Uncertainty In Radius Research Articles

Weak field deflection angle and analytical parameter estimation of the Lorentz-violating Bumblebee parameter through the black hole shadow using EHT data

We explored how the Lorentz symmetry breaking parameter ℓ affects the Reissner-Nordstöm BH solution in the context of weak field deflection angle, and the black hole shadow. We aim to derive the general expression for the weak deflection angle using the non-asymptotic version of the Gauss-Bonnet theorem, and we presented a way to simplify the calculations under the assumption that the distance of the source and the receiver are the same. Through the Solar System test, ℓ is constrained from around orders of magnitude to 0, implying challenging detection of ℓ through the deflection of light rays from the Sun. We also studied the black hole shadow in an analytic way, where we applied the EHT results under the far approximation in obtaining an estimate expression for ℓ. Using the realistic values of the black hole mass and observer distance for Sgr. A* and M87*, it was shown that is satisfied, implying the relevance and potential promise of the spontaneous Lorentz symmetry breaking parameter's role on the shadow radius uncertainties as measured by the EHT. We find constraints for ℓ to be negatively valued, where the upper and lower bounds are ∼ - 1.94 and ∼ - 2.04, respectively.

Enhancing resilience of distribution systems: Integrating mobile energy storage systems and information gap decision theory for uncertainty management

Power Distribution Systems (PDSs) have seen considerable disruption owing to events and the intrinsic uncertainty associated with renewable energy sources (RES). The fundamental purpose of this project is to identify methods to enhance the resilience of Mobile Energy Storage Systems (MESSs) against unexpected cyber and natural disasters. Information Gap Decision Theory (IGDT) is used to effectively handle the uncertainties linked with RES's outputs. It also applies the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to predict important aspects that affect the output of RES. The shift to microgrid mode prioritizes the delivery of critical loads through MESSs, which are crucial amid adverse situations. A risk-averse strategy mitigates risks associated with wind turbine and photovoltaic (PV) outputs, therefore enhancing the resilience of the PDS. An optimization model utilizing Mixed-Integer Linear Programming (MILP) was formulated. The approach's efficacy in reducing uncertainties in RES has been proved by simulations conducted on the IEEE 69 bus and Dakota transport system. Assuming the most severe situation, the Uncertainty Radius (UR) for wind turbines is 0.7385, but for PVs, it is 0.8753, thereby guaranteeing the robustness of the system against power failures and disturbances.

A risk‐averse strategy based on information gap decision theory for optimal placement of service transformers in distribution networks

AbstractIn distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO.

Observation uncertainty effects on the precision of interior planetary parameters

ABSTRACT Determining compositions of low-mass exoplanets is essential in understanding their origins. The certainty by which masses and radius are measured affects our ability to discern planets that are rocky or volatile rich. In this study, we aim to determine sound observational strategies to avoid diminishing returns. We quantify how uncertainties in mass, radius, and model assumptions propagate into errors in inferred compositions of rocky and water planets. For a target error in a planet’s iron-mass fraction or water content, we calculate the corresponding required accuracies in radius and mass. For instance, a rocky planet with a known radius error of 2 per cent (corresponding to TESS detection best errors) demands mass precision to be at 5–11 per cent to attain an 8 wt% precision in iron-mass fraction, regardless of mass. Similarly, a water world of equal radius precision requires 9–20 per cent mass precision to confine the water content within a 10 wt% margin. Lighter planets are more difficult to constrain, especially water-rich versus water-poor worlds. Studying Earth as an exoplanet, we find an ∼±5 point ’error floor’ in iron-mass fraction and ∼±7 in core-mass fraction from our lack of knowledge on mineralogy. The results presented here can quickly guide observing strategies to maximize insights into small exoplanet compositions while avoiding overobserving.

