Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra

Algal pigment composition is an indicator of phytoplankton community structure that can be estimated from optical observations. Assessing the potential capability to retrieve different types of pigments from phytoplankton absorption is critical for further applications. This study investigated the performance of three models and the utility of hyperspectral in vivo phytoplankton absorption spectra for retrieving pigment composition using a large database (n = 1392). Models based on chlorophyll-a (Chl-a model), Gaussian decomposition (Gaussian model), and partial least squares (PLS) regression (PLS model) were compared. Both the Gaussian model and the PLS model were applied to hyperspectral phytoplankton absorption data. Statistical analysis revealed the advantages and limitations of each model. The Chl-a model performed well for chlorophyll-c (Chl-c), diadinoxanthin, fucoxanthin, photosynthetic carotenoids (PSC), and photoprotective carotenoids (PPC), with a median absolute percent difference for cross-validation (MAPDCV) < 58%. The Gaussian model yielded good results for predicting Chl-a, Chl-c, PSC, and PPC (MAPDCV < 43%). The performance of the PLS model was comparable to that of the Chl-a model, and it exhibited improved retrievals of chlorophyll-b, alloxanthin, peridinin, and zeaxanthin. Additional work undertaken with the PLS model revealed the prospects of hyperspectral-resolution data and spectral derivative analyses for retrieving marker pigment concentrations. This study demonstrated the applicability of in situ hyperspectral phytoplankton absorption data for retrieving pigment composition and provided useful insights regarding the development of bio-optical algorithms from hyperspectral and satellite-based ocean-colour observations.

Amoeba: Automated Molecular Excitation Bayesian Line-fitting Algorithm

The hyperfine transitions of the ground-rotational state of the hydroxyl radical (OH) have emerged as a versatile tracer of the diffuse molecular interstellar medium. We present a novel automated Gaussian decomposition algorithm designed specifically for the analysis of the paired on-source and off-source optical depth and emission spectra of these OH transitions. In contrast to existing automated Gaussian decomposition algorithms, Amoeba (Automated Molecular Excitation Bayesian line-fitting Algorithm) employs a Bayesian approach to model selection, fitting all four optical-depth and four emission spectra simultaneously. Amoeba assumes that a given spectral feature can be described by a single centroid velocity and full width at half maximum, with peak values in the individual optical-depth and emission spectra then described uniquely by the column density in each of the four levels of the ground-rotational state, thus naturally including the real physical constraints on these parameters. Additionally, the Bayesian approach includes informed priors on individual parameters that the user can modify to suit different data sets. Here we describe Amoeba and establish its validity and reliability in identifying and fitting synthetic spectra with known (but hidden) parameters, finding that the code performs very well in a series of practical tests. Amoeba’s core algorithm could be adapted to the analysis of other species with multiple transitions interconnecting shared levels (e.g., the 700 MHz lines of the first excited rotational state of CH). Users are encouraged to adapt and modify Amoeba to suit their own use cases.

Ahead of the (ROC) Curve: A Statistical Approach to Utilizing Ex-Gaussian Parameters of Reaction Time in Diagnosing ADHD Across Three Developmental Periods

Introduction: Performance on executive function (EF) tasks is only modestly predictive of a diagnosis of Attention Deficit Hyperactivity Disorder (ADHD), despite the common assumption that EF deficits are ubiquitous to the disorder. The current study sought to determine whether ex-Gaussian parameters of simple reaction time are better able to discriminate between children and adults with and without ADHD, compared with traditional measures of inhibitory control. Methods: Receiver Operating Characteristic (ROC) analyses and the area under the curve (AUC) were used to examine the ability of performance on two commonly used tasks of inhibitory control (i.e. stop signal reaction time (SSRT) and go-no-go tasks) to predict ADHD status in preschool (N = 108), middle childhood (N = 309), and young adulthood (N = 133). Results: Across all samples, SSRT, go-no-go percentage of failed inhibits, and standard deviation of reaction (SDRT) time to “go” trials, all successfully discriminated between individuals with and without ADHD. Ex-Gaussian decomposition of the RT distribution indicated that both larger tau and larger sigma drove findings for SDRT. Contrary to predictions, traditional measures of inhibitory control were equal if not better predictors of ADHD status than ex-Gaussian parameters. Conclusions: Findings support ongoing work to quantify the separate contributions of cognitive subprocesses that drive task performance, which in turn is critical to developing and improving process-based approaches in clinical assessment.

High-Precision CO2 Column Length Analysis on the Basis of a 1.57-μm Dual-Wavelength IPDA Lidar

For high-precision measurements of the CO2 column concentration in the atmosphere with airborne integrated path differential absorption (IPDA) Lidar, the exact distance of the Lidar beam to the scattering surface, that is, the length of the column, must be measured accurately. For the high-precision inversion of the column length, we propose a set of methods on the basis of the actual conditions, including autocorrelation detection, adaptive filtering, Gaussian decomposition, and optimized Levenberg–Marquardt fitting based on the generalized Gaussian distribution. Then, based on the information of a pair of laser pulses, we use the direct adjustment method of unequal precision to eliminate the error in the distance measurement. Further, the effect of atmospheric delay on distance measurements is considered, leading to further correction of the inversion results. At last, an airborne experiment was carried out in a sea area near Qinhuangdao, China on 14 March 2019. The results showed that the ranging accuracy can reach 0.9066 m, which achieved an excellent ranging accuracy on 1.57-μm IPDA Lidar and met the requirement for high-precision CO2 column length inversion.

