A Deep Learning Pipeline for Unified Modelling of Time-Correlated Noise in Exoplanets Observations

2020 ◽  
Author(s):  
Mario Morvan ◽  
Nikos Nikolau ◽  
Angelos Tsiaras ◽  
Ingo Waldmann
Keyword(s):  
Neural Network ◽  
Systematic Errors ◽  
Light Curves ◽  
The Other ◽  
Correlated Noise ◽  
Processing Pipeline ◽  
Systematic Behaviour ◽  
Noise Correction ◽  
Host Stars

<p>The precise derivation of transit depths from stellar light curves is a key component in the construction of exoplanet transit spectra, and thereby for the characterization of exoplanet atmospheres. However, it is still deeply affected by various kinds of complex systematic errors and noises taking their source from host stars’ or instruments’ variability. On the other hand, as the volume of exoplanetary data is quickly increasing, a new way is being opened up for using machine learning as part of the data processing pipeline. By training a recurrent neural network to model the temporal dependencies in stellar light curves, our results on both real on simulated light curves highlight that it is possible to:</p> <ul> <li>Model accurately the compound of trends and periodic effects with few or no assumptions about the instrument, star, or planetary signals</li> <li>Improve the understanding of each instrument’s systematic behaviour</li> <li>Optimise a deep detrending model jointly with a transit fit</li> <li>Leverage the cross-light curves and cross-instruments information</li> </ul> <p>Such an approach therefore paves the way for a global, flexible and efficient noise-correction pipeline which will be of paramount importance to make the most of exoplanets observations and provide high precision spectra to subsequent atmospheric retrieval pipelines.</p>

scholarly journals Asteroseismic potential of CHEOPS

2018 ◽  
Vol 620 ◽  
pp. A203 ◽  
Author(s):  
A. Moya ◽  
S. Barceló Forteza ◽  
A. Bonfanti ◽  
S. J. A. J. Salmon ◽  
V. Van Grootel ◽  
...  
Keyword(s):  
Time Series ◽  
Main Sequence ◽  
Uncertainty Reduction ◽  
Light Curves ◽  
Accuracy And Precision ◽  
Mass Uncertainty ◽  
The Impact ◽  
Host Stars

Context. Asteroseismology has been impressively boosted during the last decade mainly thanks to space missions such as Kepler/K2 and CoRoT. This has a large impact, in particular, in exoplanetary sciences since the accurate characterization of the exoplanets is convoluted in most cases with the characterization of their hosting star. In the decade before the expected launch of the ESA mission PLATO 2.0, only two important missions will provide short-cadence high-precision photometric time-series: NASA–TESS and ESA–CHEOPS missions, both having high capabilities for exoplanetary sciences. Aims. In this work we want to explore the asteroseismic potential of CHEOPS time-series. Methods. Following the works estimating the asteroseismic potential of Kepler and TESS, we have analysed the probability of detecting solar-like pulsations using CHEOPS light-curves. Since CHEOPS will collect runs with observational times from hours up to a few days, we have analysed the accuracy and precision we can obtain for the estimation of νmax. This is the only asteroseismic observable we can recover using CHEOPS observations. Finally, we have analysed the impact of knowing νmax in the characterization of exoplanet host stars. Results. Using CHEOPS light-curves with the expected observational times we can determine νmax for massive G and F-type stars from late main sequence (MS) on, and for F, G, and K-type stars from post-main sequence on with an uncertainty lower than a 5%. For magnitudes V <  12 and observational times from eight hours up to two days, the HR zone of potential detectability changes. The determination of νmax leads to an internal age uncertainty reduction in the characterization of exoplanet host stars from 52% to 38%; mass uncertainty reduction from 2.1% to 1.8%; radius uncertainty reduction from 1.8% to 1.6%; density uncertainty reduction from 5.6% to 4.7%, in our best scenarios.

scholarly journals Future Observations of Transits and Light Curves from Space

2008 ◽  
Vol 4 (S253) ◽  
pp. 319-328 ◽  
Author(s):  
Charles A. Beichman ◽  
Tom Greene ◽  
John Krist
Keyword(s):  
Thermal Structure ◽  
Light Curves ◽  
Atmospheric Composition ◽  
Light Curve Analysis ◽  
James Webb Space Telescope ◽  
Sky Survey ◽  
Future Space ◽  
Host Stars

