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Douglas Caldwell

Publications and source records attributed to Douglas Caldwell.

9 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2002

Kepler Mission: A Discovery-Class Mission Designed to Determine the Frequency of Earth-Size and Larger Planets Around Solar-Like Stars

The first step in discovering the extent of life in our galaxy is to determine the number of terrestrial planets in the habitable zone (HZ). The Kepler Mission is designed around a 0.95 in aperture Schmidt-type telescope with an array of 42 CCDs designed to continuously monitor the brightness of 100,000 solar-like stars to detect the transits of Earth-size and larger planets. The photometer is scheduled to be launched into heliocentric orbit in 2007. Measurements of the depth and repetition time of transits provide the size of the planet relative to the star and its orbital period. When combined with ground-based spectroscopy of these stars to fix the stellar parameters, the true planet radius and orbit scale, hence the position relative to the HZ are determined. These spectra are also used to discover the relationships between the characteristics of planets and the stars they orbit. In particular, the association of planet size and occurrence frequency with stellar mass and metallicity will be investigated. At the end of the four year mission, hundreds of terrestrial planets should be discovered in and near the HZ of their stars if such planets are common. Extending the mission to six years doubles the expected number of Earth-size planets in the HZ. A null result would imply that terrestrial planets in the HZ occur in less than 1% of the stars and that life might be quite rare. Based on the results of the current Doppler-velocity discoveries, detection of a thousand giant planets is expected. Information on their albedos and densities of those giants showing transits will be obtained.

William Borucki↗
NASA NTRSDate not supplied

Probing ExoMiner for Effectiveness against False Alarms in Kepler Data

We present a study on the effectiveness of ExoMiner against False Alarms in Kepler data. ExoMiner is a deep learning model that was used to validate around 370 Kepler Objects of Interest. We follow the analysis conducted in Coughlin et al (2017) “DR25 Robovetter Completeness and Effectiveness” for Robovetter, a rule-based model used to vet TCEs for this data release and automatically generate the Q1-Q17 DR 25 KOI Table. The ExoMiner model is trained on observed transit data from Kepler Q1-Q17 DR25 and evaluated on Kepler inverted and scrambled data. The results provide a more comprehensive insight into the capacities and limitations of ExoMiner, especially the vetting of not-transit-like signals and, more generally, the use of deep learning models to model transit photometry data for vetting and validation purposes.

exoplanet↗
NASA NTRSDate not supplied

Science Target Prioritization Framework for Remote Sensing

Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection.While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.

Remote Sensing↗
NASA NTRSDate not supplied

ILEOS: A Novel Intelligent Observing System Enabled by High Altitude Long Endurance Uncrewed Aerial Systems

Most major global satellite surveyors of climate-relevant trace gases have relatively coarse spatial resolution or temporal sampling. While these data can be supplemented by fine-pointing satellites and aircraft, the spatial and temporal resolutions available from crewed aircraft is not sufficient to observe stochastic, ephemeral events that take place between observations. Emerging High Altitude Long Endurance (HALE) Uncrewed Aerial Systems (UAS) can operate for months at a time and loiter over targets to provide continuous daylight geostationary-like observations, allowing these new platforms to be integrated with existing satellites as part of a New Observing Strategy (NOS).To aid in the planning of future NOS missions, NASA is developing the Intelligent Long Endurance Observing System (ILEOS), a science activity planning system. ILEOS will help scientists build plans to improve spatio-temporal resolution of climate-relevant gases by fusing coarse-grained sensor data from satellites and other sources(e.g., terrain, forecasts), and plan HALE UAS flights to obtain finer-grain (high spatio-temporal) data. ILEOS will also enable observations for longer periods and of environments not accessible through in-situ observations and crewed aircraft field campaigns.

science planning pipeline↗
NASA NTRSDate not supplied

Comparing and Automatically Optimizing the Performance of Systematic Error Correctors for TESS Light Curves

Accurate and precise removal of systematic errors from TESS light curves (lcs) is critical for exoplanet, stellar, and asteroseismology studies•Various approaches exist to correct for systematics while preserving intrinsic stellar variability (Luger et al. 2016, 2018; Hedges et al. 2021; Smith et al. 2012; Stumpe et al. 2014; Aigrain et al. 2017)•However, no comprehensive analyseshave been carried out to properly compare these approaches and determine their validity on different target types in both an individual and statistical manner•Current correctors have been usually demonstrated on their own and on limited sets of hand-picked targets•To apply these corrections more generally, it is important to compare multiple correctors on larger samples•We are particularly interested in the ability to remove scattered light contaminationfrom the Earth and the Moon, which is a key systematic for TESS

David Rapetti↗
NASA NTRSDate not supplied

Deep Learning Vetting of TESS FFI Data: Results and Comparison with 2-Min Data

We present the results of vetting TCEs from the TESS SPOC full-frame images (FFI) Year 5 data using our deep learning model, and we compare the performance in this dataset against the results obtained for the TESS SPOC 2-min data. The 200-second cadence FFI data expands the search to a list of targets that not only includes 2-minute targets, but also potentially high-value targets within 100 parsecs or with H-magnitude <10, and field targets with TESS magnitude <13.5. This work aims to explore this rich dataset and increase the efficiency and throughput of the vetting process by helping unearth more high-quality planet candidates from the TESS mission.

tess spoc↗