Classification of Hipparcos variables J/MNRAS/427/2917 ivo://CDS.VizieR/J/MNRAS/427/2917 doi:10.26093/cds/vizier.74272917 CDS Rimoldini L. Dubath P. Suveges M. Lopez M. Sarro L.M. Blomme J.,De Ridder J. Cuypers J. Guy L. Mowlavi N. Lecoeur-Taibi I. Beck M.,Jan A. Nienartowicz K. Ordonez-Blanco D. Lebzelter T. Eyer L. 2024-07-19T20:17:09Z 2012-11-29T14:31:27Z CDS support team CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France cds-question@unistra.fr astronomical-models variable-stars photometry classification The Hipparcos catalogue (ESA 1997, Cat. I/239) and the AAVSO Variable Star Index (Watson et al., 2011, Cat. B/vsx) are employed to complement the training set of periodic variables of Dubath et al. (2011, Cat. J/MNRAS/414/2602) with irregular and non-periodic representatives, leading to 3881 sources in total which described 24 variability types. The attributes employed to characterize light-curve features are selected according to their relevance for classification. Classifier models are produced with random forests and a multi-stage methodology based on Bayesian networks, achieving overall misclassification rates under 12%. Both classifiers are applied to predict variability types for 6051 Hipparcos variables associated with uncertain or missing types in the literature. 2012MNRAS.427.2917R https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/427/2917 Catalog Research IsServedBy TAP VizieR generic service IsServedBy Conesearch service related-to I/239 : The Hipparcos and Tycho Catalogues (ESA 1997) I/311 : Hipparcos, the New Reduction (van Leeuwen, 2007) B/vsx : AAVSO International Variable Star Index VSX (Watson+, 2006-12) J/MNRAS/414/2602 : HIP variable automated classification (Dubath+, 2011) https://cds.unistra.fr/vizier-org/licences_vizier.html https://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/MNRAS/427/2917 https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/MNRAS/427/2917 http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/MNRAS/427/2917 https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/MNRAS/427/2917 https://vizier.iucaa.in/viz-bin/votable?-source=J/MNRAS/427/2917 http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/MNRAS/427/2917 GET text/xml+votable https://tapvizier.cds.unistra.fr/TAPVizieR/tap Cone search capability for table J/MNRAS/427/2917/table2 (Training set of Hipparcos variable stars) https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/427/2917/table2? https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/427/2917/table2? http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/427/2917/table2? GET text/xml+votable 180.0 50000 true 0.0273475 25.88645917 0.005555555555555556 Cone search capability for table J/MNRAS/427/2917/ptypes (Predicted of Hipparcos unsolved variables) https://vizier.cds.unistra.fr/viz-bin/conesearch/J/MNRAS/427/2917/ptypes? https://vizier.iucaa.in/viz-bin/conesearch/J/MNRAS/427/2917/ptypes? http://vizieridia.saao.ac.za/viz-bin/conesearch/J/MNRAS/427/2917/ptypes? GET text/xml+votable 180.0 50000 true 0.12197444 67.21677861 0.005555555555555556 5/357 814-815 899 908 928 1969 2877 2982 3320 3322 3451 3486 3501 3560 3606 3660 3687 3848 3892 3915 5204 5509 5522 5715 5905 7554 8366 9333 9467 9482 9505 9536 9816 9960 9972-9973 9982 10294 10389 10393 10398 10416 10419 10424 10439 10538 10568 10882 11363 6/14-15 17 30-31 37 46-47 88 95 108 110 118 131 147-148 161 167 170 172 174 188 193 196 202 231 250 254 262-264 268 284 297 300 342 349 351 359-360 366 375 377 382 395 400 403-404 411 414 418 433 469 472 474 490 493 524 528 546 557 560 582-583 592 620 625 633 635 657-659 680 690 694 711-712 714 722 725 730 736 742 752 783 789 797-798 808 817 842 845-846 883 888 919 924 934 954 958 962-963 966 990-991 998 1002 1004 1015 1027 1032 1035 1038 1040 1043 1060 1064 1069 1074 1077 1082 1085 1102 1105 1109 1111 1113-1116 1118 1125 1134 1153-1155 1163-1164 1169 1176 1183 1190 1198 1203 1239 1261 1276-1277 1281 1287 1293 1298-1299 1302-1303 1305-1307 1310 