scholarly journals The HARPS search for southern extra-solar planets – XLV. Two Neptune mass planets orbiting HD 13808: a study of stellar activity modelling’s impact on planet detection

2021 ◽  
Vol 503 (1) ◽  
pp. 1248-1263
Author(s):  
E Ahrer ◽  
D Queloz ◽  
V M Rajpaul ◽  
D Ségransan ◽  
F Bouchy ◽  
...  
Keyword(s):  
State Of The Art ◽  
Gaussian Process Regression ◽  
Comprehensive Analysis ◽  
Dwarf Star ◽  
Planet Detection ◽  
Stellar Activity ◽  
Sampling Algorithm ◽  
Competing Models ◽  
Activity Models ◽  
Linear Correlations

ABSTRACT We present a comprehensive analysis of 10 yr of HARPS radial velocities (RVs) of the K2V dwarf star HD 13808, which has previously been reported to host two unconfirmed planet candidates. We use the state-of-the-art nested sampling algorithm PolyChord to compare a wide variety of stellar activity models, including simple models exploiting linear correlations between RVs and stellar activity indicators, harmonic models for the activity signals, and a more sophisticated Gaussian process regression model. We show that the use of overly simplistic stellar activity models that are not well-motivated physically can lead to spurious ‘detections’ of planetary signals that are almost certainly not real. We also reveal some difficulties inherent in parameter and model inference in cases where multiple planetary signals may be present. Our study thus underlines the importance both of exploring a variety of competing models and of understanding the limitations and precision settings of one’s sampling algorithm. We also show that at least in the case of HD 13808, we always arrive at consistent conclusions about two particular signals present in the RV, regardless of the stellar activity model we adopt; these two signals correspond to the previously reported though unconfirmed planet candidate signals. Given the robustness and precision with which we can characterize these two signals, we deem them secure planet detections. In particular, we find two planets orbiting HD 13808 at distances of 0.11, 0.26 au with periods of 14.2, 53.8 d, and minimum masses of 11, 10 M⊕.

scholarly journals Social Media Adoption, Usage And Impact In Business-To-Business (B2B) Context: A State-Of-The-Art Literature Review

2021 ◽  
Author(s):  
Yogesh K. Dwivedi ◽  
Elvira Ismagilova ◽  
Nripendra P. Rana ◽  
Ramakrishnan Raman
Keyword(s):  
Social Media ◽  
Media Use ◽  
State Of The Art ◽  
Relevant Literature ◽  
Comprehensive Analysis ◽  
Social Media Use ◽  
Weight Analysis ◽  
Social Media Tools ◽  
Use Of Social Media ◽  
Business To Business

AbstractSocial media plays an important part in the digital transformation of businesses. This research provides a comprehensive analysis of the use of social media by business-to-business (B2B) companies. The current study focuses on the number of aspects of social media such as the effect of social media, social media tools, social media use, adoption of social media use and its barriers, social media strategies, and measuring the effectiveness of use of social media. This research provides a valuable synthesis of the relevant literature on social media in B2B context by analysing, performing weight analysis and discussing the key findings from existing research on social media. The findings of this study can be used as an informative framework on social media for both, academic and practitioners.

Requirements Engineering for Courseware Development

2011 ◽  
pp. 170-188 ◽  
Author(s):  
Ines Grützner ◽  
Barbara Paech
Keyword(s):  
Requirements Engineering ◽  
State Of The Art ◽  
Comprehensive Analysis ◽  
Further Education ◽  
The State ◽  
Requirements Specification ◽  
Development Projects ◽  
Learning Context ◽  
Active Involvement ◽  
The Web

Technology-enabled learning using the Web and the computer and courseware, in particular, is becoming more and more important as an addition, extension, or replacement of traditional further education measures. This chapter introduces the challenges and possible solutions for requirements engineering (RE) in courseware development projects. First the state-of-the-art in courseware requirements engineering is analyzed and confronted with the most important challenges. Then the IntView methodology is described as one solution for these challenges. The main features of IntView RE are: support of all roles from all views on courseware RE; focus on the audience supported by active involvement of audience representatives in all activities; comprehensive analysis of the sociotechnical environment of the audience and the courseware as well as of the courseware learning context; coverage of all software RE activities; and development of an explicit requirements specification documentation.

