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At least 1,549 records · Page 86

Global Aerodynamic Modeling Using Automated Local Model Networks in Real Time

A novel method is presented for automated real-time global aerodynamic modeling using local model networks, known as Smoothed Partitioning with Localized Trees in Real Time (SPLITR), as part of NASA’s Learn-to-Fly technology development initiative. The global nonlinear aerodynamics are partitioned into several local regions known as cells, with the dimension, location, and timing of each partition automatically selected based on a residual characterization procedure, under the constraints of real-time operation. Regression trees represent the successive partitioning of the global flight envelope and describe the evolution of the cell structure. Recursive equation-error least-squares parameter estimation in the time domain is used to estimate a model that represents the local aerodynamics in each region, so that it can be updated independently with non-contiguous data in the range of each cell over time. A weighted superposition of these piecewise local models across the flight envelope forms a global nonlinear model that also accurately captures the local aerodynamics. The SPLITR approach is demonstrated using both simulation and flight data, and the results are analyzed in terms of model predictive capabilities as well as interpretability. The results show that SPLITR can be used to automatically partition complex nonlinear aerodynamic behavior, produce an accurate model, and provide valuable physical insight into the local and global aerodynamics.

Weinstein, Rose↗

Mineral Dust Optical Properties for Remote Sensing and Global Modeling: A Review

Dust plays a key role in many Earth system processes and is ubiquitous in the Martian atmosphere. Various intensive field campaigns, laboratory analyses, space-based remote sensing missions, and global modeling efforts aim to characterize dust optical properties. This is a bountiful time for dust scientists, and yet the interpretation of retrievals and comparison to models remains complicated by various conflicting assumptions that are part of each algorithm. For example, the conversion of satellite radiance measurements into products like aerosol optical depth for model evaluation depends upon aerosol properties like particle size and shape that are often prescribed and not part of the retrieval. Conversely, the model calculation of aerosol optical depth often uses different assumptions. The goal of this review is to first document algorithmic assumptions by various satellite retrieval products and models, and identify where there is consistency and where there are differences. In general, the differences documented in this paper reflect uncertainties resulting from incomplete observational characterization of dust aerosols and limitations in our understanding. Second, we note what observations might reduce uncertainties in our knowledge and bring greater consistency to retrievals and models, allowing for a more rigorous and harmonious comparison. The lack of comprehensive and realistic shape models for dust is an outstanding issue, such that closure between forward modeling from particle refractive index, shape, and size and observed optical properties cannot be achieved. Limitations in the computational methods that must be applied to model scattering from complex shapes also makes accurate optical modeling for dust challenging. Field observations indicate the persistence of coarse and giant dust particles at higher altitudes and farther downwind from their source than previously expected. Remote sensing retrieval algorithms based on observations at visible wavelengths have limited sensitivity to these particles and generally do not consider them, although a recent product based on longwave radiances is encouraging. Current measurements of the refractive index of bulk dust and fundamental dust minerology components such as hematite vary widely, inhibiting attempts to represent the variability in dust optical properties and forcing, as expected from different major dust source regions on Earth that have varying mineralogical composition. Some remote sensing retrieval algorithms allow for limited refractive index variability in their inversion solutions through mixing with other fine mode aerosol models, or optimizing the single scattering albedo, but Earth system models surveyed for this paper assume a globally uniform, size-invariant refractive index. Although no Martian dust samples have yet been returned to Earth, remote sensing observations indicate that Martian dust is globally homogenous in composition, and a single spectral refractive index assumption has been widely adopted to represent Martian dust. The lack of comprehensive, statistically representative measurements of dust particle microphysical properties (size distribution, morphology, complex index of refraction spectra, internal structure heterogeneity), and the resulting optical properties, limits our ability to verify the fidelity of these assumptions. A chain of measurements is needed, ranging from characterizing individual dust mineralogy components (e.g., pure hematite and goethite) to in situ sampling of complex atmospheric aerosol mixtures. Such results could be applied to both remote sensing retrievals that characterize the optical properties of the total aerosol burden in the atmosphere from total radiance measurements, and to global models that represent the total aerosol burden in the atmosphere by building it up from the balance of individual aerosol sources and sinks.