Predicted asteroseismic detection yield for solar-like oscillating stars with PLATO

Aims.In this work, we determine the expected yield of detections of solar-like oscillations for the targets of the foreseen PLATO ESA mission. Our estimates are based on a study of the detection probability, which takes into account the properties of the target stars, using the information available in the PIC 1.1.0, including the current best estimate of the signal-to-noise ratio (S/N). The stellar samples, as defined for this mission, include those with the lowest noise level (P1 and P2 samples) and the P5 sample, which has a higher noise level. For the P1 and P2 samples, the S/N is high enough (by construction) that we can assume that the individual mode frequencies can be measured. For these stars, we estimate the expected uncertainties in mass, radius, and age due to statistical errors induced by uncertainties from the observations only.Methods.We used a formulation from the literature to calculate the detection probability. We validated this formulation and the underlying assumptions withKeplerdata. Once validated, we applied this approach to the PLATO samples. Using againKeplerdata as a calibration set, we also derived relations to estimate the uncertainties of seismically inferred stellar mass, radius, and age. We then applied those relations to the main sequence stars with masses equal to or below 1.2M⊙belonging to the PLATO P1 and P2 samples and for which we predict a positive seismic detection.Results.We found that we can expect positive detections of solar-like oscillations for more than 15 000 FGK stars in one single field after a two-year observation run. Among them, 1131 main sequence stars with masses of ≤1.2 M⊙satisfy the PLATO requirements for the uncertainties of the seismically inferred stellar masses, radii, and ages. The baseline observation programme of PLATO consists of observing two fields of similar size (one in the southern hemisphere and one in the northern hemisphere) for two years apiece. Accordingly, the expected seismic yields of the mission amount to over 30 000 FGK dwarfs and subgiants, with positive detections of solar-like oscillations. This sample of expected solar-like oscillating stars is large enough to enable the PLATO mission’s stellar objectives to be amply satisfied.Conclusions.The PLATO mission is expected to produce a catalog sample of extremely well seismically characterized stars of a quality that is equivalent to theKeplerLegacy sample, but containing a number that is about 80 times greater, when observing two PLATO fields for two years apiece. These stars are a gold mine that will make it possible to make significant advances in stellar modelling.

Rabi-oscillation spectroscopy of the hyperfine structure of the muonium atom for high-precision determination of the muon mass

Muonium (Mu) is a hydrogenlike atom comprising a positive muon and an electron, both pointlike leptons. Spectroscopy of Mu is a promising method in the search for new physics in particle-physics research, superior to that of hydrogen for which the uncertainty in the proton radius and its internal structure gives limitations. Microwave spectroscopy of the Mu hyperfine structure (HFS) provides the most precise estimation of the magnetic moment and the mass of the muon, opening a way to search for a physics beyond the Standard Model. MuSEUM Collaboration is studying Mu-HFS at the J-PARC muon facility in Japan, aiming at a precision one order of magnitude better than before. With our new spectroscopic technique which does not require any frequency sweep or Fourier transformation, the resonance frequency can be obtained directly by fitting a simulated function to the time evolution of the Rabi oscillation at a fixed microwave frequency. This method, named Rabi-oscillation spectroscopy, can improve the precision by eliminating systematic uncertainties due to power fluctuations. After a decade of our study under zero magnetic field with fruitful results, we are about to start our experiment under a high magnetic field.

The EBLM Project– XI. Mass, radius, and effective temperature measurements for 23 M-dwarf companions to solar-type stars observed with CHEOPS

