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research_details [2022/01/21 01:19]
jthaler [2011 CMS Open Data]
research_details [2022/11/16 21:49] (current)
jthaler [2011 CMS Open Data]
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 Machine learning has impacted many scientific fields, and particle physics is no exception. ​ In my research, I aim to enhance the search for new phenomena at colliders by merging the performance of deep learning algorithms with the robustness of "deep thinking"​ approaches. Machine learning has impacted many scientific fields, and particle physics is no exception. ​ In my research, I aim to enhance the search for new phenomena at colliders by merging the performance of deep learning algorithms with the robustness of "deep thinking"​ approaches.
  
-  * **SymmetryGAN:​ Symmetry Discovery with Deep Learning**. \\ Krish Desai, Benjamin Nachman, Jesse Thaler. \\ [[https://​arxiv.org/​abs/​2112.05722|arXiv:2112.05722]].+  * **Bias and Priors in Machine ​Learning ​Calibrations for High Energy Physics**. \\ Rikab Gambhir, Benjamin Nachman, ​and Jesse Thaler. \\ [[https://​doi.org/​10.1103/​PhysRevD.106.036011|Phys. Rev. D106:036011 (2022)]], ​[[https://​arxiv.org/​abs/​2205.05084|arXiv:2205.05084]].
  
-  * **Neural Conditional Reweighting**. \\ Benjamin Nachman and Jesse Thaler. \\ [[https://​arxiv.org/​abs/​2107.08979|arXiv:​2107.08979]].+  ​* **Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics**. \\ Rikab Gambhir, Benjamin Nachman, and Jesse Thaler. \\ [[https://​doi.org/​10.1103/​PhysRevLett.129.082001|Phys. Rev. Lett. 129:082001 (2022)]], [[https://​arxiv.org/​abs/​2205.03413|arXiv:​2205.03413]].  
 + 
 +  * **SymmetryGAN:​ Symmetry Discovery with Deep Learning**. \\ Krish Desai, Benjamin Nachman, Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.105.096031|Phys. Rev. D105:096031 (2022)]], [[https://​arxiv.org/​abs/​2112.05722|arXiv:​2112.05722]]. 
 + 
 +  ​* **Neural Conditional Reweighting**. \\ Benjamin Nachman and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.105.076015|Phys. Rev. D105:076015 (2022)]], ​[[https://​arxiv.org/​abs/​2107.08979|arXiv:​2107.08979]].
  
   * **E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once**. \\ Benjamin Nachman and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.103.116013|Phys. Rev. D103:116013 (2021)]], [[https://​arxiv.org/​abs/​2101.07263|arXiv:​2101.07263]].   * **E Pluribus Unum Ex Machina: Learning from Many Collider Events at Once**. \\ Benjamin Nachman and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.103.116013|Phys. Rev. D103:116013 (2021)]], [[https://​arxiv.org/​abs/​2101.07263|arXiv:​2101.07263]].
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 ===== Quantum Computation for Colliders ===== ===== Quantum Computation for Colliders =====
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 +  * **Degeneracy Engineering for Classical and Quantum Annealing: A Case Study of Sparse Linear Regression in Collider Physics**. \\ Eric R. Anschuetz, Lena Funcke, Patrick T. Komiske, Serhii Kryhin, and Jesse Thaler. \\ [[https://​doi.org/​10.1103/​PhysRevD.106.056008|Phys. Rev. D106:056008 (2022)]], [[https://​arxiv.org/​abs/​2205.10375|arXiv:​2205.10375]].
 +
 +  * **Quantum Annealing for Jet Clustering with Thrust**. \\ Andrea Delgado and Jesse Thaler. \\ [[https://​doi.org/​10.1103/​physrevd.106.094016|Phys. Rev. D106:094016 (2022)]], [[https://​arxiv.org/​abs/​2205.02814|arXiv:​2205.02814]].
  
