scholarly journals Stellar Surface Inhomogeneities as a Potential Source of the Atmospheric Signal Detected in the K2-18b Transmission Spectrum

2021 ◽  
Vol 162 (6) ◽  
pp. 300
Author(s):  
Thomas Barclay ◽  
Veselin B. Kostov ◽  
Knicole D. Colón ◽  
Elisa V. Quintana ◽  
Joshua E. Schlieder ◽  
...  
Keyword(s):  
Hubble Space Telescope ◽  
Data Sets ◽  
Transmission Spectroscopy ◽  
Yield Information ◽  
Observation Strategy ◽  
Composition And Structure ◽  
Stellar Photosphere ◽  
Best Fitting ◽  
Using Data ◽  
Rule Out

Abstract Transmission spectroscopy of transiting exoplanets is a proven technique that can yield information on the composition and structure of a planet’s atmosphere. However, transmission spectra may be compromised by inhomogeneities in the stellar photosphere. The sub-Neptune-sized habitable zone planet K2-18b has water absorption detected in its atmosphere using data from the Hubble Space Telescope (HST). Herein, we examine whether the reported planetary atmospheric signal seen from HST transmission spectroscopy of K2-18b could instead be induced by time-varying starspots. We built a time-variable spectral model of K2-18 that is designed to match the variability amplitude seen in K2 photometric data, and we used this model to simulate 1000 HST data sets that follow the K2-18b observation strategy. More than 1% of these provide a better fit to the data than the best-fitting exoplanet atmosphere model. After resampling our simulations to generate synthetic HST observations, we find that 40% of random draws would produce an atmospheric detection at a level at least as significant as that seen in the actual HST data of K2-18b. This work illustrates that the inferred detection of an atmosphere on K2-18b may alternatively be explained by stellar spectral contamination due to the inhomogeneous photosphere of K2-18. We do not rule out a detection of water in the planet’s atmosphere, but we provide a plausible alternative that should be considered and conclude that more observations are needed to fully rule out stellar contamination.

Assessment of Regional Explosion Discriminants Using Data Sets of Unparalleled Spatial Sampling

2012 ◽  
Author(s):  
Kate C. Miller ◽  
Lindsay L. Worthington ◽  
Steven Harder ◽  
Scott Phillips ◽  
Hans Hartse ◽  
...  
Keyword(s):  
Spatial Sampling ◽  
Data Sets ◽  
Using Data

scholarly journals Using Machine Learning Methods to Identify Particle Types from Doppler Lidar Measurements in Iceland

2021 ◽  
Vol 13 (13) ◽  
pp. 2433
Author(s):  
Shu Yang ◽  
Fengchao Peng ◽  
Sibylle von Löwis ◽  
Guðrún Nína Petersen ◽  
David Christian Finger
Keyword(s):  
Machine Learning ◽  
Weather Conditions ◽  
Dust Storms ◽  
Machine Learning Algorithms ◽  
Lidar Data ◽  
Data Sets ◽  
Doppler Lidar ◽  
Lidar Measurements ◽  
Using Data ◽  
Filter Noise

Doppler lidars are used worldwide for wind monitoring and recently also for the detection of aerosols. Automatic algorithms that classify the lidar signals retrieved from lidar measurements are very useful for the users. In this study, we explore the value of machine learning to classify backscattered signals from Doppler lidars using data from Iceland. We combined supervised and unsupervised machine learning algorithms with conventional lidar data processing methods and trained two models to filter noise signals and classify Doppler lidar observations into different classes, including clouds, aerosols and rain. The results reveal a high accuracy for noise identification and aerosols and clouds classification. However, precipitation detection is underestimated. The method was tested on data sets from two instruments during different weather conditions, including three dust storms during the summer of 2019. Our results reveal that this method can provide an efficient, accurate and real-time classification of lidar measurements. Accordingly, we conclude that machine learning can open new opportunities for lidar data end-users, such as aviation safety operators, to monitor dust in the vicinity of airports.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals 2D kinematics of massive stars near the Galactic Centre

