scholarly journals The CARMENES search for exoplanets around M dwarfs

2019 ◽  
Vol 622 ◽  
pp. A153 ◽  
Author(s):  
E. Nagel ◽  
S. Czesla ◽  
J. H. M. M. Schmitt ◽  
S. Dreizler ◽  
G. Anglada-Escudé ◽  
...  
Keyword(s):  
Time Series ◽  
Statistical Analysis ◽  
Likelihood Ratio Test ◽  
Ratio Test ◽  
Minimum Mass ◽  
Eccentric Orbit ◽  
M Dwarfs ◽  
High Confidence ◽  
Spectral Diagnostics ◽  
M Dwarf

We report the detection of a Neptune-mass exoplanet around the M4.0 dwarf GJ 4276 (G 232-070) based on radial velocity (RV) observations obtained with the CARMENES spectrograph. The RV variations of GJ 4276 are best explained by the presence of a planetary companion that has a minimum mass of mb sin i ≈ 16 M⊕ on a Pb = 13.35 day orbit. The analysis of the activity indicators and spectral diagnostics exclude stellar induced RV perturbations and prove the planetary interpretation of the RV signal. We show that a circular single-planet solution can be excluded by means of a likelihood ratio test. Instead, we find that the RV variations can be explained either by an eccentric orbit or interpreted as a pair of planets on circular orbits near a period ratio of 2:1. Although the eccentric single-planet solution is slightly preferred, our statistical analysis indicates that none of these two scenarios can be rejected with high confidence using the RV time series obtained so far. Based on the eccentric interpretation, we find that GJ 4276 b is the most eccentric (eb = 0.37) exoplanet around an M dwarf with such a short orbital period known today.

scholarly journals The CARMENES search for exoplanets around M dwarfs

2018 ◽  
Vol 609 ◽  
pp. L5 ◽  
Author(s):  
A. Reiners ◽  
I. Ribas ◽  
M. Zechmeister ◽  
J. A. Caballero ◽  
T. Trifonov ◽  
...  
Keyword(s):  
Time Series ◽  
Radial Velocity ◽  
Rotation Period ◽  
Major Axis ◽  
Temperate Zone ◽  
Minimum Mass ◽  
M Dwarfs ◽  
Semi Major Axis ◽  
Liquid Form

We report on the first star discovered to host a planet detected by radial velocity (RV) observations obtained within the CARMENES survey for exoplanets around M dwarfs. HD 147379 (V = 8.9 mag, M = 0.58 ± 0.08 M⊙), a bright M0.0 V star at a distance of 10.7 pc, is found to undergo periodic RV variations with a semi-amplitude of K = 5.1 ± 0.4 m s−1 and a period of P = 86.54 ± 0.06 d. The RV signal is found in our CARMENES data, which were taken between 2016 and 2017, and is supported by HIRES/Keck observations that were obtained since 2000. The RV variations are interpreted as resulting from a planet of minimum mass mP sin i = 25 ± 2 M⊕, 1.5 times the mass of Neptune, with an orbital semi-major axis a = 0.32 au and low eccentricity (e < 0.13). HD 147379 b is orbiting inside the temperate zone around the star, where water could exist in liquid form. The RV time-series and various spectroscopic indicators show additional hints of variations at an approximate period of 21.1 d (and its first harmonic), which we attribute to the rotation period of the star.

scholarly journals The Laplace Likelihood Ratio Test for Heteroscedasticity

2011 ◽  
Vol 2011 ◽  
pp. 1-7
Author(s):  
J. Martin van Zyl
Keyword(s):  
Time Series ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Laplace Distribution ◽  
Ratio Test ◽  
Fat Tails ◽  
Robust Test ◽  
Constant Variance

It is shown that the likelihood ratio test for heteroscedasticity, assuming the Laplace distribution, gives good results for Gaussian and fat-tailed data. The likelihood ratio test, assuming normality, is very sensitive to any deviation from normality, especially when the observations are from a distribution with fat tails. Such a likelihood test can also be used as a robust test for a constant variance in residuals or a time series if the data is partitioned into groups.

scholarly journals The SOPHIE search for northern extrasolar planets

2019 ◽  
Vol 625 ◽  
pp. A18 ◽  
Author(s):  
M. J. Hobson ◽  
X. Delfosse ◽  
N. Astudillo-Defru ◽  
I. Boisse ◽  
R. F. Díaz ◽  
...  
Keyword(s):  
Evolutionary History ◽  
Extrasolar Planets ◽  
Lower Boundary ◽  
Minimum Mass ◽  
Velocity Measurements ◽  
M Dwarfs ◽  
High Eccentricity ◽  
Solar Metallicity ◽  
History Of ◽  
M Dwarf

We present the detection of a warm Neptune orbiting the M dwarf Gl 378, using radial velocity measurements obtained with the SOPHIE spectrograph at the Observatoire de Haute-Provence. The star was observed in the context of the SOPHIE exoplanet consortium’s sub-programme dedicated to finding planets around M dwarfs. Gl 378 is an M1 star, of solar metallicity, at a distance of 14.96 pc. The single planet detected, Gl 378 b, has a minimum mass of 13.02 MEarth and an orbital period of 3.82 days, which place it at the lower boundary of the hot Neptune desert. As one of only a few such planets around M dwarfs, Gl 378 b provides important clues to the evolutionary history of these close-in planets. In particular, the eccentricity of 0.1 may point to a high-eccentricity migration. The planet may also have lost part of its envelope due to irradiation.

