scholarly journals pyaneti II: A multi-dimensional Gaussian process approach to analysing spectroscopic time-series

2021 ◽  
Author(s):  
Oscar Barragán ◽  
Suzanne Aigrain ◽  
Vinesh M Rajpaul ◽  
Norbert Zicher
Keyword(s):  
Time Series ◽  
Radial Velocity ◽  
Gaussian Process ◽  
Process Approach ◽  
Direct Application ◽  
Numerical Techniques ◽  
Covariance Functions ◽  
Multi Band

Abstract The two most successful methods for exoplanet detection rely on the detection of planetary signals in photometric and radial velocity time-series. This depends on numerical techniques that exploit the synergy between data and theory to estimate planetary, orbital, and/or stellar parameters. In this work we present a new version of the exoplanet modelling code pyaneti. This new release has a special emphasis on the modelling of stellar signals in radial velocity time-series. The code has a built-in multi-dimensional Gaussian process approach to modelling radial velocity and activity indicator time-series with different underlying covariance functions. This new version of the code also allows multi-band and single transit modelling; it runs on Python 3, and features overall improvements in performance. We describe the new implementation and provide tests to validate the new routines that have direct application to exoplanet detection and characterisation. We have made the code public and freely available at https://github.com/oscaribv/pyaneti. We also present the codes citlalicue and citlalatonac that allow one to create synthetic photometric and spectroscopic time-series, respectively, with planetary and stellar-like signals.

scholarly journals A Hybrid Energy System Workflow for Energy Portfolio Optimization

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4392
Author(s):  
Jia Zhou ◽  
Hany Abdel-Khalik ◽  
Paul Talbot ◽  
Cristian Rabiti
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Electricity Market ◽  
Process Model ◽  
Net Present Value ◽  
Historical Data ◽  
Energy System ◽  
Initial Guess ◽  
Reduced Order ◽  
The Impact

This manuscript develops a workflow, driven by data analytics algorithms, to support the optimization of the economic performance of an Integrated Energy System. The goal is to determine the optimum mix of capacities from a set of different energy producers (e.g., nuclear, gas, wind and solar). A stochastic-based optimizer is employed, based on Gaussian Process Modeling, which requires numerous samples for its training. Each sample represents a time series describing the demand, load, or other operational and economic profiles for various types of energy producers. These samples are synthetically generated using a reduced order modeling algorithm that reads a limited set of historical data, such as demand and load data from past years. Numerous data analysis methods are employed to construct the reduced order models, including, for example, the Auto Regressive Moving Average, Fourier series decomposition, and the peak detection algorithm. All these algorithms are designed to detrend the data and extract features that can be employed to generate synthetic time histories that preserve the statistical properties of the original limited historical data. The optimization cost function is based on an economic model that assesses the effective cost of energy based on two figures of merit: the specific cash flow stream for each energy producer and the total Net Present Value. An initial guess for the optimal capacities is obtained using the screening curve method. The results of the Gaussian Process model-based optimization are assessed using an exhaustive Monte Carlo search, with the results indicating reasonable optimization results. The workflow has been implemented inside the Idaho National Laboratory’s Risk Analysis and Virtual Environment (RAVEN) framework. The main contribution of this study addresses several challenges in the current optimization methods of the energy portfolios in IES: First, the feasibility of generating the synthetic time series of the periodic peak data; Second, the computational burden of the conventional stochastic optimization of the energy portfolio, associated with the need for repeated executions of system models; Third, the inadequacies of previous studies in terms of the comparisons of the impact of the economic parameters. The proposed workflow can provide a scientifically defendable strategy to support decision-making in the electricity market and to help energy distributors develop a better understanding of the performance of integrated energy systems.

scholarly journals Searching for g modes

2018 ◽  
Vol 617 ◽  
pp. A108 ◽  
Author(s):  
T. Appourchaux ◽  
P. Boumier ◽  
J. W. Leibacher ◽  
T. Corbard
Keyword(s):  
Time Series ◽  
Radial Velocity ◽  
Time Shift ◽  
Data Sets ◽  
Mode Amplitude ◽  
The Past ◽  
Mode Detection ◽  
Velocity Calibration ◽  
Remaining Time ◽  
Soho Spacecraft

