RoadRunner: a fast and flexible exoplanet transit model

Exomoon indicators in high-precision transit light curves

Astronomy and Astrophysics ◽

10.1051/0004-6361/202037550 ◽

2020 ◽

Vol 638 ◽

pp. A43

Author(s):

Kai Rodenbeck ◽

René Heller ◽

Laurent Gizon

Keyword(s):

Extrasolar Planets ◽

Light Curves ◽

Low Density ◽

Instrumental Effects ◽

The Moon ◽

Hill Stability ◽

Follow Up Studies ◽

Transit Model ◽

Limb Darkening

Context. While the Solar System contains about 20 times more moons than planets, no moon has been confirmed around any of the thousands of extrasolar planets discovered so far. Considering the large computational load required for the statistical vetting of exomoon candidates in a star–planet–moon framework, tools for an uncomplicated identification of the most promising exomoon candidates could be beneficial to streamline follow-up studies. Aims. Here we study three exomoon indicators that emerge if well-established planet-only models are fitted to a planet–moon transit light curve: transit timing variations (TTVs), transit duration variations (TDVs), and apparent planetary transit radius variations (TRVs). We re-evaluate under realistic conditions the previously proposed exomoon signatures in the TTV and TDV series. Methods. We simulated light curves of a transiting exoplanet with a single moon, taking into account stellar limb darkening, orbital inclinations, planet–moon occultations, and noise from both stellar granulation and instrumental effects. These model light curves were then fitted with a planet-only transit model whilst pretending there were no moon, and we explored the resulting TTV, TDV, and TRV series for evidence of the moon. Results. The previously described ellipse in the TTV-TDV diagram of an exoplanet with a moon emerges only for high-density moons. However, low-density moons distort the sinusoidal shapes of the TTV and the TDV series due to their photometric contribution to the combined planet–moon transit. Sufficiently large moons can nevertheless produce periodic apparent TRVs of their host planets that could be observable. We find that Kepler and PLATO have similar performances in detecting the exomoon-induced TRV effect around simulated bright (mV = 8) stars. Although these stars are rare in the Kepler sample, they will be abundant in the PLATO sample. Moreover, PLATO’s higher cadence yields a stronger TTV signal. We detect substantial TRVs of the Saturn-sized planet Kepler-856 b although an exomoon could only ensure Hill stability in a very narrow orbital range. Conclusions. The periodogram of the sequence of transit radius measurements can indicate the presence of a moon. The TTV and TDV series of exoplanets with moons could be more complex than previously assumed. We propose that TRVs could be a more promising means to identify exomoons in large exoplanet surveys.

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A Self-Organizing Neural Network to Approach Novelty Detection

Machine Learning ◽

10.4018/978-1-60960-818-7.ch211 ◽

2012 ◽

pp. 262-282

Author(s):

Marcelo Keese Albertini ◽

Rodrigo Fernandes de Mello

Keyword(s):

Neural Network ◽

Time Series ◽

Network Architecture ◽

Novelty Detection ◽

Analytical Models ◽

New Concepts ◽

Normal Behavior ◽

Regression Techniques ◽

Self Organizing

Machine learning is a field of artificial intelligence which aims at developing techniques to automatically transfer human knowledge into analytical models. Recently, those techniques have been applied to time series with unknown dynamics and fluctuations in the established behavior patterns, such as humancomputer interaction, inspection robotics and climate change. In order to detect novelties in those time series, techniques are required to learn and update knowledge structures, adapting themselves to data tendencies. The learning and updating process should integrate and accommodate novelty events into the normal behavior model, possibly incurring the revaluation of long-term memories. This sort of application has been addressed by the proposal of incremental techniques based on unsupervised neural networks and regression techniques. Such proposals have introduced two new concepts in time-series novelty detection. The first defines the temporal novelty, which indicates the occurrence of unexpected series of events. The second measures how novel a single event is, based on the historical knowledge. However, current studies do not fully consider both concepts of detecting and quantifying temporal novelties. This motivated the proposal of the self-organizing novelty detection neural network architecture (SONDE) which incrementally learns patterns in order to represent unknown dynamics and fluctuation of established behavior. The knowledge accumulated by SONDE is employed to estimate Markov chains which model causal relationships. This architecture is applied to detect and measure temporal and nontemporal novelties. The evaluation of the proposed technique is carried out through simulations and experiments, which have presented promising results.