Testing for the presence of sinusoidal components

1986 ◽  
Vol 23 (A) ◽  
pp. 201-210 ◽  
Author(s):  
B. G. Quinn
Keyword(s):  
Time Series ◽  
White Noise ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Gaussian White Noise ◽  
Ratio Test ◽  
Additive Gaussian White Noise

Approximate and asymptotic distributional results are obtained for the likelihood ratio test of the hypothesis that a time series is composed from s sinusoidal components, at unknown frequencies, with additive Gaussian white noise, against the hypothesis that there are an additional r sinusoidal components at unknown frequencies. The work extends that of Fisher (1929), and contains a number of simulations illustrating the results.

scholarly journals Comparing climate time series – Part 1: Univariate test

2020 ◽  
Vol 6 (2) ◽  
pp. 159-175
Author(s):  
Timothy DelSole ◽  
Michael K. Tippett
Keyword(s):  
Time Series ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Meridional Overturning Circulation ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Stationary Processes ◽  
Ratio Test ◽  
Noise Parameters ◽  
Covariance Functions

Abstract. This paper proposes a new approach to detecting and describing differences in stationary processes. The approach is equivalent to comparing auto-covariance functions or power spectra. The basic idea is to fit an autoregressive model to each time series and then test whether the model parameters are equal. The likelihood ratio test for this hypothesis has appeared in the statistics literature, but the resulting test depends on maximum likelihood estimates, which are biased, neglect differences in noise parameters, and utilize sampling distributions that are valid only for large sample sizes. This paper derives a likelihood ratio test that corrects for bias, detects differences in noise parameters, and can be applied to small samples. Furthermore, if a significant difference is detected, we propose new methods to diagnose and visualize those differences. Specifically, the test statistic can be used to define a “distance” between two autoregressive processes, which in turn can be used for clustering analysis in multi-model comparisons. A multidimensional scaling technique is used to visualize the similarities and differences between time series. We also propose diagnosing differences in stationary processes by identifying initial conditions that optimally separate predictable responses. The procedure is illustrated by comparing simulations of an Atlantic Meridional Overturning Circulation (AMOC) index from 10 climate models in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). Significant differences between most AMOC time series are detected. The main exceptions are time series from CMIP models from the same institution. Differences in stationary processes are explained primarily by differences in the mean square error of 1-year predictions and by differences in the predictability (i.e., R-square) of the associated autoregressive models.

scholarly journals Complicated Alcohol Withdrawal - an unintended consequence of COVID-19 lockdown.

2020 ◽  
Author(s):  
Lekhansh Shukla
Keyword(s):  
Time Series ◽  
Likelihood Ratio Test ◽  
Alcohol Withdrawal ◽  
Withdrawal Syndrome ◽  
Time Series Data ◽  
Alcohol Withdrawal Syndrome ◽  
Unintended Consequence ◽  
Series Data ◽  
Ratio Test ◽  
Changepoint Analysis

We report the change in incidence of severe alcohol withdrawal syndrome following COVID-19 related lockdown in India. A changepoint analysis of the time series data (between 01.01.20 to 11.04.20) shows an increase in average number of cases from 4 to 8 per day (likelihood ratio test: χ2 = 72, df = 2, p &lt; 0.001).

Testing for the presence of sinusoidal components

1986 ◽  
Vol 23 (A) ◽  
pp. 201-210 ◽  
Author(s):  
B. G. Quinn
Keyword(s):  
Time Series ◽  
White Noise ◽  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Gaussian White Noise ◽  
Ratio Test ◽  
Additive Gaussian White Noise

Approximate and asymptotic distributional results are obtained for the likelihood ratio test of the hypothesis that a time series is composed from s sinusoidal components, at unknown frequencies, with additive Gaussian white noise, against the hypothesis that there are an additional r sinusoidal components at unknown frequencies. The work extends that of Fisher (1929), and contains a number of simulations illustrating the results.

scholarly journals Change Point Detection for Diversely Distributed Stochastic Processes Using a Probabilistic Method

Inventions ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 42
Author(s):  
Muhammad Rizwan Khan ◽  
Biswajit Sarkar
Keyword(s):  
Time Series ◽  
Likelihood Ratio Test ◽  
Data Structures ◽  
Change Point ◽  
Time Series Data ◽  
Probabilistic Method ◽  
Change Point Detection ◽  
Series Data ◽  
Ratio Test ◽  
Point Detection

Unpredicted deviations in time series data are called change points. These unexpected changes indicate transitions between states. Change point detection is a valuable technique in modeling to estimate unanticipated property changes underlying time series data. It can be applied in different areas like climate change detection, human activity analysis, medical condition monitoring and speech and image analyses. Supervised and unsupervised techniques are equally used to identify changes in time series. Even though change point detection algorithms have improved considerably in recent years, several undefended challenges exist. Previous work on change point detection was limited to specific areas; therefore, more studies are required to investigate appropriate change point detection techniques applicable to any data distribution to assess the numerical productivity of any stochastic process. This research is primarily focused on the formulation of an innovative methodology for change point detection of diversely distributed stochastic processes using a probabilistic method with variable data structures. Bayesian inference and a likelihood ratio test are used to detect a change point at an unknown time (k). The likelihood of k is determined and used in the likelihood ratio test. Parameter change must be evaluated by critically analyzing the parameters expectations before and after a change point. Real-time data of particulate matter concentrations at different locations were used for numerical verification, due to diverse features, that is, environment, population densities and transportation vehicle densities. Therefore, this study provides an understanding of how well this recommended model could perform for different data structures.

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