Low Spectral Resolution Research Articles

Emerging metabolic imaging and spectroscopic methods to study neurodegenerative diseases

Proton magnetic resonance spectroscopy (1H-MRS) allows non-invasive assessment of the metabolic landscape of biological tissue. Despite demonstrating promising findings in clinical practice, single-voxel or single-slice two-dimensional 1H-MRS methods present a few challenges mainly related to limited spatial coverage and low spatial and spectral resolutions. In the recent past, the advent of more sophisticated metabolic imaging and spectroscopic sequences, such as three-dimensional echoplanar spectroscopic imaging (3D-EPSI), two-dimensional correlation spectroscopy (2D-COSY), and chemical exchange saturation technique (CEST) has revolutionized the field of metabolomics. For the metabolic characterization of diffused neurodegenerative diseases, whole brain coverage is essential for a comprehensive overview of the topography and understanding of the underlying pathophysiological processes. The 3D-EPSI sequence allows the acquisition of whole brain (volumetric) metabolite maps with high spatial resolution.1 These metabolite maps can be co-registered to anatomical images for facilitating the mapping of metabolite alterations from different brain regions in a single session, thus providing the true spatial extent of a global disease. The potential of 3D‐EPSI in characterizing several neurological and neurodegenerative disorders has been reported. On conventional one-dimensional 1H-MRS, spectral peaks due to methyl, methylene, and methine protons from N-acetyl aspartate, glutamate, glutamine, gamma-aminobutyric acid, and taurine extensively overlap in the spectral region of 2-4 ppm, often confounding the reliable detection and quantification of these metabolites. In contrast, 2D-COSY offers unambiguous identification of potentially overlapping resonances by dispersing the multiplet structure of scalar (J)-coupled spin systems into a second spectral dimension,2 especially at higher field strength3,4 and by exploiting the unlikely possibility that two metabolites would share identical chemical shifts in two-dimensions. Due to technical limitations and long acquisition time, 2D-COSY sequence has not been widely used to study neurodegenerative diseases. However, future modifications would benefit from implementing faster acquisition schemes and improved spectral fitting methods for data analysis. We believe that these new approaches could make the clinical applications of the 2D-COSY sequence faster, easier, and more versatile. CEST is a relatively novel metabolic imaging modality that allows the detection of specific exogenous and endogenous metabolites/molecules present at millimolar concentrations. Exchangeable solute protons present in chemical functional groups such as amide (-CONH), amine (-NH2) or hydroxyl (-OH) resonate at a frequency different from bulk water protons. These labile protons are selectively saturated using radiofrequency irradiation, which is subsequently transferred to the bulk water pool, leading to a decrease in the water signal intensity proportional to the concentration of solute molecules, number of labile protons and proton exchange rate.5 CEST offers more than two orders of magnitude higher sensitivity compared to 1H-MRS in detecting metabolites such as glutamate, creatine, myoinositol and mobile peptides.5 While amide proton transfer (APT) imaging has been investigated in various neurological disorders, other CEST imaging techniques such as glutamate-CEST, creatine-CEST have been performed only in pre-clinical or pilot clinical studies related to neurodegenerative diseases. We believe that these newer developments in metabolic imaging techniques will have a significant impact in reshaping our understanding of biochemical profiles of various neurodegenerative diseases. However, standardization and harmonization of acquisition parameters are required for fast-tracking the implementation of these metabolic techniques into routine clinical workflow.

Rapid Quantification of Pharmaceuticals via 1H Solid-State NMR Spectroscopy.

