scholarly journals OGLE-2017-BLG-1186: first application of asteroseismology and Gaussian processes to microlensing

2019 ◽  
Vol 488 (3) ◽  
pp. 3308-3323 ◽  
Author(s):  
S-S Li ◽  
W Zang ◽  
A Udalski ◽  
Y Shvartzvald ◽  
D Huber ◽  
...  
Keyword(s):  
Light Curve ◽  
Gaussian Processes ◽  
Angular Size ◽  
Correlated Noise ◽  
Brown Dwarf ◽  
Minimization Method ◽  
Variable Source ◽  
Galactic Model ◽  
Red Giant ◽  
Gp Model

Abstract We present the analysis of the event OGLE-2017-BLG-1186 from the 2017 Spitzer microlensing campaign. This is a remarkable microlensing event because its source is photometrically bright and variable, which makes it possible to perform an asteroseismic analysis using ground-based data. We find that the source star is an oscillating red giant with average time-scale of ∼9 d. The asteroseismic analysis also provides us source properties including the source angular size (∼27 $\mu$as) and distance (∼11.5 kpc), which are essential for inferring the properties of the lens. When fitting the light curve, we test the feasibility of Gaussian processes (GPs) in handling the correlated noise caused by the variable source. We find that the parameters from the GP model are generally more loosely constrained than those from the traditional χ2 minimization method. We note that this event is the first microlensing system for which asteroseismology and GPs have been used to account for the variable source. With both finite-source effect and microlens parallax measured, we find that the lens is likely a ∼0.045 M⊙ brown dwarf at distance ∼9.0 kpc, or a ∼0.073 M⊙ ultracool dwarf at distance ∼9.8 kpc. Combining the estimated lens properties with a Bayesian analysis using a Galactic model, we find a $\sim 35{{\ \rm per\ cent}}$ probability for the lens to be a bulge object and $\sim 65{{\ \rm per\ cent}}$ to be a background disc object.

Gaussian Processes for Hydrocarbon Depth Estimation in Forward Modeling of Seabed Logging

2019 ◽  
Vol 24 (3) ◽  
pp. 399-408
Author(s):  
Muhammad Naeim Mohd Aris ◽  
Hanita Daud ◽  
Sarat Chandra Dass ◽  
Khairul Arifin Mohd Noh
Keyword(s):  
Electric Field ◽  
Gaussian Processes ◽  
Depth Estimation ◽  
Forward Modeling ◽  
Hydrocarbon Reservoir ◽  
Offset Distance ◽  
Data Points ◽  
Gp Model ◽  
D Model ◽  
True Values

Seabed logging (SBL) is an application of the marine controlled-source electromagnetic (CSEM) technique to discover offshore hydrocarbon reservoirs underneath the seabed. This application is based on electrical resistivity contrast between hydrocarbon and its surroundings. In this paper, simulation and forward modeling were performed to estimate the hydrocarbon depths in one-dimensional (1-D) SBL data. 1-D data, consisted offset distance (input) and magnitude of electric field (output), were acquired from SBL models developed using computer simulation technology (CST) software. The computer simulated outputs were observed at various depths of hydrocarbon reservoir (250 m–2,750 m with an increment of 250 m) with frequency of 0.125 Hz. Gaussian processes (GP) was employed in the forward modeling by utilizing prior information which is electric field (E-field) at all observed inputs to provide E-field profile at unobserved/untried inputs with uncertainty quantification in terms of variance. The concept was extended for two-dimensional (2-D) model. All observations of E-field were then investigated with the 2-D forward GP model. Root mean square error (RMSE) and coefficient of variation (CV) were utilized to compare the acquired and modeled data at random untried hydrocarbon depths at 400 m, 950 m, 1,450 m, 2,100 m and 2,600 m. Small RMSE and CV values have indicated that developed model can fit well the SBL data at untried hydrocarbon depths. The measured variances of the untried inputs revealed that the data points (true values) were very close to the estimated values, which was 0.003 (in average). RMSEs obtained were very small as an average of 0.049, and CVs found as very reliable percentages, an average of 0.914%, which implied well fitting of the GP model. Hence, the 2-D forward GP model is believed to be capable of predicting unobserved hydrocarbon depths.

