Evidence for Circumburst Extinction of Gamma-Ray Bursts with Dark Optical Afterglows and Evidence for a Molecular Cloud Origin of Gamma-Ray Bursts

First, we show that the gamma-ray bursts with dark optical afterglows (DOAs) cannot be explained by a failure to image deeply enough quickly enough, and argue that circumburst extinction is the most likely solution. If so, many DOAs will be ``revived'' with rapid follow up and NIR searches in the HETE-2 and Swift eras. Next, we consider the effects of dust sublimation and fragmentation, and show that DOAs occur in clouds of size R > 10L_{49}^{1/2} pc and mass M > 3x10^5L_{49} M_{sun}, where L is the luminosity of the optical flash. Stability considerations show that such clouds cannot be diffuse, but must be molecular. Consequently, we compute the expected column density distribution of bursts that occur in Galactic-like molecular clouds, and show that the column density measurements from X-ray spectra of afterglows, DOAs and otherwise, satisfy this expectation in the source frame.Comment: Invited Review. To appear in Procs. of Gamma-Ray Burst and Afterglow Astronomy 2001: A Workshop Celebrating the First Year of the HETE Mission, 8 pages, 8 figures, LaTe

A SVM-Based Multi-Resolution Procedure for the Estimation of the DOAS of Interfering Signals in a Communication System

In this work, the use of a planar antenna system for the estimation of the directions of arrivals (DOAs) of multiple signals impinging on the receiver has been considered. Towards this end, an efficient multi-resolution method based on a SVM-classifier is proposed for determining a probabilitic map of the DOAs of the unknown interfering signals. Numerical results dealing with multiple interferers scenarios in noisy environments are provided in order to assess the feasibility as well as the capability of the proposed approach

A Semi-Blind Source Separation Method for Differential Optical Absorption Spectroscopy of Atmospheric Gas Mixtures

Differential optical absorption spectroscopy (DOAS) is a powerful tool for detecting and quantifying trace gases in atmospheric chemistry \cite{Platt_Stutz08}. DOAS spectra consist of a linear combination of complex multi-peak multi-scale structures. Most DOAS analysis routines in use today are based on least squares techniques, for example, the approach developed in the 1970s uses polynomial fits to remove a slowly varying background, and known reference spectra to retrieve the identity and concentrations of reference gases. An open problem is to identify unknown gases in the fitting residuals for complex atmospheric mixtures. In this work, we develop a novel three step semi-blind source separation method. The first step uses a multi-resolution analysis to remove the slow-varying and fast-varying components in the DOAS spectral data matrix $X$. The second step decomposes the preprocessed data $\hat{X}$ in the first step into a linear combination of the reference spectra plus a remainder, or $\hat{X} = A\,S + R$, where columns of matrix $A$ are known reference spectra, and the matrix $S$ contains the unknown non-negative coefficients that are proportional to concentration. The second step is realized by a convex minimization problem $S = \mathrm{arg} \min \mathrm{norm}\,(\hat{X} - A\,S)$, where the norm is a hybrid $\ell_1/\ell_2$ norm (Huber estimator) that helps to maintain the non-negativity of $S$. The third step performs a blind independent component analysis of the remainder matrix $R$ to extract remnant gas components. We first illustrate the proposed method in processing a set of DOAS experimental data by a satisfactory blind extraction of an a-priori unknown trace gas (ozone) from the remainder matrix. Numerical results also show that the method can identify multiple trace gases from the residuals.Comment: submitted to Journal of Scientific Computin

Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite – Part 2: Methane

Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. SCIAMACHY on ENVISAT is the first satellite instrument whose measurements are sensitive to concentration changes of the two gases at all altitude levels down to the Earth's surface where the source/sink signals are largest. We have processed three years (2003–2005) of SCIAMACHY near-infrared nadir measurements to simultaneously retrieve vertical columns of CO2 (from the 1.58 µm absorption band), CH4 (1.66 µm) and oxygen (O2 A-band at 0.76 µm) using the scientific retrieval algorithm WFM-DOAS. We show that the latest version of WFM-DOAS, version 1.0, which is used for this study, has been significantly improved with respect to its accuracy compared to the previous versions while essentially maintaining its high processing speed (~1 min per orbit, corresponding to ~6000 single measurements, and per gas on a standard PC). The greenhouse gas columns are converted to dry air column-averaged mole fractions, denoted XCO2 (in ppm) and XCH4 (in ppb), by dividing the greenhouse gas columns by simultaneously retrieved dry air columns. For XCO2 dry air columns are obtained from the retrieved O2 columns. For XCH4 dry air columns are obtained from the retrieved CO2 columns because of better cancellation of light path related errors compared to using O2 columns retrieved from the spectrally distant O2 A-band. Here we focus on a discussion of the XCH4 data set. The XCO2 data set is discussed in a separate paper (Part 1). For 2003 we present detailed comparisons with the TM5 model which has been optimally matched to highly accurate but sparse methane surface observations. After accounting for a systematic low bias of ~2% agreement with TM5 is typically within 1–2%. We investigated to what extent the SCIAMACHY XCH4 is influenced by the variability of atmospheric CO2 using global CO2 fields from NOAA's CO2 assimilation system CarbonTracker. We show that the CO2 corrected and uncorrected XCH4 spatio-temporal pattern are very similar but that agreement with TM5 is better for the CarbonTracker CO2 corrected XCH4. In line with previous studies (e.g., Frankenberg et al., 2005b) we find higher methane over the tropics compared to the model. We show that tropical methane is also higher when normalizing the CH4 columns with retrieved O2 columns instead of CO2. In consistency with recent results of Frankenberg et al. (2008b) it is shown that the magnitude of the retrieved tropical methane is sensitive to the choice of the spectroscopic line parameters of water vapour. Concerning inter-annual variability we find similar methane spatio-temporal pattern for 2003 and 2004. For 2005 the retrieved methane shows significantly higher variability compared to the two previous years, most likely due to somewhat larger noise of the spectral measurement

