Bringing 2D Eclipse Mapping out of the Shadows with Leave-one-out Cross Validation

被引:5
|
作者
Challener, Ryan C. [1 ,2 ]
Welbanks, Luis [3 ]
McGill, Peter [4 ,5 ]
机构
[1] Univ Michigan, Dept Astron, 1085 S Univ Ave, Ann Arbor, MI 48109 USA
[2] Cornell Univ, Dept Astron, 122 Sci Dr, Ithaca, NY 14853 USA
[3] Arizona State Univ, Sch Earth & Space Explorat, Tempe, AZ 85257 USA
[4] Univ Calif Santa Cruz, Dept Astron & Astrophys, Santa Cruz, CA 93105 USA
[5] Lawrence Livermore Natl Lab, 7000 East Ave, Livermore, CA USA
来源
ASTRONOMICAL JOURNAL | 2023年 / 166卷 / 06期
基金
美国国家航空航天局;
关键词
PHASE; MAP;
D O I
10.3847/1538-3881/ad0366
中图分类号
P1 [天文学];
学科分类号
0704 ;
摘要
Eclipse mapping is a technique for inferring 2D brightness maps of transiting exoplanets from the shape of an eclipse light curve. With JWST's unmatched precision, eclipse mapping is now possible for a large number of exoplanets. However, eclipse mapping has only been applied to two planets, and the nuances of fitting eclipse maps are not yet fully understood. Here, we use Leave-one-out Cross Validation (LOO-CV) to investigate eclipse mapping, with application to a JWST NIRISS/SOSS observation of the ultrahot Jupiter WASP-18b. LOO-CV is a technique that provides insight into the out-of-sample predictive power of models on a data-point-by-data-point basis. We show that constraints on planetary brightness patterns behave as expected, with large-scale variations driven by the phase-curve variation in the light curve and smaller-scale structures constrained by the eclipse ingress and egress. For WASP-18b we show that the need for higher model complexity (smaller-scale features) is driven exclusively by the shape of the eclipse ingress and egress. We use LOO-CV to investigate the relationship between planetary brightness map components when mapping under a positive-flux constraint to better understand the need for complex models. Finally, we use LOO-CV to understand the degeneracy between the competing "hot spot" and "plateau" brightness map models of WASP-18b, showing that the plateau model is driven by the ingress shape and the hot spot model is driven by the egress shape, but preference for neither model is due to outliers or unmodeled signals. Based on this analysis, we make recommendations for the use of LOO-CV in future eclipse-mapping studies.
引用
收藏
页数:9
相关论文
共 50 条
  • [31] Algorithmic stability and sanity-check bounds for leave-one-out cross-validation
    Kearns, M
    Ron, D
    [J]. NEURAL COMPUTATION, 1999, 11 (06) : 1427 - 1453
  • [32] Ensemble Kalman Filter Regularization Using Leave-One-Out Data Cross-Validation
    Rayo, Lautaro
    Hoteit, Ibrahim
    [J]. NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2012), VOLS A AND B, 2012, 1479 : 1247 - 1250
  • [33] Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction
    Hao Cheng
    Dorian J. Garrick
    Rohan L. Fernando
    [J]. Journal of Animal Science and Biotechnology, 8
  • [34] Enhanced Kriging leave-one-out cross-validation in improving model estimation and optimization
    Pang, Yong
    Wang, Yitang
    Lai, Xiaonan
    Zhang, Shuai
    Liang, Pengwei
    Song, Xueguan
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2023, 414
  • [35] Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
    Wong, Tzu-Tsung
    [J]. PATTERN RECOGNITION, 2015, 48 (09) : 2839 - 2846
  • [36] Determining optimal neighborhood size for ecological studies using leave-one-out cross validation
    Deok Ryun Kim
    Mohammad Ali
    Dipika Sur
    Ahmed Khatib
    Thomas F Wierzba
    [J]. International Journal of Health Geographics, 11
  • [37] A leave-one-out cross-validation SAS macro for the identification of markers associated with survival
    Rushing, Christel
    Bulusu, Anuradha
    Hurwitz, Herbert I.
    Nixon, Andrew B.
    Pang, Herbert
    [J]. COMPUTERS IN BIOLOGY AND MEDICINE, 2015, 57 : 123 - 129
  • [38] Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction
    Cheng, Hao
    Garrick, Dorian J.
    Fernando, Rohan L.
    [J]. JOURNAL OF ANIMAL SCIENCE AND BIOTECHNOLOGY, 2017, 8
  • [39] Determining optimal neighborhood size for ecological studies using leave-one-out cross validation
    Kim, Deok Ryun
    Ali, Mohammad
    Sur, Dipika
    Khatib, Ahmed
    Wierzba, Thomas F.
    [J]. INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS, 2012, 11
  • [40] Efficient Leave-One-Out Cross-Validation-based Regularized Extreme Learning Machine
    Shao, Zhifei
    Er, Meng Joo
    [J]. NEUROCOMPUTING, 2016, 194 : 260 - 270