The physicochemical properties of active pharmaceutical ingredients (APIs) can depend on their solid-state forms. Therefore, characterization of API forms is crucial for upholding the performance of pharmaceutical products. Solid-state nuclear magnetic resonance (SSNMR) spectroscopy is a powerful technique for API quantification due to its selectivity. However, quantitative SSNMR experiments can be time consuming, sometimes requiring days to perform. Sensitivity can be considerably improved using 1H SSNMR spectroscopy. Nonetheless, quantification via 1H can be a challenging task due to low spectral resolution. Here, we offer a novel 1H SSNMR method for rapid API quantification, termed CRAMPS-MAR. The technique is based on combined rotation and multiple-pulse spectroscopy (CRAMPS) and mixture analysis using references (MAR). CRAMPS-MAR can provide high 1H spectral resolution with standard equipment, and data analysis can be accomplished with ease, even for structurally complex APIs. Using several API species as model systems, we show that CRAMPS-MAR can provide a lower quantitation limit than standard approaches such as fast MAS with peak integration. Furthermore, CRAMPS-MAR was found to be robust for cases that are inapproachable by conventional ultra-fast (i.e., 100 kHz) MAS methods even when state-of-the-art SSNMR equipment was employed. Our results demonstrate CRAMPS-MAR as an alternative quantification technique that can generate new opportunities for analytical research.

Improving the accuracy of copolymer sequence length determination by pyrolysis gas chromatography: A comprehensive study

Many industrial polymers which find application in contemporary materials are copolymers. Copolymers feature multiple distributions, that govern their physical properties, including the sequence distribution. Styrene-acrylate copolymers are an important class of polymers, their monomer sequence is typically determined by 13C NMR which suffers from low sensitivity and spectral resolution. A series of studies have shown that Py-GC can be applied to determine the sequence length of copolymers. The accuracy of the trimer assignments and the appropriate calibration approaches yielding reliable data have however not yet been validated. In the present study we propose a comprehensive workflow to ensure the accuracy of the sequence determination by Py-GC, next to NMR. In-depth analysis of the trimers observed in the Py-GC pyrograms of model styrene-acrylate copolymers was performed and specific MS fragments relating to the trimer sequence were assigned. A comparison of a series of copolymers yielded reliable assignments for the trimer signals. The obtained sequence lengths were in agreement with those calibrated with the benchmark method, 13C NMR. Py-GC was found to consistently underestimate the acrylate sequence length. Py-GC calibration with 13C NMR was thus found to be indispensable for the accurate absolute quantification of the sequence length by Py-GC. The calculated randomness did not vary significantly after NMR calibration, indicating that NMR calibration might not be required in all cases to obtain (relative) information on the sequence of a copolymer.

Physical-Based Spatial-Spectral Deep Fusion Network for Chlorophyll-a Estimation Using MODIS and Sentinel-2 MSI Data

Satellite-derived Chlorophyll-a (Chl-a) is an important environmental evaluation indicator for monitoring water environments. However, the available satellite images either have a coarse spatial or low spectral resolution, which restricts the applicability of Chl-a retrieval in coastal water (e.g., less than 1 km from the shoreline) for large- and medium-sized lakes/oceans. Considering Lake Chaohu as the study area, this paper proposes a physical-based spatial-spectral deep fusion network (PSSDFN) for Chl-a retrieval using Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 Multispectral Instrument (MSI) reflectance data. The PSSDFN combines residual connectivity and attention mechanisms to extract effective features, and introduces physical constraints, including spectral response functions and the physical degradation model, to reconcile spatial and spectral information. The fused and MSI data were used as input variables for collaborative retrieval, while only the MSI data were used as input variables for MSI retrieval. Combined with the Chl-a field data, a comparison between MSI and collaborative retrieval was conducted using four machine learning models. The results showed that collaborative retrieval can greatly improve the accuracy compared with MSI retrieval. This research illustrates that the PSSDFN can improve the estimated accuracy of Chl-a for coastal water (less than 1 km from the shoreline) in large- and medium-sized lakes/oceans.

Generating Daily Land Surface Temperature Downscaling Data Based on Sentinel-3 Images