scholarly journals On the Photometric Variations of the Irregular Variable Red Giant HD 65750 in the Reflection Nebula IC 2220

1977 ◽  
Vol 42 ◽  
pp. 601-605
Author(s):  
J. Dachs ◽  
J. Isserstedt ◽  
J. Rahe
Keyword(s):  
Light Curve ◽  
Time Scale ◽  
Reflection Nebula ◽  
Irregular Variable ◽  
Red Giant

AbstractThe light-curve between 1964 and 1977 for the variable M2II giant HD 65750 = V341 Car is derived from 77 photographic and 83 photoelectric UBV measurements and analyzed. It is concluded that the light variations of the star are irregular and due to variable extinction in the circumstellar nebula. The appearence of the visible reflection nebula IC 2220 into which HD 65750 is embedded, has been found to vary on a time scale of four years.

scholarly journals Asymmetric Transit Curves as Indication of Orbital Obliquity: Clues from the Brown Dwarf Companion in KOI-13

2011 ◽  
Vol 7 (S282) ◽  
pp. 139-140
Author(s):  
G. M. Szabó ◽  
R. Szabó ◽  
J. M. Benkö ◽  
H. Lehmann ◽  
G. Mezö ◽  
...  
Keyword(s):  
Light Curve ◽  
Surface Brightness ◽  
Rotating Stars ◽  
Brown Dwarf ◽  
Rapidly Rotating Stars

AbstractExoplanets orbiting rapidly rotating stars may have unusual light curve shapes. These objects transit across an oblate disk with non-isotropic surface brightness, caused by the gravitational darkening. If such asymmetries are measured, one can infer the orbital obliquity of the exoplanet and the gravity darkened star, even without the analysis of the Rossiter-McLaughlin effect or interferometry. Here we introduce KOI-13 as the first example of a transiting system with a gravity darkened star.

scholarly journals NEPTUNE’S DYNAMIC ATMOSPHERE FROMKEPLER K2OBSERVATIONS: IMPLICATIONS FOR BROWN DWARF LIGHT CURVE ANALYSES

2016 ◽  
Vol 817 (2) ◽  
pp. 162 ◽  
Author(s):  
Amy A. Simon ◽  
Jason F. Rowe ◽  
Patrick Gaulme ◽  
Heidi B. Hammel ◽  
Sarah L. Casewell ◽  
...  
Keyword(s):  
Light Curve ◽  
Brown Dwarf

Robust Optimization With Parameter and Model Uncertainties Using Gaussian Processes With Limited Samples

2017 ◽  
Author(s):  
Yanjun Zhang ◽  
Mian Li
Keyword(s):  
Robust Optimization ◽  
Engineering Design ◽  
Gaussian Processes ◽  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Computational Cost ◽  
Random Errors ◽  
Model Uncertainties ◽  
Uncertainty Model ◽  
Gp Model

Uncertainty is inevitable in engineering design. The existence of uncertainty may change the optimality and/or the feasibility of the obtained optimal solutions. In simulation-based engineering design, uncertainty could have various types of sources, such as parameter uncertainty, model uncertainty, and other random errors. To deal with uncertainty, robust optimization (RO) algorithms are developed to find solutions which are not only optimal but also robust with respect to uncertainty. Parameter uncertainty has been taken care of by various RO approaches. While model uncertainty has been ignored in majority of existing RO algorithms with the hypothesis that the simulation model used could represent the real physical system perfectly. In the authors’ earlier work, a RO framework was proposed to consider both parameter and model uncertainties using the Bayesian approach with Gaussian processes (GP), where metamodeling uncertainty introduced by GP modeling is ignored by assuming the constructed GP model is accurate enough with sufficient training samples. However, infinite samples are impossible for real applications due to prohibitive time and/or computational cost. In this work, a new RO framework is proposed to deal with both parameter and model uncertainties using GP models but only with limited samples. The compound effect of parameter, model, and metamodeling uncertainties is derived with the form of the compound mean and variance to formulate the proposed RO approach. The proposed RO approach will reduce the risk for the obtained robust optimal designs considering parameter and model uncertainties becoming non-optimal and/or infeasible due to insufficiency of samples for GP modeling. Two test examples with different degrees of complexity are utilized to demonstrate the applicability and effectiveness of the proposed approach.