Aeolian dust↗

Improved Methods for Modeling Functional Transition Metal Compounds in Complex Environments: Ground States, Excited States, and Spectroscopies

Rapid advances in energy applications require new theory and computational models to provide guidance for interpretation of experimental results and mechanistic understanding. New theory development is necessary to treat systems of increasing complexity, size, and relevance to real applications. Functional transition metal compounds, including molecules, clusters, nanoparticles, surfaces, and solids, provide particular promise for magnetic, optical, and catalytic applications. However, such systems can be exceptionally challenging to model. To make progress in understanding and designing transition metal compounds for energy applications, theory must be able to simulate such systems in complex environments, as well as simulate the spectra of such complex systems to provide direct connections with experiment. Leveraging the independent expertise of the team’s members, this project aimed to make inroads to the theoretical and computational challenges associated with studying transition metal compounds, their reaction chemistry, photophysics and photochemistry, and response to spectroscopic interrogation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Using a Simple Water Balance Framework to Quantify the Impact of Soil Moisture Initialization on Subseasonal Evapotranspiration and Air Temperature Forecasts

Past studies have shown that accurate soil moisture initialization can contribute significant skill to near-surface air temperature (T2M) forecasts at subseasonal leads. The mechanisms by which soil moisture contributes such skill are examined here with a simple water balance-based model that captures the essence of soil moisture behavior in a state-of-the-art subseasonal-to-seasonal (S2S) forecasting system. The simple model successfully transforms initial soil moisture contents into average “forecasted” ET values at 16-30 day lead that agree well, during summer, with the values forecasted by the full NASA GEOS S2S system, indicating that soil moisture initialization dominates over forecasted meteorology in determining ET fluxes at subseasonal leads. When the simple model’s ET anomalies are interpreted in terms of T2M anomalies, a similar conclusion is reached for T2M: soil moisture initialization explains much (about 50% in the eastern half of the continental US) of the T2M anomalies produced by the full GEOS S2S system at 16-30 day lead, and the T2M forecasts produced by the simple model capture about half of the skill attained by the full system. The simple model’s framework is particularly conducive to an analysis of uncertainty in forecasts. Drier soils are generally found to induce larger uncertainty in ET (and thus T2M) forecasts, a result linked to the functional form relating ET to soil moisture in the simple model and verified by an analysis of the ensemble spreads within the forecasts produced by the full GEOS S2S system

Randal D Koster↗

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗

A Parallel Kinetic Model for Surface and Bulk Charge Storage in ε -MnO 2 Pseudocapacitors

Pseudocapacitive materials such as manganese dioxide (MnO 2 ) are attractive for energy storage applications due to their ability to combine the fast kinetics of capacitors with the higher energy density of battery-type systems. However, the electrochemical behavior of MnO 2 remains difficult to interpret mechanistically, in part because existing models often fail to distinguish between surface-based redox processes and bulk intercalation mechanisms. In this work, we develop a physics-based model that represents MnO 2 pseudocapacitance as a linear combination of two independent, parallel electrochemical processes: (i) the surface or near-surface redox storage and (ii) lithium ion intercalation into the bulk material. These two processes are treated with distinct kinetic and thermodynamic parameters and are assumed to proceed independently. The total measured current is assumed to be the sum of these two partial currents. We validate the model using rate-dependent cyclic voltammetry experiments, demonstrating that it captures key trends and provides physically interpretable parameters reflecting the relative contributions of surface and bulk processes. By enabling a clear separation between these mechanisms, the model offers a useful framework for analyzing pseudocapacitive materials and can guide the rational design of high-performance energy storage electrodes.

