in velocity distribution of atoms undergoing collisions, and the resulting fluorescence lineshape. The experiment is conducted with a nominally ‘buffer-gas free’ vapour cell; our results show that fine structure changing collisions are important with such media, and point out possible implications for quantum-optics experiments in thermal vapours producing entangled photon pairs with the double ladder configuration, and solar physics magneto-optical filters.

Application of Hyperspectral Image for Monitoring in Coastal Area with Deep Learning: A Case Study of Green Algae on Artificial Structure

Remote sensing is a powerful technique for classifying and quantifying objects. However, the elaborate classification of objects in coastal waters with complex structures is still challenging due to the high possibility of class mixing. The classification through the hyperspectral images can be a reasonable alternative to problems related to such precise classification work because it has high spectral resolution over a wide bandwidth. This study introduced the results of the case study using a novel method to classify green algae on an artificial structure based on hyperspectral data and deep-learning models. The spectral characteristics of the attached green algae on the artificial structure were observed using a ground-based hyperspectral camera. The observed image had a total of three classes (concrete, dense green algae, and sparse green algae). A certain area of the image was used as learning data to create classification models for three classes. The classification models were created from one machine-learning (support vector machine, SVM) and two deep-learning models (convolutional neural network, CNN; and dense convolutional network, DenseNet). As a result, the performance for the classification results of green algae predicted from two deep-learning models was higher than that of the machine-learning model. Additionally, the deep-learning model successfully classified the interface area between concrete and green algae. This study suggests that the combination of hyperspectral data and deep learning could enable more precise classification of objects in coastal areas.

On the theory of spectral compression-assisted optical temporal differentiation

Bandwidth limitation represents a significant factor that degrades the performance of optical devices. The dimensions, composition and configuration of optical devices impose intrinsic constraints on processing broadband optical pulse signals. The enhancement of the response bandwidth of optical devices represents a significant challenge. In this study, we put forward the theory of self-similar spectral compression (SSSC), which involves solving the nonlinear Schrödinger equation with variable coefficients by using the Taylor expansion and residual theorem. The spectral waveform can be precisely preserved in the process of SSSC, leading to a predictable compression factor without pedestals. To demonstrate the effectiveness of the proposed SSSC, we present a case study by designing an on-chip optical time-domain differentiator (OTD) system including a silicon-based tapered spiral waveguide. A 200-fs chirped pulse is well differentiated at multiple orders in the OTD system. Although the linear loss of spiral waveguide has a detrimental impact on SSSC, the broadband spectrum can still be self-similarly compressed, leading to a reduction of differentiation deviation of 22.5 times. The proposed SSSC theory offers valuable guidance for designing all-optical signal processing systems with high spectral resolution and low signal error.

Classification of Hyperspectral Images of Explosive Fragments Based on Spatial–Spectral Combination

The identification and recovery of explosive fragments can provide a reference for the evaluation of explosive power and the design of explosion-proof measures. At present, fragment detection usually uses a few bands in the visible light or infrared bands for imaging, without fully utilizing multi-band spectral information. Hyperspectral imaging has high spectral resolution and can provide multidimensional reference information for the fragments to be classified. Therefore, this article proposed a spatial–spectral joint method for explosive fragment classification by combining hyperspectral imaging technology. In a laboratory environment, this article collected hyperspectral images of explosion fragments scattered in simulated scenes. In order to extract effective features from redundant spectral information and improve classification accuracy, this paper adopted a classification framework based on deep learning. This framework used a convolutional neural network–bidirectional long short-term memory network (CNN-BiLSTM) as the spectral information classification model and a U-shaped network (U-Net) as the spatial segmentation model. The experimental results showed that the overall accuracy exceeds 95.2%. The analysis results indicated that the method of spatial–spectral combination can accurately identify explosive fragment targets. It validated the feasibility of using hyperspectral imaging for explosive fragment classification in laboratory environments. Due to the complex environment of the actual explosion site, this study still needs to be validated in outdoor environments. Our next step is to use airborne hyperspectral imaging to identify explosive fragments in outdoor environments.

