An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval

Machine learning is now used in many areas of astrophysics, from detecting exoplanets in Kepler transit signals to removing telescope systematics. Recent work demonstrated the potential of using machine learning algorithms for atmospheric retrieval by implementing a random forest to perform retrievals in seconds that are consistent with the traditional, computationally-expensive nested-sampling retrieval method. We expand upon their approach by presenting a new machine learning model, \texttt{plan-net}, based on an ensemble of Bayesian neural networks that yields more accurate inferences than the random forest for the same data set of synthetic transmission spectra. We demonstrate that an ensemble provides greater accuracy and more robust uncertainties than a single model. In addition to being the first to use Bayesian neural networks for atmospheric retrieval, we also introduce a new loss function for Bayesian neural networks that learns correlations between the model outputs. Importantly, we show that designing machine learning models to explicitly incorporate domain-specific knowledge both improves performance and provides additional insight by inferring the covariance of the retrieved atmospheric parameters. We apply \texttt{plan-net} to the Hubble Space Telescope Wide Field Camera 3 transmission spectrum for WASP-12b and retrieve an isothermal temperature and water abundance consistent with the literature. We highlight that our method is flexible and can be expanded to higher-resolution spectra and a larger number of atmospheric parameters

The use of dithiothreitol for the quantitative analysis of elemental sulfur concentrations and isotopes in environmental samples

Determining the concentration and isotopic composition of elemental sulfur in modern and ancient environments is essential to improved interpretation of the mechanisms and pathways of sulfur utilization in biogeochemical cycles. Elemental sulfur can be extracted from sediment or water samples and quantified by converting to hydrogen sulfide. Alternatively, elemental sulfur concentrations can themselves be analyzed using HPLC and other methodologies; however, the preparation and analysis times can be long and these methods are not amenable to stable isotopic analysis. Current reduction methods involve the use of costly and specialized glassware in addition to toxins such as chromium chloride or cyanide to reduce the sulfur to hydrogen sulfide. The novel reduction method presented here uses dithiothreitol (DTT) as a less toxic reducing agent to obtain both elemental sulfur concentrations and isotopic composition from the same sample. The sample is dissolved in an aqueous or organic liquid medium and upon reaction with DTT, the elemental sulfur is volatilized as hydrogen sulfide and collected in a sulfide trap using an inexpensive gas extraction apparatus. The evolved sulfide concentrations can easily be measured for concentration, by absorbance spectrophotometery or voltammetry techniques, and then analyzed for sulfur isotopic composition. The procedure is quantitative at >93% recovery to dissolved elemental sulfur with no observed sulfur isotope fractionation during reduction and recovery. Controlled experiments also demonstrate that DTT is not reactive to sulfate, sulfite, pyrite, or organic sulfur

Alternative Pest Control Methods for Homeowners

This fact sheet explains how using a comprehensive, or integrated pest management approach, will help home gardeners reduce their reliance on pesticides for pest control

Accurate Machine Learning Atmospheric Retrieval via a Neural Network Surrogate Model for Radiative Transfer

Atmospheric retrieval determines the properties of an atmosphere based on its measured spectrum. The low signal-to-noise ratio of exoplanet observations require a Bayesian approach to determine posterior probability distributions of each model parameter, given observed spectra. This inference is computationally expensive, as it requires many executions of a costly radiative transfer (RT) simulation for each set of sampled model parameters. Machine learning (ML) has recently been shown to provide a significant reduction in runtime for retrievals, mainly by training inverse ML models that predict parameter distributions, given observed spectra, albeit with reduced posterior accuracy. Here we present a novel approach to retrieval by training a forward ML surrogate model that predicts spectra given model parameters, providing a fast approximate RT simulation that can be used in a conventional Bayesian retrieval framework without significant loss of accuracy. We demonstrate our method on the emission spectrum of HD 189733 b and find good agreement with a traditional retrieval from the Bayesian Atmospheric Radiative Transfer (BART) code (Bhattacharyya coefficients of 0.9843--0.9972, with a mean of 0.9925, between 1D marginalized posteriors). This accuracy comes while still offering significant speed enhancements over traditional RT, albeit not as much as ML methods with lower posterior accuracy. Our method is ~9x faster per parallel chain than BART when run on an AMD EPYC 7402P central processing unit (CPU). Neural-network computation using an NVIDIA Titan Xp graphics processing unit is 90--180x faster per chain than BART on that CPU.Comment: 16 pages, 4 figures, submitted to PSJ 3/4/2020, revised 1/22/2021. Text restructured and updated for clarity, model updated and expanded to work for range of hot Jupiters, results/plots updated, two new appendices to further justify model selection and methodolog