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Modelling ground deformation in Tenerife (Canary Islands) during 2003-2010

10.5194/egusphere-egu21-11023 ◽

2021 ◽

Author(s):

Monika Przeor ◽

Luca D'Auria ◽

Susi Pepe ◽

Pietro Tizzani

Keyword(s):

Time Series ◽

Ground Deformation ◽

Point Of View ◽

Analytical Models ◽

Volcanic Complex ◽

Statistical Tool ◽

Noise Component ◽

Envisat Asar ◽

Tectonic Features ◽

Teide Volcano

Tenerife is the biggest island of the Canaries and one of the most active from the volcanological point of view. The island is geologically complex, and its main volcano-tectonic features are three volcanic rifts and the composite volcanic complex of Teide-Las Ca&#241;adas. The latter is located in the central part of the island at the intersection of Tenerife principal rifts. Teide volcano, with its 3718 m of elevation constitutes the most prominent topographical feature of the island. Being a densely populated active volcanic island, Tenerife is characterised by a high volcanic risk. For this reason, the island requires an advanced and efficient volcano monitoring system. Among the geophysical parameters that could be useful to forecast an oncoming volcanic eruption, the ground deformation is relevant for detecting the approach of magma to the surface.This study aim is to analyse the ground deformation in the surroundings of the Teide-Las Ca&#241;adas complex. &#160;For this purpose, we studied the ground deformation of Tenerife by using a set of Synthetic Aperture Radar (SAR) images acquired between 2003 and 2010 by the ENVISAT ASAR sensor and processed through a DInSAR-SBAS technique. The DInSAR SBAS time series revealed a ground deformation in the central part of the island, coinciding with the Teide volcano. A similar deformation was already evidenced by Fern&#225;ndez et al. (2009) from 2004 to 2005.We investigated the source of this ground deformation by applying the statistical tool of Independent Component Analysis (ICA) to the dataset. ICA allowed separating the spatial patterns of deformation into four components. We attributed three of them to an actual ground deformation, while the fourth seems to be only related to the noise component of data. The first component (ICA1) displays a spatial pattern localised in Teide volcano neighbourhoods and consists of a ground uplift of few centimetres. The deformation associated with this component starts in 2005 and persists along the rest of the time series. The second component (ICA2) of the ground deformation is localised in the South/South-West part of Las Ca&#241;adas rim while the third component (ICA3) is localised to the East of Teide volcano. We performed inverse modelling to analyse the source of the ground deformation related to ICA1 to retrieve the location, the geometry and the temporal evolution of this source. The inversion was based on analytical models of ground deformation as well as on Finite-Element-Modelling. The result showed that the ground deformation is associated with a shallow sill-like structure, located beneath Teide volcano, possibly reflecting a hydrothermal reservoir. The knowledge of this source geometry could be of significant interest to better understand ground deformation data of possible future volcanic crisis.&#160;

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DETRENDED FLUCTUATION ANALYSIS OF THE TCP-RED ALGORITHM

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127409025328 ◽

2009 ◽

Vol 19 (12) ◽

pp. 4237-4245 ◽

Cited By ~ 1

Author(s):

XI CHEN ◽

SIU-CHUNG WONG ◽

CHI K. TSE ◽

LJILJANA TRAJKOVIĆ

Keyword(s):

Time Series ◽

Long Range ◽

Power Law ◽

Detrended Fluctuation Analysis ◽

Time Series Data ◽

Analytical Models ◽

Series Data ◽

Fluctuation Analysis ◽

Scaling Exponent ◽

Detrended Fluctuation

It has been observed that Internet gateways employing Transport Control Protocol (TCP) and the Random Early Detection (RED) control algorithm may exhibit instability and oscillatory behavior. Most control methods proposed in the past have been based on analytical models that rely on statistical measurements of network parameters. In this paper, we apply the detrended fluctuation analysis (DFA) method to analyze stability of the TCP-RED system. The DFA is used to analyze time-series data and generate power-law scaling exponents, which indicate the long-range correlations of the time series. We quantify the stability of the TCP-RED system by examining the variation of the DFA power-law scaling exponent when the system parameters are varied. We also study the long-range power-law correlations of TCP window periods.

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Estimating and Comparing Dam Deformations Using Classical and GNSS Techniques

10.20944/preprints201801.0209.v1 ◽

2018 ◽

Author(s):

Riccardo Barzaghi ◽

Noemi Emanuela Cazzaniga ◽

Carlo Iapige De Gaetani ◽

Livio Pinto ◽

Vincenza Tornatore

Keyword(s):

Time Series ◽

Standard Deviation ◽

Data Processing ◽

Time Span ◽

Analytical Models ◽

Monitoring Applications ◽

Gnss Receivers ◽

The One ◽

Gnss Data ◽

Dam Monitoring

GNSS receivers are nowadays commonly used in monitoring applications, e.g., in estimating crustal and infrastructure deformations. This is basically due to the recent improvements in GNSS instruments and methodologies that allow high precision positioning, 24 h availability and semiautomatic data processing. In this paper, GNSS estimated deformations on a dam structure have been analyzed and compared with pendulum data. This study has been carried out for the Eleonora D’Arborea (Cantoniera) dam, which is in the Sardinia Island. Time series of pendulum and GNSS over a time span of 2.5 years have been aligned so to be comparable. Analytical models fitting these time series have been estimated and compared. Those models were able to properly fit pendulum data and GNSS data, with standard deviation of residuals smaller than one millimeter. This encouraging results led to the conclusion that GNSS technique can be profitably applied to dam monitoring allowing a denser description, both in space and time, of the dam displacements than the one based on pendulum observations.

