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Inclusive Searches for Supersymmetry at √ S = 13 Tev Using Razor

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Inclusive Searches for Supersymmetry at √ S = 13 Tev Using Razor p Inclusive searches for supersymmetry at s = 13 TeV using razor kinematic variables, and data scouting using the CMS trigger system Thesis by Dustin James Anderson In Partial Fulfillment of the Requirements for the degree of Doctor of Philosophy CALIFORNIA INSTITUTE OF TECHNOLOGY Pasadena, California 2018 Defended 27 April 2018 ii © 2018 Dustin James Anderson ORCID: 0000-0001-8173-3182 All rights reserved iii ACKNOWLEDGEMENTS First, I thank my advisor Maria Spiropulu, who oversaw my journey from particle physics beginner to CERN veteran. Her vision for the Caltech CMS group, and her nonstop efforts to realize it, made the work documented in this thesis possible. Second, I thank the others who helped make my Ph.D. experience meaningful and worthwhile. I am deeply grateful to the following people: • Cristián Peña, for being a great friend and role model, both at work and out- side it. • Javier Duarte, for helping me learn the ropes of HEP, for getting me interested in statistics, and for trying to get me interested in soccer. • Yi Chen, for inspiring me in my first year by pulling off amazing feats and then kindly explaining them. • My fellow grad students at CERN: Thong Nguyen, Jay Lawhorn, Zhicai Zhang, and Jiajing Mao. You made life there much more colorful. Special thanks to Thong for our quality Slack discussions. • Adi Bornheim, for being a source of wisdom, sanity, and warmth, and for occasional help with car trouble. • Si Xie, for masterminding our search program and for helping me move in the right direction in the face of uncertainty. • Maurizio Pierini, for being my mentor at CERN, and for showing me how to navigate a very confusing world and make a real impact on it. • Jean-Roch Vlimant, for always having bigger ideas than me, bravely pursuing them, and often convincing me to follow. • Others in the group who took the time to help me when I needed it: Emanuele Di Marco, Josh Bendavid, Artur Apresyan, and Dorian Kcira. Thanks also to Harvey Newman for many useful discussions. • The Friday night dinner and D&D group: Tony Bartolotta, Jason Pollack, Kevin Barkett, Jeremy Brouillet, Jon Blackman, and Dan Brooks. You’ve all been amazing friends and have made the Ph.D. process a lot less lonely. iv • Shao Min Tan; I’m glad we were there to support each other at CERN. • Tristan McKinney and Belinda Pang (and Cristian again), for the best lunch conversations anyone could ask for. • My parents Ann and Ken Anderson. Sorry that I’m always thousands of miles away, and thank you for everything. • My brothers Adam and Alex, and my sister Kelly: you’re the best siblings I’ve ever had. Keep it up. • My girlfriend Jiejing, for helping me find much-needed balance these past several months. Your wonderful optimism and perspective have made such a difference. v ABSTRACT We present two searches for supersymmetric particles using proton-proton colli- p sion data collected by the CMS experiment at s = 13 TeV. The searches use razor kinematic variables for signal discrimination and target the pair production of heavy gluinos and squarks in R-parity conserving supersymmetry. The first search is performed on 2.3 fb−1 of data collected by CMS in 2015. Two complete, inde- pendent background predictions are made: one based on fits using a parameterized functional form, and the other based on Monte Carlo simulation corrected via con- trol samples in data. The second search is an expanded version of the first search, and is performed using the Monte Carlo-based background prediction method on 35.9 fb−1 of data collected in 2016. Both searches obtain results compatible with standard model background expectations. The null results are interpreted as limits on the masses and cross sections of gluinos, squarks, and higgsinos in the context of simplified models of supersymmetry. We discuss the outlook for the fit-based search strategy and explore how the technique of gaussian process regression may be useful as a tool to combat the challenges of this analysis methodology. We also describe a new paradigm for trigger-level collider data analysis, which we refer to as data scouting. In this paradigm, searches for new physics are per- formed using event information reconstructed within the experiment’s trigger soft- ware. This circumvents traditional event rate constraints, such as disk space and the latency of offline reconstruction. We provide details on the implementation of a framework for data scouting in the CMS High-Level Trigger system and its successful use in Run II of the LHC. We discuss the impact of scouting on the physics program of CMS and demonstrate that it enables searches for new physics