1 Introduction
2 Eclipsing binaries with O’Connell effect
2.1 Signatures and theories
- Starspots result from chromospheric activity, causing a consistent decrease in brightness of the star when viewed as a point source. While magnetic surface activity will cause both flares (brightening) and spots (darkening), flares tend to be transient, whereas spots tend to have longer-term effects on the observed binary flux. Thus, between the two, starspots are the favored hypothesis for causing long-term consistent asymmetry; often binary simulations (such as the Wilson–Devinney code) can be used to model O’Connell effect binaries via including an often large starspot (Zboril and Djurasevic 2006).
- Gas stream impact results from matter transferring between stars (smaller to larger) through the L1 point and onto a specific position on the larger star, resulting in a consistent brightening on the leading/trailing side of the secondary/primary.
- The circumstellar matter theory proposes to describe the increase in brightness via free-falling matter being swept up, resulting in energy loss and heating, again causing an increase in amplitude. Alternatively, circumstellar matter in orbit could result in attenuation, i.e., the difference in maximum magnitude of the phased light curve results from dimming and not brightening.
2.2 Characterization of OEEB
3 Variable star data
3.1 Kepler Villanova eclipsing catalog
10123627 | 11924311 | 5123176 | 8696327 | 11410485 | 7696778 | 7516345 | 9654476 |
10815379 | 2449084 | 5282464 | 8822555 | 7259917 | 6223646 | 4350454 | 9777987 |
10861842 | 2858322 | 5283839 | 9164694 | 7433513 | 9717924 | 5820209 | 7584739 |
11127048 | 4241946 | 5357682 | 9290838 | 8394040 | 7199183 |
10007533 | 10544976 | 11404698 | 5560831 | 7119757 | 5685072 | 7335517 | 5881838 |
10024144 | 10711646 | 11442348 | 12470530 | 3954227 | 10084115 | 10736223 | 11444780 |
10095469 | 10794878 | 11652545 | 2570289 | 4037163 | 10216186 | 10802917 | 12004834 |
10253421 | 10880490 | 12108333 | 3127873 | 4168013 | 10257903 | 10920314 | 12109845 |
10275747 | 11076176 | 12157987 | 3344427 | 4544587 | 10383620 | 11230837 | 12216706 |
10485137 | 11395117 | 12218858 | 3730067 | 4651526 | 9007918 | 8196180 | 7367833 |
9151972 | 8248812 | 7376500 | 6191574 | 4672934 | 9179806 | 8285349 | 7506446 |
9205993 | 8294484 | 7518816 | 6283224 | 4999357 | 9366988 | 8298344 | 7671594 |
9394601 | 8314879 | 7707742 | 6387887 | 5307780 | 9532219 | 8481574 | 7879399 |
9639491 | 8608490 | 7950964 | 6431545 | 5535061 | 9700154 | 8690104 | 8074045 |
9713664 | 8758161 | 8087799 | 6633929 | 5606644 | 9715925 | 8804824 | 8097553 |
9784230 | 8846978 | 8155368 | 7284688 | 5785551 | 9837083 | 8949316 | 8166095 |
9935311 | 8957887 | 8182360 | 7339345 | 5956776 | 9953894 | 3339563 | 4474193 |
12400729 | 3832382 | 12553806 | 4036687 | 2996347 | 4077442 | 3557421 | 4554004 |
6024572 | 4660997 | 6213131 | 4937217 | 6370361 | 5296877 | 6390205 | 5374999 |
6467389 |
3.1.1 Light curve/feature space
3.1.2 Training/testing data
3.2 Data untrained
Type | Count | Percentage |
---|---|---|
Algol | 287 | 5.6 |
Contact Binary | 1805 | 35.6 |
Delta Scuti | 68 | 1.3 |
RRab | 2189 | 43.0 |
RRc | 737 | 14.5 |
4 PMML classification algorithm
4.1 Prior research
4.2 Distribution field
4.3 Metric learning
4.4 k-NN classifier
5 Results of the classification
5.1 Training on Kepler data
- The selection of partitions is a random process (there is a random selection algorithm in the partitioning algorithm). Therefore the resulting errors produced as part of the cross-validation process aren’t guaranteed to be the same on subsequent runs (i.e. different partitions selection = different error rates).
- Likewise, one of the alternative classification methodologies requires the use of clustering, so similar to (3) there is some inherent randomness associated with the process that may result in different results from run to run.
5.2 Application on unlabeled Kepler data
5.2.1 Design considerations
5.2.2 Results
5.2.3 Subgroup analysis
Cluster | Δm | \({\sigma _{\Delta m}}/{\Delta m}\) | OER | \({\sigma _{\mathrm{OER}}}/{\mathrm{OER}}\) | LCA | \({\sigma _{\mathrm{LCA}}}/{\mathrm{LCA}}\) | # |
---|---|---|---|---|---|---|---|
1 | 0.13 | 0.11 | 1.16 | 0.02 | 7.62 | 0.25 | 17 |
2 | 0.28 | 0.17 | 1.41 | 0.07 | 8.92 | 0.16 | 9 |
3 | 0.14 | 0.78 | 1.20 | 0.30 | 7.13 | 0.25 | 15 |
4 | 0.09 | 0.24 | 1.08 | 0.02 | 6.95 | 0.23 | 22 |
5 | 0.15 | 0.55 | 1.17 | 0.16 | 8.54 | 0.58 | 15 |
6 | −0.14 | −0.36 | 0.86 | 0.08 | 8.36 | 0.19 | 8 |
7 | 0.17 | 0.36 | 1.25 | 0.08 | 9.41 | 0.82 | 24 |
8 | 0.20 | 0.31 | 1.22 | 0.08 | 8.03 | 0.36 | 19 |
5.3 LINEAR catalog
13824707 | 19752221 | 257977 | 458198 | 7087932 | 4306725 | 23202141 | 15522736’ |
1490274 | 21895776 | 2941388 | 4919865 | 8085095 | 4320508 | 23205293 | 17074729’ |
1541626 | 22588921 | 346722 | 4958189 | 8629192 | 6946624’ |
6 Discussion on the methodology
6.1 Comparative studies
PPML | Method A | Method B | |
---|---|---|---|
Error rate | 12.5% | 15.6% | 12.7% |