H I filaments are cold and associated with dark molecular gas

Context. There are significant amounts of H2 in the Milky Way. Due to its symmetry H2 does not radiate at radio frequencies. CO is thought to be a tracer for H2; however, CO is formed at significantly higher opacities than H2. Thus, toward high Galactic latitudes significant amounts of H2 are hidden and are called CO–dark. Aims. We demonstrate that the dust-to-gas ratio is a tool for identifying locations and column densities of CO–dark H2. Methods. We adopt the hypothesis of a constant E(B−V)∕NH ratio, independent of phase transitions from H I to H2. We investigate the Doppler temperatures TD, from a Gaussian decomposition of HI4PI data, to study temperature dependences of E(B−V)∕NHI. Results. The E(B−V)∕NHI ratio in the cold H I gas phase is high in comparison to the warmer phase. We consider this as evidence that cold H I gas toward high Galactic latitudes is associated with H2. Beyond CO–bright regions, for TD ≤ 1165 K we find a correlation (NHI + 2NH2)∕NHI ∝−logTD. In combination with a factor XCO = 4.0 × 1020 cm−2 (K km s−1)−1 this yields NH∕E(B−V) ~ 5.1 to 6.7 × 1021 cm−2 mag−1 for the full sky, which is compatible with X-ray scattering and UV absorption line observations. Conclusions. Cold H I with TD ≤ 1165 K contains on average 46% CO–dark H2. Prominent filaments have TD ≤ 220 K and typical excitation temperatures Tex ~ 50 K. With a molecular gas fraction of ≥61% they are dominated dynamically by H2.

The interpretation of protoplanetary disc wind diagnostic lines from X-ray photoevaporation and analytical MHD models

ABSTRACT High-resolution spectra of typical wind diagnostics ([O i] 6300 Å and other forbidden emission lines) can often be decomposed into multiple components: high-velocity components with blueshifts up to several 100 km s−1 are usually attributed to fast jets, while narrow (NLVC) and broad (BLVC) low-velocity components are believed to trace slower disc winds. Under the assumption that the line broadening is dominated by Keplerian rotation, several studies have found that the BLVCs should trace gas launched between 0.05 and 0.5 au and correlations between the properties of BLVCs and NLVCs have been interpreted as evidence for the emission tracing an extended magnetohydrodynamics (MHD) wind and not a photoevaporative wind. We calculated synthetic line profiles obtained from detailed photoionization calculations of an X-ray photoevaporation model and a simple MHD wind model and analysed the emission regions of different diagnostic lines and the resulting spectral profiles. The photoevaporation model reproduces most of the observed NLVCs but not the BLVCs or HVCs. The MHD model is able to reproduce all components but produces Keplerian double peaks at average inclinations that are rarely observed. The combination of MHD and photoevaporative winds could solve this problem. Our results suggest that the Gaussian decomposition does not allow for a clear distinction of flux from different wind regions and that the line broadening is often dominated by the velocity gradient in the outflow rather than by Keplerian rotation. We show that observed correlations between BLVC and NLVC do not necessarily imply a common origin in an extended MHD wind.

Modeling and Analysis of an Echo Laser Pulse Waveform for the Orientation Determination of Space Debris

Orientation information of space debris is required to improve the orbital prediction accuracy for mitigation or elimination of a significant threat to not only human space activities but also operational satellites. Obtaining orientation information is currently achievable by applying photometry, adaptive optics (AO) and satellite laser ranging (SLR) technologies. In this study, a new method is proposed based on an echo laser pulse waveform (ELPW) for the orientation determination of space debris; its feasibility was also investigated by numerical simulations. Unlike the photometry and AO technologies available just under the sun-illumination condition and the SLR technology applicable only for cooperative targets, the ELPW is achievable by using a high power laser regardless of the above measurement constraints. A mathematical model is derived to generate the ELPW, and the beam broadening and spreading due to the atmospheric turbulence is taken into account. The Gaussian decomposition based on a genetic algorithm was employed to the ELPWs in order to analyze the orientation features. It is demonstrated from the numerical simulations that the ELPWs have distinctive shapes characterizing the orientation of space debris and therefore our approach was capable of providing orientation information.

Autonomous Gaussian decomposition of the Galactic Ring Survey

The analysis of large molecular line surveys of the Galactic plane is essential for our understanding of the gas kinematics on Galactic scales and, in particular, its link with the formation and evolution of dense structures in the interstellar medium. An approximation of the emission peaks with Gaussian functions allows for an efficient and straightforward extraction of useful physical information contained in the shape and Doppler-shifted frequency of the emission lines contained in these enormous data sets. In this work, we present an overview and the first results of a Gaussian decomposition of the entire Galactic Ring Survey (GRS) 13CO (1–0) data that consists of about 2.3 million spectra. We performed the decomposition with the fully automated GAUSSPY+ algorithm and fitted about 4.6 million Gaussian components to the GRS spectra. These decomposition results enable novel and unexplored ways to interpret and study the gas velocity structure. We discuss the statistics of the fit components and relations between the fitted intensities, velocity centroids, and velocity dispersions. We find that the magnitude of the velocity dispersion values increase towards the inner Galaxy and around the Galactic midplane, which we speculate is partly due to the influence of the Galactic bar and regions with higher non-thermal motions located in the midplane, respectively. We also used our decomposition results to infer global properties of the gas emission and find that the number of fit components used per spectrum is indicative of the amount of structure along the line of sight. We find that the emission lines from regions located on the far side of the Galaxy show increased velocity dispersion values, which are likely due to beam averaging effects. We demonstrate how this trend has the potential to aid in characterising Galactic structure by disentangling emission that belongs to the nearby Aquila Rift molecular cloud from emission that is more likely associated with the Perseus and Outer spiral arms. With this work, we also make our entire decomposition results available.