Identifying the source regions of coronal mass ejections (CMEs) is crucial for understanding their origins and improving space weather forecasting. We present an automated algorithm for matching CMEs detected by the Large Angle Spectrometric Coronagraph with their source active regions, specifically Space Weather HMI Active Region Patches (SHARPs), between 2010 May and 2019 January. Our method uses posteruptive signatures, including flares and coronal dimmings, to associate CMEs with potential source regions. Out of 4190 CMEs, we successfully match 1132, achieving a recall rate of ~57% for frontside events. We find that the algorithm performs better for complex SHARP regions containing multiple NOAA regions and for faster CMEs, consistent with expectations that more energetic events produce stronger eruption signatures. We find that CME–flare association rates increase with flare intensity, aligning with previous studies. While our approach has limitations, such as focusing exclusively on SHARP regions and relying on a limited set of posteruptive signatures, it significantly reduces the time required for CME source identification while providing transparent, reproducible results. We encourage the solar physics community to build upon this work, developing improved automated tools for CME source identification. The resulting catalog of CME–source region associations is made publicly available, offering a valuable resource for statistical studies and machine learning applications in solar physics and space weather forecasting.

We investigate the effect of observing cadence on the precision of radius ratio values obtained from transit light curves by performing uniform Markov chain Monte Carlo fits of 46 exoplanets observed by the Transiting Exoplanet Survey Satellite (TESS) in multiple cadences. We find median improvements of almost 50 per cent when comparing fits to 20 and 120 s cadence light curves to 1800 s cadence light curves, and of 37 per cent when comparing 600 s cadence to 1800 s cadence. Such improvements in radius precision are important, for example, to precisely constrain the properties of the radius valley or to characterize exoplanet atmospheres. We also implement a numerical information analysis to predict the precision of parameter estimates for different observing cadences. We tested this analysis on our sample and found that it reliably predicts the effect of shortening observing cadence with errors in the predicted percentage precision of ≲ 0.5 per cent for most cases. We apply this method to 157 TESS objects of interest that have only been observed with 1800 s cadence to predict the precision improvement that could be obtained by reobservations with shorter cadences and provide the full table of expected improvements. We report the 10 planet candidates that would benefit the most from reobservations at short cadence. Our implementation of the information analysis for the prediction of the precision of exoplanet parameters, Prediction of Exoplanet Precisions using Information in Transit Analysis, is made publicly available.