ABSTRACT Observations of low-mass stars have frequently shown a disagreement between observed stellar radii and radii predicted by theoretical stellar structure models. This ‘radius inflation’ problem could have an impact on both stellar and exoplanetary science. We present the final results of our observation programme with the CHaracterising ExOPlanet Satellite (CHEOPS) to obtain high-precision light curves of eclipsing binaries with low-mass stellar companions (EBLMs). Combined with the spectroscopic orbits of the solar-type companions, we can derive the masses, radii, and effective temperatures of 23 M-dwarf stars. We use the pycheops data analysis software to analyse their primary and secondary occultations. For all but one target, we also perform analyses with Transiting Exoplanet Survey Satellite (TESS) light curves for comparison. We have assessed the impact of starspot-induced variation on our derived parameters and account for this in our radius and effective temperature uncertainties using simulated light curves. We observe trends in inflation with both metallicity and orbital separation. We also observe a strong trend in the difference between theoretical and observational effective temperatures with metallicity. There is no such trend with orbital separation. These results are not consistent with the idea that the observed inflation in stellar radius combines with lower effective temperature to preserve the luminosity predicted by low-mass stellar models. Our EBLM systems provide high-quality and homogeneous measurements that can be used in further studies of radius inflation.

Suppression for Phase Error of Fringe Projection Profilometry Using Outlier-Detection Model: Development of an Easy and Accurate Method for Measurement

Fringe projection is an important technology in three-dimensional measurement and target recognition. The measurement accuracy depends heavily on the calibration of the absolute phase and projector pixels. An easy-to-implement calibration method based on the Random Sample Consensus (RANSAC) algorithm is proposed to exterminate the phase error data and elevate the measurement accuracy in a fringe projection system. The reconstruction experiments of a double-sphere standard demonstrate that the uncertainties in radius and sphere-distance measurement are reduced to one thousandth of the measured value or even less, and the standard deviation in multiple measurements is restricted to within 50 μm. The measurement accuracy provided by the proposed RANSAC method can be improved by up to 44% compared with that provided by traditional least squared method (LSM). The proposed calibration method is easy and simple to implement, and it does not need additional hardware, but rather a calibration board.

ATMOSPHERIX: II- Characterizing exoplanet atmospheres through transmission spectroscopy with SPIRou

ABSTRACT In a companion paper, we introduced a publicly available pipeline to characterize exoplanet atmospheres through high-resolution spectroscopy. In this paper, we use this pipeline to study the biases and degeneracies that arise in atmospheric characterization of exoplanets in near-infrared ground-based transmission spectroscopy. We inject synthetic planetary transits into sequences of SPIRou spectra of the well known M dwarf star Gl 15 A, and study the effects of different assumptions on the retrieval. We focus on (i) mass and radius uncertainties, (ii) non-isothermal vertical profiles, and (iii) identification and retrieval of multiple species. We show that the uncertainties on mass and radius should be accounted for in retrievals and that depth-dependent temperature information can be derived from high-resolution transmission spectroscopy data. Finally, we discuss the impact of selecting wavelength orders in the retrieval and the issues that arise when trying to identify a single species in a multispecies atmospheric model. This analysis allows us to understand better the results obtained through transmission spectroscopy and their limitations in preparation to the analysis of actual SPIRou data.

ExoMDN: Rapid characterization of exoplanet interior structures with mixture density networks

Aims.Characterizing the interior structure of exoplanets is essential for understanding their diversity, formation, and evolution. As the interior of exoplanets is inaccessible to observations, an inverse problem must be solved, where numerical structure models need to conform to observable parameters such as mass and radius. This is a highly degenerate problem whose solution often relies on computationally expensive and time-consuming inference methods such as Markov chain Monte Carlo.Methods.We present ExoMDN, a machine-learning model for the interior characterization of exoplanets based on mixture density networks (MDN). The model is trained on a large dataset of more than 5.6 million synthetic planets below 25 Earth masses consisting of an iron core, a silicate mantle, a water and high-pressure ice layer, and a H/He atmosphere. We employ log-ratio transformations to convert the interior structure data into a form that the MDN can easily handle.Results.Given mass, radius, and equilibrium temperature, we show that ExoMDN can deliver a full posterior distribution of mass fractions and thicknesses of each planetary layer in under a second on a standard Intel i5 CPU. Observational uncertainties can be easily accounted for through repeated predictions from within the uncertainties. We used ExoMDN to characterize the interiors of 22 confirmed exoplanets with mass and radius uncertainties below 10 and 5%, respectively, including the well studied GJ 1214 b, GJ 486 b, and the TRAPPIST-1 planets. We discuss the inclusion of the fluid Love numberk2as an additional (potential) observable, showing how it can significantly reduce the degeneracy of interior structures. Utilizing the fast predictions of ExoMDN, we show that measuringk2with an accuracy of 10% can constrain the thickness of core and mantle of an Earth analog to ≈13% of the true values.

