Signal mixture estimation for degenerate heavy Higgses using a deep neural network

If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scen...

Full description

Saved in:

Bibliographic Details
Published in:	The European physical journal. C, Particles and fields Vol. 78; no. 12; pp. 1 - 11
Main Authors:	Kvellestad, Anders, Maeland, Steffen, Strümke, Inga
Format:	Journal Article
Language:	English
Published:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2018 Springer Springer Nature B.V SpringerOpen
Subjects:	Artificial neural networks Astronomy Astrophysics and Cosmology Bosons Elementary Particles Hadrons Heavy Ions Higgs bosons Machine learning Measurement Science and Instrumentation Neural networks Nuclear Energy Nuclear Physics Parameter estimation Physics Physics and Astronomy Quantum Field Theories Quantum Field Theory Regular Article - Experimental Physics String Theory
ISSN:	1434-6044, 1434-6052
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Abstract	If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a ∼ 20 % improvement in the estimate uncertainty.
AbstractList	If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a \[\sim 20\%\] improvement in the estimate uncertainty. If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a [Formula omitted] improvement in the estimate uncertainty. If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a ∼ 20 % improvement in the estimate uncertainty. Abstract If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to multiple near-degenerate states. We investigate the performance of a deep learning approach to signal mixture estimation for the challenging scenario of a ditau signal coming from a pair of degenerate Higgs bosons of opposite CP charge. This constitutes a parameter estimation problem for a mixture model with highly overlapping features. We use an unbinned maximum likelihood fit to a neural network output, and compare the results to mixture estimation via a fit to a single kinematic variable. For our benchmark scenarios we find a $$\sim 20\%$$ ∼20% improvement in the estimate uncertainty.
ArticleNumber	1010
Audience	Academic
Author	Kvellestad, Anders Strümke, Inga Maeland, Steffen
Author_xml	– sequence: 1 givenname: Anders orcidid: 0000-0002-5267-7705 surname: Kvellestad fullname: Kvellestad, Anders organization: Department of Physics, University of Oslo, Blackett Laboratory, Department of Physics, Imperial College London – sequence: 2 givenname: Steffen orcidid: 0000-0002-4652-4753 surname: Maeland fullname: Maeland, Steffen email: steffen.maeland@uib.no organization: Department of Physics and Technology, University of Bergen – sequence: 3 givenname: Inga orcidid: 0000-0003-1820-6544 surname: Strümke fullname: Strümke, Inga email: inga.strumke@uib.no organization: Department of Physics and Technology, University of Bergen
BookMark	eNqFkU9v1DAQxSNUJNqFr4AiceKQ1k78J5G4VBXQlSohtXC2HHscvOzai-1A20_PbIOKyqXywdbovTfj-Z1URyEGqKq3lJxSysgZ7DfmLFNCeNsQ2jeCcd7cv6iOKetYI7B89Phm7FV1kvOGENIy0h9X1zd-Cnpb7_xtmRPUkIvf6eJjqF1MtYUJAiRdoP4O-tddfemnKUOu5-zDVGsUwL4OMCfMCFB-x_TjdfXS6W2GN3_vVfXt08evF5fN1ZfP64vzq8Zw2pfGUul4T0ZhRdsOVAs7EDL2OJjgVAjNiXZuYE5SDiMdO7CkGxihzkoz9qbrVtV6ybVRb9Q-4dzpTkXt1UMhpknpVLzZgnKjlo5wx4WxzMi2Z1IOUltHnZaUUcx6t2TtU_w54xLUJs4JF5NVSzkbBB2GFlWni2rSGOqDiyVpg8fCzhuk4jzWz7nADpLgylfV-ycG1BS4LZOec1brm-unWrFoTYo5J3CPX6JEHUCrA2i1gFYIWh1Aq3s0fvjPaHx5QIjT-e3zdrnYM_YLE6R_X3_G-QeNksPN
CitedBy_id	crossref_primary_10_1007_s00521_020_05316_5 crossref_primary_10_1140_epjc_s10052_023_11532_9
Cites_doi	10.1007/JHEP09(2018)007 10.1103/PhysRevD.68.013001 10.1038/ncomms5308 10.1016/0370-2693(85)91028-7 10.1007/JHEP11(2015)147 10.1007/JHEP12(2014)024 10.1007/BF01557231 10.1016/0550-3213(91)90356-3 10.1103/PhysRevD.92.075004 10.1214/aos/1016218223 10.1016/j.cpc.2011.07.015 10.1103/PhysRevD.94.093001 10.1016/j.cpc.2009.09.011 10.1016/0550-3213(92)90284-I 10.1007/JHEP01(2018)055 10.1088/1126-6708/2006/05/026 10.1103/PhysRevLett.121.191802 10.1103/PhysRevD.96.073002 10.1016/S0370-2693(02)02445-0 10.1016/j.cpc.2009.09.003 10.1103/PhysRevD.45.2685 10.1142/9789812839657_0001 10.1142/9789814307505_0001 10.1016/S0010-4655(00)00243-5 10.1016/S0010-4655(00)00155-7 10.1103/PhysRevLett.114.111801 10.1016/j.cpc.2016.10.015 10.1016/j.physletb.2008.10.018 10.1016/j.cpc.2008.01.036 10.1016/0550-3213(89)90211-3 10.1140/epjc/s10052-015-3650-z 10.1103/PhysRevLett.88.201801 10.1140/epjc/s10052-013-2693-2 10.1088/1674-1137/40/10/100001 10.1103/PhysRevD.92.096012 10.1088/1126-6708/2005/12/015 10.1214/aoms/1177732360 10.1016/j.cpc.2013.02.006 10.1088/1742-6596/762/1/012034