2020 ◽  
Vol 500 (3) ◽  
pp. 3213-3239
Author(s):  
Mattia Libralato ◽  
Daniel J Lennon ◽  
Andrea Bellini ◽  
Roeland van der Marel ◽  
Simon J Clark ◽  
...  
Keyword(s):  
Moving Objects ◽  
Hubble Space Telescope ◽  
Massive Stars ◽  
Line Of Sight ◽  
Galactic Centre ◽  
Harsh Environment ◽  
Proper Motions ◽  
Sgr A ◽  
Better Than ◽  
Rule Out

ABSTRACT The presence of massive stars (MSs) in the region close to the Galactic Centre (GC) poses several questions about their origin. The harsh environment of the GC favours specific formation scenarios, each of which should imprint characteristic kinematic features on the MSs. We present a 2D kinematic analysis of MSs in a GC region surrounding Sgr A* based on high-precision proper motions obtained with the Hubble Space Telescope. Thanks to a careful data reduction, well-measured bright stars in our proper-motion catalogues have errors better than 0.5 mas yr−1. We discuss the absolute motion of the MSs in the field and their motion relative to Sgr A*, the Arches, and the Quintuplet. For the majority of the MSs, we rule out any distance further than 3–4 kpc from Sgr A* using only kinematic arguments. If their membership to the GC is confirmed, most of the isolated MSs are likely not associated with either the Arches or Quintuplet clusters or Sgr A*. Only a few MSs have proper motions, suggesting that they are likely members of the Arches cluster, in agreement with previous spectroscopic results. Line-of-sight radial velocities and distances are required to shed further light on the origin of most of these massive objects. We also present an analysis of other fast-moving objects in the GC region, finding no clear excess of high-velocity escaping stars. We make our astro-photometric catalogues publicly available.

POSTPARTUM AMENORRHOEA IN RURAL EASTERN UTTAR PRADESH, INDIA

1998 ◽  
Vol 30 (2) ◽  
pp. 227-243
Author(s):  
K. N. S. YADAVA ◽  
S. K. JAIN
Keyword(s):  
Higher Education ◽  
Uttar Pradesh ◽  
Current Status Data ◽  
North India ◽  
Current Status ◽  
Data Sets ◽  
Survival Status ◽  
The Mean ◽  
The Difference ◽  
Using Data

This paper calculates the mean duration of the postpartum amenorrhoea (PPA) and examines its demographic, and socioeconomic correlates in rural north India, using data collected through 'retrospective' (last but one child) as well as 'current status' (last child) reporting of the duration of PPA.The mean duration of PPA was higher in the current status than in the retrospective data;n the difference being statistically significant. However, for the same mothers who gave PPA information in both the data sets, the difference in mean duration of PPA was not statistically significant. The correlates were identical in both the data sets. The current status data were more complete in terms of the coverage, and perhaps less distorted by reporting errors caused by recall lapse.A positive relationship of the mean duration of PPA was found with longer breast-feeding, higher parity and age of mother at the birth of the child, and the survival status of the child. An inverse relationship was found with higher education of a woman, higher education of her husband and higher socioeconomic status of her household, these variables possibly acting as proxies for women's better nutritional status.

scholarly journals Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

2018 ◽  
Vol 7 (2.28) ◽  
pp. 312
Author(s):  
Manu Kohli
Keyword(s):  
Data Extraction ◽  
Indirect Costs ◽  
True Positive Rate ◽  
Spare Parts ◽  
Machine Learning Algorithms ◽  
Machine Language ◽  
Support Vector ◽  
Data Sets ◽  
Equipment Failure ◽  
Using Data