The land surface temperature (LST) images obtained by thermal infrared remote sensing sensors are of great significance for numerous fields of research. However, the low spatial resolution is a drawback of LST images. Downscaling is an effective way to solve this problem. The traditional downscaling methods, however, have various drawbacks, including their low temporal and spectral resolutions, difficult processes, numerous errors, and single downscaling factor. They also rely on two or more separate satellite platforms. These drawbacks can be partially compensated for by the Sentinel-3 satellite’s ability to acquire LST and multispectral images simultaneously. This paper proposes a downscaling model based on Sentinel-3 satellite and ASTER GDEM images—D-DisTrad—and compares the effects of the D-DisTrad model with DisTrad model and TsHARP model over four sites and four seasons. The mean bias (MB) range of the D-DisTrad model is −0.001–0.017 K, the mean absolute error (MAE) range is 0.103–0.891 K, and the root mean square error (RMSE) range is 0.220–1.235 K. The Pearson correlation coefficient (PCC) and R2 ranges are 0.938–0.994 and 0.889–0.989, respectively. The D-DisTrad model has the smallest error, the highest correlation, and the best visual effect, and can eliminate some “mosaic” effects in the original image. This paper shows that the D-DisTrad model can improve the spatial resolution and visual effects of LST images while maintaining high temporal resolution, and discusses the influence of the terrain and land cover on LST data.

Inference detection and classification of illicit drugs by a modest Raman spectrometer with a convolutional neural network analyzer

Faced with widespread of emerging new psychoactive substances (NPS) and illicit drugs, real-time and on-site detection techniques are urgently in need but remain a challenge. For this purpose, we present a simple but accurate in-house developed portable Raman spectral imaging system. The system consists of a compact, cost-effective and redesigned Raman spectrometer and a responsive analyzer for chemical compound identification based on convolutional neural network (CNN) techniques. To test the system, twelve substances are selected and divided into the library (established in database) and the challenge (unknown) groups. The challenge validation shows that the system achieves excellent predictive accuracy and high sensitivity even on low spectral resolution. The illicit drugs in the challenge set are identified as suspicious illicit compounds at an accuracy rate of about 92%. This accuracy results from that the spectral signatures of the functional groups or molecular structural similarities of the chemical compounds are recognized by the CNN model.

Hubble WFC3 Spectroscopy of the Rocky Planet L 98–59 b: No Evidence for a Cloud-free Primordial Atmosphere

We are using archived data from HST of transiting exoplanet L 98-59 b to place constraints on its potentially hot atmosphere. We analyze the data from five transit visits and extract the final combined transmission spectrum using Iraclis. Then we use the inverse atmospheric retrieval code TauREx to analyze the combined transmission spectrum. There is a weak absorption feature near 1.40 μm and 1.55 μm in the transmission spectrum, which can be modeled by a cloudy atmosphere with abundant hydrogen cyanide (HCN). However, the unrealistically high abundance of HCN derived cannot be explained by any equilibrium chemical model with reasonable assumptions. Thus, the likeliest scenario is that L 98-59 b has a flat, featureless transmission spectrum in the WFC3/G141 bandpass due to a thin atmosphere with high mean molecular weight, an atmosphere with an opaque aerosol layer, or no atmosphere, and it is very unlikely for L 98-59 b to have a clear hydrogen-dominated primary atmosphere. Due to the narrow wavelength coverage and low spectral resolution of HST/WFC3 G141 grism observation, we cannot tell these different scenarios apart. Our simulation shows future higher precision measurements over wider wavelengths from the James Webb Space Telescope can be used to better characterize the planetary atmosphere of L 98-59 b.

Telluric absorption lines in the ALMA spectra of η Car

ABSTRACT The massive binary system formed by η Car and an unknown companion is a strong source at millimetre and submillimetre wavelengths. Close to the stars, continuum bremsstrahlung and radio recombination lines originate in the massive ionized wind of η Car and in several compact sources of high density plasma. Molecular lines are also detected at these wavelengths, some of them are seen in absorption towards the continuum emission region, many of them revealed by ALMA observations. However, because the ALMA atmospheric calibration is performed in a low spectral resolution mode, telluric lines can still be present in some high-resolution spectra of scientific products, which could lead to a false identification of molecules. In this work, we explore three different sets of ALMA archive data of η Car, including high resolution (0.065 arcsec × 0.043 arcsec) observations recently published by our group, to verify which of these absorption lines are real and discuss their origin. We conclude that some of them truly originate in clouds close to the binary system, while others are artefacts of a faulty elimination of telluric lines during ALMA calibration procedure. We found that these absorption lines are not present in the phase calibrators because they are much weaker than η Car, where the absorption line appears because the high intensity continuum enhances the small individual systematic calibration errors.