Risk‐averse optimal operation of an on‐grid photovoltaic/battery/diesel generator hybrid energy system using information gap decision theory

AbstractThis paper focuses on risk‐averse‐based optimal operation of a grid‐connected hybrid energy system (HES) composed of photovoltaic (PV), diesel generator, and battery storage system (BSS). For this goal, information gap decision theory (IGDT) is used to model load demand uncertainty. The aim of the optimal operation is to minimize cost of PV/diesel/BSS by optimal determination of the power purchased from the electricity grid. Since in the risk‐averse strategy, load demand has an undesirable impact on the objective function, the decision maker attempts to maximize the uncertainty radius in a way that any deviation of the uncertain parameter leads to an objective function value which is not worse than the critical value. Over the case studies (considering different radiations), simulation results indicate that in the risk‐neutral strategy, at high, medium, and low radiations, the operation cost is 28.88, 36.10, and 42.63$, respectively. In the risk‐averse strategy, when the radiation is high, by increase of the deviation factor from 0.1 to 0.25, the optimal uncertainty radius increases from 6.98% to 15.72% (increase of around 125%) and the operation cost increases from 31.768 to 36.101$. When the radiation is low, by increase of the deviation factor from 0.1 to 0.25, the uncertainty radius increases from 8.64% to 16.9% (increase of around 96%) and the operation cost increases from 46.895 to 53.291$.

Information gap decision theory with risk aversion strategy for robust planning of hybrid photovoltaic/wind/battery storage system in distribution networks considering uncertainty

In this paper, robust planning of hybrid photovoltaic/wind/battery storage (PV/WT/Battery) system is performed in the distribution network to minimize power losses cost, purchasing power cost from the hybrid system as well as purchasing power cost from the upstream network considering uncertainties of network demand and renewable generation using information gap decision theory (IGDT) based on risk aversion strategy (RAS). The decision variables are considered as the optimal installation location and capacity of the hybrid system components in the network as well as the uncertainty radius of the uncertain parameters, which is determined using the flow direction algorithm (FDA), which is inspired by the movement of surface water flow to the pond outlet. The planning problem is implemented with deterministic and robust-based IGDT approaches. The proposed methodology is performed on the IEEE 33- and 69-bus distribution networks. The deterministic results indicate that the optimal hybrid PV/WT/Battery system planning in the networks significantly reduces system costs by injecting the optimal renewable power into the network and the battery power reserve management. The better capability of the FDA in deterministic planning is confirmed compared to the particle swarm optimization (PSO), crow search algorithm (CSA), and moth-flame optimizer (MFO) in achieving lower system cost. Moreover, in the IGDT robust approach, the maximum uncertainty radius of the renewable generation and network load is determined for the different uncertainties budget. The IGDT effectiveness is proved compared with deterministic and Monte Carlo simulation (MCS) methods. The total cost of the 33-bus network in deterministic, IGDT, and MCS is obtained at $ 17978.18, $ 20996.19, and $ 26178.02, respectively, and for the 69-bus network are achieved at $ 16846.26, $ 20215.39, and $ 20597.84, respectively. A comparison of the results showed that the MCS strongly depends on the defined scenarios, is very time-consuming, and is also not able to provide a level of cost assurance, the deterministic method is not able to determine the effect of uncertainties on system planning, but the IGDT can easily determine the cost of the system based on changes in uncertainty parameters. Also, planning based on the IGDT has resulted in the achievement of a robust hybrid system against forecasting errors caused by uncertainties.