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38409-38411 38413 38416 38432-38433 38437 38441 38444-38445 38447 38449-38450 38453 38458 38464 38466 38468 38471 38473 38489-38490 38494 38498-38499 38508 38519 38523 38526-38527 38531 38538-38540 38543 38550 38556-38558 38561-38562 38565 38568 38582 38586 38590 38593-38594 38597 38599-38600 38605 38621 38625-38630 38648 38660 38666 38670 38678 38680 38685 38688 38691 38695 38699 38701 38706 38711 38718 38720-38721 38723 38730 38736 38741 38765-38766 38788-38789 38802 38806 38809 38821 38825 38845 38853 38855 38860 38875-38877 38891 38895 38905 38918 38920 38924-38925 38932-38933 38941-38942 38947 38950 38957 38963 38968 38970 38975-38976 38978 38985 38992 38995-38996 39002-39003 39008 39011 39017 39020 39023-39024 39028 39031-39033 39035 39040 39044 39052 39059-39060 39064 39070 39081 39085 39087 39089 39092 39094-39095 39099 39114 39125 39130 39138 39144 39149-39150 39156-39157 39163-39164 39174-39175 39177 39183 39194-39196 39201 39208-39209 39211 39214 39217-39218 39221-39222 39224 39227 39229-39230 39232-39233 39235-39236 39238 39241 39244-39245 39248 39262-39263 39268 39270 39273 39277 39280 39284 39288 39293-39294 39298 39300 39302-39304 39308 39313-39315 39317-39318 39324 39326-39327 39332 39334-39336 39339 39341-39342 39345 39347-39349 39351-39352 39354-39356 39365 39367-39368 39370-39371 39374 39379-39380 39383 39385-39390 39394 39397 39404-39405 39410 39412-39413 39416-39418 39434 39440 39443 39447 39450 39464 39474-39475 39477 39484 39486-39487 39492 39504 39514 39523 39533 39562-39563 39569 39591 39596 39598 39606 39614 39625 39627-39629 39639 39648 39650 39661-39662 39670-39671 39677 39679 39681 39688 39691 39693 39695-39696 39701 39704 39706 39714 39720 39723 39729-39730 39732-39733 39735 39739-39741 39743-39745 39749 39751 39753 39755 39757-39758 39760-39761 39765-39766 39769-39771 39776 39780 39782-39786 39788-39790 39794 39797 39802 39804 39806 39808 39811 39814-39816 39822 39825 39829-39833 39838-39839 39853 39857 39861 39863-39864 39866-39868 39871 39873-39874 39877-39878 39880-39882 39884 39886-39887 39896-39897 39901 39903 39905 39907-39908 39910 39912-39913 39919 39922-39923 39926 39932-39933 39942 39946 39948-39949 39951 39954 39970 39975 39979 39982 39986 39991 39995 39999 40002-40004 40008-40009 40028 40030 40035-40036 40038 40042 40049 40062 40068 40070-40071 40074-40076 40078-40079 40085 40089 40093-40094 40099 40101 40104 40115 40119-40120 40134 40138 40146 40162 40165 40167 40197 40221 40233 40238 40242 40244 40250 40259 40283 40285 40288 40292 40300 40302-40303 40318 40321 40324 40328 40332 40336-40337 40345 40350 40383 40386-40387 40400 40405 40407 40421-40422 40427 40439 40441 40455 40457 40463 40466 40468 40474 40477 40483 40486-40488 40490-40494 40496-40497 40508 40517-40518 40527 40549 40551 40554 40556 40577 40586 40588 40592 40594 40600 40608 40613 40615 40619 40625 40634-40635 40637 40641-40643 40671 40676 40682 40688 40707-40708 40713 40716-40718 40743 40746 40757 40763 40766-40767 40772 40784 40788 40793 40795 40797 40800 40804 40807 40811 40820 40828-40830 40844 40850 40855 40858 40863-40864 40869 40876 40878 40885 40890 40900 40912 40936 40960 40965 40976 40979 40982 40985 40987-40988 40991 40998 41010 41015 41022 41027-41029 41037 41039 41041 41043 41047 41061 41077 41083 41105 41110 41115 41117-41119 41130 41145 41150 41153 41157-41158 41161 41163 41168 41171 41173-41175 41181-41182 41186 41192 41195 41199 41202 41204 41206 41209-41211 41214-41216 41222 41225 41231 41235 41247 41249 41259 41263 41266 41270-41272 41276-41277 41281-41282 41285 41293 41295 41297 41299 41310 41312 41314 41317-41318 41323 41337-41339 41351 41355 41357 41363 41366 41369 41371 41374-41375 41379-41380 41383 41385-41386 41394 41400 41403-41404 41407 41411 41413 41415 41419 41428-41429 41436 41441 41445 41461 41464 41466 41468-41469 41472 41476 41479-41481 41488 41494 41500 41502-41503 41511-41512 41516 41519-41520 41522 41524-41525 41532 41534 41539 41543 41546 41549-41551 