東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Development of machine learning τ tr...

SpringerLink (Online service)

Linked to FindBook

Google Book

Amazon

博客來

Development of machine learning τ trigger algorithms and search for Higgs boson pair production = in the bbττ decay channel with the CMS detector at the LHC /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Development of machine learning τ trigger algorithms and search for Higgs boson pair production/ by Jona Motta.
Reminder of title:	in the bbττ decay channel with the CMS detector at the LHC /
Author:	Motta, Jona.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xxvi, 350 p. :ill. (chiefly col.), digital ;24 cm.
Notes:	"Doctoral thesis prepared at École Polytechnique, Palaiseau, France and accepted by Institut Polytechnique de Paris, Palaiseau, France."
Contained By:	Springer Nature eBook
Subject:	Higgs bosons. -
Online resource:	https://doi.org/10.1007/978-3-031-96288-2
ISBN:	9783031962882

Development of machine learning τ trigger algorithms and search for Higgs boson pair production = in the bbττ decay channel with the CMS detector at the LHC /
Motta, Jona.

Development of machine learning τ trigger algorithms and search for Higgs boson pair productionin the bbττ decay channel with the CMS detector at the LHC /[electronic resource] :by Jona Motta. - Cham :Springer Nature Switzerland :2025. - xxvi, 350 p. :ill. (chiefly col.), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral thesis prepared at École Polytechnique, Palaiseau, France and accepted by Institut Polytechnique de Paris, Palaiseau, France."

This book reports the successful optimization of the Compact Mupn Solenoid (CMS) tau trigger algorithm for the Run-3 (Phase-1) of the Large Hadron Collider (LHC) and a completely new and original design of a machine learning based tau triggering algorithm for the High Luminosity LHC (or Phase-2). A large proportion of searches at collider experiments relies on datasets collected with a dedicated tau lepton selection algorithm, particularly difficult to operate in intense hadronic environments, making the work descirbed in this book of prime importance. The second part of the book describes a major and very challenging data analysis, aiming to detect Higgs boson pair production. The book summarizes these contributions in clear, pedagogical prose while keeping an adequate and coherent balance between the technical and data analysis aspects. Machine learning techniques were used extensively throughout this research; therefore, special care has been taken to describe their core principles and application in high-energy physics, as well as potential future developments for sophisticated low-latency trigger algorithms and modern signal extraction methods.

ISBN: 9783031962882

Standard No.: 10.1007/978-3-031-96288-2doiSubjects--Topical Terms:

940517
Higgs bosons.

LC Class. No.: QC793.5.B62

Dewey Class. No.: 539.721

Development of machine learning τ trigger algorithms and search for Higgs boson pair production = in the bbττ decay channel with the CMS detector at the LHC /
LDR:02442nmm a2200337 a 4500 001 2422878
003 DE-He213
005 20260102120756.0
006 m d
007 cr nn 008maaau
008 260505s2025 sz s 0 eng d
020 $a 9783031962882 $q (electronic bk.)
020 $a 9783031962875 $q (paper)
024 7 $a 10.1007/978-3-031-96288-2 $2 doi
035 $a 978-3-031-96288-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QC793.5.B62
072 7 $a PHP $2 bicssc
072 7 $a SCI057000 $2 bisacsh
072 7 $a PHP $2 thema
082 0 4 $a 539.721 $2 23
090 $a QC793.5.B62 $b M921 2025
100 1 $a Motta, Jona. $3 3805026
245 1 0 $a Development of machine learning τ trigger algorithms and search for Higgs boson pair production $h [electronic resource] : $b in the bbττ decay channel with the CMS detector at the LHC / $c by Jona Motta.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xxvi, 350 p. : $b ill. (chiefly col.), digital ; $c 24 cm.
490 1 $a Springer theses, $x 2190-5061
500 $a "Doctoral thesis prepared at École Polytechnique, Palaiseau, France and accepted by Institut Polytechnique de Paris, Palaiseau, France."
520 $a This book reports the successful optimization of the Compact Mupn Solenoid (CMS) tau trigger algorithm for the Run-3 (Phase-1) of the Large Hadron Collider (LHC) and a completely new and original design of a machine learning based tau triggering algorithm for the High Luminosity LHC (or Phase-2). A large proportion of searches at collider experiments relies on datasets collected with a dedicated tau lepton selection algorithm, particularly difficult to operate in intense hadronic environments, making the work descirbed in this book of prime importance. The second part of the book describes a major and very challenging data analysis, aiming to detect Higgs boson pair production. The book summarizes these contributions in clear, pedagogical prose while keeping an adequate and coherent balance between the technical and data analysis aspects. Machine learning techniques were used extensively throughout this research; therefore, special care has been taken to describe their core principles and application in high-energy physics, as well as potential future developments for sophisticated low-latency trigger algorithms and modern signal extraction methods.
650 0 $a Higgs bosons. $3 940517
650 1 4 $a Elementary Particles, Quantum Field Theory. $3 894026
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Particle Physics. $3 3538893
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer theses. $3 1314442
856 4 0 $u https://doi.org/10.1007/978-3-031-96288-2
950 $a Physics and Astronomy (SpringerNature-11651)