Energy - Storage↗

High Accuracy Indicators of Androgen Suppression Therapy Failure for Prostate Cancer—A Modeling Study

Prostate cancer is a serious public health concern in the United States. The primary obstacle to effective long-term management for prostate cancer patients is the eventual development of treatment resistance. Due to the uniquely chaotic nature of the neoplastic genome, it is difficult to determine the evolution of tumor composition over the course of treatment. Hence, a drug is often applied continuously past the point of effectiveness, thereby losing any potential treatment combination with that drug permanently to resistance. If a clinician is aware of the timing of resistance to a particular drug, then they may have a crucial opportunity to adjust the treatment to retain the drug’s usefulness in a potential treatment combination or strategy. In this study, we investigate new methods of predicting treatment failure due to treatment resistance using a novel mechanistic model built on an evolutionary interpretation of Droop cell quota theory. We analyze our proposed methods using patient PSA and androgen data from a clinical trial of intermittent treatment with androgen deprivation therapy. Our results produce two indicators of treatment failure. The first indicator, proposed from the evolutionary nature of the cancer population, is calculated using our mathematical model with a predictive accuracy of 87.3% (sensitivity: 96.1%, specificity: 65%). The second indicator, conjectured from the implication of the first indicator, is calculated directly from serum androgen and PSA data with a predictive accuracy of 88.7% (sensitivity: 90.2%, specificity: 85%). Our results demonstrate the potential and feasibility of using an evolutionary tumor dynamics model in combination with the appropriate data to aid in the adaptive management of prostate cancer.

59 BASIC BIOLOGICAL SCIENCES↗

A model of the variability of the Venus ionopause altitude

A model for the variability of the Venus ionopause as a function of solar wind dynamic pressure and EUV flux during quiescent solar wind conditions is presented. The radio occultation measurements of the Venus ionopause from Mariner 5, 10 and Venera 9, 10 spacecraft, as well as recent in situ Pioneer Venus measurements are interpreted in terms of this model. An ionospheric model consistent with observations in the 400-1000 km region is predominantly O(+) with densities of about 10,000/cu cm and (T sub e + T sub i) approximately equal to 4500-6500 K. For ionopause measurements below 400 km the ionosphere appears severely compressed and density and temperature profiles cannot be simply described, although a strong correlation with solar wind dynamic pressure is observed. Possible effects of IMF direction switching on the dynamics and structure of the ionosphere are also considered and compared with available ionopause data.

Wolff, R. S.↗

Sensitivity of Temperature Profiles Retrieved from Mars Global Surveyor Thermal Emission Spectrometer (MGS/TES) Observations to the GSFC Synthetic Mars Model Atmosphere

Part of the task of interpreting IR spectral features observed by MGS/TES due to surface minerals requires distinguishing those IR signatures from atmospheric signatures of gas and dust. Surface-atmosphere separation for MGS/TES depends on knowledge of the retrieved temperature profile. In turn, the temperature retrieval Erom the observed data depends on molecular parameters including 15 micron CO2 line shape or line intensities which contribute to defining the Mars synthetic radiative transfer model. Using a simple isothermal, homogeneous single layer model of Pinnock and Shine, we find the ratio of (the error in degrees Kelvin of the retrieved temperature profile) to (the percentage error in the absorption coefficient) (deg K/percent) to be 0.4 at 200K. This ratio at 150K and 250K is 0.2 and 0.6, respectively. A more refined model, incorporating observed MGS/TES retrieved temperature profiles, the TES instrumental resolution and the most recent molecular modelling, will yield an improved knowledge of this error sensitivity. We present results of such a sensitivity study to determine the dependence of temperature profiles inverted from MGS/TES on these and other molecular parameters. This work was supported in part by NASA's Mars Data Analysis Program.