The miniJPAS survey: White dwarf science with 56 optical filters

Aims. We analyze the white dwarf population in miniJPAS, the first square degree observed with 56 medium-band, 145 Å in width optical filters by the Javalambre Physics of the accelerating Universe Astrophysical Survey (J-PAS), to provide a data-based forecast for the white dwarf science with low-resolution (R ∼ 50) photo-spectra. Methods. We define the sample of the bluest point-like sources in miniJPAS with r < 21.5 mag, a point-like probability larger than 0.5, (u − r)< 0.80 mag, and (g − i)< 0.25 mag. This sample comprises 33 sources with spectroscopic information: 11 white dwarfs and 22 quasi-stellar objects (QSOs). We estimate the effective temperature (Teff), the surface gravity, and the composition of the white dwarf population by a Bayesian fitting to the observed photo-spectra. Results. The miniJPAS data are sensitive to the Balmer series and the presence of polluting metals. Our results, combined with those from the Javalambre Photometric Local Universe Survey (J-PLUS) which has a lower spectral resolution but has already observed thousands of white dwarfs, suggest that J-PAS photometry would permit – down to r ∼ 21.5 mag and at least for sources with 7000 < Teff < 22 000 K – both the classification of the observed white dwarfs into H-dominated and He-dominated with 99% confidence and the detection of calcium absorption for equivalent widths larger than 15 Å. The effective temperature is estimated with a 2% uncertainty, which is close to the 1% from spectroscopy. A precise estimation of the surface gravity depends on the available parallax information. In addition, the white dwarf population at Teff > 7000 K can be segregated from the bluest extragalactic QSOs, providing a clean sample based on optical photometry alone. Conclusions. The J-PAS low-resolution photo-spectra would produce precise effective temperatures and atmospheric compositions for white dwarfs, complementing the data from Gaia. J-PAS will also detect and characterize new white dwarfs beyond the Gaia magnitude limit, providing faint candidates for spectroscopic follow-up.

The Mantis Network II: examining the 3D high-resolution observable properties of the UHJs WASP-121b and WASP-189b through GCM modelling

ABSTRACT The atmospheres of ultra hot Jupiters (UHJs) are prime targets for the detection of molecules and atoms at both low and high spectral resolution. We study the atmospheres of the UHJs WASP-121b and WASP-189b by performing 3D general circulation models (GCMs) of these planets using high temperature correlated-k opacity schemes with ultra-violet (UV) absorbing species included. The GCM results are then post-processed at low and high spectral resolutions and compared to available data. The high resolution results are cross-correlated with molecular and atomic templates to produce mock molecular detections. Our GCM models produce similar temperature-pressure (T-p) structure trends to previous 1D radiative-convective equilibrium models of UHJs. Furthermore, the inclusion of UV opacities greatly shapes the thermal and dynamical properties of the high-altitude, low-pressure regions of the UHJ atmospheres, with sharp T-p inversions due to the absorption of UV light. This suggests that optical wavelength, high-resolution observations probe a dynamically distinct upper atmospheric region, rather than the deeper jet forming layers.

UAV-based classification of maritime Antarctic vegetation types using GEOBIA and random forest

Development of vegetation communities in areas of Antarctica without permanent ice cover emphasizes the need for effective remote sensing techniques for proper monitoring of local environmental changes. Detection and mapping of vegetation by image classification remains limited in the Antarctic environment due to the complexity of its surface cover, and the spatial heterogeneity and spectral homogeneity of cryptogamic vegetation. As ultra-high resolution aerial images allow a comprehensive analysis of vegetation, this study aims to identify different types of vegetation cover (i.e., algae, mosses, and lichens) in an ice-free area of Hope Bay, on the northern tip of the Antarctic Peninsula. Using the geographic object-based image analysis (GEOBIA) approach, remote sensing data sets are tested in the random forest classifier in order to distinguish vegetation classes within vegetated areas. Because species of algae, mosses, and lichens may have similar spectral characteristics, subclasses are established. The results show that when only the mean values of green, red, and NIR bands are considered, the subclasses have low separability. Variations in accuracy and visual changes are identified according to the set of features used in the classification. Accuracy improves when multilayer information is used. A combination of spectral and morphometric products and by-products provides the best result for the detection and delineation of different types of vegetation, with an overall accuracy of 0.966 and a Kappa coefficient of 0.946. The method allowed for the identification of units primarily composed of algae, mosses, and lichens as well as differences in communities. This study demonstrates that ultra-high spatial resolution data can provide the necessary properties for the classification of vegetation in Maritime Antarctica, even in images obtained by sensors with low spectral resolution.