ContentType	Journal Article
Copyright	The Author(s) 2018 COPYRIGHT 2018 Springer The European Physical Journal C is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml	– notice: The Author(s) 2018 – notice: COPYRIGHT 2018 Springer – notice: The European Physical Journal C is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
DBID	C6C AAYXX CITATION ISR 7U5 8FD 8FE 8FG ABUWG AFKRA ARAPS AZQEC BENPR BGLVJ CCPQU DWQXO H8D HCIFZ L7M P5Z P62 PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS DOA
DOI	10.1140/epjc/s10052-018-6455-z
DatabaseName	Springer Nature OA Free Journals CrossRef Gale In Context: Science Solid State and Superconductivity Abstracts Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials ProQuest Central Technology collection ProQuest One Community College ProQuest Central Aerospace Database SciTech Premium Collection Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Database ProQuest Advanced Technologies & Aerospace Collection ProQuest Databases ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China DOAJ Directory of Open Access Journals
DatabaseTitle	CrossRef Publicly Available Content Database Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences Aerospace Database ProQuest Central Korea ProQuest Central (New) Advanced Technologies Database with Aerospace Advanced Technologies & Aerospace Collection ProQuest One Academic Eastern Edition ProQuest Technology Collection ProQuest SciTech Collection Advanced Technologies & Aerospace Database ProQuest One Academic UKI Edition Solid State and Superconductivity Abstracts ProQuest One Academic ProQuest One Academic (New)
DatabaseTitleList	Publicly Available Content Database
Database_xml	– sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database
DeliveryMethod	fulltext_linktorsrc
Discipline	Physics
EISSN	1434-6052
EndPage	11
ExternalDocumentID	oai_doaj_org_article_fba7f05f56cd4c72847797adf1fa7141 A567287014 10_1140_epjc_s10052_018_6455_z
GroupedDBID	-5F -5G -A0 -BR -Y2 -~X .86 0R~ 199 1SB 29G 29Q 2JY 2P1 30V 4.4 408 409 40D 5GY 5VS 67Z 6NX 78A 8FE 8FG 8TC 8UJ 95. 95~ AAFWJ AAKKN ABDBF ABEEZ ABMNI ABQSL ABTEG ACACY ACGFS ACNCT ACUHS ACULB ADBBV ADINQ ADKPE ADMLS AENEX AFBBN AFFNX AFGXO AFKRA AFPKN AFWTZ AGJBK AGWIL AHSBF AHYZX AI. AIBLX ALMA_UNASSIGNED_HOLDINGS AMKLP ARAPS ASPBG AVWKF AZFZN B0M BA0 BCNDV BENPR BGLVJ BGNMA C24 C6C CAG CCPQU COF CS3 CSCUP DL5 DU5 EAD EAP EAS EBS EJD EMK EPL ER. ESX FEDTE GQ6 GQ8 GROUPED_DOAJ GXS H13 HCIFZ HF~ HG5 HG6 HMJXF HVGLF HZ~ H~9 I-F I09 IAO IGS IHE ISR IXC IZIGR IZQ I~X KDC KOV LAS M4Y MA- N2Q NB0 NU0 O9- O93 OK1 P62 P9T PIMPY PROAC PT5 QOS R89 R9I RED RID RNS ROL RPX RSV RZK S1Z S27 S3B SDH SOJ SPH SZN T13 TN5 TSK TSV TUC TUS U2A VC2 VH1 WK8 Z45 Z7Y ~8M AAYXX ABFSG ACSTC ADHKG AEZWR AFFHD AFHIU AGQPQ AHWEU AIXLP CITATION PHGZM PHGZT PQGLB 7U5 8FD ABUWG AZQEC DWQXO H8D L7M PKEHL PQEST PQQKQ PQUKI PRINS
ID	FETCH-LOGICAL-c518t-d17f580b6d62291a6d900b800265166a50aff94f715eb1b3ed039401fd7cb8c33
IEDL.DBID	RSV
ISICitedReferencesCount	2
ISICitedReferencesURI	http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000453066200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN	1434-6044
IngestDate	Fri Oct 03 12:50:46 EDT 2025 Sat Oct 18 23:48:48 EDT 2025 Mon Oct 20 16:30:25 EDT 2025 Thu Oct 16 15:04:41 EDT 2025 Sat Nov 29 02:58:04 EST 2025 Tue Nov 18 21:10:20 EST 2025 Fri Feb 21 02:39:34 EST 2025
IsDoiOpenAccess	true
IsOpenAccess	true
IsPeerReviewed	true
IsScholarly	true
Issue	12
Language	English
LinkModel	DirectLink
MergedId	FETCHMERGED-LOGICAL-c518t-d17f580b6d62291a6d900b800265166a50aff94f715eb1b3ed039401fd7cb8c33
Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ORCID	0000-0002-4652-4753 0000-0002-5267-7705 0000-0003-1820-6544
OpenAccessLink	https://link.springer.com/10.1140/epjc/s10052-018-6455-z
PQID	2154961992
PQPubID	2034659
PageCount	11