41553 41555 41561 41564 41567-41568 41570 41577 41580-41582 41584 41587-41588 41591 41596 41598-41599 41602 41605-41606 41608-41610 41613-41617 41621 41626 41628-41629 41631 41646-41648 41650-41653 41657 41661-41662 41668 41671-41673 41680-41681 41683 41687-41689 41691-41692 41704 41706-41707 41712 41715 41717-41718 41722-41723 41726 41729 41731 41734 41736-41737 41739-41741 41746-41747 41752 41760-41762 41765-41767 41770 41772 41775 41779 41788 41796 41798 41802 41810-41811 41813-41815 41830 41832-41833 41853-41858 41862-41864 41866-41867 41869-41872 41875 41889 41891 41893-41894 41896 41899-41900 41911 41917-41918 41920 41928-41929 41945 41948 41960 41963 41965 41971 41979 41982-41983 41985 41994-41995 41997 42002 42007 42014-42016 42018 42021 42024 42026-42027 42029 42031 42034 42037-42038 42040 42043 42046 42053 42057 42059-42060 42063 42072 42075-42076 42078 42080-42081 42086 42092 42096 42101 42105 42111 42113 42116 42125-42126 42129 42136 42142-42143 42145 42147 42150 42156-42158 42160 42162 42176 42183 42185-42186 42189 42201-42202 42205 42209 42216-42217 42219 42221 42231 42233-42237 42245 42247 42250 42252 42255 42260 42263 42266 42269 42279 42283 42285 42290 42297 42301 42303 42305 42317 42323 42325-42326 42333-42334 42336 42347 42352 42355-42356 42366-42370 42373 42377-42378 42382 42385 42388 42391 42394-42395 42399 42403-42404 42408 42410 42412 42416 42420-42421 42424 42426 42428-42429 42431 42433 42435-42437 42439 42442 42444 42449 42451 42458 42461 42464 42471 42477 42481 42483 42486 42489 42502 42504 42515-42516 42524 42530-42531 42546 42555 42561 42564 42574 42588 42595-42596 42600-42601 42604-42605 42616-42617 42619 42625 42632 42635 42640 42645-42646 42650-42651 42653 42655 42660 42666-42667 42670 42672-42674 42679 42683 42687-42688 42690 42694-42695 42700 42702 42708 42722 42736 42738 42741 42747 42751 42760 42764 42766 42769 42773 42779 42791 42794-42795 42807 42811 42815 42820-42821 42829 42840-42841 42860 42872 42876 42897 42903 42910 42929-42930 42936 42939-42940 42953 42955-42956 42960 42966 42977 42985 42988 43008 43011 43015-43017 43019 43022 43024 43027-43028 43030-43031 43033 43035-43036 43038 43041 43047-43048 43051 43053 43056 43059-43060 43062 43071-43072 43078-43079 43082 43086 43088 43091 43093-43096 43099 43107 43110 43113-43114 43117 43119-43120 43127 43129-43130 43132 43134 43137 43140 43143 43145 43148-43150 43162 43164 43172 43174 43177-43178 43184 43188 43193 43200-43201 43203 43206-43207 43209-43211 43217-43219 43221 43234-43235 43242-43244 43258 43270 43272 43283-43284 43286 43290-43292 43294 43302 43308 43310 43316 43337-43339 43342 43345 43353-43354 43360-43361 43369 43373 43375 43378 43381 43392 43395 43399 43401 43405 43407 43418 43420 43439-43440 43443 43452 43454-43455 43460-43461 43464 43474 43480 43488 43490 43494 43500-43501 43521-43523 43526-43527 43532-43533 43537 43550 43552 43555 43557 43570-43571 43574-43575 43609 43617 43619 43621 43624 43631 43637-43638 43640-43641 43646-43648 43651-43652 43657 43659 43664 43666 43673 43689 43702-43703 43708-43710 43716-43717 43723 43729 43732 43744 43749 43753-43754 43757 43761 43764 43774 43777 43786 43799 43804 43807 43810 43812 43814-43815 43828 43832 43842-43843 43847-43848 43855 43862 43869 43877 43880 43882-43883 43890 43896 43898 43901-43902 43910 43914 43922 43932 43942 43944 43946 43952 43963 43982 43984 44006 44018 44020 44036 44039-44040 44044 44052 44056 44059 44066-44067 44069 44074 44084-44085 44101 44108 44112 44119 44126 44133 44136 44139 44142 44144-44145 44157 44160 44162 44173 44176 44198 44206 44212 44216 44227-44228 44240 44245 44250 44254 44258 44263-44264 44268 44271-44272 44275 44305 44309-44310 44312 44316 44323 44326 44328 44332 44354 44356 44360-44361 44364-44365 44371 44385 44396 44413 44417 44431 44437 44479 44482 44488 44496 44500 44502 44510 44514 44520 44537 44541 44546 44550 44580 44583 44603 44611 44619 44624 44627 44651 44655 44676-44677 44695 44701 44711 44717 44720 44728 44746-44747 44751 44762 44767 44774 44784-44786 