Intercomparison of field measurements of nitrous acid (HONO) during the SHARP campaign

Because of the importance of HONO as a radical reservoir, consistent and accurate measurements of its concentration are needed. As part of SHARP (Study of Houston Atmospheric Radical Precursors), time series of HONO were obtained by six different measurement techniques on the roof of the Moody Tower at the University of Houston. Techniques used were long path differential optical absorption spectroscopy (DOAS), stripping coil-visible absorption photometry (SC-AP), long path absorption photometry (LOPAP® ), mist chamber/ion chromatography (MC-IC), quantum cascade-tunable infrared laser differential absorption spectroscopy (QC-TILDAS), and ion drift-chemical ionization mass spectrometry (ID-CIMS). Various combinations of techniques were in operation from 15 April through 31 May 2009. All instruments recorded a similar diurnal pattern of HONO concentrations with higher median and mean values during the night than during the day. Highest values were observed in the final 2 weeks of the campaign. Inlets for the MC-IC, SC-AP, and QC-TILDAS were collocated and agreed most closely with each other based on several measures. Largest differences between pairs of measurements were evident during the day for concentrations ~100 parts per trillion (ppt). Above ~ 200 ppt, concentrations from the SC-AP, MC-IC, and QC-TILDAS converged to within about 20%, with slightly larger discrepancies when DOAS was considered. During the first 2 weeks, HONO measured by ID-CIMS agreed with these techniques, but ID-CIMS reported higher values during the afternoon and evening of the final 4 weeks, possibly from interference from unknown sources. A number of factors, including building related sources, likely affected measured concentrations

A Method for Finding Structured Sparse Solutions to Non-negative Least Squares Problems with Applications

Demixing problems in many areas such as hyperspectral imaging and differential optical absorption spectroscopy (DOAS) often require finding sparse nonnegative linear combinations of dictionary elements that match observed data. We show how aspects of these problems, such as misalignment of DOAS references and uncertainty in hyperspectral endmembers, can be modeled by expanding the dictionary with grouped elements and imposing a structured sparsity assumption that the combinations within each group should be sparse or even 1-sparse. If the dictionary is highly coherent, it is difficult to obtain good solutions using convex or greedy methods, such as non-negative least squares (NNLS) or orthogonal matching pursuit. We use penalties related to the Hoyer measure, which is the ratio of the $l_1$ and $l_2$ norms, as sparsity penalties to be added to the objective in NNLS-type models. For solving the resulting nonconvex models, we propose a scaled gradient projection algorithm that requires solving a sequence of strongly convex quadratic programs. We discuss its close connections to convex splitting methods and difference of convex programming. We also present promising numerical results for example DOAS analysis and hyperspectral demixing problems.Comment: 38 pages, 14 figure

The PiSpec: A Low-Cost, 3D-Printed Spectrometer for Measuring Volcanic SO2 Emission Rates

Spectroscopy has been used to quantify volcanic gas emission rates, most commonly SO2, for a number of decades. Typically, commercial spectrometers costing 1000s USD are employed for this purpose. The PiSpec is a new, custom-designed, 3D-printed spectrometer based on smartphone sensor technology. This unit has ≈1 nm spectral resolution and a spectral range in the ultraviolet of ≈280–340 nm, and is specifically configured for the remote sensing of SO2 using Differential Optical Absorption Spectroscopy (DOAS). Here we report on the first field deployment of the PiSpec on a volcano, to demonstrate the proof of concept of the device’s functionality in this application area. The study was performed on Masaya Volcano, Nicaragua, which is one of the largest emitters of SO2 on the planet, during a period of elevated activity where a lava lake was present in the crater. Both scans and traverses were performed, with resulting emission rates ranging from 3.2 to 45.6 kg s−1 across two measurement days; these values are commensurate with those reported elsewhere in the literature during this activity phase (Aiuppa et al., 2018; Stix et al., 2018). Furthermore, we tested the PiSpec’s thermal stability, finding a wavelength shift of 0.046 nm/∘C between 2.5 and 45∘C, which is very similar to that of some commercial spectrometers. Given the low build cost of these units (≈500 USD for a one-off build, with prospects for further price reduction with volume manufacture), we suggest these units hold considerable potential for volcano monitoring operations in resource limited environments

The influence of random element displacement on DOA estimates obtained with (Khatri-Rao-)root-MUSIC

Although a wide range of direction of arrival (DOA) estimation algorithms has been described for a diverse range of array configurations, no specific stochastic analysis framework has been established to assess the probability density function of the error on DOA estimates due to random errors in the array geometry. Therefore, we propose a stochastic collocation method that relies on a generalized polynomial chaos expansion to connect the statistical distribution of random position errors to the resulting distribution of the DOA estimates. We apply this technique to the conventional root-MUSIC and the Khatri-Rao-root-MUSIC methods. According to Monte-Carlo simulations, this novel approach yields a speedup by a factor of more than 100 in terms of CPU-time for a one-dimensional case and by a factor of 56 for a two-dimensional case