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Register a new userUsing color photometry to separate transiting exoplanets from false positives
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B. Tingley
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The radial velocity technique is currently used to classify transiting objects. While capable of identifying grazing binary eclipses, this technique cannot reliably identify blends, a chance overlap of a faint background eclipsing binary with an ordinary foreground star. Blends generally have no observable radial velocity shifts, as the foreground star is brighter by several magnitudes and therefore dominates the spectrum, but their combined light can produce events that closely resemble those produced by transiting exoplanets. The radial velocity technique takes advantage of the mass difference between planets and stars to classify exoplanet candidates. However, the existence of blends renders this difference an unreliable discriminator. Another difference must therefore be utilized for this classification -- the physical size of the transiting body. Due to the dependence of limb darkening on color, planets and stars produce subtly different transit shapes. These differences can be relatively weak, little more than 1/10th the transit depth. However, the presence of even small color differences between the individual components of the blend increases this difference. This paper will show that this color difference is capable of discriminating between exoplanets and blends reliably, theoretically capable of classifying even terrestrial-class transits, unlike the radial velocity technique.
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The Transiting Exoplanet Survey Satellite (TESS) is currently concluding its 2-year primary science mission searching 85% of the sky for transiting exoplanets. TESS has already discovered well over one thousand TESS objects of interest (TOIs), but these candidate exoplanets must be distinguished from astrophysical false positives using other instruments or techniques. The 3-band Multi-color Simultaneous Camera for Studying Atmospheres of Transiting Planets (MuSCAT), as well as the 4-band MuSCAT2, can be used to validate TESS discoveries. Transits of exoplanets are achromatic when observed in multiple bandpasses, while transit depths for false positives often vary with wavelength. We created software tools to simulate MuSCAT/MuSCAT2 TESS follow-up observations and reveal which planet candidates can be efficiently distinguished from blended eclipsing binary (BEB) false positives using these two instruments, and which must be validated using other techniques. We applied our software code to the Barclay et al. (2018) predicted TESS discoveries, as well as to TOIs downloaded from the ExoFOP-TESS website. We estimate that MuSCAT (MuSCAT2 values in parentheses) will be able to use its multi-color capabilities to distinguish BEB false positives for $sim$17% ($sim$18%) of all TESS discoveries, and $sim$13% ($sim$15%) of $R_{rm pl} < 4R_oplus$ discoveries. Our TOI analysis shows that MuSCAT (MuSCAT2) can distinguish BEB false positives for $sim$55% ($sim$52%) of TOIs with transit depths greater than 0.001, for $sim$64% ($sim$61%) of TOIs with transit depths greater than 0.002, and for $sim$70% ($sim$68%) of TOIs with transit depth greater than 0.003. Our work shows that MuSCAT and MuSCAT2 can validate hundreds of $R_{rm pl} < 4R_oplus$ candidate exoplanets, thus supporting the TESS mission in achieving its Level 1 Science Requirement.
Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network using an SVM classifier trained with features derived from the uncertainty map of the neural network prediction. We demonstrate the effectiveness of this method for the detection of liver lesions on a dataset of abdominal MR images. We find that the use of a dropout rate of 0.5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.
The Kepler Mission was launched on March 6, 2009 to perform a photometric survey of more than 100,000 dwarf stars to search for Earth-size planets with the transit technique. The reliability of the resulting planetary candidate list relies on the ability to identify and remove false positives. Major sources of astrophysical false positives are planetary transits and stellar eclipses on background stars. We describe several new techniques for the identification of background transit sources that are separated from their target stars, indicating an astrophysical false positive. These techniques use only Kepler photometric data. We describe the concepts and construction of these techniques in detail as well as their performance and relative merits.
Astrophysical false positives due to stellar eclipsing binaries pose one of the greatest challenges to ground-based surveys for transiting Hot Jupiters. We have used known properties of multiple star systems and Hot Jupiter systems to predict, a priori, the number of such false detections and the number of genuine planet detections recovered in two hypothetical but realistic ground-based transit surveys targeting fields close to the galactic plane (b~10 degrees): a shallow survey covering a magnitude range 10<V<13, and a deep survey covering a magnitude range 15<V<19. Our results are consistent with the commonly-reported experience of false detections outnumbering planet detections by a factor of ~10 in shallow surveys, while in our synthetic deep survey we find ~1-2 false detections for every planet detection. We characterize the eclipsing binary configurations that are most likely to cause false detections and find that they can be divided into three main types: (i) two dwarfs undergoing grazing transits, (ii) two dwarfs undergoing low-latitude transits in which one component has a substantially smaller radius than the other, and (iii) two eclipsing dwarfs blended with one or more physically unassociated foreground stars. We also predict that a significant fraction of Hot Jupiter detections are blended with the light from other stars, showing that care must be taken to identify the presence of any unresolved neighbors in order to obtain accurate estimates of planetary radii. This issue is likely to extend to terrestrial planet candidates in the CoRoT and Kepler transit surveys, for which neighbors of much fainter relative brightness will be important.
In this paper, we investigate the cause of the high false positive rate in Visual Relationship Detection (VRD). We observe that during training, the relationship proposal distribution is highly imbalanced: most of the negative relationship proposals are easy to identify, e.g., the inaccurate object detection, which leads to the under-fitting of low-frequency difficult proposals. This paper presents Spatially-Aware Balanced negative pRoposal sAmpling (SABRA), a robust VRD framework that alleviates the influence of false positives. To effectively optimize the model under imbalanced distribution, SABRA adopts Balanced Negative Proposal Sampling (BNPS) strategy for mini-batch sampling. BNPS divides proposals into 5 well defined sub-classes and generates a balanced training distribution according to the inverse frequency. BNPS gives an easier optimization landscape and significantly reduces the number of false positives. To further resolve the low-frequency challenging false positive proposals with high spatial ambiguity, we improve the spatial modeling ability of SABRA on two aspects: a simple and efficient multi-head heterogeneous graph attention network (MH-GAT) that models the global spatial interactions of objects, and a spatial mask decoder that learns the local spatial configuration. SABRA outperforms SOTA methods by a large margin on two human-object interaction (HOI) datasets and one general VRD dataset.
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