The role of refractive indices in measuring mineral dust with high-spectral-resolution infrared satellite sounders: application to the Gobi Desert

Abstract. Mineral dust significantly influences the Earth's climate system by affecting the radiative balance through the emission, absorption and scattering of solar and terrestrial radiation. Estimating the dust radiative effect remains challenging due to the lack of detailed information on the physical and chemical properties of dust. High-spectral-resolution instruments in the infrared (IR) spectrum, such as the Infrared Atmospheric Sounding Instrument (IASI), have demonstrated the ability to quantify these aerosol properties. A crucial parameter for characterizing mineral dust from space is the complex refractive index (CRI), as it links the dust's physical and chemical properties to its optical properties. This paper examines the impact of six prior laboratory CRI datasets to improve the characterization of dust microphysical properties using IASI. The CRIs include older measurements obtained through the classical pellet method, commonly employed in mineral dust applications, as well as newer datasets that incorporate the latest advancements in laboratory measurement techniques for aerosol generation. These datasets are tested on IASI measurements during a dust storm event over the Gobi Desert in May 2017. We evaluate the sensitivity of IASI to different CRI datasets using the Atmospheric Radiation Algorithm for High-Spectral Resolution Measurements from Infrared Spectrometer (ARAHMIS) radiative transfer algorithm and explore the datasets' impact on retrieving size distribution parameters by mapping their spatial distributions. The results indicate that the laboratory CRI datasets decrease the total error in the covariance matrix by 30 %. In addition, we assess the ability to accurately reconstruct IASI detections and the extent to which we can retrieve the microphysical properties of dust particles. The choice of CRI significantly impacts the accuracy of dust detection and characterization from satellite observations. Notably, datasets that incorporate recent aerosol generation techniques with higher spectral resolution and samples from the case study region show improved compatibility with IASI observations. The outcomes of this research emphasize two key points: the crucial link between chemical composition of dust and its optical properties and the importance of considering the specific composition of the CRI dataset for improved retrieval of the microphysical parameters. Furthermore, this study highlights the critical role of ongoing enhancements in CRI measurement approaches, as well as the potential of high-spectral-resolution infrared sounders for aerosol atmospheric investigation and for understanding the radiative impacts of sounders.

The VIRUS-dE Survey. I. Stars in Dwarf Elliptical Galaxies—3D Dynamics and Radially Resolved Stellar Initial Mass Functions

We analyze the stellar structure of a sample of dwarf ellipticals (dEs) inhabiting various environments within the Virgo cluster. Integral-field observations with a high spectral resolution allow us to robustly determine their low-velocity dispersions (∼25 km s−1) and higher-order kinematic moments out to the half-light radius. We find the dEs exhibit a diversity in ages, with the younger dEs being less enhanced than the older, suggesting a complex star formation history for those dEs that recently entered Virgo, while others have been quenched shortly after reionization. Orbit-superposition modeling allowed us to recover viewing angles, stellar mass-to-light ratios (with gradients), as well as the intrinsic orbit structure. We find that the angular momentum of the dEs is strongly suppressed compared to ordinary early-type galaxies and correlates with the environment. Flattened dEs are so because of a suppressed kinetic energy perpendicular to their equatorial plane. Combining population and dynamical modeling results, we find an age-dependent stellar initial mass function or, alternatively, evidence for a more extended star formation history for those galaxies that have had higher initial mass and/or inhabited lower-density environments. dEs appear to have a spatially homogeneous stellar structure, but the state they were “frozen” in as they stopped forming stars varies dramatically according to their initial conditions.

