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--- research_details [2022/05/24 01:23]
jthaler [Quantum Computation for Colliders]
+++ research_details [2022/11/16 21:49] (current)
jthaler [2011 CMS Open Data]
@@ Line 18: / Line 18: @@
 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.
-  * **Bias and Priors in Machine Learning Calibrations for High Energy Physics**. \\ Rikab Gambhir, Benjamin Nachman, Jesse Thaler. \\ [[https://arxiv.org/abs/2205.05084|arXiv:2205.05084]].
+  * **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]].
-  * **Learning Uncertainties the Frequentist Way: Calibration and Correlation in High Energy Physics**. \\ Rikab Gambhir, Benjamin Nachman, Jesse Thaler. \\ [[https://arxiv.org/abs/2205.03413|arXiv:2205.03413]].
+  * **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. \\ [[https://arxiv.org/abs/2112.05722|arXiv:2112.05722]].
+  * **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]].
@@ Line 49: / Line 49: @@
 ===== Quantum Computation for Colliders =====
-  * **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, Jesse Thaler. \\ [[https://arxiv.org/abs/2205.10375|arXiv:2205.10375]].
+  * **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, Jesse Thaler. \\ [[https://arxiv.org/abs/2205.02814|arXiv:2205.02814]].
+  * **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]].
@@ Line 60: / Line 60: @@
 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.
-  * **Power Counting Energy Flow Polynomials**. \\ Pedro Cal, Jesse Thaler, Wouter J. Waalewijn. \\ [[https://arxiv.org/abs/2205.06818|arXiv:2205.06818]].
+  * **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]].
@@ Line 153: / Line 153: @@
 ===== 2011 CMS Open Data ====
-  * **Disentangling Quarks and Gluons with CMS Open Data**. \\ Patrick T. Komiske, Serhii Kryhin, Jesse Thaler. \\ [[https://arxiv.org/abs/2205.04459|arXiv:2205.04459]].
+  * **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, Hua Xing Zhu. \\ [[https://arxiv.org/abs/2205.02857|arXiv:2205.02857]].
+  * **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]].