44793 44798 44800 44822 44825 44839 44853 44857 44860 44866 44868 44872-44873 44891-44892 44894 44900-44901 44907 44924-44925 44936 44950 44959 44981 44992-44993 44998 45008-45009 45027 45043 45055-45057 45060-45061 45066 45069 45075 45078 45081 45090 45105 45113 45115 45118-45119 45131-45132 45137 45149 45152 45155 45162 45165 45179 45191 45194 45196 45201 45204 45209 45216-45217 45223 45226 45228 45232 45237-45239 45249 45258 45261 45264 45270 45276 45278-45279 45282 45284 45286-45287 45293 45296 45299-45300 45303 45315 45319 45327 45343 45360 45362 45377 45382 45386-45389 45410 45420-45421 45424 45426-45429 45434-45435 45444 45448-45450 45458 45462 45469-45470 45472 45475 45478 45480 45483 45487 45489 45493 45496 45506-45507 45509-45510 45512 45519 45522 45524 45528 45530-45531 45534 45538 45541-45542 45549 45552 45557 45559 45587 45590-45593 45601 45606 45610-45611 45613 45615 45617 45620-45621 45623-45625 45629 45641 45645 45651-45652 45663 45667 45675 45679-45680 45684-45685 45688 45694 45701 45708 45711 45717 45725 45727 45731 45737-45738 45746 45754 45765-45766 45770 45773 45782 45784-45785 45790 45809 45814-45815 45817 45826 45828 45830 45832 45838 45840 45845 45858 45861 45867-45868 45878 45881 45883 45886 45888 45890 45893 45895 45897 45903 45906 45913 45915 45917-45918 45921 45927 45929 45933-45934 45938 45941-45942 45944 45952 45958 45964-45965 45980 45988 45990 45997 46004 46007 46010 46014 46016-46017 46036 46044 46046 46054 46056-46057 46061 46069-46070 46072-46073 46076 46079 46084 46089 46091 46096 46098-46099 46113 46115-46116 46119 46123-46126 46128 46131 46135 46137-46138 46146 46156 46160 46162 46164-46165 46167 46169-46170 46172 46175 46180 46186 46192 46194 46198 46200-46201 46203 46214 46218 46224 46227-46228 46234 46236 46238 46242 46250 46257-46258 46262 46269 46275 46277 46281-46282 46289 46304-46305 46308 46313-46314 46317-46318 46334 46336 46349 46367 46369 46374 46377 46382 46384 46387 46396 46402 46407 46419 46425 46432 46434 46471 46476 46487-46488 46492 46498-46499 46509 46520 46529 46538 46561 46563 46566-46567 46571 46574 46584 46589 46592 46599 46606 46610 46615 46623 46630 46633 46640 46656 46669 46685 46691 46694-46695 46700-46702 46709 46721-46722 46729 46732 46734 46746-46747 46749 46762 46768 46780 46792 46800 46805 46808-46809 46822-46823 46834 46851 46859 46866 46874 46897 46901 46930-46932 46934 46939 46952 46954 46956 46970 46975-46977 46983 46990 47007 47009 47013 47033 47060 47081 47091 47097-47099 47106 47110 47113-47114 47119 47121 47126-47127 47131 47133 47137 47140 47143 47152 47154 47158 47174 47180 47185 47189 47194 47198-47199 47207 47212 47217 47219 47226 47232 47243 47246 47251 47262 47265 47270 47277 47279 47299 47305 47313 47333 47336 47342 47345-47346 47348 47358 47361 47374 47376-47377 47385 47392 47395 47397 47402-47403 47411 47418 47424 47428 47437 47450 47461 47467 47469 47471 47480 47483-47484 47498 47506 47509 47513 47516 47531 47542 47548-47549 47558-47560 47562 47567 47570-47571 47576 47584 47586 47589 47600 47607 47615 47623 47638 47641 47645 47648 47652 47657-47658 47663 47668 47672 47681-47682 47698 47706 47708-47710 47724 47731 47739 47749-47751 47753 47757 47773 47777 47783-47784 47788 47790-47791 47798 47809 47811 47821-47822 47824-47825 47827 47829-47831 47845 47852 47854 47866 47872-47873 47876 47904 47907 47912 47918 47928 47930-47931 47933 47935 47945-47946 47957-47959 47962-47964 47967 47986-47987 48008 48012 48016 48027 48031 48038 48040 48042 48082 48113 48118 48121 48127 48133 48135-48136 48142 48152 48160 48164 48169-48170 48174 48178 48188 48195 48225 48229-48230 48233 48248-48249 48257 48269 48274 48281 48285 48297-48298 48301 48305 48330 48344 48347 48362 48367 48398 48414 48429 48435 48459 48464-48465 48468 48481 48485 48488 48493 48510 48515 48529 48531 48534-48535 48539 48555 48572 48575 48579 48582 48584 48589 48591 48597 48604 48613 48620 48635 48646 48650 48676 48687 48689 48700 48706 48710 48728 48732 48737-48738 48741 48746 48753 48760 48768 48785 48798 