Reservoir sediment characterisation by diffuse reflectance spectroscopy in a semiarid region to support sediment reuse for soil fertilization

PurposeSoil erosion by water yields sediment to surface reservoirs, reducing their storage capacities, changing their geometry, and degrading water quality. Sediment reuse, i.e., fertilization of agricultural soils with the nutrient-enriched sediment from reservoirs, has been proposed as a recovery strategy. However, the sediment needs to meet certain criteria. In this study, we characterize sediments from the densely dammed semiarid Northeast Brazil by VNIR-SWIR spectroscopy and assess the effect of spectral resolution and spatial scale on the accuracy of N, P, K, C, electrical conductivity, and clay prediction models.MethodsSediment was collected in 10 empty reservoirs, and physical and chemical laboratory analyses as well as spectral measurements were performed. The spectra, initially measured at 1 nm spectral resolution, were resampled to 5 and 10 nm, and samples were analysed for both high and low spectral resolution at three spatial scales, namely (1) reservoir, (2) catchment, and (3) regional scale.ResultsPartial least square regressions performed from good to very good in the prediction of clay and electrical conductivity from reservoir (< 40 km2) to regional (82,500 km2) scales. Models for C and N performed satisfactorily at the reservoir scale, but degraded to unsatisfactory at the other scales. Models for P and K were more unstable and performed from unsatisfactorily to satisfactorily at all scales. Coarsening spectral resolution by up to 10 nm only slightly degrades the models’ performance, indicating the potential of characterizing sediment from spectral data captured at lower resolutions, such as by hyperspectral satellite sensors.ConclusionBy reducing the costly and time-consuming laboratory analyses, the method helps to promote the sediment reuse as a practice of soil and water conservation.

Bathymetry retrieval from CubeSat image sequences with short time lags

The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.

Evaluation of Simulated AVIRIS-NG Imagery Using a Spectral Reconstruction Method for the Retrieval of Leaf Chlorophyll Content

The leaf chlorophyll content (LCC) is a vital parameter that indicates plant production, stress, and nutrient availability. It is critically needed for precision farming. There are several multispectral images available freely, but their applicability is restricted due to their low spectral resolution, whereas hyperspectral images which have high spectral resolution are very limited in availability. In this work, hyperspectral imagery (AVIRIS-NG) is simulated using a multispectral image (Sentinel-2) and a spectral reconstruction method, namely, the universal pattern decomposition method (UPDM). UPDM is a linear unmixing technique, which assumes that every pixel of an image can be decomposed as a linear composition of different classes present in that pixel. The simulated AVIRIS-NG was very similar to the original image, and its applicability in estimating LCC was further verified by using the ground based measurements, which showed a good correlation value (R = 0.65). The simulated image was further classified using a spectral angle mapper (SAM), and an accuracy of 87.4% was obtained, moreover a receiver operating characteristic (ROC) curve for the classifier was also plotted, and the area under the curve (AUC) was calculated with values greater than 0.9. The obtained results suggest that simulated AVIRIS-NG is quite useful and could be used for vegetation parameter retrieval.

Seven Europium(III) Complexes in Solution – The Importance of Reporting Data When Investigating Luminescence Spectra and Electronic Structure

AbstractDespite significant effort, the complexity of europium(III) luminescence in general – and sample composition in particular – has precluded the publication of standard spectra of specific complexes in aqueous solution. Further, low spectral resolution, weighted averages with contributions from several species, as well as poor reproduction hampers the use of literature data, with the note that high quality data from e. g. Bünzli, Latva, Carnall, Parker, Heller, Balzani and Sabatini is from the pre‐electronic era. Here, luminescence spectra of Eu.DOTA, Eu.EDTA, Eu.DPA1, Eu.DPA2. Eu.DPA3, Eu.DTPA and the Eu(III) aqua ion was determined in samples with known pH and conductivity. The ligand concentrations were varied in a buffered medium (MES) with a significant salt background. In this manner, full complexation could be ensured, while pH and ionic strength were monitored. The spectra corresponding to the complexes were extracted, and the electronic structure of the ground state 7FJ manifold was investigated and contrasted to literature data. All spectroscopic data is made available and we suggest this becomes part of the standard procedure when investigating lanthanide(III) complexes.

