Search for tt̄H Production in the H → bb̅ Decay Channel: Using Deep Learning Techniques with the CMS Experiment
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing
2021
Cham Springer |
| Ausgabe: | 1st ed. 2021 |
| Schriftenreihe: | Springer Theses, Recognizing Outstanding Ph.D. Research
|
| Schlagworte: | |
| Online-Zugang: | BFB01 TUM01 UBM01 UBT01 UBY01 URL des Erstveröffentlichers Buchcover |
| Beschreibung: | 1 Online-Ressource (XIII, 217 p. 82 illus., 73 illus. in color) |
| ISBN: | 9783030653804 |
| ISSN: | 2190-5053 |
| DOI: | 10.1007/978-3-030-65380-4 |
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| edition | 1st ed. 2021 |
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| index_date | 2024-07-03T16:44:24Z |
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| isbn | 9783030653804 |
| issn | 2190-5053 |
| language | English |
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| spelling | Rieger, Marcel Verfasser aut Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment by Marcel Rieger 1st ed. 2021 Cham Springer International Publishing 2021 Cham Springer 1 Online-Ressource (XIII, 217 p. 82 illus., 73 illus. in color) txt rdacontent c rdamedia cr rdacarrier Springer Theses, Recognizing Outstanding Ph.D. Research 2190-5053 Elementary Particles, Quantum Field Theory Statistics, general Machine Learning Particle and Nuclear Physics Elementary particles (Physics) Quantum field theory Statistics Machine learning Nuclear physics Erscheint auch als Druck-Ausgabe 978-3-030-65379-8 Erscheint auch als Druck-Ausgabe 978-3-030-65381-1 Erscheint auch als Druck-Ausgabe 978-3-030-65382-8 https://doi.org/10.1007/978-3-030-65380-4 Verlag URL des Erstveröffentlichers Volltext SWB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032580605&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Buchcover |
| spellingShingle | Rieger, Marcel Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment Elementary Particles, Quantum Field Theory Statistics, general Machine Learning Particle and Nuclear Physics Elementary particles (Physics) Quantum field theory Statistics Machine learning Nuclear physics |
| title | Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment |
| title_auth | Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment |
| title_exact_search | Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment |
| title_exact_search_txtP | Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment |
| title_full | Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment by Marcel Rieger |
| title_fullStr | Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment by Marcel Rieger |
| title_full_unstemmed | Search for tt̄H Production in the H → bb̅ Decay Channel Using Deep Learning Techniques with the CMS Experiment by Marcel Rieger |
| title_short | Search for tt̄H Production in the H → bb̅ Decay Channel |
| title_sort | search for tth production in the h → bb decay channel using deep learning techniques with the cms experiment |
| title_sub | Using Deep Learning Techniques with the CMS Experiment |
| topic | Elementary Particles, Quantum Field Theory Statistics, general Machine Learning Particle and Nuclear Physics Elementary particles (Physics) Quantum field theory Statistics Machine learning Nuclear physics |
| topic_facet | Elementary Particles, Quantum Field Theory Statistics, general Machine Learning Particle and Nuclear Physics Elementary particles (Physics) Quantum field theory Statistics Machine learning Nuclear physics |
| url | https://doi.org/10.1007/978-3-030-65380-4 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032580605&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT riegermarcel searchfortthproductioninthehbbdecaychannelusingdeeplearningtechniqueswiththecmsexperiment |