Combined TESS-CHEOPS data on 5620 flares shows ED-based FFDs follow power laws but bolometric-energy FFDs are best described by a truncated power law with break at 1.8e35 erg, with low-energy flattening attributed to detection biases rather than intrinsic lognormal behavior.
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Machine learning classification of TESS data for 6 million stars in the LOPS2 field identifies 28% as candidate variables after filtering out 72% instrumental signals, producing one of the largest automated variability catalogs.
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Extending TESS flare frequency distributions with CHEOPS: Power-law versus lognormal
Combined TESS-CHEOPS data on 5620 flares shows ED-based FFDs follow power laws but bolometric-energy FFDs are best described by a truncated power law with break at 1.8e35 erg, with low-energy flattening attributed to detection biases rather than intrinsic lognormal behavior.
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Variability classification of TESS targets in LOPS2, the first long-term pointing field of PLATO. Version 1 of the public variability catalogue
Machine learning classification of TESS data for 6 million stars in the LOPS2 field identifies 28% as candidate variables after filtering out 72% instrumental signals, producing one of the largest automated variability catalogs.