Rubin-LSST

Abstract:

Automated planetary transit detection has become vital to identify and prioritize candidates for expert analysis and verification given the scale of modern telescopic surveys. Current methods for short-period exoplanet detection work effectively due to periodicity in the transit signals, but a robust approach for detecting single-transit events is lacking. However, volunteer-labeled transits collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find that this model recovers planet candidates (TESS objects of interest; TOIs) at a precision and recall rate exceeding those of volunteers, with a 20% improvement in the area under the precision-recall curve and 10% more TOIs identified in the top 500 predictions on average per sector. Importantly, the model also recovers almost all planet candidates found by volunteers but missed by current automated methods (PHT community TOIs). Finally we retrospectively utilise the model to simulate live deployment in PHT to reprioritize candidates for analysis. We also find that multiple promising planet candidates, originally missed by PHT, would have been found using our approach, showing promise for upcoming real-world deployment.

Abstract:

The dipole anisotropy induced by our peculiar motion in the sky distribution of cosmologically distant sources is an important consistency test of the standard Friedmann-Lemaître-Robertson-Walker cosmology. In this work, we formalize how to compute the kinematic matter dipole in redshift bins. Apart from the usual terms arising from angular aberration and flux boosting, there is a contribution from the boosting of the redshifts that becomes important when considering a sample selected on observed redshift, leading to nonvanishing correction terms. We discuss examples and provide expressions to incorporate arbitrary redshift selection functions. We also discuss the effect of redshift measurement uncertainties in this context, in particular in upcoming surveys for which we provide estimates of the correction terms. Depending on the shape of a sample’s redshift distribution and on the applied redshift cuts, the correction terms can become substantial, even to the degree that the direction of the dipole is reversed. Lastly, we discuss how cuts on variables correlated with observed redshift, such as color, can induce additional correction terms. Published by the American Physical Society 2025