scholarly journals Bayesian Planet Searches for the 10 cm/s Radial Velocity Era

2015 ◽  
Vol 11 (A29A) ◽  
pp. 205-207
Author(s):  
Philip C. Gregory
Keyword(s):  
Radial Velocity ◽  
State Of The Art ◽  
Simulated Data ◽  
Model Parameters ◽  
Data Sets ◽  
Stellar Activity ◽  
Bayesian Fusion ◽  
Multiple State ◽  
Simulated Data Sets ◽  
Apodization Function

AbstractA new apodized Keplerian model is proposed for the analysis of precision radial velocity (RV) data to model both planetary and stellar activity (SA) induced RV signals. A symmetrical Gaussian apodization function with unknown width and center can distinguish planetary signals from SA signals on the basis of the width of the apodization function. The general model for m apodized Keplerian signals also includes a linear regression term between RV and the stellar activity diagnostic In (R'hk), as well as an extra Gaussian noise term with unknown standard deviation. The model parameters are explored using a Bayesian fusion MCMC code. A differential version of the Generalized Lomb-Scargle periodogram provides an additional way of distinguishing SA signals and helps guide the choice of new periods. Sample results are reported for a recent international RV blind challenge which included multiple state of the art simulated data sets supported by a variety of stellar activity diagnostics.

scholarly journals Frequency multiplexed coherent φ-OTDR

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hannah M. Ogden ◽  
Matthew J. Murray ◽  
Joseph B. Murray ◽  
Clay Kirkendall ◽  
Brandon Redding
Keyword(s):  
Numerical Analysis ◽  
Pulse Train ◽  
State Of The Art ◽  
Average Power ◽  
Comprehensive Analysis ◽  
Experimental Measurements ◽  
Rayleigh Backscattering ◽  
Sensor Performance ◽  
Sensor Platform ◽  
Lower Noise

AbstractWe present a comprehensive analysis of a frequency multiplexed phase-measuring φ-OTDR sensor platform. The system uses a train of frequency-shifted pulses to increase the average power injected into the fiber and provide a diversity of uncorrelated Rayleigh backscattering measurements. Through a combination of simulations, numerical analysis, and experimental measurements, we show that this approach not only enables lower noise and mitigates interference fading, but also improves the sensor linearity. We investigate the sensor dependence on the length of the pulse train and characterize the sensor performance as a function of range, demonstrating operation from 1 to 50 km. Despite its relative simplicity, this platform enables state-of-the-art performance, including low crosstalk, high linearity, and a minimum detectable strain of only 0.6 p$$\varepsilon /\sqrt{\text{Hz}}$$ ε / Hz in a 10 km fiber with 10 m spatial resolution and a bandwidth of 5 kHz.

scholarly journals Biases in retrieving planetary signals in the presence of quasi-periodic stellar activity

2019 ◽  
Vol 489 (2) ◽  
pp. 2555-2571 ◽  
Author(s):  
M Damasso ◽  
M Pinamonti ◽  
G Scandariato ◽  
A Sozzetti
Keyword(s):  
Time Series ◽  
Gaussian Process Regression ◽  
Data Sets ◽  
Correlated Noise ◽  
Stellar Activity ◽  
Activity Component ◽  
Low Mass ◽  
Host Stars ◽  
Correlated Signals