scholarly journals A Trust-based Mixture of Gaussian Processes Model for Reliable Regression in Participatory Sensing

2017 ◽  
Author(s):  
Qikun Xiang ◽  
Jie Zhang ◽  
Ido Nevat ◽  
Pengfei Zhang
Keyword(s):  
Real World ◽  
Gaussian Processes ◽  
Spatial Regression ◽  
Participatory Sensing ◽  
Sensing Applications ◽  
Different Types ◽  
Inaccurate Estimation ◽  
Real World Datasets ◽  
Gp Model ◽  
Mixture Of Gaussian

Data trustworthiness is a crucial issue in real-world participatory sensing applications. Without considering this issue, different types of worker misbehavior, especially the challenging collusion attacks, can result in biased and inaccurate estimation and decision making. We propose a novel trust-based mixture of Gaussian processes (GP) model for spatial regression to jointly detect such misbehavior and accurately estimate the spatial field. We develop a Markov chain Monte Carlo (MCMC)-based algorithm to efficiently perform Bayesian inference of the model. Experiments using two real-world datasets show the superior robustness of our model compared with existing approaches.

scholarly journals Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors (IDETC2021-71570)

2021 ◽  
pp. 1-36
Author(s):  
Liwei Wang ◽  
Suraj Yerramilli ◽  
Akshay Iyer ◽  
Daniel Apley ◽  
Ping Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gaussian Processes ◽  
Building Blocks ◽  
Variational Inference ◽  
Data Driven ◽  
Ternary Oxide ◽  
Latent Space ◽  
Multiple Materials ◽  
Qualitative Factors ◽  
Gp Model

Abstract Scientific and engineering problems often require the use of artificial intelligence to aid understanding and the search for promising designs. While Gaussian processes (GP) stand out as easy-to-use and interpretable learners, they have difficulties in accommodating big datasets, qualitative inputs, and multi-type responses obtained from different simulators, which has become a common challenge for data-driven design applications. In this paper, we propose a GP model that utilizes latent variables and functions obtained through variational inference to address the aforementioned challenges simultaneously. The method is built upon the latent variable Gaussian process (LVGP) model where qualitative factors are mapped into a continuous latent space to enable GP modeling of mixed-variable datasets. By extending variational inference to LVGP models, the large training dataset is replaced by a small set of inducing points to address the scalability issue. Output response vectors are represented by a linear combination of independent latent functions, forming a flexible kernel structure to handle multi-type responses. Comparative studies demonstrate that the proposed method scales well for large datasets, while outperforming state-of-the-art machine learning methods without requiring much hyperparameter tuning. In addition, an interpretable latent space is obtained to draw insights into the effect of qualitative factors, such as those associated with “building blocks” of architectures and element choices in metamaterial and materials design. Our approach is demonstrated for machine learning of ternary oxide materials and topology optimization of a multiscale compliant mechanism with aperiodic microstructures and multiple materials.

scholarly journals TESS first look at evolved compact pulsators

2019 ◽  
Vol 632 ◽  
pp. A90 ◽  
Author(s):  
S. Charpinet ◽  
P. Brassard ◽  
G. Fontaine ◽  
V. Van Grootel ◽  
W. Zong ◽  
...  
Keyword(s):  
Light Curve ◽  
Seismic Analysis ◽  
Compact Objects ◽  
Optimal Model ◽  
Light Curve Analysis ◽  
The Core ◽  
Trigonometric Parallax ◽  
Derived Properties ◽  
Red Giant ◽  
First Time