that would not otherwise be possible due to trigger constraints, such as hadronic resonance searches at low mass and searches for leptonic decays of dark photons. vi PUBLISHED CONTENT AND CONTRIBUTIONS [1] Dustin Anderson. “Data Scouting in CMS”. In: PoS ICHEP2016 (2016), p. 190. D.A. designed and implemented the data scouting framework described here with input from CMS trigger coordination and guidance from the Physics Performance and Dataset group. p [2] Searches for dijet resonances in pp collisions at s = 13 TeV using data collected in 2016. Tech. rep. CMS-PAS-EXO-16-056. Geneva: CERN, 2017. url: https://cds.cern.ch/record/2256873. D.A. developed data processing and analysis code for this search, and de- fined the trigger strategy for event selection and efficiency measurements in the low-mass search. [3] Vardan Khachatryan et al. “Inclusive search for supersymmetry using razor variables in pp collisions at sqrt(s) = 13 TeV”. In: Phys. Rev. D95.1 (2017), p. 012003. doi: 10.1103/PhysRevD.95.012003. arXiv: 1609.07658 [hep-ex]. D.A. was one of the leading contributors to this search. He carried out the background prediction using the Monte Carlo-based strategy, contributed significantly to the fit-based background prediction, and performed the sta- tistical interpretation of the search. [4] D. Anderson et al. “On timing properties of LYSO-based calorimeters”. In: Nuclear Instruments and Methods in Physics Research Section A: Accel- erators, Spectrometers, Detectors and Associated Equipment 794 (2015), pp. 7–14. issn: 0168-9002. doi: https://doi.org/10.1016/j.nima. 2015 . 04 . 013. url: http : / / www . sciencedirect . com / science / article/pii/S0168900215004829. D.A. participated in the test beam experiments to characterize LYSO crystal calorimeters, and assisted in the analysis of the data. [5] Farrell, Steven et al. “The HEP.TrkX Project: deep neural networks for HL- LHC online and offline tracking”. In: EPJ Web Conf. 150 (2017), p. 00003. doi: 10.1051/epjconf/201715000003. url: https://doi.org/10. 1051/epjconf/201715000003. D.A. designed, implemented, and tested the neural network models for track parameter prediction, with input from others in the HEP.TrkX collaboration. [6] D. Anderson, J.-R. Vlimant, and M. Spiropulu. “An MPI-Based Python Framework for Distributed Training with Keras”. In: ArXiv e-prints (Dec. 2017). arXiv: 1712.05878 [cs.DC]. D.A. wrote the mpi_learn library described in this paper, and assisted in the benchmarking of the software on the Cooley supercomputing cluster. vii TABLE OF CONTENTS Acknowledgements . iii Abstract . v Published Content and Contributions . vi Table of Contents . vii List of Illustrations . x List of Tables . xxx I CMS and the Search for Supersymmetry 1 Chapter I: Introduction . 2 Chapter II: Physics with the CMS detector . 5 2.1 Proton-proton collisions at the LHC . 5 2.2 Description of the CMS detector . 7 2.3 The trigger system . 14 2.4 Physics object reconstruction . 18 2.5 MC simulation . 25 Chapter III: Supersymmetry and searches at the LHC . 26 3.1 Brief overview of the standard model . 26 3.2 Theory of supersymmetry . 32 3.3 SUSY in the SM . 33 3.4 Natural SUSY and simplified signal models . 38 3.5 SUSY searches in LHC Run I . 40 3.6 SUSY at 13 TeV . 44 3.7 Razor kinematic variables . 44 II Towards Trigger-Based Searches: Data Scouting 50 Chapter IV: Data scouting trigger development . 51 4.1 Motivation: limits to CMS event processing . 51 4.2 The data scouting paradigm . 53 4.3 History of data scouting in CMS . 55 4.4 Design of a multipurpose scouting framework for Run II . 58 4.5 Data parking and its relationship with scouting . 66 4.6 Scouting and parking evolution in 2015-2017 . 67 4.7 Design of a new di-muon scouting trigger for 2017 . 72 Chapter V: Impact of data scouting . 74 5.1 Dijet resonance searches with Run II scouting data . 74 5.2 Planned hadronic scouting searches . 79 5.3 Dark photon searches using scouting triggers . 83 viii 5.4 Outlook for the future . 85 III Supersymmetry Searches using Razor Variables 87 Chapter VI: Fit-based razor search on 2015 data . 88 6.1 Motivation for the fit-based razor analysis . 88 6.2 Razor fit function . 89 6.3 Sideband fits and full fits . 92 6.4 Razor triggers . 93 6.5 Event selection and categorization . 96 6.6 Validation of the fit . 102 6.7 Signal region predictions and uncertainties . 106 Chapter VII: Monte Carlo-based razor search on 2015 data . 113 7.1 Paradigm for MC-assisted, data-driven search . 113 7.2 Primary background processes . 114 7.3 MC simulation and reweighting . 115 7.4 Estimation of tt¯+jets and W(! `ν)+jets backgrounds . 117 7.5 Estimation of Z(! νν)+jets background . 126 7.6 Estimation of QCD multijet background . 134 7.7 Systematic uncertainties . 138 7.8 Comparison with the fit-based method . 141 7.9 Search region results .
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