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Collider signatures for Hidden Valley jets

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dc.contributor.advisor Desai, Nishita
dc.contributor.author SAOJI, PUSHKAR
dc.date.accessioned 2024-01-24T05:28:33Z
dc.date.available 2024-01-24T05:28:33Z
dc.date.issued 2024-01
dc.identifier.citation 65 en_US
dc.identifier.uri http://dr.iiserpune.ac.in:8080/xmlui/handle/123456789/8414
dc.description.abstract This thesis delves into the realm of Hidden Valley models, which introduce the intriguing concept of a new gauge group alongside the Standard Model gauge group, connected by a heavy mediator particle. Seeking to discern their signatures at the Large Hadron Collider (LHC), a binary classification MLP (Multi-Layer Perceptron) neural network serves as a key tool for signal discrimination. The introduction of the SIFT algorithm enhances the extraction of dark meson mass scales and discriminating the Hidden Valley jets against ordinary quark or gluon jets. We also introduce other benchmarks to explore sensitivity to heavy and light quark decays of Hidden Valley mesons. The use CNNs, with jet-images as inputs, offers an alternative avenue to study the properties of Hidden Valley jets. This comprehensive analysis unveils the potential to decipher elusive signals of the Hidden Valley. en_US
dc.language.iso en en_US
dc.subject Hidden Valley en_US
dc.subject Particle en_US
dc.subject High Energy en_US
dc.subject Dark Matter en_US
dc.subject Collider en_US
dc.title Collider signatures for Hidden Valley jets en_US
dc.type Thesis en_US
dc.description.embargo No Embargo en_US
dc.type.degree BS-MS en_US
dc.contributor.department Dept. of Physics en_US
dc.contributor.registration 20181191 en_US


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  • MS THESES [1705]
    Thesis submitted to IISER Pune in partial fulfilment of the requirements for the BS-MS Dual Degree Programme/MSc. Programme/MS-Exit Programme

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