48802-48803 48813 48816 48823-48824 48827 48837 48842 48845 48864 48868 48873-48874 48880 48896 48903 48905 48911 48914 48916 48918 48923 48933 48937-48938 48958 48962 48965 49002 49008 49020 49023 49035 49045-49046 49052 49056 49064 49066 49085-49086 49116 49123 49127-49128 49133 49136 49138 49147 https://cdsarc.cds.unistra.fr/viz-bin/moc/J/MNRAS/427/2917?format=ascii default J/MNRAS/427/2917/table2 Training set of Hipparcos variable stars recno Record number assigned by the VizieR team. Should Not be used for identification. meta.record int HIP [1/120404] Hipparcos number meta.id;meta.main int V-I Reddened V-I colour index in Cousins' system, as provided by ESA (1997) (1) mag phot.color;em.opt.V;em.opt.I float Skew Unbiased skewness of the distribution of HIP magnitudes (Skewness) (2) stat.fit.param float logAmp Decadic logarithm of the difference between the faintest and the brightest values of the light-curve model (LogAmplitude) (3) log(mag) stat.value;arith.diff float logPer Decadic logarithm of the period (LogPeriod) (4) log(d) time.period float MAG Absolute magnitude in the Hipparcos band (AbsoluteMag) (5) mag phot.mag;em.opt.V float logFAP [,0] Decadic logarithm of the probability that the maximum peak in the Lomb-Scargle periodogram (Scargle 1982ApJ...263..835S) is due to noise rather than the true signal (6) stat.fit.param float logP2P Decadic logarithm of the point-to-point scatter of the time series (LogP2PscatterFoldedRaw) (7) stat.fit.param float logQSOvar Decadic logarithm of the reduced chi-square of the source variability with respect to a parametrized QSO variance model (8) stat.fit.chi2 float logScRaw Decadic logarithm of the ratio between the median of absolute deviations from the median of the raw time series and the median of absolute values of the residual time series (logScatterRawRes) (9) stat.fit.residual float logPlx Decadic logarithm of the parallax value as provided by ESA (2007) (LogParallax) (10) log(mas) pos.parallax.trig float logSt Decadic logarithm of the unbiased standard deviation of the residual time series (logStdDevRes) (11) log(mag) stat.fit.residual float logSVar Decadic logarithm of the average of absolute values of magnitude differences between all pairs of measurements separated by time-scales from 0.01 to 0.1 day (logShortVar) (12) log(mag) stat.fit.residual float Sum Ratio between the sum of squared residuals of the model from the raw data and the sum of squared deviations of the raw time series from its mean value (SumSqResRaw) (13) stat.fit.residual float |b| Absolute value of the Galactic latitude of the source position (AbsGLAT) (14) deg pos.galactic.lat float eFreq Error estimate of the derived frequency (FrequencyError) (15) 0.0001d**-1 stat.error;em.freq float Type Variability type, only in table2 (16) meta.note;src.var char HIPdata Display HIP data meta.ref.url char Simbad Simbad column added by the CDS meta.ref char _RA Positions from HIP2 reduction (Cat. I/311) (right ascension part) deg pos.eq.ra;meta.main _DE Positions from HIP2 reduction (Cat. I/311) (declination part) deg pos.eq.dec;meta.main
J/MNRAS/427/2917/ptypes Predicted of Hipparcos unsolved variables recno Record number assigned by the VizieR team. Should Not be used for identification. meta.record int HIP [1/120404] Hipparcos number meta.id;meta.main int primary V-I Reddened V-I colour index in Cousins' system, as provided by ESA (1997) (1) mag phot.color;em.opt.V;em.opt.I float Skew Unbiased skewness of the distribution of HIP magnitudes (Skewness) (2) stat.fit.param float logAmp Decadic logarithm of the difference between the faintest and the brightest values of the light-curve model (LogAmplitude) (3) log(mag) stat.value;arith.diff float logPer Decadic logarithm of the period (LogPeriod) (4) log(d) time.period float MAG Absolute magnitude in the Hipparcos band (AbsoluteMag) (5) mag phot.mag;em.opt.V float logFAP [,0] Decadic logarithm of the probability that the maximum peak in the Lomb-Scargle periodogram (Scargle 1982ApJ...263..835S) is