   * **Quantum Algorithms for Jet Clustering**. ​ \\ Annie Y. Wei, Preksha Naik, Aram W. Harrow, and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.101.094015|Phys. Rev. D101:094015 (2020)]], [[https://​arxiv.org/​abs/​1908.08949|arXiv:​1908.08949]].   * **Quantum Algorithms for Jet Clustering**. ​ \\ Annie Y. Wei, Preksha Naik, Aram W. Harrow, and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.101.094015|Phys. Rev. D101:094015 (2020)]], [[https://​arxiv.org/​abs/​1908.08949|arXiv:​1908.08949]].
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 Jets are collimated sprays of particles arising from the fragmentation of short-distance quarks and gluons. ​ In traditional collider studies, these jets are reconstructed using jet algorithms, which assign clusters of particles to jet four-vectors. ​ I have shown that the substructure of jets can provide valuable information about the underlying short-distance physics. ​ In extreme cases, physics that would otherwise be unobservable using traditional jet algorithms can be made prominent through jet substructure techniques. Jets are collimated sprays of particles arising from the fragmentation of short-distance quarks and gluons. ​ In traditional collider studies, these jets are reconstructed using jet algorithms, which assign clusters of particles to jet four-vectors. ​ I have shown that the substructure of jets can provide valuable information about the underlying short-distance physics. ​ In extreme cases, physics that would otherwise be unobservable using traditional jet algorithms can be made prominent through jet substructure techniques.
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 +  * **Power Counting Energy Flow Polynomials**. \\ Pedro Cal, Jesse Thaler, and Wouter J. Waalewijn. \\ [[https://​doi.org/​10.1007/​JHEP09(2022)021|JHEP 2209:021 (2022)]], [[https://​arxiv.org/​abs/​2205.06818|arXiv:​2205.06818]].
  
   * **Cutting Multiparticle Correlators Down to Size**. \\ Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.101.036019|Phys. Rev. D101:036019 (2020)]], [[https://​arxiv.org/​abs/​1911.04491|arXiv:​1911.04491]].   * **Cutting Multiparticle Correlators Down to Size**. \\ Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.101.036019|Phys. Rev. D101:036019 (2020)]], [[https://​arxiv.org/​abs/​1911.04491|arXiv:​1911.04491]].
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 ===== 2011 CMS Open Data ==== ===== 2011 CMS Open Data ====
  
-    ​* **Analyzing N-point Energy Correlators Inside Jets with CMS Open Data**. \\  Patrick T. Komiske, Ian Moult, Jesse Thaler, Hua Xing Zhu. \\ [[https://​arxiv.org/​abs/​2201.07800|arXiv:​2201.07800]].+  * **Disentangling Quarks and Gluons with CMS Open Data**. \\ Patrick T. Komiske, Serhii Kryhin, and Jesse Thaler. \\ [[https://​doi.org/​10.1103/​PhysRevD.106.056008|Phys Rev D106:056008 (2022)]], [[https://​arxiv.org/​abs/​2205.04459|arXiv:​2205.04459]]. 
 + 
 +  * **Non-Gaussianities in Collider Energy Flux**. \\ Hao Chen, Ian Moult, Jesse Thaler, and Hua Xing Zhu. \\ [[https://​dx.doi.org/​10.1007/​JHEP07(2022)146|JHEP 2207:146 (2022)]], [[https://​arxiv.org/​abs/​2205.02857|arXiv:​2205.02857]]. 
 + 
 +  ​* **Analyzing N-point Energy Correlators Inside Jets with CMS Open Data**. \\  Patrick T. Komiske, Ian Moult, Jesse Thaler, Hua Xing Zhu. \\ [[https://​arxiv.org/​abs/​2201.07800|arXiv:​2201.07800]].
  
   * **Exploring the Space of Jets with CMS Open Data**. \\ Patrick T. Komiske, Radha Mastandrea, Eric M. Metodiev, Preksha Naik, and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.101.034009|Phys. Rev. D101:034009 (2020)]], [[https://​arxiv.org/​abs/​1908.08542|arXiv:​1908.08542]].   * **Exploring the Space of Jets with CMS Open Data**. \\ Patrick T. Komiske, Radha Mastandrea, Eric M. Metodiev, Preksha Naik, and Jesse Thaler. \\ [[http://​dx.doi.org/​10.1103/​PhysRevD.101.034009|Phys. Rev. D101:034009 (2020)]], [[https://​arxiv.org/​abs/​1908.08542|arXiv:​1908.08542]].
research_details.1642727998.txt.gz · Last modified: 2022/01/21 01:19 by jthaler