Engineering topics

Joseph Twicken

Publications and source records attributed to Joseph Twicken.

3 records

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

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↗