AbstractA variety of new observational opportunities have made transit and more generally light curve analysis central to the study of exoplanets. Talks at this IAU 253 Symposium have dramatically highlighted the measurement of the radius, density, atmospheric composition and atmospheric thermal structure, presently for relatively large, hot planets, but soon for smaller planets orbiting further from their host stars. On-going and future space observations will play a key role in the detection and characterization of these planetary systems. After a brief review, I focus on two topics: the need for a sensitive all-sky survey for planets transiting the brightest, closest stars and the follow-up opportunities afforded by the James Webb Space Telescope (JWST).

scholarly journals Detection of exomoons in simulated light curves with a regularized convolutional neural network

2020 ◽  
Vol 640 ◽  
pp. A41
Author(s):  
Rasha Alshehhi ◽  
Kai Rodenbeck ◽  
Laurent Gizon ◽  
Katepalli R. Sreenivasan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Optimization ◽  
Extrasolar Planets ◽  
Classical Method ◽  
Light Curves ◽  
Training Process ◽  
Classification Tasks ◽  
Host Stars ◽  
Better Than

Context. Many moons have been detected around planets in our Solar System, but none has been detected unambiguously around any of the confirmed extrasolar planets. Aims. We test the feasibility of a supervised convolutional neural network to classify photometric transit light curves of planet-host stars and identify exomoon transits, while avoiding false positives caused by stellar variability or instrumental noise. Methods. Convolutional neural networks are known to have contributed to improving the accuracy of classification tasks. The network optimization is typically performed without studying the effect of noise on the training process. Here we design and optimize a 1D convolutional neural network to classify photometric transit light curves. We regularize the network by the total variation loss in order to remove unwanted variations in the data features. Results. Using numerical experiments, we demonstrate the benefits of our network, which produces results comparable to or better than the standard network solutions. Most importantly, our network clearly outperforms a classical method used in exoplanet science to identify moon-like signals. Thus the proposed network is a promising approach for analyzing real transit light curves in the future.

A very rapid in situ Kikuchi technique to determine relative crystal misorientation

1988 ◽  
Vol 46 ◽  
pp. 830-831
Author(s):  
J. I. Bennetch
Keyword(s):  
Electron Beam ◽  
Analytical Techniques ◽  
Superplastic Forming ◽  
The Other ◽  
Kikuchi Pattern ◽  
Kikuchi Line ◽  
Line Analysis

In a recent study of the superplastic forming (SPF) behavior of certain Al-Li-X alloys, the relative misorientation between adjacent (sub)grains proved to be an important parameter. It is well established that the most accurate way to determine misorientation across boundaries is by Kikuchi line analysis. However, the SPF study required the characterization of a large number of (sub)grains in each sample to be statistically meaningful, a very time-consuming task even for comparatively rapid Kikuchi analytical techniques.In order to circumvent this problem, an alternate, even more rapid in-situ Kikuchi technique was devised, eliminating the need for the developing of negatives and any subsequent measurements on photographic plates. All that is required is a double tilt low backlash goniometer capable of tilting ± 45° in one axis and ± 30° in the other axis. The procedure is as follows. While viewing the microscope screen, one merely tilts the specimen until a standard recognizable reference Kikuchi pattern is centered, making sure, at the same time, that the focused electron beam remains on the (sub)grain in question.

Rapid Isolation of High Molecular Weight Urokinase from Native Human Urine

1982 ◽  
Vol 47 (03) ◽  
pp. 197-202 ◽  
Author(s):  
Kurt Huber ◽  
Johannes Kirchheimer ◽  
Bernd R Binder
Keyword(s):  
Molecular Weight ◽  
High Molecular Weight ◽  
Human Urine ◽  
Gel Filtration ◽  
Chain Structure ◽  
The Other ◽  
Single Chain ◽  
Apparent Homogeneity ◽  
Sequential Adsorption