due to noise rather than the true signal (6) stat.fit.param float logP2P Decadic logarithm of the point-to-point scatter of the time series (LogP2PscatterFoldedRaw) (7) stat.fit.param float logQSOvar Decadic logarithm of the reduced chi-square of the source variability with respect to a parametrized QSO variance model (8) stat.fit.chi2 float logScRaw Decadic logarithm of the ratio between the median of absolute deviations from the median of the raw time series and the median of absolute values of the residual time series (logScatterRawRes) (9) stat.fit.residual float logPlx Decadic logarithm of the parallax value as provided by ESA (2007) (LogParallax) (10) log(mas) pos.parallax.trig float logSt Decadic logarithm of the unbiased standard deviation of the residual time series (logStdDevRes) (11) log(mag) stat.fit.residual float logSVar Decadic logarithm of the average of absolute values of magnitude differences between all pairs of measurements separated by time-scales from 0.01 to 0.1 day (logShortVar) (12) log(mag) stat.fit.residual float Sum Ratio between the sum of squared residuals of the model from the raw data and the sum of squared deviations of the raw time series from its mean value (SumSqResRaw) (13) stat.fit.residual float |b| Absolute value of the Galactic latitude of the source position (AbsGLAT) (14) deg pos.galactic.lat float eFreq Error estimate of the derived frequency (FrequencyError) (15) 0.0001d**-1 stat.error;em.freq float Set Hipparcos sets from which the sources have been selected (HipparcosSet) (17) phot.mag;em.opt.V char HIPtype Variability types as listed in HIP (HipparcosType) (18) meta.code;src.var char VXtype Variability types as listed in AAVSO (19) meta.code;src.var char RFtype Variability types predicted by random forests (PredictedTypeRF) (20) meta.code;src.var char MBtype Variability types predicted by a multi-stage methodology based on Bayesian networks (PredictedTypeMB) (21) meta.code;src.var char prRF [0/1] Probability of the variability type predicted by random forests (ProbabilityRF) stat.fit.goodness float prMB [0/1] Probability of the variability type predicted by a multi-stage methodology based on Bayesian networks (ProbabilityMB) stat.fit.goodness float HIPdata Display HIP data meta.ref.url char Simbad Simbad column added by the CDS meta.ref char _RA Positions from HIP2 reduction (Cat. I/311) (right ascension part) deg pos.eq.ra;meta.main _DE Positions from HIP2 reduction (Cat. I/311) (declination part) deg pos.eq.dec;meta.main
J/MNRAS/427/2917/tablec1 Full random forest prediction probability arrays recno Record number assigned by the VizieR team. Should Not be used for identification. meta.record int HIP [1/120404] Hipparcos number meta.id;meta.main int IX Probability of the source to be of type I_X, as predicted by random forests (ProbabilityI_X) (G1) stat.fit.goodness float LPVP Probability of the source to be of type LPV_P, as predicted by random forests (ProbabilityLPV_P) (G1) stat.fit.goodness float LPVX Probability of the source to be of type LPV_X, as predicted by random forests (ProbabilityLPV_X) (G1) stat.fit.goodness float RS+BYP Probability of the source to be of type RS+BY_P, as predicted by random forests (ProbabilityRS+BY_P) (G1) stat.fit.goodness float RS+BYX Probability of the source to be of type RS+BY_X, as predicted by random forests (ProbabilityRS+BY_X) (G1) stat.fit.goodness float BE+GCASP Probability of the source to be of type BE+GCAS_P as predicted by random forests (ProbabilityBE+GCAS_P) (G1) stat.fit.goodness float BE+GCASX Probability of the source to be of type BE+GCAS_X as predicted by random forests (ProbabilityBE+GCAS_X) (G1) stat.fit.goodness float SPBP Probability of the source to be of type SPB_P, as predicted by random forests (ProbabilitySPB_P) (G1) stat.fit.goodness float ACVP Probability of the source to be of type ACV_P, as predicted by random forests (ProbabilityACV_P) (G1) stat.fit.goodness float ACVX Probability of the source to be of type ACV_X, as