Characterization of diverse bacteriohopanepolyols in a permanently stratified, hyper-euxinic lake

Bacteriohopanepolyols (BHPs) are a diverse class of bacterial lipids that hold promise as biomarkers of specific microbes, microbial processes, and environmental conditions. BHPs have been characterized in a variety of terrestrial and aquatic environments, but less is known about their distribution and abundance in extreme environmental systems. In the present study, samples taken from the water column and upper sediments of the hyper-euxinic, meromictic Mahoney Lake (Canada) were analyzed for BHPs. Analyses show distinct BHP distributions within the oxic mixolimnion, the chemocline, and the euxinic monimolimnion. Bacteriohopanetetrol (BHT) and unsaturated BHT are the dominant BHPs found in the oxic mixolimnion and at the chemocline, whereas a novel BHP (tentatively identified as diunsaturated aminotriol) dominates the euxinic monimolimnion. Along with the novel BHP structure, composite BHPs (i.e., BHT-cyclitol ether and BHT-glucosamine) were observed in the euxinic monimolimnion and sediments, indicating their production by anaerobic bacteria. Complementary metagenomic analysis of genes involved in BHP biosynthesis (i.e., shc, hpnH, hpnO, hpnP, and hpnR) further revealed that BHPs in Mahoney Lake are most likely produced by bacteria belonging to Deltaproteobacteria, Chloroflexi, Planctomycetia, and Verrucomicrobia. The combined observations of BHP distribution and metagenomic analyses additionally indicate that 2- and 3-methyl BHTs are produced within the euxinic sediments in response to low oxygen and high osmotic concentrations, as opposed to being diagnostic biomarkers of cyanobacteria and aerobic metabolisms

Bovine Viral Diarrhea diagnostic testing results in the Intermountain West- comparison between test methods, age, sex and beef vs. dairy breeds

Prevalence of Bovine viral diarrhea virus (BVDV) (“detected” test results) among all bovine samples tested at the Utah Veterinary Diagnostic Laboratory from 2008 - 2013 was calculated, and results were compared by age, sex, or breed of the cattle and BVDV diagnostic test methods. Necropsies were tested for BVDV when lesions suggestive of infection were identified. Adults, juveniles and most calves were tested by antigen (Ag) capture ELISA, while fetuses and some calves were tested by real-time reverse transcriptase PCR. Cattle originated from Utah and surrounding states. Chi-square analyses were used to test for significant differences in BVDV prevalence between age, sex, breed and test methods. Bovine viral diarrhea virus was detected in 105/8,975 samples (1.2%), including 22/180 necropsies (12.2%). Detection of BVDV by each test method was: Ag Capture ELISA-skin 79/7,692 (1.0%); Ag Capture ELISA-serum 19/1,195 (1.6%); PCR 7/88 (8.0%). Detection of BVDV by age, sex, breed was: male 5/215 (2.3%); female 9/382 (2.4%); fetus 3/36 (8.3%); calf (1-200 days old) 29/579 (5.0%); juvenile (201-729 days old) 4/183 (2.2%); adult (≥ 730 days old) 4/75 (5.3%); dairy 25/750 (3.3%); beef 26/1,600 (1.6%). There were no significant differences in BVDV detection by age or sex. Necropsied animals (P\u3c0.0001), those tested with PCR (P\u3c0.0001) and dairy breeds (P=0.07), were more likely to be detected with BVDV. When prevalence of BVDV has been reported over the last 20 years, it has focused on the 0.1% prevalence of persistently infected (PI) cattle, but PI cattle are a source of infection for large numbers of herdmates. The 8% prevalence in aborted fetuses and overall prevalence of \u3e1% demonstrates that despite the low reported prevalence of persistently infected cattle, BVDV remains an important bovine disease