Asset intensive Organizations have searched long for a framework model that would timely predict equipment failure. Timely prediction of equipment failure substantially reduces direct and indirect costs, unexpected equipment shut-downs, accidents, and unwarranted emission risk. In this paper, the author proposes a model that can predict equipment failure by using data from SAP Plant Maintenance module. To achieve that author has applied data extraction algorithm and numerous data manipulations to prepare a classification data model consisting of maintenance records parameters such as spare parts usage, time elapsed since last completed maintenance and the period to the next scheduled maintained and so on. By using unsupervised learning technique of clustering, the author observed a class to cluster evaluation of 80% accuracy. After that classifier model was trained using various machine language (ML) algorithms and subsequently tested on mutually exclusive data sets with an objective to predict equipment breakdown. The classifier model using ML algorithms such as Support Vector Machine (SVM) and Decision Tree (DT) returned an accuracy and true positive rate (TPR) of greater than 95% to predict equipment failure. The proposed model acts as an Advanced Intelligent Control system contributing to the Cyber-Physical Systems for asset intensive organizations. 

Relationship Between Environmental Temperature and Yields of Subarctic and Temperate Zone Fish Species

1983 ◽  
Vol 40 (10) ◽  
pp. 1829-1837 ◽  
Author(s):  
David A. Schlesinger ◽  
Henry A. Regier
Keyword(s):  
Optimal Growth ◽  
Temperate Zone ◽  
Data Sets ◽  
Coregonus Clupeaformis ◽  
Influence Of Temperature On ◽  
Northern United States ◽  
Using Data ◽  
Curvilinear Relationships ◽  
Fish Yields

Fishes inhabiting subarctic and temperate zone lakes exhibit distinct optimal growth temperatures and temperature preferenda. However, within regional data sets, attempts to correlate fish yields with temperature variables have generally been unsuccessful. In our study, curvilinear relationships between "long-term mean annual air temperature" (TEMP) and sustained yields of three species were fitted using data from 23 intensively fished lakes in Canada and the northern United States. Optimum TEMP values for sustained yield were approximately −1.0, 1.5, and 2 °C, respectively, for lake whitefish (Coregonus clupeaformis), northern pike (Esox lucius), and walleye (Stizostedion vitreum vitreum). These differences suggest that the influence of temperature on sustained fish yields from subarctic and temperate zone lakes may, in the past, have been underestimated.

Developing an Empirical Yield-Prediction Model Based on Wheat and Wild Oat (Avena fatua) Density, Nitrogen and Herbicide Rate, and Growing-Season Precipitation

Weed Science ◽  
2007 ◽  
Vol 55 (6) ◽  
pp. 652-664 ◽  
Author(s):  
N. C. Wagner ◽  
B. D. Maxwell ◽  
M. L. Taper ◽  
L. J. Rew
Keyword(s):  
Field Data ◽  
Wheat Yield ◽  
Growing Season ◽  
Predictor Variables ◽  
Data Sets ◽  
Wild Oat ◽  
Best Fitting ◽  
Weed Infestation ◽  
Growing Season Precipitation ◽  
Better Than

To develop a more complete understanding of the ecological factors that regulate crop productivity, we tested the relative predictive power of yield models driven by five predictor variables: wheat and wild oat density, nitrogen and herbicide rate, and growing-season precipitation. Existing data sets were collected and used in a meta-analysis of the ability of at least two predictor variables to explain variations in wheat yield. Yield responses were asymptotic with increasing crop and weed density; however, asymptotic trends were lacking as herbicide and fertilizer levels were increased. Based on the independent field data, the three best-fitting models (in order) from the candidate set of models were a multiple regression equation that included all five predictor variables (R2= 0.71), a double-hyperbolic equation including three input predictor variables (R2= 0.63), and a nonlinear model including all five predictor variables (R2= 0.56). The double-hyperbolic, three-predictor model, which did not include herbicide and fertilizer influence on yield, performed slightly better than the five-variable nonlinear model including these predictors, illustrating the large amount of variation in wheat yield and the lack of concrete knowledge upon which farmers base their fertilizer and herbicide management decisions, especially when weed infestation causes competition for limited nitrogen and water. It was difficult to elucidate the ecological first principles in the noisy field data and to build effective models based on disjointed data sets, where none of the studies measured all five variables. To address this disparity, we conducted a five-variable full-factorial greenhouse experiment. Based on our five-variable greenhouse experiment, the best-fitting model was a new nonlinear equation including all five predictor variables and was shown to fit the greenhouse data better than four previously developed agronomic models with anR2of 0.66. Development of this mathematical model, through model selection and parameterization with field and greenhouse data, represents the initial step in building a decision support system for site-specific and variable-rate management of herbicide, fertilizer, and crop seeding rate that considers varying levels of available water and weed infestation.