A novel information gap decision theory‐based demand response scheduling for a smart residential community considering deep uncertainties

AbstractDemand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively.

Improved radius determinations for the transiting brown dwarf population in the era of Gaia and TESS

ABSTRACT I report updates to the substellar mass–radius diagram for 11 transiting brown dwarfs (BDs) and low-mass stars published before the third data release from the Gaia mission (Gaia DR3). I re-analyse these transiting BD systems whose physical parameters were published between 2008 and 2019 and find that when using the parallax measurements from Gaia DR3, seven BDs show significant differences in their radius estimate or an improvement in the radius uncertainty. This has important implications for how these BDs are used to test substellar evolutionary models in the mass–radius diagram. The remaining four BDs show mass–radius estimates that are consistent with their previous pre-Gaia DR3 measurements. The seven BDs that show significant deviation from the original mass–radius measurements are AD 3116b, CoRoT-3b, CoRoT-15b, EPIC 201702477b, Kepler-39b, KOI-205b, and KOI-415b. Of these, AD 3116b is a known member of the Praesepe cluster at an age of 600 Myr. Additionally, some of the previously smallest known transiting BDs, KOI-205b and KOI-415b, are not as small as once thought, leaving the mass–radius region for the very oldest BDs relatively sparse as a result of this work.

Discovery of TOI-1260d and the characterization of the multiplanet system

ABSTRACT We report the discovery of a third planet transiting the star TOI-1260, previously known to host two transiting sub-Neptune planets with orbital periods of 3.127 and 7.493 d, respectively. The nature of the third transiting planet with a 16.6-d orbit is supported by ground-based follow-up observations, including time-series photometry, high-angular resolution images, spectroscopy, and archival imagery. Precise photometric monitoring with CHEOPS allows to improve the constraints on the parameters of the system, improving our knowledge on their composition. The improved radii of TOI-1260b and TOI-1260c are $2.36 \pm 0.06 \rm R_{\oplus }$, $2.82 \pm 0.08 \rm R_{\oplus }$, respectively while the newly discovered third planet has a radius of $3.09 \pm 0.09 \rm R_{\oplus }$. The radius uncertainties are in the range of 3 per cent, allowing a precise interpretation of the interior structure of the three planets. Our planet interior composition model suggests that all three planets in the TOI-1260 system contains some fraction of gas. The innermost planet TOI-1260b has most likely lost all of its primordial hydrogen-dominated envelope. Planets c and d were also likely to have experienced significant loss of atmospheric through escape, but to a lesser extent compared to planet b.

Risk-averse Volt-VAr management scheme to coordinate distributed energy resources with demand response program

Distributed energy resources (DERs), such as renewable energy sources (RESs) and energy storage systems (ESSs), are inevitable parts of future power grids. However, the high penetration of RESs may cause voltage violation issues in distribution levels. To this end, the application of the Volt-VAr management (VVM) scheme is highlighted. This paper aims to maintain voltage levels within standard ranges with optimal coordination of different resources at minimum operational costs and voltage deviation. In more detail, this article develops a new VVM in distribution networks to effectively coordinate novel smart grid technologies such as PV inverters, time of use demand response program (TOU-DRP), energy storage systems (ESSs) with traditional devices, including on-line tap changer transformer (OLTC) and switchable capacitor banks (SCBs). Because the coordinated VVM is a bi-objective model that minimizes both operating cost and voltage deviation, it is proposed to use the ε-constraint technique to identify optimal Pareto solutions. To handle the uncertainty related to RESs and load demands, a robust technique based on information gap decision theory (IGDT) is also utilized. The formulation is configured as a mixed-integer linear program (MILP) and solved by CPLEX. It was implemented on the 118-bus distribution network under various conditions. The simulation results clearly show that the coordination of DERs and traditional devices has a significant impact on maintaining voltage levels within permissible ranges while minimizing operational costs. A higher degree of uncertainty radius can also be obtained if DRP participates in the VVM scheme.