predicted by random forests (ProbabilityACV_X) (G1) stat.fit.goodness float EAP Probability of the source to be of type EA_P, as predicted by random forests (ProbabilityEA_P) (G1) stat.fit.goodness float EAX Probability of the source to be of type EA_X, as predicted by random forests (ProbabilityEA_X) (G1) stat.fit.goodness float EBP Probability of the source to be of type EB_P, as predicted by random forests (ProbabilityEB_P) (G1) stat.fit.goodness float EWP Probability of the source to be of type EW_P, as predicted by random forests (ProbabilityEW_P) (G1) stat.fit.goodness float ELLP Probability of the source to be of type ELL_P, as predicted by random forests (ProbabilityELL_P) (G1) stat.fit.goodness float ACYGP Probability of the source to be of type ACYG_P, as predicted by random forests (ProbabilityACYG_P) (G1) stat.fit.goodness float ACYGX Probability of the source to be of type ACYG_X, as predicted by random forests (ProbabilityACYG_X) (G1) stat.fit.goodness float BCEPP Probability of the source to be of type BCEP_P, as predicted by random forests (ProbabilityBCEP_P) (G1) stat.fit.goodness float BCEPX Probability of the source to be of type BCEP_X, as predicted by random forests (ProbabilityBCEP_X) (G1) stat.fit.goodness float DCEPSP Probability of the source to be of type DCEPS_P, as predicted by random forests (ProbabilityDCEPS_P) (G1) stat.fit.goodness float DCEPP Probability of the source to be of type DCEP_P, as predicted by random forests (ProbabilityDCEP_P) (G1) stat.fit.goodness float CEP(B)P Probability of the source to be of type CEP(B)_P, as predicted by random forests (ProbabilityCEP(B)_P) (G1) stat.fit.goodness float RRABP Probability of the source to be of type RRAB_P, as predicted by random forests (ProbabilityRRAB_P) (G1) stat.fit.goodness float RRCP Probability of the source to be of type RRC_P, as predicted by random forests (ProbabilityRRC_P) (G1) stat.fit.goodness float GDORP Probability of the source to be of type GDOR_P, as predicted by random forests (ProbabilityGDOR_P) (G1) stat.fit.goodness float GDORX Probability of the source to be of type GDOR_X, as predicted by random forests (ProbabilityGDOR_X) (G1) stat.fit.goodness float DSCTP Probability of the source to be of type DSCT_P, as predicted by random forests (ProbabilityDSCT_P) (G1) stat.fit.goodness float DSCTX Probability of the source to be of type DSCT_X, as predicted by random forests (ProbabilityDSCT_X) (G1) stat.fit.goodness float DSCTCP Probability of the source to be of type DSCTC_P, as predicted by random forests (ProbabilityDSCTC_P) (G1) stat.fit.goodness float DSCTCX Probability of the source to be of type DSCTC_X, as predicted by random forests (ProbabilityDSCTC_X) (G1) stat.fit.goodness float CWAP Probability of the source to be of type CWA_P, as predicted by random forests (ProbabilityCWA_P) (G1) stat.fit.goodness float CWBP Probability of the source to be of type CWB_P, as predicted by random forests (ProbabilityCWB_P) (G1) stat.fit.goodness float SXARIP Probability of the source to be of type SXARI_P, as predicted by random forests (ProbabilitySXARI_P) (G1) stat.fit.goodness float RVP Probability of the source to be of type RV_P, as predicted by random forests (ProbabilityRV_P) (G1) stat.fit.goodness float
J/MNRAS/427/2917/tablec2 Full multi-stage Bayesian nets prediction probability arrays EWP Probability of the source to be of type EW_P, as predicted by random forests (ProbabilityEW_P) (G1) stat.fit.goodness float recno Record number assigned by the VizieR team. Should Not be used for identification. meta.record int HIP [1/120404] Hipparcos number meta.id;meta.main int IX Probability of the source to be of type I_X, as predicted by random forests (ProbabilityI_X) (G1) stat.fit.goodness float LPVP Probability of the source to be of type LPV_P, as predicted by random forests (ProbabilityLPV_P) (G1) stat.fit.goodness float LPVX Probability of the source to be of type LPV_X, as predicted by random forests (ProbabilityLPV_X) (G1) stat.fit.goodness