Abstract Gaussian process regression is a widespread tool used to mitigate stellar correlated noise in radial velocity (RV) time series. It is particularly useful to search for and determine the properties of signals induced by small-sized low-mass planets (Rp < 4 R⊕, mp < 10 M⊕). By using extensive simulations based on a quasi-periodic representation of the stellar activity component, we investigate the ability in retrieving the planetary parameters in 16 different realistic scenarios. We analyse systems composed by one planet and host stars having different levels of activity, focusing on the challenging case represented by low-mass planets, with Doppler semi-amplitudes in the range 1–3 $\rm{\,m\,s^{-1}}$. We consider many different configurations for the quasi-periodic stellar activity component, as well as different combinations of the observing epochs. We use commonly employed analysis tools to search for and characterize the planetary signals in the data sets. The goal of our injection-recovery statistical analysis is twofold. First, we focus on the problem of planet mass determination. Then, we analyse in a statistical way periodograms obtained with three different algorithms, in order to explore some of their general properties, as the completeness and reliability in retrieving the injected planetary and stellar activity signals with low false alarm probabilities. This work is intended to provide some understanding of the biases introduced in the planet parameters inferred from the analysis of RV time series that contain correlated signals due to stellar activity. It also aims to motivate the use and encourage the improvement of extensive simulations for planning spectroscopic follow-up observations.

Computer Programs

1965 ◽  
Vol 19 (1) ◽  
pp. 84-84
Keyword(s):  
Data Processing ◽  
State Of The Art ◽  
Electronic Computer ◽  
Rapid Change ◽  
Computer Programs ◽  
Comprehensive Analysis ◽  
Computer Hardware ◽  
Output Data ◽  
General Program ◽  
Selection Of

“The state of the art” of computer programs, which have been developed for geodetic purposes, is portrayed in the following six papers. Each report has been condensed to give an indication of what the program can do, but no attempt has been made to indicate any detail of the programs. It is generally the experience of programers that a program is never static. By the time one edition has been checked out (“debugged” is the jargon of the craft), the programer has already started to work on the next edition. In this sense, many of the programs that were discussed in October may be superseded before this report is printed. In part, this reflects the rapid change in computer hardware. Computers, themselves, are frequently obsolete soon after they are put into service. Each change of computer requires at least minor changes in programs; more flexibility in programs, in turn, encourages more comprehensive analysis of the data and the selection of alternative formats for output data. In general, a much better job of data processing can be, and is being, done with the help of the electronic computer. It is the consensus that the user of a program must understand it and be capable of modifying it to suit his particular requirements. The dream of a general program into which anyone can feed his data and from which he will get perfect results remains a dream.

scholarly journals Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

2021 ◽  
Vol 3 ◽  
Author(s):  
Marieke van Erp ◽  
Christian Reynolds ◽  
Diana Maynard ◽  
Alain Starke ◽  
Rebeca Ibáñez Martín ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Domain Knowledge ◽  
State Of The Art ◽  
Interdisciplinary Approach ◽  
Comprehensive Analysis ◽  
The State ◽  
Use Cases

In this paper, we discuss the use of natural language processing and artificial intelligence to analyze nutritional and sustainability aspects of recipes and food. We present the state-of-the-art and some use cases, followed by a discussion of challenges. Our perspective on addressing these is that while they typically have a technical nature, they nevertheless require an interdisciplinary approach combining natural language processing and artificial intelligence with expert domain knowledge to create practical tools and comprehensive analysis for the food domain.

scholarly journals Flexible Causal Inference for Political Science

2018 ◽  
Vol 26 (1) ◽  
pp. 54-71 ◽  
Author(s):  
Bear F. Braumoeller ◽  
Giampiero Marra ◽  
Rosalba Radice ◽  
Aisha E. Bradshaw
Keyword(s):  
Causal Inference ◽  
Political Science ◽  
Functional Form ◽  
State Of The Art ◽  
State Behavior ◽  
Competing Models ◽  
Causal Impact ◽  
Substantial Bias ◽  
The Impact ◽  
The Relationship

Measuring the causal impact of state behavior on outcomes is one of the biggest methodological challenges in the field of political science, for two reasons: behavior is generally endogenous, and the threat of unobserved variables that confound the relationship between behavior and outcomes is pervasive. Matching methods, widely considered to be the state of the art in causal inference in political science, are generally ill-suited to inference in the presence of unobserved confounders. Heckman-style multiple-equation models offer a solution to this problem; however, they rely on functional-form assumptions that can produce substantial bias in estimates of average treatment effects. We describe a category of models, flexible joint likelihood models, that account for both features of the data while avoiding reliance on rigid functional-form assumptions. We then assess these models’ performance in a series of neutral simulations, in which they produce substantial (55% to ${>}$90%) reduction in bias relative to competing models. Finally, we demonstrate their utility in a reanalysis of Simmons’ (2000) classic study of the impact of Article VIII commitment on compliance with the IMF’s currency-restriction regime.