Context. The recent claims of g-mode detection have restarted the search for these potentially extremely important modes. These claims can be reassessed in view of the different data sets available from the SoHO instruments and ground-based instruments. Aims. We produce a new calibration of the GOLF data with a more consistent p-mode amplitude and a more consistent time shift correction compared to the time series used in the past. Methods. The calibration of 22 yr of GOLF data is done with a simpler approach that uses only the predictive radial velocity of the SoHO spacecraft as a reference. Using p modes, we measure and correct the time shift between ground- and space-based instruments and the GOLF instrument. Results. The p-mode velocity calibration is now consistent to within a few percent with other instruments. The remaining time shifts are within ±5 s for 99.8% of the time series.

Conditioned Simulation of Ground-Motion Time Series at Uninstrumented Sites Using Gaussian Process Regression

2021 ◽  
Author(s):  
Aidin Tamhidi ◽  
Nicolas Kuehn ◽  
S. Farid Ghahari ◽  
Arthur J. Rodgers ◽  
Monica D. Kohler ◽  
...  
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Ground Motion ◽  
San Francisco ◽  
Earthquake Engineering ◽  
Seismic Analysis ◽  
Ground Motions ◽  
Free Field ◽  
Gaussian Process Regression ◽  
Motion Time

ABSTRACT Ground-motion time series are essential input data in seismic analysis and performance assessment of the built environment. Because instruments to record free-field ground motions are generally sparse, methods are needed to estimate motions at locations with no available ground-motion recording instrumentation. In this study, given a set of observed motions, ground-motion time series at target sites are constructed using a Gaussian process regression (GPR) approach, which treats the real and imaginary parts of the Fourier spectrum as random Gaussian variables. Model training, verification, and applicability studies are carried out using the physics-based simulated ground motions of the 1906 Mw 7.9 San Francisco earthquake and Mw 7.0 Hayward fault scenario earthquake in northern California. The method’s performance is further evaluated using the 2019 Mw 7.1 Ridgecrest earthquake ground motions recorded by the Community Seismic Network stations located in southern California. These evaluations indicate that the trained GPR model is able to adequately estimate the ground-motion time series for frequency ranges that are pertinent for most earthquake engineering applications. The trained GPR model exhibits proper performance in predicting the long-period content of the ground motions as well as directivity pulses.

scholarly journals Efficient Gaussian Process-Based Modelling and Prediction of Image Time Series

2015 ◽  
pp. 626-637 ◽  
Author(s):  
Marco Lorenzi ◽  
Gabriel Ziegler ◽  
Daniel C. Alexander ◽  
Sebastien Ourselin
Keyword(s):  
Time Series ◽  
Gaussian Process

scholarly journals The runaway supergiant HD 188209 (O9.5Iab): a binary or a pulsating star?

1999 ◽  
Vol 193 ◽  
pp. 69-70
Author(s):  
Garik Israelian ◽  
Artemio Herrero ◽  
E. Santolaya-Rey ◽  
A. Kaufer ◽  
F. Musaev ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mass Loss ◽  
Radial Velocity ◽  
Loss Rate ◽  
State Of The Art ◽  
Mass Loss Rate ◽  
Fundamental Parameters ◽  
Binary Nature ◽  
Period 2

We report radial velocity studies of photospheric absorption lines from spectral time series of the late O-type runaway supergiant HD 188209. Radial velocity variations with a quasi-period ∼ 2 days have been detected in high-resolution echelle spectra and most probably indicate that the supergiant is pulsating. Night-to-night variations in the position and strength of the central emission reversal of the Hα profile have been observed. The fundamental parameters of the star have been derived using state-of-the-art plane-parallel and unified non-LTE model atmospheres, these last including the mass-loss rate. The binary nature of this star is not suggested either from Hipparcos photometry or from radial-velocity curves.

Gaussian process approach for metric learning

2019 ◽  
Vol 87 ◽  
pp. 17-28 ◽  
Author(s):  
Ping Li ◽  
Songcan Chen
Keyword(s):  
Gaussian Process ◽  
Metric Learning ◽  
Process Approach
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