float RS+BYP Probability of the source to be of type RS+BY_P, as predicted by random forests (ProbabilityRS+BY_P) (G1) stat.fit.goodness float RS+BYX Probability of the source to be of type RS+BY_X, as predicted by random forests (ProbabilityRS+BY_X) (G1) stat.fit.goodness float BE+GCASP Probability of the source to be of type BE+GCAS_P as predicted by random forests (ProbabilityBE+GCAS_P) (G1) stat.fit.goodness float BE+GCASX Probability of the source to be of type BE+GCAS_X as predicted by random forests (ProbabilityBE+GCAS_X) (G1) stat.fit.goodness float SPBP Probability of the source to be of type SPB_P, as predicted by random forests (ProbabilitySPB_P) (G1) stat.fit.goodness float ACVP Probability of the source to be of type ACV_P, as predicted by random forests (ProbabilityACV_P) (G1) stat.fit.goodness float ACVX Probability of the source to be of type ACV_X, as predicted by random forests (ProbabilityACV_X) (G1) stat.fit.goodness float EAP Probability of the source to be of type EA_P, as predicted by random forests (ProbabilityEA_P) (G1) stat.fit.goodness float EAX Probability of the source to be of type EA_X, as predicted by random forests (ProbabilityEA_X) (G1) stat.fit.goodness float EBP Probability of the source to be of type EB_P, as predicted by random forests (ProbabilityEB_P) (G1) stat.fit.goodness float ELLP Probability of the source to be of type ELL_P, as predicted by random forests (ProbabilityELL_P) (G1) stat.fit.goodness float ACYGP Probability of the source to be of type ACYG_P, as predicted by random forests (ProbabilityACYG_P) (G1) stat.fit.goodness float ACYGX Probability of the source to be of type ACYG_X, as predicted by random forests (ProbabilityACYG_X) (G1) stat.fit.goodness float BCEPP Probability of the source to be of type BCEP_P, as predicted by random forests (ProbabilityBCEP_P) (G1) stat.fit.goodness float BCEPX Probability of the source to be of type BCEP_X, as predicted by random forests (ProbabilityBCEP_X) (G1) stat.fit.goodness float DCEPSP Probability of the source to be of type DCEPS_P, as predicted by random forests (ProbabilityDCEPS_P) (G1) stat.fit.goodness float DCEPP Probability of the source to be of type DCEP_P, as predicted by random forests (ProbabilityDCEP_P) (G1) stat.fit.goodness float CEP(B)P Probability of the source to be of type CEP(B)_P, as predicted by random forests (ProbabilityCEP(B)_P) (G1) stat.fit.goodness float RRABP Probability of the source to be of type RRAB_P, as predicted by random forests (ProbabilityRRAB_P) (G1) stat.fit.goodness float RRCP Probability of the source to be of type RRC_P, as predicted by random forests (ProbabilityRRC_P) (G1) stat.fit.goodness float GDORP Probability of the source to be of type GDOR_P, as predicted by random forests (ProbabilityGDOR_P) (G1) stat.fit.goodness float GDORX Probability of the source to be of type GDOR_X, as predicted by random forests (ProbabilityGDOR_X) (G1) stat.fit.goodness float DSCTP Probability of the source to be of type DSCT_P, as predicted by random forests (ProbabilityDSCT_P) (G1) stat.fit.goodness float DSCTX Probability of the source to be of type DSCT_X, as predicted by random forests (ProbabilityDSCT_X) (G1) stat.fit.goodness float DSCTCP Probability of the source to be of type DSCTC_P, as predicted by random forests (ProbabilityDSCTC_P) (G1) stat.fit.goodness float DSCTCX Probability of the source to be of type DSCTC_X, as predicted by random forests (ProbabilityDSCTC_X) (G1) stat.fit.goodness float CWAP Probability of the source to be of type CWA_P, as predicted by random forests (ProbabilityCWA_P) (G1) stat.fit.goodness float CWBP Probability of the source to be of type CWB_P, as predicted by random forests (ProbabilityCWB_P) (G1) stat.fit.goodness float SXARIP Probability of the source to be of type SXARI_P, as predicted by random forests (ProbabilitySXARI_P) (G1) stat.fit.goodness float RVP Probability of the source to be of type RV_P, as predicted by random forests (ProbabilityRV_P) (G1) stat.fit.goodness float