All-dielectric transmissive narrow-band filters adjustable within wide spectral range

The challenge of obtaining transmission spectra with both a wider tunable spectral range and high resolution persists. In this paper, a novel all- dielectric transmissive grating structure based on Mie resonance is proposed. The structure excites the Mie resonance by embedding a high refractive index contrast grating in a KF-Si film system band-stop filter. The dipole resonance energy is confined within the high refractive index silicon grating material, expanding the cutoff bandwidth of the KF-Si film system to over 400 nm and achieving single-peak transmission in the visible range. And the continuous transmission spectrum has an adjustable range of 300 nm with the highest spectral resolution (FWHM) (∼20 nm) can be achieved by adjusting the grating period. To suppress the coupling energy excitation between the higher-order magnetic dipole and the substrate, a silicon-rich nitride (SRN) matching layer is introduced between the KF-Si film system and the substrate. This layer enhances the intensity of the resonance peaks while simultaneously suppressing the short-wavelength sidebands, thereby improving the saturation and purity of the spectrum. In addition, we obtained a large angular tolerance of up to 15° by stacking the silicon film system. This work is of great significance for the advancement of hyperspectral imaging and display technology.

SpectraTrack: megapixel, hundred-fps, and thousand-channel hyperspectral imaging

Hyperspectral imaging (HSI) finds broad applications in various fields due to its substantial optical signatures for the intrinsic identification of physical and chemical characteristics. However, it faces the inherent challenge of balancing spatial, temporal, and spectral resolution due to limited bandwidth. Here we present SpectraTrack, a computational HSI scheme that simultaneously achieves high spatial, temporal, and spectral resolution in the visible-to-near-infrared (VIS-NIR) spectral range. We deeply investigated the spatio-temporal-spectral multiplexing principle inherent in HSI videos. Based on this theoretical foundation, the SpectraTrack system uses two cameras including a line-scan imaging spectrometer for temporal-multiplexed hyperspectral data and an auxiliary RGB camera to capture motion flow. The motion flow guides hyperspectral reconstruction by reintegrating the scanned spectra into a 4D video. The SpectraTrack system can achieve around megapixel HSI at 100 fps with 1200 spectral channels, demonstrating its great application potential from drone-based anti-vibration video-rate HSI to high-throughput non-cooperative anti-spoofing.

GA-NIFS: JWST/NIRSpec IFS view of the z ∼ 3.5 galaxy GS5001 and its close environment at the core of a large-scale overdensity

We present JWST Near-Infrared Spectrograph (NIRSpec) observations in integral field spectroscopic (IFS) mode of the galaxy GS5001 at redshift z = 3.47, the central member of a candidate protocluster in the GOODS-S field. The data cover a field of view (FoV) of 4″ × 4″ (∼30 × 30 kpc2) and were obtained as part of the Galaxy Assembly with NIRSpec IFS (GA-NIFS) GTO programme. The observations include both high (R ∼ 2700) and low (R ∼ 100) spectral resolution data, spanning the rest-frame wavelength ranges 3700–6780 Å and 1300–11850 Å, respectively. These observations enable the detection and mapping of the main optical emission lines from [O II]λλ3726, 29 to [S III]λ9531. We analysed the spatially resolved ionised gas kinematics and interstellar medium properties, including obscuration, gas metallicity, excitation, ionisation parameter, and electron density. In addition to the main galaxy (GS5001), the NIRSpec FoV covers a close companion in the south, with three sub-structures with velocities blueshifted by ∼ − 150 km s−1 with respect to GS5001, and another source in the north redshifted by ∼200 km s−1. Optical line ratio diagnostics indicate star formation ionisation and electron densities of ∼500 cm−3 across all sources in the FoV. The gas-phase metallicity in the main galaxy is 12+log(O/H) = 8.45 ± 0.04, and slightly lower in the companions (12+log(O/H) = 8.34 − 8.42), consistent with the mass-metallicity relation at z ∼ 3. We find peculiar line ratios (high log[N II]/Hα = [−0.45, −0.3], low log[O III]/Hβ = [0.06, 0.10]) in the northern part of GS5001. These could be attributed to either higher metallicity, or to shocks resulting from the interaction of the main galaxy with the northern source. We identify a spatially resolved outflow in the main galaxy, traced by a broad symmetric component in Hα and [O III], with an extension of about 3 kpc. We find maximum outflow velocities of ∼400 km s−1, an outflow mass of (1.7 ± 0.4)×108 M⊙, a mass outflow rate of 23 ± 5 M⊙ yr−1, and a mass loading factor of 0.23. These properties are compatible with star formation being the driver of the outflow. Our analysis of these JWST NIRSpec IFS data therefore provides valuable, unprecedented insights into the interplay between star formation, galactic outflows, and interactions in the core of a z ∼ 3.5 candidate protocluster.