Maguire, William C.↗

Superior CO 2 /N 2 separation performance of highly branched Poly(1,3 dioxolane) plasticized by polyethylene glycol

With its high content of CO 2 -philic ether oxygen groups, poly(1,3-dioxolane) (PDXLA) has emerged as an attractive platform to achieve excellent CO 2 /N 2 separation properties for post-combustion carbon capture. Herein we demonstrate that the separation properties of PDXLA can be further enhanced by plasticizing with miscible polyethylene glycol (PEG)-based additives using an integrated experimentation and modeling approach. The effects of the chain end groups and loading level of the additives on the physical properties of the blends are thoroughly investigated, including glass transition temperature (T g ), fractional free volume, and gas transport properties, and the effects can be satisfactorily described using models available for homogeneous blends. Notably, a T g -integrated free volume model is adapted to successfully interpret the unified effect of the blend composition and temperature on gas diffusivity and permeability. A sample containing 45 mass% PEG dimethyl ether (PEGDME with a molecular mass of 240 g/mol) displays stable mixed-gas CO 2 permeability of 1540 Barrer and CO 2 /N 2 selectivity of 40 when challenged with a model flue gas at 60 °C, outperforming Robeson's 2008 upper bound. Elucidating how small plasticizers impact gas transport in homogeneous blends may unravel a facile way to design high-performance membranes for gas separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Surface temperature of a magnetized neutron star and interpretation of the ROSAT data. 1: Dipole fields

We model the temperature distribution at the surface of a magnetized neutron star and study the effects on the observed X-ray spectra and light curves. Generalrelativistic effects, i.e., redshift and lensing, are fully taken into account. Atmospheric effects on the emitted spectral flux are not included: we consider only blackbody emission at the local effective temperature. In this first paper we restrict ourselves to dipole fields. General features are studied and compared with the ROSAT data from the pulsars 0833 - 45 (Vela), 0656 + 14, 0630 + 178 (Geminga), and 1055 - 52, the four cases for which there is strong evidence that thermal radiation from the stellar surface is detected. The composite spectra we obtain are not very different from a blackbody spectrum at the star's effective temperature. We conclude that, as far as blackbody spectra are considered, temperature estimates using single-temperature models give results practically identical to our composite models. The change of the (composite blackbody) spectrum with the star's rotational phase is also not very large and may be unobservable inmost cases. Gravitational lensing strongly suppresses the light curve pulsations. If a dipole field is assumed, pulsed fractions comparable to the observed ones can be obtained only with stellar radii larger than those which are predicted by current models of neutron star struture, or with low stellar masses. Moreover, the shapes of the theoretical light curves with dipole fields do not correspond to the observations. The use of magnetic spectra may raise the pulsed fraction sufficiently but will certainly make the discrepancy with the light curve shapes worse: dipole fields are not sufficient to interpret the data. Many neutron star models with a meson condensate or hypersons predict very small radii, and hence very strong lensing, which will require highly nondipolar fields to be able to reproduce the observed pulsed fractions, if possible at all: this may be a new tool to constrain the size of neutron stars. The pulsed fractions obtained in all our models increase with photon energy: the strong decrease observed in Geminga at energies 0.3-0.5 keV is definitely a genuine effect of the magnetic field on the spectrum in contradistinction to the magnetic effects on the surface temperature considered her. Thus, a detailed analysis of thermal emission from the four pulsars we consider will require both complex surface field configurations and the inclusion of magnetic effects in the atmosphere (i.e., on the emitted spectrum).