Generation and Optimization of Spectral Cluster Maps to Enable Data Fusion of CaSSIS and CRISM Datasets

Four-band color imaging of the Martian surface using the Color and Stereo Surface Imaging System (CaSSIS) onboard the European Space Agency’s ExoMars Trace Gas Orbiter exhibits a high color diversity in specific regions. Not only is the correlation of color diversity maps with local morphological properties desirable, but mineralogical interpretation of the observations is also of great interest. The relatively high spatial resolution of CaSSIS data mitigates its low spectral resolution. In this paper, we combine the broad-band imaging of the surface of Mars, acquired by CaSSIS with hyperspectral data from the Compact Reconnaissance Imaging Spectrometer (CRISM) onboard NASA’s Mars Reconnaissance Orbiter to achieve a fusion of both datasets. We achieve this using dimensionality reduction and data clustering of the high dimensional datasets from CRISM. In the presented research, CRISM data from the Coprates Chasma region of Mars are tested with different machine learning methods and compared for robustness. With the help of a suitable metric, the best method is selected and, in a further step, an optimal cluster number is determined. To validate the methods, the so-called “summary products” derived from the hyperspectral data are used to correlate each cluster with its mineralogical properties. We restrict the analysis to the visible range in order to match the generated clusters to the CaSSIS band information in the range of 436–1100 nm. In the machine learning community, the so-called UMAP method for dimensionality reduction has recently gained attention because of its speed compared to the already established t-SNE. The results of this analysis also show that this method in combination with the simple K-Means outperforms comparable methods in its efficiency and speed. The cluster size obtained is between three and six clusters. Correlating the spectral cluster maps with the given summary products from CRISM shows that four bands, and especially the NIR bands and VIS albedo, are sufficient to discriminate most of these clusters. This demonstrates that features in the four-band CaSSIS images can provide robust mineralogical information, despite the limited spectral information using semi-automatic processing.

Prospects of measuring a metallicity trend and spread in globular clusters from low-resolution spectroscopy

Context.The metallicity spread, or the metallicity trend along the evolutionary sequence of a globular cluster, is a rich source of information to help understand the cluster physics (e.g., multiple populations) and stellar physics (e.g., atomic diffusion). Low-resolution integral-field-unit spectroscopy in the optical with the MUSE spectrograph is an attractive prospect if it can provide these diagnostics because it allows us to efficiently extract spectra of a large fraction of the cluster stars with only a few telescope pointings.Aims.We investigate the possibilities of full-spectrum fitting to derive stellar parameters and chemical abundances at low spectral resolution (R ∼ 2000).Methods.We reanalysed 1584 MUSE spectra of 1061 stars above the turn-off of NGC 6397 using FERRE and employing two different synthetic libraries.Results.We derive the equivalent iron abundance [Fe/H]efor fixed values of [α/Fe] (solar or enhanced). We find that (i) the interpolation schema and grid mesh are not critical for the precision, metallicity spread, and trend; (ii) with the two considered grids, [Fe/H]eincreases by ∼0.2 dex along the sub-giant branch, starting from the turn-off of the main sequence; (iii) restricting the wavelength range to the optical decreases the precision significantly; and (iv) the precision obtained with the synthetic libraries is lower than the precision obtained previously with empirical libraries.Conclusions.Full-spectrum fitting provides reproducible results that are robust to the choice of the reference grid of synthetic spectra and to the details of the analysis. The [Fe/H]eincrease along the sub-giant branch is in stark contrast with the nearly constant iron abundance previously found with empirical libraries. The precision of the measurements (0.05 dex on [Fe/H]e) is currently not sufficient to assess the intrinsic chemical abundance spreads, but this may change with deeper observations. Improvements of the synthetic spectra are still needed to deliver the full possibilities of full-spectrum fitting.