scholarly journals A Comprehensive Analysis of Multilayer Community Detection Algorithms for Application to EEG-Based Brain Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Maria Grazia Puxeddu ◽  
Manuela Petti ◽  
Laura Astolfi
Keyword(s):  
Community Detection ◽  
Simulation Study ◽  
State Of The Art ◽  
Brain Networks ◽  
Comprehensive Analysis ◽  
The State ◽  
Modular Organization ◽  
High Temporal Resolution ◽  
Eeg Signals ◽  
Modular Structures

Modular organization is an emergent property of brain networks, responsible for shaping communication processes and underpinning brain functioning. Moreover, brain networks are intrinsically multilayer since their attributes can vary across time, subjects, frequency, or other domains. Identifying the modular structure in multilayer brain networks represents a gateway toward a deeper understanding of neural processes underlying cognition. Electroencephalographic (EEG) signals, thanks to their high temporal resolution, can give rise to multilayer networks able to follow the dynamics of brain activity. Despite this potential, the community organization has not yet been thoroughly investigated in brain networks estimated from EEG. Furthermore, at the state of the art, there is still no agreement about which algorithm is the most suitable to detect communities in multilayer brain networks, and a way to test and compare them all under a variety of conditions is lacking. In this work, we perform a comprehensive analysis of three algorithms at the state of the art for multilayer community detection (namely, genLouvain, DynMoga, and FacetNet) as compared with an approach based on the application of a single-layer clustering algorithm to each slice of the multilayer network. We test their ability to identify both steady and dynamic modular structures. We statistically evaluate their performances by means of ad hoc benchmark graphs characterized by properties covering a broad range of conditions in terms of graph density, number of clusters, noise level, and number of layers. The results of this simulation study aim to provide guidelines about the choice of the more appropriate algorithm according to the different properties of the brain network under examination. Finally, as a proof of concept, we show an application of the algorithms to real functional brain networks derived from EEG signals collected at rest with closed and open eyes. The test on real data provided results in agreement with the conclusions of the simulation study and confirmed the feasibility of multilayer analysis of EEG-based brain networks in both steady and dynamic conditions.

scholarly journals Simulated mass measurements of the young planet K2-33b

2020 ◽  
Vol 493 (1) ◽  
pp. L92-L97 ◽  
Author(s):  
Baptiste Klein ◽  
J-F Donati
Keyword(s):  
Near Infrared ◽  
Gaussian White Noise ◽  
Gaussian Process Regression ◽  
Stellar Rotation ◽  
Mass Measurements ◽  
Radial Magnetic Field ◽  
Stellar Activity ◽  
Central Wavelength ◽  
Bright Surface ◽  
Good Coverage

ABSTRACT In this paper, we carry out simulations of radial velocity (RV) measurements of the mass of the 8–11 Myr Neptune-sized planet K2-33b using high-precision near-infrared velocimeters like SPIRou at the Canada–France–Hawaii Telescope. We generate an RV curve containing a planet signature and a realistic stellar activity signal, computed for a central wavelength of 1.8 µm and statistically compatible with the light curve obtained with K2. The modelled activity signal includes the effect of time-evolving dark and bright surface features hosting a 2 kG radial magnetic field, resulting in an RV signal of semi-amplitude ∼30 m s−1. Assuming a 3-month visibility window, we build RV time series including Gaussian white noise from which we retrieve the planet mass while filtering the stellar activity signal using Gaussian process regression. We find that 35/50 visits spread over three consecutive bright-time runs on K2-33 allow one to reliably detect planet RV signatures of, respectively, 10 and 5 m s−1 at precisions &gt;3σ. We also show that 30 visits may end up being insufficient in some cases to provide a good coverage of the stellar rotation cycle, with the result that the planet signature can go undetected or the mass estimation be plagued by large errors.

Sign in / Sign up

Export Citation Format

Share Document