ParticipantIDs	doaj_primary_oai_doaj_org_article_fba7f05f56cd4c72847797adf1fa7141 proquest_journals_2154961992 gale_infotracacademiconefile_A567287014 gale_incontextgauss_ISR_A567287014 crossref_primary_10_1140_epjc_s10052_018_6455_z crossref_citationtrail_10_1140_epjc_s10052_018_6455_z springer_journals_10_1140_epjc_s10052_018_6455_z
PublicationCentury	2000
PublicationDate	20181200
PublicationDateYYYYMMDD	2018-12-01
PublicationDate_xml	– month: 12 year: 2018 text: 20181200
PublicationDecade	2010
PublicationPlace	Berlin/Heidelberg
PublicationPlace_xml	– name: Berlin/Heidelberg – name: Heidelberg
PublicationSubtitle	Particles and Fields
PublicationTitle	The European physical journal. C, Particles and fields
PublicationTitleAbbrev	Eur. Phys. J. C
PublicationYear	2018
Publisher	Springer Berlin Heidelberg Springer Springer Nature B.V SpringerOpen
Publisher_xml	– name: Springer Berlin Heidelberg – name: Springer – name: Springer Nature B.V – name: SpringerOpen
References	BechtlePBreinOHeinemeyerSWeigleinGWilliamsKEComput. Phys. Commun.20101811382010CoPhC.181..138B10.1016/j.cpc.2009.09.003 SirunyanAMJHEP2018090072018JHEP...09..007S10.1007/JHEP09(2018)007 BaldiPSadowskiPWhitesonDPhys. Rev. Lett.2015114111118012015PhRvL.114k1801B10.1103/PhysRevLett.114.111801 SrivastavaNHintonGEKrizhevskyASutskeverISalakhutdinovRJ. Mach. Learn. Res.201415119293231592 BaldiPSadowskiPWhitesonDNat. Commun.2014543082014NatCo...5E4308B10.1038/ncomms5308 M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from http://tensorflow.org A. Haarr, A. Kvellestad, T.C. Petersen (2016) preprint. arXiv:1611.05757 Dell’AquilaJRNelsonCANucl. Phys. B19893201611989NuPhB.320...61D10.1016/0550-3213(89)90211-3 HarlanderRKantPJHEP2005120152005JHEP...12..015H10.1088/1126-6708/2005/12/015 ErikssonDRathsmanJStalOComput. Phys. Commun.20101811892010CoPhC.181..189E10.1016/j.cpc.2009.09.011 CranmerKPavezJLouppeGBrooksWKJ. Phys. Conf. Ser.2016762101203410.1088/1742-6596/762/1/012034 BechtlePBreinOHeinemeyerSWeigleinGWilliamsKEComput. Phys. Commun.201118226052011CoPhC.182.2605B10.1016/j.cpc.2011.07.015 P.S. Bhupal Dev, A. Pilaftsis, JHEP 12, 024 (2014). https://doi.org/10.1007/JHEP11(2015)147. https://doi.org/10.1007/JHEP12(2014)024. [Erratum: JHEP 11, 147 (2015)] PatrignaniCChin. Phys. C201640101000012016ChPhC..40j0001P10.1088/1674-1137/40/10/100001 AaboudMJHEP2018010552018JHEP...01..055A10.1007/JHEP01(2018)055 BergeSBernreutherWKirchnerSPhys. Rev. D2015920960122015PhRvD..92i6012B10.1103/PhysRevD.92.096012 JózefowiczRRichter-WasEWasZPhys. Rev. D20169490930012016PhRvD..94i3001J10.1103/PhysRevD.94.093001 F. Chollet, et al. Keras. https://github.com/fchollet/keras (2015) ActisSPassarinoGSturmCUcciratiSPhys. Lett. B2008670122008PhLB..670...12A10.1016/j.physletb.2008.10.018 D.P. Kingma, J. Ba, CoRR arXiv:1412.6980 (2014) K. He, X. Zhang, S. Ren, J. Sun, CoRR arXiv:1502.01852 (2015) BernonJGunionJFHaberHEJiangYKramlSPhys. Rev. D20159270750042015PhRvD..92g5004B10.1103/PhysRevD.92.075004 BowerGRPierzchalaTWasZWorekMPhys. Lett. B20025432272002PhLB..543..227B10.1016/S0370-2693(02)02445-0 HarlanderRVKilgoreWBPhys. Rev. D2003680130012003PhRvD..68a3001H10.1103/PhysRevD.68.013001 TurokNZadroznyJNucl. Phys. B19923697291992NuPhB.369..729T10.1016/0550-3213(92)90284-I KuzminVARubakovVAShaposhnikovMEPhys. Lett. B1985155361985PhLB..155...36K10.1016/0370-2693(85)91028-7 HarlanderRVLieblerSMantlerHComput. Phys. Commun.20172122392017CoPhC.212..239H10.1016/j.cpc.2016.10.015 SjostrandTMrennaSSkandsPZComput. Phys. Commun.20081788522008CoPhC.178..852S10.1016/j.cpc.2008.01.036 HarlanderRVKilgoreWBPhys. Rev. Lett.2002882018012002PhRvL..88t1801H10.1103/PhysRevLett.88.201801 BechtlePHeinemeyerSStalOStefaniakTWeigleinGEur. Phys. J. C20157594212015EPJC...75..421B10.1140/epjc/s10052-015-3650-z A. Farbin, PoS ICHEP2016, 180 (2016) BarberioELeBRichter-WasEWasZZanziDZarembaJPhys. Rev. D20179670730022017PhRvD..96g3002B10.1103/PhysRevD.96.073002 HarlanderRVLieblerSMantlerHComput. Phys. Commun.201318416052013CoPhC.184.1605H10.1016/j.cpc.2013.02.006 AndersonGWHallLJPhys. Rev. D19924526851992PhRvD..45.2685A10.1103/PhysRevD.45.2685 TurokNZadroznyJNucl. Phys. B19913584711991NuPhB.358..471T10.1016/0550-3213(91)90356-3 S.P. Martin (1997). https://doi.org/10.1142/9789812839657_0001. https://doi.org/10.1142/9789814307505_0001. [Adv. Ser. Direct. High Energy Phys. 18, 1 (1998)] FriedmanJHastieTTibshiraniRAnn. Stat.200028233710.1214/aos/1016218223 AndersenJOGordaTHelsetANiemiLTenkanenTVITranbergAVuorinenAWeirDJPhys. Rev. Lett.2018121191918022018PhRvL.121s1802A10.1103/PhysRevLett.121.191802 SjostrandTMrennaSSkandsPZJHEP2006050262006JHEP...05..026S10.1088/1126-6708/2006/05/026 CranmerKSComput. Phys. Commun.20011361982001CoPhC.136..198C10.1016/S0010-4655(00)00243-5 BechtlePBreinOHeinemeyerSStalOStefaniakTWeigleinGWilliamsKPoS2012CHARGED2012024 KramerMKuhnJHStongMLZerwasPMZ. Phys. C199464211994ZPhyC..64...21K10.1007/BF01557231 A.L. Maas, A.Y. Hannun, A.Y. Ng, in International conference on machine learning, vol. 30, p. 3 (2013) BechtlePBreinOHeinemeyerSStalOStefaniakTWeigleinGWilliamsKEEur. Phys. J. C201474326932014EPJC...74.2693B10.1140/epjc/s10052-013-2693-2 ChetyrkinKGKuhnJHSteinhauserMComput. Phys. Commun.2000133432000CoPhC.133...43C10.1016/S0010-4655(00)00155-7 K. Cranmer, J. Pavez, G. Louppe (2015) preprint. arXiv:1506.02169 S. Ioffe, C. Szegedy, CoRR arXiv:1502.03167 (2015) WilksSSAnn. Math. Stat.1938916010.1214/aoms/1177732360 N Turok (6455_CR7) 1992; 369 N Srivastava (6455_CR33) 2014; 15 M Kramer (6455_CR16) 1994; 64 KS Cranmer (6455_CR41) 2001; 136 P Bechtle (6455_CR45) 2011; 182 GR Bower (6455_CR17) 2002; 543 D Eriksson (6455_CR28) 2010; 181 P Baldi (6455_CR1) 2014; 5 M Aaboud (6455_CR9) 2018; 01 T Sjostrand (6455_CR29) 2006; 05 T Sjostrand (6455_CR30) 2008; 178 6455_CR3 VA Kuzmin (6455_CR4) 1985; 155 P Bechtle (6455_CR46) 2012; CHARGED2012 6455_CR40 JR Dell’Aquila (6455_CR15) 1989; 320 RV Harlander (6455_CR23) 2002; 88 J Friedman (6455_CR39) 2000; 28 AM Sirunyan (6455_CR10) 2018; 09 E Barberio (6455_CR20) 2017; 96 R Harlander (6455_CR26) 2005; 12 N Turok (6455_CR6) 1991; 358 SS Wilks (6455_CR42) 1938; 9 KG Chetyrkin (6455_CR27) 2000; 133 RV Harlander (6455_CR22) 2017; 212 GW Anderson (6455_CR5) 1992; 45 RV Harlander (6455_CR21) 2013; 184 S Actis (6455_CR25) 2008; 670 J Bernon (6455_CR11) 2015; 92 RV Harlander (6455_CR24) 2003; 68 P Bechtle (6455_CR44) 2010; 181 C Patrignani (6455_CR31) 2016; 40 6455_CR32 6455_CR13 6455_CR35 P Baldi (6455_CR2) 2015; 114 6455_CR14 6455_CR36 R Józefowicz (6455_CR19) 2016; 94 6455_CR12 6455_CR34 P Bechtle (6455_CR48) 2015; 75 K Cranmer (6455_CR43) 2016; 762 6455_CR37 S Berge (6455_CR18) 2015; 92 6455_CR38 JO Andersen (6455_CR8) 2018; 121 P Bechtle (6455_CR47) 2014; 74
References_xml	– reference: KramerMKuhnJHStongMLZerwasPMZ. Phys. C199464211994ZPhyC..64...21K10.1007/BF01557231 – reference: AndersenJOGordaTHelsetANiemiLTenkanenTVITranbergAVuorinenAWeirDJPhys. Rev. Lett.2018121191918022018PhRvL.121s1802A10.1103/PhysRevLett.121.191802 – reference: TurokNZadroznyJNucl. Phys. B19923697291992NuPhB.369..729T10.1016/0550-3213(92)90284-I – reference: HarlanderRVKilgoreWBPhys. Rev. D2003680130012003PhRvD..68a3001H10.1103/PhysRevD.68.013001 – reference: SirunyanAMJHEP2018090072018JHEP...09..007S10.1007/JHEP09(2018)007 – reference: A. Haarr, A. Kvellestad, T.C. Petersen (2016) preprint. arXiv:1611.05757 – reference: SjostrandTMrennaSSkandsPZJHEP2006050262006JHEP...05..026S10.1088/1126-6708/2006/05/026 – reference: BaldiPSadowskiPWhitesonDNat. Commun.2014543082014NatCo...5E4308B10.1038/ncomms5308 – reference: ErikssonDRathsmanJStalOComput. Phys. Commun.20101811892010CoPhC.181..189E10.1016/j.cpc.2009.09.011 – reference: Dell’AquilaJRNelsonCANucl. Phys. B19893201611989NuPhB.320...61D10.1016/0550-3213(89)90211-3 – reference: ChetyrkinKGKuhnJHSteinhauserMComput. Phys. Commun.2000133432000CoPhC.133...43C10.1016/S0010-4655(00)00155-7 – reference: F. Chollet, et al. Keras. https://github.com/fchollet/keras (2015) – reference: ActisSPassarinoGSturmCUcciratiSPhys. Lett. B2008670122008PhLB..670...12A10.1016/j.physletb.2008.10.018 – reference: CranmerKSComput. Phys. Commun.20011361982001CoPhC.136..198C10.1016/S0010-4655(00)00243-5 – reference: SrivastavaNHintonGEKrizhevskyASutskeverISalakhutdinovRJ. Mach. Learn. Res.201415119293231592 – reference: BernonJGunionJFHaberHEJiangYKramlSPhys. Rev. D20159270750042015PhRvD..92g5004B10.1103/PhysRevD.92.075004 – reference: KuzminVARubakovVAShaposhnikovMEPhys. Lett. B1985155361985PhLB..155...36K10.1016/0370-2693(85)91028-7 – reference: M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G.S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, X. Zheng. TensorFlow: Large-scale machine learning on heterogeneous systems (2015). Software available from http://tensorflow.org/ – reference: S.P. Martin (1997). https://doi.org/10.1142/9789812839657_0001. https://doi.org/10.1142/9789814307505_0001. [Adv. Ser. Direct. High Energy Phys. 18, 1 (1998)] – reference: A.L. Maas, A.Y. Hannun, A.Y. Ng, in International conference on machine learning, vol. 30, p. 3 (2013) – reference: AaboudMJHEP2018010552018JHEP...01..055A10.1007/JHEP01(2018)055 – reference: BechtlePHeinemeyerSStalOStefaniakTWeigleinGEur. Phys. J. C20157594212015EPJC...75..421B10.1140/epjc/s10052-015-3650-z – reference: BechtlePBreinOHeinemeyerSWeigleinGWilliamsKEComput. Phys. Commun.20101811382010CoPhC.181..138B10.1016/j.cpc.2009.09.003 – reference: HarlanderRVKilgoreWBPhys. Rev. Lett.2002882018012002PhRvL..88t1801H10.1103/PhysRevLett.88.201801 – reference: SjostrandTMrennaSSkandsPZComput. Phys. Commun.20081788522008CoPhC.178..852S10.1016/j.cpc.2008.01.036 – reference: HarlanderRVLieblerSMantlerHComput. Phys. Commun.20172122392017CoPhC.212..239H10.1016/j.cpc.2016.10.015 – reference: FriedmanJHastieTTibshiraniRAnn. Stat.200028233710.1214/aos/1016218223 – reference: HarlanderRVLieblerSMantlerHComput. Phys. Commun.201318416052013CoPhC.184.1605H10.1016/j.cpc.2013.02.006 – reference: D.P. Kingma, J. Ba, CoRR arXiv:1412.6980 (2014) – reference: TurokNZadroznyJNucl. Phys. B19913584711991NuPhB.358..471T10.1016/0550-3213(91)90356-3 – reference: BechtlePBreinOHeinemeyerSStalOStefaniakTWeigleinGWilliamsKEEur. Phys. J. C201474326932014EPJC...74.2693B10.1140/epjc/s10052-013-2693-2 – reference: K. He, X. Zhang, S. Ren, J. Sun, CoRR arXiv:1502.01852 (2015) – reference: CranmerKPavezJLouppeGBrooksWKJ. Phys. Conf. Ser.2016762101203410.1088/1742-6596/762/1/012034 – reference: JózefowiczRRichter-WasEWasZPhys. Rev. D20169490930012016PhRvD..94i3001J10.1103/PhysRevD.94.093001 – reference: BarberioELeBRichter-WasEWasZZanziDZarembaJPhys. Rev. D20179670730022017PhRvD..96g3002B10.1103/PhysRevD.96.073002 – reference: K. Cranmer, J. Pavez, G. Louppe (2015) preprint. arXiv:1506.02169 – reference: BaldiPSadowskiPWhitesonDPhys. Rev. Lett.2015114111118012015PhRvL.114k1801B10.1103/PhysRevLett.114.111801 – reference: HarlanderRKantPJHEP2005120152005JHEP...12..015H10.1088/1126-6708/2005/12/015 – reference: AndersonGWHallLJPhys. Rev. D19924526851992PhRvD..45.2685A10.1103/PhysRevD.45.2685 – reference: BergeSBernreutherWKirchnerSPhys. Rev. D2015920960122015PhRvD..92i6012B10.1103/PhysRevD.92.096012 – reference: WilksSSAnn. Math. Stat.1938916010.1214/aoms/1177732360 – reference: A. Farbin, PoS ICHEP2016, 180 (2016) – reference: P.S. Bhupal Dev, A. Pilaftsis, JHEP 12, 024 (2014). https://doi.org/10.1007/JHEP11(2015)147. https://doi.org/10.1007/JHEP12(2014)024. [Erratum: JHEP 11, 147 (2015)] – reference: S. Ioffe, C. Szegedy, CoRR arXiv:1502.03167 (2015) – reference: BechtlePBreinOHeinemeyerSStalOStefaniakTWeigleinGWilliamsKPoS2012CHARGED2012024 – reference: BowerGRPierzchalaTWasZWorekMPhys. Lett. B20025432272002PhLB..543..227B10.1016/S0370-2693(02)02445-0 – reference: PatrignaniCChin. Phys. C201640101000012016ChPhC..40j0001P10.1088/1674-1137/40/10/100001 – reference: BechtlePBreinOHeinemeyerSWeigleinGWilliamsKEComput. Phys. Commun.201118226052011CoPhC.182.2605B10.1016/j.cpc.2011.07.015 – volume: 09 start-page: 007 year: 2018 ident: 6455_CR10 publication-title: JHEP doi: 10.1007/JHEP09(2018)007 – volume: 68 start-page: 013001 year: 2003 ident: 6455_CR24 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.68.013001 – volume: 5 start-page: 4308 year: 2014 ident: 6455_CR1 publication-title: Nat. Commun. doi: 10.1038/ncomms5308 – volume: 155 start-page: 36 year: 1985 ident: 6455_CR4 publication-title: Phys. Lett. B doi: 10.1016/0370-2693(85)91028-7 – ident: 6455_CR14 doi: 10.1007/JHEP11(2015)147 10.1007/JHEP12(2014)024 – volume: 64 start-page: 21 year: 1994 ident: 6455_CR16 publication-title: Z. Phys. C doi: 10.1007/BF01557231 – volume: 358 start-page: 471 year: 1991 ident: 6455_CR6 publication-title: Nucl. Phys. B doi: 10.1016/0550-3213(91)90356-3 – volume: 92 start-page: 075004 issue: 7 year: 2015 ident: 6455_CR11 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.92.075004 – volume: 28 start-page: 