High Spectral Resolution Observations of Propynal (HCCCHO) toward TMC-1 from the GOTHAM Large Program on the GBT

We used new high spectral resolution observations of propynal (HCCCHO) toward TMC-1 and in the laboratory to update the spectral line catalog available for transitions of HCCCHO—specifically at frequencies lower than 30 GHz, which were previously discrepant in a publicly available catalog. The observed astronomical frequencies provided a high enough spectral resolution that, when combined with high-resolution (∼2 kHz) measurements taken in the laboratory, a new, consistent fit to both the laboratory and astronomical data was achieved. Now with a nearly exact (<1 kHz) frequency match to the J = 2–1 and 3–2 transitions in the astronomical data, using a Markov Chain Monte Carlo analysis, a best fit to the total HCCCHO column density of 7.28−1.94+4.08×1012 cm−2 was found with a surprisingly low excitation temperature of just over 3 K. This column density is around a factor of 5 times larger than reported in previous studies. Finally, this work highlights that care is needed when using publicly available spectral catalogs to characterize astronomical spectra. The availability of these catalogs is essential to the success of modern astronomical facilities and will only become more important as the next generation of facilities comes online.

Characterization of Crystal Properties and Defects in CdZnTe Radiation Detectors

CdZnTe-based detectors are highly valued because of their high spectral resolution, which is an essential feature for nuclear medical imaging. However, this resolution is compromised when there are substantial defects in the CdZnTe crystals. In this study, we present a learning-based approach to determine the spatially dependent bulk properties and defects in semiconductor detectors. This characterization allows us to mitigate and compensate for the undesired effects caused by crystal impurities. We tested our model with computer-generated noise-free input data, where it showed excellent accuracy, achieving an average RMSE of 0.43% between the predicted and the ground truth crystal properties. In addition, a sensitivity analysis was performed to determine the effect of noisy data on the accuracy of the model.

Correlation Hyperspectral Imaging.

Hyperspectral imaging aims at providing information on both the spatial and the spectral distribution of light, with high resolution. However, state-of-the-art protocols are characterized by an intrinsic trade-off imposing to sacrifice either resolution or image acquisition speed. We address this limitation by exploiting light intensity correlations, which are shown to enable overcoming the typical downsides of traditional hyperspectral imaging techniques, both scanning and snapshot. The proposed approach also opens possibilities that are not otherwise achievable, such as sharper imaging and natural filtering of broadband spectral components that would otherwise hide the spectrum of interest. The enabled combination of high spatial and spectral resolution, high speed, and insensitivity to undesired spectral features shall lead to a paradigm change in hyperspectral imaging devices and open up new application scenarios.

Extending radiowave frequency detection range with dressed states of solid-state spin ensembles

Quantum sensors using solid-state spin defects excel in the detection of radiofrequency (RF) fields, serving various applications in communication, ranging, and sensing. For this purpose, pulsed dynamical decoupling (PDD) protocols are typically applied, which enhance sensitivity to RF signals. However, these methods are limited to frequencies of a few megahertz, which poses a challenge for sensing higher frequencies. We introduce an alternative approach based on a continuous dynamical decoupling (CDD) scheme involving dressed states of nitrogen vacancy (NV) ensemble spins driven within a microwave resonator. We compare the CDD methods to established PDD protocols and demonstrate the detection of RF signals up to ~85 MHz, about ten times the current limit imposed by the PDD approach under identical conditions. Implementing the CDD method in a heterodyne/synchronized protocol combines the high-frequency detection with high spectral resolution. This advancement extends to various domains requiring detection in the high frequency (HF) and very high frequency (VHF) ranges of the RF spectrum, including spin sensor-based magnetic resonance spectroscopy at high magnetic fields.