Page, Dany↗

Search for light pseudoscalar boson pairs produced from Higgs boson decays using the 4τ and 2μ2τ final states in proton-proton collisions at $\sqrt{s}=13$ TeV

A search for a pair of light pseudoscalar bosons (a 1 ) produced in the decay of the 125 GeV Higgs boson is presented. The analysis examines decay modes where one a 1 decays into a pair of tau leptons and the other decays into either another pair of tau leptons or a pair of muons. The a1 boson mass probed in this study ranges from 4 to 15 GeV. The data sample was recorded by the CMS experiment in proton-proton collisions at a center-of-mass energy of 13 TeV and corresponds to an integrated luminosity of 138 fb −1 . No excess above standard model (SM) expectations is observed. The study combines the 4τ and 2μ2τ channels to set upper limits at 95% confidence level (CL) on the product of the Higgs boson production cross section and the branching fraction to the 4τ final state, relative to the Higgs boson production cross section predicted by the SM. In this interpretation, the a 1 boson is assumed to have Yukawa-like couplings to fermions, with coupling strengths proportional to the respective fermion masses. The observed (expected) upper limits range between 0.007 (0.011) and 0.079 (0.066) across the mass range considered. The results are also interpreted in the context of models with two Higgs doublets and an additional complex singlet field (2HD+S). The tightest constraints are obtained for the Type III 2HD+S model. In this case, assuming the Higgs boson production cross section equals the SM prediction, values of the branching ratio for the Higgs boson decay into a pair of a1 bosons exceeding 16% are excluded at 95% CL for a1 boson masses between 5 and 15 GeV and tan β > 2, with the exception of scenarios in which the a1 boson mixes with charm or bottom quark-antiquark bound states.

Higgs physics↗

Implementation of AI/DEEP learning disruption predictor into a plasma control system

Abstract This paper reports on advances in the state‐of‐the‐art deep learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced in a 2019 NATURE publication [ https://doi.org/10.1038/s41586‐019‐1116‐4 ]. In particular, the predictor now features not only the “disruption score,” as an indicator of the probability of an imminent disruption, but also a “sensitivity score” in real time to indicate the underlying reasons for the imminent disruption. This adds valuable physics interpretability for the deep learning model and can provide helpful guidance for control actuators now implemented into a modern plasma control system (PCS). The advance is a significant step forward in moving from modern deep learning disruption prediction to real‐time control and brings novel AI‐enabled capabilities relevant for application to the future burning plasma ITER system. Our analyses use large amounts of data from JET and DIII‐D vetted in the earlier NATURE publication. In addition to “when” a shot is predicted to disrupt, this paper addresses reasons “why” by carrying out sensitivity studies. FRNN is accordingly extended to use more channels of information, including measured DIII‐D signals such as (i) the “n1rms” signal that is correlated with the n = 1 modes with finite frequency, including neoclassical tearing mode and sawtooth dynamics; (ii) the bolometer data indicative of plasma impurity control; and (iii) “q‐min”—the minimum value of the safety factor relevant to the key physics of kink modes. The additional channels and interpretability features expand the ability of the deep learning FRNN software to provide information about disruption subcategories as well as more precise and direct guidance for the actuators in a PCS.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Data from: Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model

This data collection represents all UH8to2 model data used for the publication "Arctic ice-ocean interactions in an 8-to-2 kilometer resolution global model." This includes ocean and sea ice characteristics from the 2017-2020 UH8to2 model run. Further details about the model setup, forcing, and our interpretation of these data can be found in the publication.

58 GEOSCIENCES↗

Experimental evidence for chemo-mechanical coupling during carbon mineralization in ultramafic rocks