SummaryUrokinase (UK) could be purified to apparent homogeneity starting from crude urine by sequential adsorption and elution of the enzyme to gelatine-Sepharose and agmatine-Sepharose followed by gel filtration on Sephadex G-150. The purified product exhibited characteristics of the high molecular weight urokinase (HMW-UK) but did contain two distinct entities, one of which exhibited a two chain structure as reported for the HMW-UK while the other one exhibited an apparent single chain structure. The purification described is rapid and simple and results in an enzyme with probably no major alterations. Yields are high enough to obtain purified enzymes for characterization of UK from individual donors.

scholarly journals Analysis of the Mechanism by Which Glucose Inhibits Maltose Induction of MAL Gene Expression in Saccharomyces

Genetics ◽  
2000 ◽  
Vol 154 (1) ◽  
pp. 121-132
Author(s):  
Zhen Hu ◽  
Yingzi Yue ◽  
Hua Jiang ◽  
Bin Zhang ◽  
Peter W Sherwood ◽  
...  
Keyword(s):  
Gene Expression ◽  
Glucose Repression ◽  
Protein A ◽  
The Other ◽  
Maltose Permease ◽  
Inducer Exclusion ◽  
Glucose Inhibition ◽  
Independent Mechanism ◽  
Mal Genes

Abstract Expression of the MAL genes required for maltose fermentation in Saccharomyces cerevisiae is induced by maltose and repressed by glucose. Maltose-inducible regulation requires maltose permease and the MAL-activator protein, a DNA-binding transcription factor encoded by MAL63 and its homologues at the other MAL loci. Previously, we showed that the Mig1 repressor mediates glucose repression of MAL gene expression. Glucose also blocks MAL-activator-mediated maltose induction through a Mig1p-independent mechanism that we refer to as glucose inhibition. Here we report the characterization of this process. Our results indicate that glucose inhibition is also Mig2p independent. Moreover, we show that neither overexpression of the MAL-activator nor elimination of inducer exclusion is sufficient to relieve glucose inhibition, suggesting that glucose acts to inhibit induction by affecting maltose sensing and/or signaling. The glucose inhibition pathway requires HXK2, REG1, and GSF1 and appears to overlap upstream with the glucose repression pathway. The likely target of glucose inhibition is Snf1 protein kinase. Evidence is presented indicating that, in addition to its role in the inactivation of Mig1p, Snf1p is required post-transcriptionally for the synthesis of maltose permease whose function is essential for maltose induction.

scholarly journals On a New Geometric Constant Related to the Euler-Lagrange Type Identity in Banach Spaces

Mathematics ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 116
Author(s):  
Qi Liu ◽  
Yongjin Li
Keyword(s):  
Banach Spaces ◽  
Product Space ◽  
Sufficient Conditions ◽  
Normal Structure ◽  
The Other ◽  
Inner Product ◽  
Equivalent Characterization ◽  
Geometric Constant ◽  
Geometric Constants

In this paper, we will introduce a new geometric constant LYJ(λ,μ,X) based on an equivalent characterization of inner product space, which was proposed by Moslehian and Rassias. We first discuss some equivalent forms of the proposed constant. Next, a characterization of uniformly non-square is given. Moreover, some sufficient conditions which imply weak normal structure are presented. Finally, we obtain some relationship between the other well-known geometric constants and LYJ(λ,μ,X). Also, this new coefficient is computed for X being concrete space.

The development of body and organ shape

BMC Zoology ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Ansa E. Cobham ◽  
Christen K. Mirth
Keyword(s):  
Relative Position ◽  
Body Shape ◽  
Single Cells ◽  
The Other ◽  
Geometric Features ◽  
Main Text ◽  
Size Characterization ◽  
Organ Shape ◽  
The Many

Abstract Background Organisms show an incredibly diverse array of body and organ shapes that are both unique to their taxon and important for adapting to their environment. Achieving these specific shapes involves coordinating the many processes that transform single cells into complex organs, and regulating their growth so that they can function within a fully-formed body. Main text Conceptually, body and organ shape can be separated in two categories, although in practice these categories need not be mutually exclusive. Body shape results from the extent to which organs, or parts of organs, grow relative to each other. The patterns of relative organ size are characterized using allometry. Organ shape, on the other hand, is defined as the geometric features of an organ’s component parts excluding its size. Characterization of organ shape is frequently described by the relative position of homologous features, known as landmarks, distributed throughout the organ. These descriptions fall into the domain of geometric morphometrics. Conclusion In this review, we discuss the methods of characterizing body and organ shape, the developmental programs thought to underlie each, highlight when and how the mechanisms regulating body and organ shape might overlap, and provide our perspective on future avenues of research.