Aerosol Vertical Turbulent Mass Flux Retrievals Through Novel Remote Sensing Algorithm

AbstractIntegrated measurements of aerosol, radiation, cloud, and turbulent transport in the planetary boundary layer (PBL) are essential for understanding and modeling climate and air quality. Here, we developed a new technique for the identification of convective turbulent regions and deriving the vertical distribution of aerosol turbulent mass fluxes within PBL. The algorithm uses retrievals from coherent Doppler lidars and a high spectral resolution lidar. The technique was applied to study particle mass fluxes over 2 months (November–December 2020) during the campaign conducted at the DOE Atmospheric Radiation Measurement Southern Great Plains (SGP) site in Lamont, Oklahoma. The algorithm developed here is capable of continuously deriving vertically resolved (curtains) aerosol mass fluxes. Our data analysis shows that at the site, the 30‐min averaged fluxes at 135 m above the surface were mainly positive (upward) at ∼1 μg m−2 s−1, suggesting that the surface is the primary source of the particle mass supplied to the boundary layer at the SGP site. Analyses of the individual case studies have revealed that not all the derived fluxes can be linked to surface emissions. Both positive and negative values in a range of ±5 μg m−2 s−1 can be caused by convective thermals interacting between the residual layer and the mixed layer and by rotation of the horizontal wind with the height. Large erroneous negative fluxes can also be caused by drizzling/precipitating clouds. We anticipate that the application of the current technique will lead to a more realistic representation of aerosol mass budgets and bidirectional mixing rates.

A deep convolutional neural network for blind element error correction of spatial heterodyne spectrometer using line selective convolutional blocks

The "GF Special Project" is a massive remote sensing technology initiative including a number of satellites and various observation platforms. GF-5 is the satellite with the most payloads, the highest spectral resolution, and the most difficulty in development, and it can monitor a variety of environmental elements using spatial heterodyne spectroscopy (SHS) technology, including atmospheric aerosols, carbon dioxide, methane, terrestrial vegetation, straw burning, and urban heat islands. In this study, a novel blind element error correction technique based on deep learning network is investigated and developed for spatial heterodyne interferograms, as well as the formation mechanism and distribution characteristics of the SHS interferometric data. LSConv-Net, a new CNN model, was created and trained to denoise in the presence of high-density and ultra-high-density blind element errors. We do this by introducing a new line-selective convolutional (LSConv) block. Simultaneously, experimental validation of blind element error correction utilizing laboratory water vapor interferometric data and atmospheric CO2 absorption interferometric data from GF-5, and the change in FWHM before and after the experiment was tested using potassium lamp interferograms. Experiments show that the Deep neural networks trained with this model may successfully suppress the effect of blind element noise on spectra, recover spectra that have been overwhelmed by high-density blind element noise without any effect on other non-blind pixels, and surpass all similar techniques in terms of spectral recovery.