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SUBSIDENCE IN MACEIO, BRAZIL, CHARACTERIZED BY DINSAR AND INVERSE MODELING

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-3-w2-2020-7-2020 ◽

2020 ◽

Vol IV-3/W2-2020 ◽

pp. 7-11

Author(s):

P. A. Euillades ◽

L. E. Euillades ◽

P. Rosell ◽

Y. Roa

Keyword(s):

Time Series ◽

Deformation Source ◽

Time Span ◽

Analytical Models ◽

Fault System ◽

Mining Operations ◽

Mean Velocity ◽

Displacement Patterns ◽

The City ◽

Series Technique

Abstract. The city of Maceió has been historically affected by cracks and sink events in buildings and city infrastructure. Availability of a consistent Sentinel 1 Mission dataset between 2014 and 2019 allows characterizing the undergoing crustal deformation process that provokes such effects. We processed a dataset of 81 SAR scenes using the DInSAR-SBAS time-series technique, which allowed us to obtain mean velocity of deformation and deformation time series. Detected displacement patterns show subsidence concentrated in the Mundaú lagoon coast in front of Mutange, Pinheiro and Levada neighbourhoods. Inversion of the results, using analytical models, locates a sill-like source at ∼400 m depth and with a radius of ∼0.8 km. Its depth would be compatible with re-activation of the Mutange fault system, possibly related to salt mining operations in the area. Further investigation is needed to better constrain the deformation source and to identify if the observed process was active before the analysed time span.

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Deep learning convolutional neural network in rainfall–runoff modelling

Journal of Hydroinformatics ◽

10.2166/hydro.2020.095 ◽

2020 ◽

Vol 22 (3) ◽

pp. 541-561 ◽

Cited By ~ 4

Author(s):

Song Pham Van ◽

Hoang Minh Le ◽

Dat Vi Thanh ◽

Thanh Duc Dang ◽

Ho Huu Loc ◽

...

Keyword(s):

Neural Network ◽

Time Series ◽

Deep Learning ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Water Cycle ◽

Machine Learning Techniques ◽

Analytical Models ◽

Rainfall Runoff ◽

Convolutional Network

Abstract Rainfall–runoff modelling is complicated due to numerous complex interactions and feedback in the water cycle among precipitation and evapotranspiration processes, and also geophysical characteristics. Consequently, the lack of geophysical characteristics such as soil properties leads to difficulties in developing physical and analytical models when traditional statistical methods cannot simulate rainfall–runoff accurately. Machine learning techniques with data-driven methods, which can capture the nonlinear relationship between prediction and predictors, have been rapidly developed in the last decades and have many applications in the field of water resources. This study attempts to develop a novel 1D convolutional neural network (CNN), a deep learning technique, with a ReLU activation function for rainfall–runoff modelling. The modelling paradigm includes applying two convolutional filters in parallel to separate time series, which allows for the fast processing of data and the exploitation of the correlation structure between the multivariate time series. The developed modelling framework is evaluated with measured data at Chau Doc and Can Tho hydro-meteorological stations in the Vietnamese Mekong Delta. The proposed model results are compared with simulations of long short-term memory (LSTM) and traditional models. Both CNN and LSTM have better performance than the traditional models, and the statistical performance of the CNN model is slightly better than the LSTM results. We demonstrate that the convolutional network is suitable for regression-type problems and can effectively learn dependencies in and between the series without the need for a long historical time series, is a time-efficient and easy to implement alternative to recurrent-type networks and tends to outperform linear and recurrent models.

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Capturing Two Consecutive Green Water Events by Convolution

Volume 7B: Ocean Engineering ◽

10.1115/omae2019-96747 ◽

2019 ◽

Cited By ~ 1

Author(s):

Jassiel V. Hernández-Fontes ◽

Rodolfo Silva-Casarín ◽

Edgar Mendoza

Keyword(s):

Time Series ◽

Green Water ◽

Analytical Models ◽

Regular Waves ◽

Convolution Model ◽

Water Elevations ◽

Convolution Approach ◽

Fixed Structure ◽

Hydrodynamic Effects ◽

Selection Of

Abstract Capturing the propagation of green water events on in ships and other marine structures is of importance when studying the hydrodynamic effects on their motion and the structure’s behavior. Analytical models used to predict green water elevations, such as dam-break models, have been considered to represent time series of water elevations of single green water events. This paper presents the use of a convolution approach to represent the time series of water elevations of two consecutive green water events on deck of a fixed structure. The procedure is described considering green water events, generated with regular waves, on a barge-type fixed structure. Its application is compared with results available elsewhere in the literature. With the assumptions related with the selection of input parameters of the convolution model, and considering only the first green water event, the results show that this methodology allows two consecutive green water events to be captured acceptably. It is hoped that this methodology will be useful in further time-domain applications which study the dynamic behavior of structures subjected to green water.