It is known that storing carbon dioxide in the subsurface as carbonate minerals has the benefit of long-term stability and immobility. Ultramafic rock formations have been suggested as a potential reservoir for this type of storage due to the availability of cations to react with dissolved carbon dioxide and the fast reaction rates associated with minerals common in ultramafic formations; however, the rapid reactions have the potential to couple with the mechanical and hydraulic behavior of the rocks and little is known about the extent and mechanisms of this coupling. In this study, we argue that the dissolution of primary minerals and the precipitation of secondary minerals along pre-existing fractures in samples lead to reductions in both the apparent Young's modulus and shear strength of aggregates, accompanied by reduction in permeability. Hydrostatic and triaxial deformation experiments were run on dunite samples saturated with de-ionized water and carbon dioxide-rich solutions while stress, strain, permeability and pore fluid chemistry were monitored. Sample microstructures were examined after reaction and deformation using scanning electron microscopy (SEM). The results show that channelized dissolution and carbonate mineral precipitation in the samples saturated with carbon dioxide-rich solutions modify the structure of grain boundaries, leading to the observed reductions in stiffness, strength and permeability. A geochemical model was run to help interpret fluid chemical data, and we find that the apparent reaction rates in our experiments are faster than rates calculated from powder reactors, suggesting mechanically enhanced reaction rates. In conclusion, we find that chemo-mechanical coupling during carbon mineralization in dunites leads to substantial modification of mechanical and hydraulic behavior that needs to be accounted for in future modeling efforts of in situ carbon mineralization projects.

58 GEOSCIENCES↗

Actinide signatures in low electron fraction kilonova ejecta

ABSTRACT Neutron star (NS) mergers are known to produce heavy elements through rapid neutron capture (r-process) nucleosynthesis. Actinides are expected to be created solely by the r-process in the most neutron-rich environments. Confirming if NS mergers provide the requisite conditions for actinide creation is therefore central to determining their origin in the Universe. Actinide signatures in kilonova (KN) spectra may yield an answer, provided adequate models are available in order to interpret observational data. In this study, we investigate actinide signatures in neutron-rich merger ejecta. We use three ejecta models with different compositions and radioactive power, generated by nucleosynthesis calculations using the same initial electron fraction ($Y_e = 0.15$) but with different nuclear physics inputs and thermodynamic expansion history. These are evolved from 10 to 100 d after merger using the sumo non-local thermodynamic equilibrium (NLTE) radiative transfer code. We highlight how uncertainties in nuclear properties, as well as choices in thermodynamic trajectory, may yield entirely different outputs for equal values of $Y_e$. We consider an actinide-free model and two actinide-rich models, and find that the emergent spectra and light-curve evolution are significantly different depending on the amount of actinides present, and the overall decay properties of the models. We also present potential key actinide spectral signatures, of which doubly ionized $_{89}$Ac and $_{90}$Th may be particularly interesting as spectral indicators of actinide presence in KN ejecta.

Pognan, Quentin (ORCID:0000000152550782)↗

One Planet or Two Planets? The Ultra-sensitive Extreme-magnification Microlensing Event KMT-2019-BLG-1953

We present the analysis of a very high-magnification (A ∼ 900) microlensing event KMT-2019-BLG-1953. A single-lens single-source (1L1S) model appears to approximately delineate the observed light curve, but the residuals from the model exhibit small but obvious deviations in the peak region. A binary-lens (2L1S) model with a mass ratio of q ∼ 2 × 10{sup −3} improves the fits by Δχ {sup 2} = 181.8, indicating that the lens possesses a planetary companion. From additional modeling by introducing an extra planetary lens component (3L1S model) and an extra source companion (2L2S model), it is found that the residuals from the 2L1S model further diminish, but claiming these interpretations is difficult due to the weak signals with Δχ {sup 2} = 16.0 and 13.5 for the 3L1S and 2L2L models, respectively. From a Bayesian analysis, we estimate that the host of the planets has a mass of M{sub host}=0.31{sub −0.17}{sup +0.37} M{sub ⊙} and that the planetary system is located at a distance of D{sub L}=7.04{sub −1.33}{sup +1.10} kpc toward the Galactic center. The mass of the securely detected planet is M{sub p}=0.64{sub −0.35}{sup +0.76} M{sub J}. The signal of the potential second planet could have been confirmed if the peak of the light curve had been more densely observed by follow-up observations, and thus the event illustrates the need for intensive follow-up observations for very high-magnification events even in the current generation of high-cadence surveys.

79 ASTRONOMY AND ASTROPHYSICS↗