Modeling the Microgrid Operator Participation in Day-Ahead Energy and Reserve Markets Considering Stochastic Decisions in the Real-Time Market

The penetration of the distributed energy resources in the distribution networks is facilitated by the structure of the microgrids (MGs). The MG operator (MGO) can schedule the MG resources to meet the local load and participate in the wholesale markets. In this article, a new model is developed for the MGO participation in the day-ahead (DA) (energy and reserve) and the real-time (RT) energy markets under uncertainties. For this purpose, the effect of the uncertainties of demand and generation from renewable energy sources on the MGO decisions is represented in a two-stage stochastic model. The MGO bids in the DA and RT markets are modeled as the first and the second stage decisions, respectively. Moreover, the information gap decision theory method is used to model the behavior of the MGO to address the uncertainties of the RT energy market price and the probability of calling the reserve. The results show that as the RT price uncertainty radius increases, the energy sold to the RT market decreases/increases in the risk-averse/risk-taker strategy. Furthermore, to manage the uncertainty related to the probability of calling the reserve, the reserve capacity provided by the MGO in the risk-averse and the risk-taker strategies decreases and increases, respectively.

Georeferencing Historic Collection Data

Collection data from historic collections often contain vague or non-specific location information of where the specimen was found. Now, during the mass-digitization era of natural history collections, this presents a challenge as we intend to georeference these locations without specific details of where they were found. In a case study at the Museum für Naturkunde Berlin (MfN), a system was developed to georeference these vague locations. There are three types of geospatial vector data that should be considered in georeferencing: points, lines, and polygons. In most collections, objects were taken from a place that can be represented by a point coordinate (x, y or latitude and longitude). However, if these coordinates were not captured at the time, or information has been lost, making a polygon (i.e. a bounding area) is more appropriate for georeferencing a collection site (Hill 2009). Many databases and standards, however, expect point coordinate information with a field to account for uncertainty. TDWG’s Darwin Core Standard includes terms for decimalLatitude, decimalLongitude, geodeticDatum, and coordinateUncertaintyInMeters, among additional georeferencing fields (Wieczorek et al. 2012). Therefore, the following process results in relevant information to fulfill this standard. MfN’s Neuroptera collection required the georeferencing of their historic collection of specimens found in Germany. Their data contained verbatim place names for the objects collected between 1758 and 1906. Some of these places were vague and non-specific, referring to entire states or cities, while others were more detailed descriptions. This georeferencing process ultimately resulted in a searchable table of place names in Germany, at different administrative levels. For each, there was a latitude, longitude and uncertainty radius assigned. Open geospatial data sources of polygon boundaries of Germany’s regions at different administrative levels were used as a basis for the project. These were transformed with QGIS. First, the polygon layer was used to create a circular polygon, encompassing the entire area. Next, a measurement of each circle’s circumference was taken and stored in the data’s attribute table. Then the centroids of each circle were calculated. These became the latitudes and longitudes for each area. This visualisation is shown in Fig. 1. Finally, the data was tidied, and the radius of the circle was calculated — this became the uncertainty measurement for each area, as it was measured from the centroid of the polygon to the maximum possible distance edge of the polygon. In total there were six output tables (see Fig. 2) — the four administrative levels, and two additional levels for Berlin, which is organised differently (Thorn 2022). These tables allowed a user to search for the verbatim place name they had in the data, and assign coordinates and an uncertainty radius to it. This method for georeferencing historic locations could be replicated in different countries, eventually creating a comprehensive database that would aid in georeferencing historic (and recent) collections. If this project were to be used more widely, additional outputs could be created using historic boundaries. The outputs from this process can be repeatedly reused, therefore saving collection staff from manually finding coordinates for everything in their collections. While currently the output tables still have to be searched to find relevant data, this may be automated in the future, creating an efficient georeferencing process.