337 issue: 2 year: 2000 ident: 6455_CR39 publication-title: Ann. Stat. doi: 10.1214/aos/1016218223 – ident: 6455_CR40 – volume: 182 start-page: 2605 year: 2011 ident: 6455_CR45 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2011.07.015 – volume: 94 start-page: 093001 issue: 9 year: 2016 ident: 6455_CR19 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.94.093001 – volume: 181 start-page: 189 year: 2010 ident: 6455_CR28 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2009.09.011 – volume: 369 start-page: 729 year: 1992 ident: 6455_CR7 publication-title: Nucl. Phys. B doi: 10.1016/0550-3213(92)90284-I – volume: 01 start-page: 055 year: 2018 ident: 6455_CR9 publication-title: JHEP doi: 10.1007/JHEP01(2018)055 – volume: 05 start-page: 026 year: 2006 ident: 6455_CR29 publication-title: JHEP doi: 10.1088/1126-6708/2006/05/026 – ident: 6455_CR35 – volume: 121 start-page: 191802 issue: 19 year: 2018 ident: 6455_CR8 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.121.191802 – volume: 96 start-page: 073002 issue: 7 year: 2017 ident: 6455_CR20 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.96.073002 – ident: 6455_CR3 – volume: 543 start-page: 227 year: 2002 ident: 6455_CR17 publication-title: Phys. Lett. B doi: 10.1016/S0370-2693(02)02445-0 – ident: 6455_CR37 – volume: 181 start-page: 138 year: 2010 ident: 6455_CR44 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2009.09.003 – volume: 45 start-page: 2685 year: 1992 ident: 6455_CR5 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.45.2685 – volume: 15 start-page: 1929 issue: 1 year: 2014 ident: 6455_CR33 publication-title: J. Mach. Learn. Res. – ident: 6455_CR12 – volume: CHARGED2012 start-page: 024 year: 2012 ident: 6455_CR46 publication-title: PoS – ident: 6455_CR13 doi: 10.1142/9789812839657_0001 10.1142/9789814307505_0001 – volume: 136 start-page: 198 year: 2001 ident: 6455_CR41 publication-title: Comput. Phys. Commun. doi: 10.1016/S0010-4655(00)00243-5 – volume: 133 start-page: 43 year: 2000 ident: 6455_CR27 publication-title: Comput. Phys. Commun. doi: 10.1016/S0010-4655(00)00155-7 – volume: 114 start-page: 111801 issue: 11 year: 2015 ident: 6455_CR2 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.114.111801 – volume: 212 start-page: 239 year: 2017 ident: 6455_CR22 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2016.10.015 – volume: 670 start-page: 12 year: 2008 ident: 6455_CR25 publication-title: Phys. Lett. B doi: 10.1016/j.physletb.2008.10.018 – volume: 178 start-page: 852 year: 2008 ident: 6455_CR30 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2008.01.036 – volume: 320 start-page: 61 issue: 1 year: 1989 ident: 6455_CR15 publication-title: Nucl. Phys. B doi: 10.1016/0550-3213(89)90211-3 – volume: 75 start-page: 421 issue: 9 year: 2015 ident: 6455_CR48 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-015-3650-z – volume: 88 start-page: 201801 year: 2002 ident: 6455_CR23 publication-title: Phys. Rev. Lett. doi: 10.1103/PhysRevLett.88.201801 – ident: 6455_CR32 – ident: 6455_CR34 – volume: 74 start-page: 2693 issue: 3 year: 2014 ident: 6455_CR47 publication-title: Eur. Phys. J. C doi: 10.1140/epjc/s10052-013-2693-2 – volume: 40 start-page: 100001 issue: 10 year: 2016 ident: 6455_CR31 publication-title: Chin. Phys. C doi: 10.1088/1674-1137/40/10/100001 – ident: 6455_CR36 – volume: 92 start-page: 096012 year: 2015 ident: 6455_CR18 publication-title: Phys. Rev. D doi: 10.1103/PhysRevD.92.096012 – volume: 12 start-page: 015 year: 2005 ident: 6455_CR26 publication-title: JHEP doi: 10.1088/1126-6708/2005/12/015 – volume: 9 start-page: 60 issue: 1 year: 1938 ident: 6455_CR42 publication-title: Ann. Math. Stat. doi: 10.1214/aoms/1177732360 – ident: 6455_CR38 – volume: 184 start-page: 1605 year: 2013 ident: 6455_CR21 publication-title: Comput. Phys. Commun. doi: 10.1016/j.cpc.2013.02.006 – volume: 762 start-page: 012034 issue: 1 year: 2016 ident: 6455_CR43 publication-title: J. Phys. Conf. Ser. doi: 10.1088/1742-6596/762/1/012034
SSID	ssj0002408
Score	2.2802706
Snippet	If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is due to... Abstract If a new signal is established in future LHC data, a next question will be to determine the signal composition, in particular whether the signal is...