Remote detection of polluted gases based on thermal infrared spectral imaging in the field

This paper reports the new progress of long wave infrared remote sensing in the field, focusing on the implementation process of window scanning spatiotemporal modulation Fourier spectroscopic imaging technology. Utilizing the self-developed CHIPED-1 device, the spatial modulation interference was achieved through the use of a cone mirror Michelson interferometer. By combining it with a cooled long-wave infrared focal plane detector component and applying data processing steps such as acquisition, recombination, calibration, etc., high-spectral resolution imaging in long-wave infrared region was accomplished. The detection sensitivity index nesr (Noise Equivalent Spectral Radiance) of the self-developed CHIPED-1 long wave infrared hyperspectral imaging principle experimental device reaches 5.6 × 10−8w/(cm−1.sr.cm2) in single pixel，Equivalent to commercial time modulation interferometric hyperspectral imagers; It reflects the progressiveness of the technology, and leaves much room for improvement.By testing the transmittance curve of polypropylene film, the spectral response range of CHIPED-1 infrared hyperspectral imaging principle experimental device reached 11.5 μm.The article also studied the hyperspectral imaging detection method for two-dimensional distributed chemical gas VOCs, taking the detection experiments of field high-rise buildings and ether gas as examples.Under complex backgrounds and low experimental concentrations, the presence of ether vapor cannot be observed from the infrared spectrum slices at the same wave number. However, after differential spectral processing, the spatial distribution of ether vapor can be clearly seen.The hyperspectral method is applied in the field of infrared detection of organic vapor VOCs, which has many advantages over wide-band thermal imaging methods, such as high sensitivity, strong anti-interference ability, and wide recognition range.

Assessing field scale spatiotemporal heterogeneity in salinity dynamics using aerial data assimilation

Soil moisture and salinity shifts within the root zone can significantly alter crop yields. Thus, spatiotemporal dynamics of these parameters are essential for precision crop management. Airborne or spaceborne earth observation methods based on vegetation and soil observations have sometimes been used with limited success to indirectly understand parameters like soil salinity. These datasets lack spatiotemporal resolution to discern field-scale heterogeneities, and estimates’ accuracy is poor. A Metropolis-Hasting Markov Chain-Monte Carlo (MCMC) based method was developed to estimate field-scale soil salinity by assimilating estimated evapotranspiration (ET) data obtained from aerial canopy temperature sensing with ET outputs from a one-dimensional soil-water transport model. By aligning the two estimated ET values, we inferred anticipated soil salinity levels in a mature pecan orchard (28,951 m2). Our results aligned closely with in-situ measurements with a spatial cross-correlation more than 0.86 and highlighted the expected heterogeneities and nonlinearities. This research offers an approach to refine the current state-of-the-art crop models by accounting for field scale heterogeneities using remotely sensed data. This assimilation method will pave the way for a more inclusive agricultural system modeling that can infer critical but hard-to-measure soil properties from easier-to-obtain remotely sensed datasets. Though this paper concentrates on aerial observations, we anticipate similar methods can be used for satellite-based imagery, especially those with high spatial, temporal, and spectral resolutions.

Multimode light generation in an injection semiconductor laser based on a chiral AlAs/(Al, Ga)As/GaAs microcavity

Experimental investigations of chiral injection AlAs/(Al, Ga)As/GaAs vertical-cavity surface-emitting lasers in the multimode generation regime are performed. A high circular polarization degree 70% of different generation modes measured with a high spectral resolution, is demonstrated. Detailed maps of spatial and angular distribution of laser radiation intensity were constructed.

A simple spectrogram model for high-accuracy spectral calibration of VIPA spectrometers.

The virtually imaged phased array (VIPA) spectrometer uses the orthogonal dispersion method and has the advantages of compact structure, high spectral resolution, and wide wavelength coverage. It has been widely used in different fields. However, due to the non-linear dispersion of the VIPA etalon and the cross-dispersion structure of the VIPA spectrometer, simple and high-accuracy wavelength calibration remains a challenge. In this paper, a new and simple five-parameter spectrogram model is developed by simplifying the phase-matching equation of the VIPA etalon and considering the angle between the camera and dispersion direction, which can achieve a frequency accuracy better than one pixel. The performance of the model is demonstrated by measuring the CO2 absorption spectrum in the range of 1.42 to 1.45 μm using a self-designed near-infrared VIPA spectrometer . The reported method is simple and easy to solve with high accuracy, which is conducive to promoting the application of VIPA spectrometers in precision measurement.