Context. The TESS satellite was launched in 2018 to perform high-precision photometry from space over almost the whole sky in a search for exoplanets orbiting bright stars. This instrument has opened new opportunities to study variable hot subdwarfs, white dwarfs, and related compact objects. Targets of interest include white dwarf and hot subdwarf pulsators, both carrying high potential for asteroseismology. Aims. We present the discovery and detailed asteroseismic analysis of a new g-mode hot B subdwarf (sdB) pulsator, EC 21494−7018 (TIC 278659026), monitored in TESS first sector using 120-s cadence. Methods. The TESS light curve was analyzed with standard prewhitening techniques, followed by forward modeling using our latest generation of sdB models developed for asteroseismic investigations. By simultaneously best-matching all the observed frequencies with those computed from models, we identified the pulsation modes detected and, more importantly, we determined the global parameters and structural configuration of the star. Results. The light curve analysis reveals that EC 21494−7018 is a sdB pulsator counting up to 20 frequencies associated with independent g-modes. The seismic analysis singles out an optimal model solution in full agreement with independent measurements provided by spectroscopy (atmospheric parameters derived from model atmospheres) and astrometry (distance evaluated from Gaia DR2 trigonometric parallax). Several key parameters of the star are derived. Its mass (0.391 ± 0.009 M⊙) is significantly lower than the typical mass of sdB stars and suggests that its progenitor has not undergone the He-core flash; therefore this progenitor could originate from a massive (≳2 M⊙) red giant, which is an alternative channel for the formation of sdBs. Other derived parameters include the H-rich envelope mass (0.0037 ± 0.0010 M⊙), radius (0.1694 ± 0.0081 R⊙), and luminosity (8.2 ± 1.1 L⊙). The optimal model fit has a double-layered He+H composition profile, which we interpret as an incomplete but ongoing process of gravitational settling of helium at the bottom of a thick H-rich envelope. Moreover, the derived properties of the core indicate that EC 21494−7018 has burnt ∼43% (in mass) of its central helium and possesses a relatively large mixed core (Mcore = 0.198 ± 0.010 M⊙), in line with trends already uncovered from other g-mode sdB pulsators analyzed with asteroseismology. Finally, we obtain for the first time an estimate of the amount of oxygen (in mass; X(O)core = 0.16+0.13−0.05) produced at this stage of evolution by an helium-burning core. This result, along with the core-size estimate, is an interesting constraint that may help to narrow down the still uncertain 12C(α, γ)16O nuclear reaction rate.

scholarly journals Further Evidence of a Brown Dwarf Orbiting the Post-Common Envelope Eclipsing Binary V470 Cam (HS 0705+6700)

2017 ◽  
Vol 26 (1) ◽  
Author(s):  
David Bogensberger ◽  
Fraser Clarke ◽  
Anthony Eugene Lynas-Gray
Keyword(s):  
Mass Loss ◽  
Travel Time ◽  
Centre Of Mass ◽  
Third Body ◽  
Brown Dwarf ◽  
Common Envelope ◽  
Gravitational Pull ◽  
Orbital Periods ◽  
Red Giant ◽  
Time Changes

AbstractSeveral post-common envelope binaries have slightly increasing, decreasing or oscillating orbital periods. One of several possible explanations is light travel-time changes, caused by the binary centre-of-mass being perturbed by the gravitational pull of a third body. Further studies are necessary because it is not clear how a third body could have survived subdwarf progenitor mass-loss at the tip of the Red Giant Branch, or formed subsequently. Thirty-nine primary eclipse times for V470 Cam were secured with the Philip Wetton Telescope during the period 2016 November 25

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