SourceID	doaj proquest gale crossref springer
SourceType	Open Website Aggregation Database Enrichment Source Index Database Publisher
StartPage	1
SubjectTerms	Artificial neural networks Astronomy Astrophysics and Cosmology Bosons Elementary Particles Hadrons Heavy Ions Higgs bosons Machine learning Measurement Science and Instrumentation Neural networks Nuclear Energy Nuclear Physics Parameter estimation Physics Physics and Astronomy Quantum Field Theories Quantum Field Theory Regular Article - Experimental Physics String Theory
SummonAdditionalLinks	– databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1bSx0xEA5FLPSlaC90vZRQCn0KJ9ncdh9VlBaKFLXFt5DN5XBKXQ_uUdRfbyaboxWhvvR1d7JsJpPJTGbmG4Q-d7yN6dwJJIQAYUbKiRVakQxW5Tnv2gw8_-u7PjxsTk_bH3-1-oKcsBEeeGTcJHZWRyqjVM4Lp0Gb6lZbH1m0muWS9TpZPUtnquhgAO7KdUVcEEWFKLXByZuYhPlvB5VzVEJOQkOUkJLcPjqWMnr_Ux39JFiaz6CDNfS6GI94Z_zpdfQi9G_Qy5zE6Ya36Oh4NoX3Z7NriAtgANAYKxNxMk2xD9OMMb0IOGngqxsM181DGDDkvk-xTQRhjgHgMn2jH9PD36GfB_sne19J6ZlAnGTNgnimo2xop7yq65ZZ5VtKO7AKlWRKWUltjK2ImsmkpTsePIXe6Cx67brGcf4erfTnffiAsHJOCx9tW1uenDjWqZi8CwE3VyImzlZILllmXAEUh74Wf8xY7EwNsNqMrDaJ1QZYbW4rNLkfNx8hNZ4dsQsrck8NkNj5QRIUUwTFPCcoFfoE62kA9KKHrJqpvRwG8-34yOxIpSHgy0SFvhSieJ7m4mwpUkgcAZysR5RbS7kwZdsPpgbAOwUZvRWiS1l5eP3vWW78j1luolc1SHVOuNlCK4uLy7CNVt3VYjZcfMyb5A6eNxFe priority: 102 providerName: Directory of Open Access Journals – databaseName: ProQuest Central dbid: BENPR link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3daxQxEA96VfDFb3G1ShDBp3DJbj52n6SVFgU5ylWlbyGbj-NE966311L715vJ5q6UQn3wdTMJZGcyk8zHbxB631ZNiHbHE-89hBlpRQxXkiSwKldVbZOA5398VZNJfXLSHGWHW5_TKjc6MSlqt7DgIx-XgCUmIVny4_KUQNcoiK7mFhp30Q4glfER2tk_mBxNt7oYALxSfVHFiaSc5xrh-KoY--VPCxV0VEBuQk0kF4JcXjNPCcX_pq6-ETRNtujw0f_u4jF6mG-heG8Qmyfoju-eovspG9T2z9D0eD6D8d_zCwgwYEDiGEoccbzjYudnCax67XFU5ed_MPite99jSKKfYRMJ_BIDUmZcoxvyzJ-j74cH3z59Jrn5ArGC1WvimAqipq10siwbZqRrKG3heikFk9IIakJoeFBMRHXfVt5RaLLOglO2rW1VvUCjbtH5lwhLaxV3wTSlqeJrkLUyxGcKBxcYD5E1BRKbf65tRiaHBhm_9FA1TTXwSg-80pFXGnilLws03s5bDtgc_5yxDyzdUgO2dvqwWM10Pqo6tEYFKoKQ1nGrwH6rRhkXWDCKcVagdyAQGtAzOkjPmZmzvtdfjqd6T0gFkWPGC_QhE4VF3Is1udoh_hEA3LpGubsRFZ31R6-v5KRAdCNsV8O37_LV7Su-Rg9KEPiUk7OLRuvVmX-D7tnz9bxfvc0n6C_10SBK priority: 102 providerName: ProQuest
Title	Signal mixture estimation for degenerate heavy Higgses using a deep neural network
URI	https://link.springer.com/article/10.1140/epjc/s10052-018-6455-z https://www.proquest.com/docview/2154961992 https://doaj.org/article/fba7f05f56cd4c72847797adf1fa7141
Volume	78
WOSCitedRecordID	wos000453066200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText	1
inHoldings	1
isFullTextHit
isPrint
journalDatabaseRights	– providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1434-6052 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002408 issn: 1434-6044 databaseCode: DOA dateStart: 20140101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVIAO databaseName: SCOAP3 Journals customDbUrl: eissn: 1434-6052 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002408 issn: 1434-6044 databaseCode: ER. dateStart: 20140101 isFulltext: true titleUrlDefault: https://scoap3.org/ providerName: SCOAP3 (Sponsoring Consortium for Open Access Publishing in Particle Physics) – providerCode: PRVAVX databaseName: SpringerLINK customDbUrl: eissn: 1434-6052 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002408 issn: 1434-6044 databaseCode: C24 dateStart: 19980301 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1434-6052 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0002408 issn: 1434-6044 databaseCode: RSV dateStart: 19980103 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature
link	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB71ARIX3hWBsooQEqeoduJHcmyrVlSC1WoXqsLFchw7WgTZ1WZbQX89HidZVFWA4JJDbEfxeDxje775DPC6zArn_Y5NrLUYZiRZopkUSSCrqrKsLALx_Pk7OR7nFxfFZAvyIRcmoN2HkGSw1B2fLTmwyy8GM94IRyxBngjGeXK9Dbve5eU4Jaez840NRuKukFeUsUQQxvrc4N9_54ZbCuz9t230rWBp8EGnD_7_7x_C_X7dGR92ivIItmzzGO4G_Kdpn8B0Nq-x_Nv8O4YUYuTe6JIaY7-qjStbB3rqtY298b76EeNJdWvbGGHzdax9BbuMkRvTf6PpkOVP4ePpyYfjt0l_3UJiOM3XSUWl4zkpRSXStKBaVAUhJS4oBadCaE60cwVzknJv4MvMVgSvVaeukqbMTZbtwU6zaOwziIUxklVOF6nO_P6PlsL5jQnDQy_m_KBEwAdpK9NzkeOVGF9VlydNFMpLdfJSXl4K5aWuIzjYtFt2bBx_bXGEg7mpjWza4cViVat-cipXaukId1yYihmJHlsWUleOOi0poxG8QlVQyJfRICCn1pdtq85mU3XIhcRYMWURvOkruYXvi9F9foOXCFJs3ai5P6iU6i1Gq1LkyhMIBo6ADCr0q_jPvXz-701ewL0UFTEgc_ZhZ726tC_hjrlaz9vVCHaPTsaT6Qi2j1M2CgcV_jnhn33J5Oz95NMozLefZ7Yg0Q
linkProvider	Springer Nature
linkToHtml	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaqAoILb8RCAQuBOEVrJ34kB4TKo-qqy6pqC6q4GMePaKuyu2y2hfZH8RvxOMlWVaVy6oFr_JAcfzNje2a-QehVmRU-2B2XOOfAzUiyRDMpkkhWZbOsLCLx_NehHI3y_f1iewX96XJhIKyy04lRUdupgTfyfgpcYgKCJd_NfiZQNQq8q10JjQYWW-7kV7iy1W8HH8P-vk7TjU97HzaTtqpAYjjNF4ml0vOclMKKNC2oFrYgpIRzk-BUCM2J9r5gXlIe9FiZOUugejj1VpoyN_AAGlT-NcbCYoL8bPNvS80PdGExmyljiSCMtRnJ4Q7Td7MDA_l6hEMkRJ4Ixnlyes4YxpoBFy3DBRdttHwbd_63f3YX3W7P2Hi9EYp7aMVN7qMbMdbV1A_Qzu64gvYf49_gPsHAM9IkcOJwgsfWVZGKe-FwMFTHJxhe5WtXY0gRqLAOHdwMAw9omGPSRNE_RF-uZEGP0OpkOnGPERbGSGa9LlKdhbsuLYUPlzAGD3zMByj0EO_2WJmWdx3KfxyqJiecKMCGarChAjYUYEOd9lB_OW7WMI_8c8R7gNCyNzCHxw_TeaVaRaR8qaUn3HNhLDMSTieykNp66rWkjPbQSwCgAm6QCQQfVfqortVgd0etcyHBL05ZD71pO_lpWIvRbS5H-CNAJ3au51oHTdVqx1qd4bKHSAfus-bLV_nk8hlfoJube5-HajgYbT1Ft1IQthh9tIZWF_Mj9wxdN8eLcT1_HmUXo-9XDfy_ptB6Xw
linkToPdf	http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwELaq8hAX3ohAAQuBOEVrJ34kB4RayopVq9WqBVRxMY4fq0Wwu2y2hfan8evqcZKtqkrl1APX-CE5_jzj8cx8g9CrKi990Dsudc6Bm5HkqWZSpJGsyuZ5VUbi-S-7cjgsDg7K0Rr62-XCQFhlJxOjoLYzA2_kvQy4xAQES_Z8GxYx2u6_m_9KoYIUeFq7choNRHbc8e9gvtVvB9thr19nWf_Dp_cf07bCQGo4LZappdLzglTCiiwrqRa2JKSCO5TgVAjNifa-ZF5SHmRalTtLoJI49VaaqjDwGBrE_zUZbEw4XSP-daUFgDosZjblLBWEsTY7OdgzPTf_biB3j3CIiihSwThPT84pxlg_4KKWuOCujVqwf-d__n930e327o03m8NyD6256X10I8bAmvoB2tufjKH95-QPuFUw8I80iZ043OyxdeNI0b10OCiwo2MMr_W1qzGkDoyxDh3cHAM_aJhj2kTXP0Sfr2RBj9D6dDZ1jxEWxkhmvS4znQcbmFbCB-OMwcMf8wEWCeLdfivT8rFDWZAfqskVJwpwohqcqIATBThRJwnqrcbNG0aSf47YAjitegOjePwwW4xVK6CUr7T0hHsujGVGwq1FllJbT72WlNEEvQQwKuAMmQJ6xvqwrtVgf09tciHBX05Zgt60nfwsrMXoNscj_BGgGTvXc6ODqWqlZq3OMJog0gH9rPnyVT65fMYX6GbAu9odDHeeolsZnLsYlLSB1peLQ_cMXTdHy0m9eB6PMUbfrhr3p4UDgzI
openUrl	ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Signal+mixture+estimation+for+degenerate+heavy+Higgses+using+a+deep+neural+network&rft.jtitle=The+European+physical+journal.+C%2C+Particles+and+fields&rft.au=Kvellestad%2C+Anders&rft.au=Maeland%2C+Steffen&rft.au=Str%C3%BCmke%2C+Inga&rft.date=2018-12-01&rft.pub=Springer+Berlin+Heidelberg&rft.issn=1434-6044&rft.eissn=1434-6052&rft.volume=78&rft.issue=12&rft_id=info:doi/10.1140%2Fepjc%2Fs10052-018-6455-z&rft.externalDocID=10_1140_epjc_s10052_018_6455_z
thumbnail_l	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1434-6044&client=summon
thumbnail_m	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1434-6044&client=summon
thumbnail_s	http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1434-6044&client=summon