Large Interferometer For Exoplanets (LIFE)

Context.Temperate terrestrial exoplanets are likely to be common objects, but their discovery and characterization is very challenging because of the small intrinsic signal compared to that of their host star. Various concepts for optimized space missions to overcome these challenges are currently being studied. The Large Interferometer For Exoplanets (LIFE) initiative focuses on the development of a spacebased mid-infrared (MIR) nulling interferometer probing the thermal emission of a large sample of exoplanets.Aims.This study derives the minimum requirements for the signal-to-noise ratio (S/N), the spectral resolution (R), and the wavelength coverage for the LIFE mission concept. Using an Earth-twin exoplanet as a reference case, we quantify how well planetary and atmospheric properties can be derived from its MIR thermal emission spectrum as a function of the wavelength range,S/N,andR.Methods.We combined a cloud-free 1D atmospheric radiative transfer model, a noise model for observations with the LIFE interferometer, and the nested sampling algorithm for Bayesian parameter inference to retrieve planetary and atmospheric properties. We simulated observations of an Earth-twin exoplanet orbiting a G2V star at 10 pc from the Sun with different levels of exozodiacal dust emissions. We investigated a grid of wavelength ranges (3–20 μm, 4–18.5 μm, and 6–17 μm),S/Ns(5, 10, 15, and 20 determined at a wavelength of 11.2 μm), andRs (20, 35, 50, and 100).Results.We find that H2O, CO2, and O3are detectable ifS/N ≥10 (uncertainty ≤ ± 1.0 dex). We find upper limits for N2O (abundance ≲10−3). In conrtrast, CO, N2, and O2are unconstrained. The lower limits for a CH4detection areR= 50 andS/N= 10. Our retrieval framework correctly determines the exoplanet’s radius (uncertainty ≤ ± 10%), surface temperature (uncertainty ≤ ± 20 K), and surface pressure (uncertainty ≤ ± 0.5 dex) in all cloud-free retrieval analyses. Based on our current assumptions, the observation time required to reach the specifiedS/Nfor an Earth-twin at 10 pc when conservatively assuming a total instrument throughput of 5% amounts to ≈6−7 weeks with four 2m apertures.Conclusions.We provide first order estimates for the minimum technical requirements for LIFE via the retrieval study of an Earth-twin exoplanet. We conclude that a minimum wavelength coverage of 4–18.5 μm, anRof 50, and anS/Nof at least 10 is required. With the current assumptions, the atmospheric characterization of several Earth-like exoplanets at a distance of 10 pc and within a reasonable amount of observing time will require apertures ≥ 2m.

A Guide to Realistic Uncertainties on the Fundamental Properties of Solar-type Exoplanet Host Stars

Abstract Our understanding of the properties and demographics of exoplanets critically relies on our ability to determine the fundamental properties of their host stars. The advent of Gaia and large spectroscopic surveys has now made it possible, in principle, to infer the properties of individual stars, including most exoplanet hosts, to very high precision. However, we show that, in practice, such analyses are limited by uncertainties in both the fundamental scale and our models of stellar evolution, even for stars similar to the Sun. For example, we show that current uncertainties on measured interferometric angular diameters and bolometric fluxes set a systematic uncertainty floor of ≈2.4% in temperature, ≈2.0% in luminosity, and ≈4.2% in radius. Comparisons between widely available model grids suggest uncertainties of order ≈5% in mass and ≈20% in age for main-sequence and subgiant stars. While the radius uncertainties are roughly constant over this range of stars, the model-dependent uncertainties are a complex function of luminosity, temperature, and metallicity. We provide open-source software for approximating these uncertainties for individual targets and discuss strategies for reducing these uncertainties in the future.