On the measurement of flame temperature and emissivity based on multispectral imaging technique

Due to the low spatial resolution of spectrometer and low spectral resolution of CCD, a multi-wavelength radiation thermometry method employing multispectral imaging technology was proposed to measure flame temperature and emissivity. The calculated radiation intensity lines were obtained by the Hottel emissivity model and the least square method. The temperature and flame absorption capacity with the calculated spectral line closest to the measured spectral line were considered as the optimum results. The experimental results showed that maximum temperature of the flame was 1943 K appeared in the thin-wick paraffin. At any position, the emissivity of the thin-wick paraffin flame was lower than that of the thick-wick, but the emissivity of the biomass candle flame near the flame center was the highest. The measured temperature distribution was validated by employing a spectrometer system and the relative error was 2.66%.

On the Effect of Stellar Activity on Low-resolution Transit Spectroscopy and the use of High Resolution as Mitigation

We present models designed to quantify the effects of stellar activity on exoplanet transit spectroscopy and atmospheric characterization at low (R = 100) and high (R = 100,000) spectral resolution. We study three model classes mirroring planetary system archetypes: a hot Jupiter around an early-K star (HD 189733 b); a mini-Neptune around an early-M dwarf (K2-18 b); and terrestrial planets around a late-M dwarf (TRAPPIST-1). We map photospheres with temperatures and radial velocities (RV) and integrate specific intensity stellar models. We obtain transit spectra affected by stellar contamination, the Rossiter–McLaughlin effect (RME), and center-to-limb variations (CLV). We find that, at low resolution, for later-type stars, planetary water features become difficult to distinguish from contamination. Many distributions of unocculted active regions can induce planetary-like features of similar amplitudes in the case of a late-M dwarf. Atmospheric characterization of planets around late-type stars will likely continue to suffer from degeneracy with stellar activity unless active regions' parameters can be constrained using additional information. For the early-K star, stellar contamination mostly manifests itself through a slope at optical wavelengths similar to Rayleigh scattering. In all cases, contamination induces offsets in measured planet radii. At high resolution, we show that we can determine the origin of H2O and CO detection signals and lift the degeneracy observed at low resolution, provided sufficient planet RV variation during transit and adequate correction for the RME and CLV when required. High-resolution spectroscopy may therefore help resolve issues arising from stellar contamination for favorable systems.

The average dust attenuation curve at z ∼ 1.3 based on HST grism surveys

ABSTRACT We present the first characterization of the average dust attenuation curve at z ∼ 1.3 by combining rest-frame ultraviolet through near-IR photometry with Balmer decrement (H α/H β) constraints for ∼900 galaxies with 8 ≲ log (M⋆/M⊙) &amp;lt; 10.2 at 0.75 &amp;lt; z &amp;lt; 1.5 in the HST WFC3 IR Spectroscopic Parallel and 3D-HST grism surveys. Using galaxies in SDSS, we establish that the (H α + [N ii])/[O iii] line ratio and stellar mass are good proxies for the Balmer decrement in low-spectral resolution grism data when only upper limits on H β are available and/or H α is blended with [N ii]. The slope of the z ∼ 1.3 attenuation curve ($A(0.15\, \rm{\mu m})/A(V)=3.15$) and its normalization (RV = 3.26) lie in-between the values found for z = 0 and z ∼ 2 dust attenuation curves derived with similar methods. These provide supporting evidence that the average dust attenuation curve of star-forming galaxies evolves continuously with redshift. The z ∼ 1.3 curve has a mild 2175 Å feature (bump amplitude, Eb = 0.83; ∼25 per cent that of the MW extinction curve), which is comparable to several other studies at 0 &amp;lt; z ≲ 3, and suggests that the average strength of this feature may not evolve significantly with redshift. The methods we develop to constrain dust attenuation from HST grism data can be applied to future grism surveys with JWST, Euclid, and RST. These new facilities will detect millions of emission line galaxies and offer the opportunity to significantly improve our understanding of how and why dust attenuation curves evolve.