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.
PLATO input catalogs for technical calibration and fine guidance
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
A few weeks after launch, the PLATO spacecraft is expected to start its payload commissioning, which will be completed within the first three months of the mission. This phase includes the in-orbit verification, calibration, and configuration of the instrument prior to nominal science operations. During this mission-critical period, and again later during regular spacecraft rotations and re-pointings, a set of reference stars is required to complete various calibration steps. This set, referred to as the calibration PLATO Input Catalog (cPIC), is part of the PIC. The cPIC comprises various stellar samples, each serving a dedicated technical calibration purpose, and it contains 71671 unique stellar targets across PLATO's entire field of view (FoV). Once the spacecraft commences science observations, the on-board Fine Guidance System (FGS) will rely on a small set of guide stars. These stars must be particularly bright and will be observed with the two fast cameras, which cover only a smaller central region of PLATO's FoV. This target list, referred to as the fine-guidance PLATO Input Catalog (fgPIC), contains 2640 unique targets, of which about 30 are used by the FGS at any given time. In this paper, we present the selection criteria for both the cPIC and the fgPIC, and asses their impact on the construction of these calibration catalogs for PLATO.
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astro-ph.SR 1years
2026 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
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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.