scholarly journals Development of a New Pasta Product by the Incorporation of Chestnut Flour and Bee Pollen

2021 ◽  
Vol 11 (14) ◽  
pp. 6617
Author(s):  
Maëlys Brochard ◽  
Paula Correia ◽  
Maria João Barroca ◽  
Raquel P. F. Guiné
Keyword(s):  
Wheat Flour ◽  
Nutritional Composition ◽  
Cooking Time ◽  
The Other ◽  
Physical Characterization ◽  
Chemical Analyses ◽  
High Fiber ◽  
Chestnut Flour ◽  
Cooking Yield

This work aimed at developing fortified pastas incorporating chestnut flour (25–55%) and powdered pollen (5–20%), either separately or in combination, as well as the characterization of the products obtained. To this, a physical characterization was carried out (analyzing texture and color), complemented with chemical analyses to determine the nutritional composition. Results showed that adding chestnut flour over 40% to wheat-flour pasta shortened optimum cooking time and lowered cooking yield, and the addition to pasta prepared with wheat flour and eggs maintained approximately constant the cooking yield. Additionally, the incorporation of pollen powder (up to 20%) in pasta prepared with wheat flour and water or fresh egg shortened the cooking time and cooking yield, in both fresh and dried pasta. The most suitable percentages of the new ingredients were 50% for chestnut and 10% for pollen. Comparing with the control pasta recipe (wheat flour and egg), the addition of chestnut flour (50%) or pollen powder (10%) increased stickiness, adhesiveness and the darkening of the final product (fresh or dried) but maintained the firmness of the pasta. The cooking of fresh or dried pasta enriched with both ingredients turned the pasta clearer and slightly stickier. On the other hand, the addition of chestnut flour and pollen powder in pasta formulation delivered a nutritionally balanced product with high fiber, vitamins and minerals. Overall, chestnut flour and powdered pollen represent promising ingredients for the development of functional fresh and dried pasta formulations.

scholarly journals Fast Accurate and Automatic Brushstroke Extraction

2021 ◽  
Vol 17 (2) ◽  
pp. 1-24
Author(s):  
Yunfei Fu ◽  
Hongchuan Yu ◽  
Chih-Kuo Yeh ◽  
Tong-Yee Lee ◽  
Jian J. Zhang
Keyword(s):  
Neural Network ◽  
Efficient Algorithm ◽  
Deep Neural Network ◽  
High Efficiency ◽  
State Of The Art ◽  
High Reliability ◽  
The Other ◽  
Manual Annotation ◽  
Stroke Extraction ◽  
Art Research

Brushstrokes are viewed as the artist’s “handwriting” in a painting. In many applications such as style learning and transfer, mimicking painting, and painting authentication, it is highly desired to quantitatively and accurately identify brushstroke characteristics from old masters’ pieces using computer programs. However, due to the nature of hundreds or thousands of intermingling brushstrokes in the painting, it still remains challenging. This article proposes an efficient algorithm for brush Stroke extraction based on a Deep neural network, i.e., DStroke. Compared to the state-of-the-art research, the main merit of the proposed DStroke is to automatically and rapidly extract brushstrokes from a painting without manual annotation, while accurately approximating the real brushstrokes with high reliability. Herein, recovering the faithful soft transitions between brushstrokes is often ignored by the other methods. In fact, the details of brushstrokes in a master piece of painting (e.g., shapes, colors, texture, overlaps) are highly desired by artists since they hold promise to enhance and extend the artists’ powers, just like microscopes extend biologists’ powers. To demonstrate the high efficiency of the proposed DStroke, we perform it on a set of real scans of paintings and a set of synthetic paintings, respectively. Experiments show that the proposed DStroke is noticeably faster and more accurate at identifying and extracting brushstrokes, outperforming the other methods.

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