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A Self-Organizing Neural Network to Approach Novelty Detection

Intelligent Systems for Automated Learning and Adaptation ◽

10.4018/978-1-60566-798-0.ch003 ◽

2010 ◽

pp. 49-71 ◽

Cited By ~ 2

Author(s):

Marcelo Keese Albertini ◽

Rodrigo Fernandes de Mello

Keyword(s):

Neural Network ◽

Time Series ◽

Network Architecture ◽

Novelty Detection ◽

Analytical Models ◽

New Concepts ◽

Normal Behavior ◽

Regression Techniques ◽

Self Organizing

Machine learning is a field of artificial intelligence which aims at developing techniques to automatically transfer human knowledge into analytical models. Recently, those techniques have been applied to time series with unknown dynamics and fluctuations in the established behavior patterns, such as humancomputer interaction, inspection robotics and climate change. In order to detect novelties in those time series, techniques are required to learn and update knowledge structures, adapting themselves to data tendencies. The learning and updating process should integrate and accommodate novelty events into the normal behavior model, possibly incurring the revaluation of long-term memories. This sort of application has been addressed by the proposal of incremental techniques based on unsupervised neural networks and regression techniques. Such proposals have introduced two new concepts in time-series novelty detection. The first defines the temporal novelty, which indicates the occurrence of unexpected series of events. The second measures how novel a single event is, based on the historical knowledge. However, current studies do not fully consider both concepts of detecting and quantifying temporal novelties. This motivated the proposal of the self-organizing novelty detection neural network architecture (SONDE) which incrementally learns patterns in order to represent unknown dynamics and fluctuation of established behavior. The knowledge accumulated by SONDE is employed to estimate Markov chains which model causal relationships. This architecture is applied to detect and measure temporal and nontemporal novelties. The evaluation of the proposed technique is carried out through simulations and experiments, which have presented promising results.

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Analysis of full-disc Ca II K spectroheliograms

Astronomy and Astrophysics ◽

10.1051/0004-6361/202037746 ◽

2020 ◽

Vol 639 ◽

pp. A88 ◽

Cited By ~ 2

Author(s):

Theodosios Chatzistergos ◽

Ilaria Ermolli ◽

Natalie A. Krivova ◽

Sami K. Solanki ◽

Dipankar Banerjee ◽

...

Keyword(s):

Time Series ◽

Synthetic Data ◽

Solar Cycles ◽

Surface Magnetism ◽

Central Wavelength ◽

Photometric Calibration ◽

The Past ◽

Limb Darkening ◽

Selection Of

Context. Studies of long-term solar activity and variability require knowledge of the past evolution of the solar surface magnetism. The archives of full-disc Ca II K observations that have been performed more or less regularly at various sites since 1892 can serve as an important source of such information. Aims. We derive the plage area evolution over the last 12 solar cycles by employing data from all Ca II K archives that are publicly available in digital form, including several as-yet-unexplored Ca II K archives. Methods. We analysed more than 290 000 full-disc Ca II K observations from 43 datasets spanning the period between 1892–2019. All images were consistently processed with an automatic procedure that performs the photometric calibration (if needed) and the limb-darkening compensation. The processing also accounts for artefacts affecting many of the images, including some very specific artefacts, such as bright arcs found in Kyoto and Yerkes data. Our employed methods have previously been tested and evaluated on synthetic data and found to be more accurate than other methods used in the literature to treat a subset of the data analysed here. Results. We produced a plage area time-series from each analysed dataset. We found that the differences between the plage areas derived from individual archives are mainly due to the differences in the central wavelength and the bandpass used to acquire the data at the various sites. We empirically cross-calibrated and combined the results obtained from each dataset to produce a composite series of plage areas. The ’backbone’ approach was used to bridge the series together. We have also shown that the selection of the backbone series has little effect on the final composite of the plage area. We quantified the uncertainty of determining the plage areas with our processing due to shifts in the central wavelength and found it to be less than 0.01 in fraction of the solar disc for the average conditions found on historical data. We also found the variable seeing conditions during the observations to slightly increase the plage areas during the activity maxima. Conclusions. We provide the most complete so far time series of plage areas based on corrected and calibrated historical and modern Ca II K images. Consistent plage areas are now available on 88% of all days from 1892 onwards and on 98% from 1907 onwards.

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