Three-centre C—H...O hydrogen bonds in the DNA minor groove: analysis of oligonucleotide crystal structures

1999 ◽  
Vol 55 (12) ◽  
pp. 2005-2012 ◽  
Author(s):  
Anirban Ghosh ◽  
Manju Bansal
Keyword(s):  
Hydrogen Bonds ◽  
Crystal Structures ◽  
Base Pair ◽  
Minor Groove ◽  
Local Geometry ◽  
Data Sets ◽  
Base Pairs ◽  
Dna Minor Groove ◽  
Close Proximity ◽  
Using Data

AA·TT and GA·TC dinucleotide steps in B-DNA-type oligomeric crystal structures and in protein-bound DNA fragments (solved using data with resolution <2.6 Å) show very small variations in their local dinucleotide geometries. A detailed analysis of these crystal structures reveals that in AA·TT and GA·TC steps the electropositive C2—H2 group of adenine is in very close proximity to the keto O atoms of both the pyrimidine bases in the antiparallel strand of the duplex structure, suggesting the possibility of intra-base pair as well as cross-strand inter-base pair C—H...O hydrogen bonds in the DNA minor groove. The C2—H2...O2 hydrogen bonds in the A·T base pairs could be a natural consequence of Watson–Crick pairing. However, the cross-strand interactions between the bases at the 3′-end of the AA·TT and GA·TC steps obviously arise owing to specific local geometry of these steps, since a majority of the H2...O2 distances in both data sets are considerably shorter than their values in the uniform fibre model (3.3 Å) and many are even smaller than the sum of the van der Waals radii. The analysis suggests that in addition to already documented features such as the large propeller twist of A·T base pairs and the hydration of the minor groove, these C2—H2...O2 cross-strand interactions may also play a role in the narrowing of the minor groove in A-tract regions of DNA and help explain the high structural rigidity and stability observed for poly(dA)·poly(dT).

The Thermal Volatilization of Petrodisel/Biodiesel from Waste Oil

2011 ◽  
Vol 347-353 ◽  
pp. 2656-2660
Author(s):  
Xiu Chen ◽  
Yin Nan Yuan ◽  
Yong Bin Lai
Keyword(s):  
Chemical Composition ◽  
Methyl Esters ◽  
Gas Chromatography Mass Spectrometry ◽  
Waste Oil ◽  
Compression Ignition Engine ◽  
Yield Information ◽  
Composition And Structure ◽  
Similar Chemical ◽  
Ignition Quality ◽  
Long Chain

Thermogravimetry (TG) has been employed to yield information on the thermal volatilization of the fuels since the volatility influences the ignition quality of the fuels in a compression ignition engine. The chemical composition of -10 petrodiesel (-10PD) and waste oil biodiesel (WME) was analyzed by gas chromatography-mass spectrometry. The thermal volatilization of biodiesel and its blends was investigated by TG and liquid volatile theory. Volatile index was put forward for describing biodiesel/petrodisel volatility. A good correlation model was proposed for calculate the biodiesel/petrodiesel volatility by biodiesel blending ratio. The study showed that -10PD and WME had similar chemical composition and structure. -10PD was mainly composed of long chain alkanes: C8–C26. WME was mainly composed of long chain fatty acid methyl esters: C14:0–C22:0, C16:1–C22:1, C18:2 and C18:3. The volatile indexes of WME and -10PD were 1.47E-04 and 3.64E-05, respectively. The biodiesel was considerably more volatile in comparison to the petrodiesel. The WME/-10PD volatility was better with increasing the biodiesel blending ratio.

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