Adding 2022 Water Cherenkov generative network paper arXiv:2202.01276

HEPML.bib

@@ -11512,6 +11512,20 @@ @article{Mallick:2022alr
     year = "2022"
 }
 
+% June 17, 2022
+@article{Jia:2022ulh,
+    author = "Jia, Mo and Kumar, Karan and Mackey, Liam S. and Putra, Alexander and Vilela, Cristovao and Wilking, Michael J. and Xia, Junjie and Yanagisawa, Chiaki and Yang, Karan",
+    title = "{Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks}",
+    eprint = "2202.01276",
+    archivePrefix = "arXiv",
+    primaryClass = "hep-ex",
+    doi = "10.3389/fdata.2022.868333",
+    journal = "Front. Big Data",
+    volume = "5",
+    pages = "868333",
+    year = "2022"
+}
+
 % 2022-06-16
 @article{Alvi:2022fkk,
     author = "Alvi, Sulaiman and Bauer, Christian and Nachman, Benjamin",

HEPML.tex

@@ -179,7 +179,7 @@
 		\\\textit{These approaches learn the density or perform generative modeling using transformer-based networks.}
 		\item \textbf{Physics-inspired}~\cite{Abasov:2024hyq,Larkoski:2023xam,Barenboim:2021vzh,Lai:2020byl,1808876,Andreassen:2019txo,Andreassen:2018apy}
 		\\\textit{A variety of methods have been proposed to use machine learning tools (e.g. neural networks) combined with physical components.}
-		\item \textbf{Mixture Models}~\cite{Vermunt:2023fsr,Liu:2022dem,Graziani:2021vai,Burton:2021tsd,Chen:2020uds}
+		\item \textbf{Mixture Models}~\cite{Vermunt:2023fsr,Liu:2022dem,Jia:2022ulh,Graziani:2021vai,Burton:2021tsd,Chen:2020uds}
 		\\\textit{A mixture model is a superposition of simple probability densities.  For example, a Gaussian mixture model is a sum of normal probability densities.  Mixture density networks are mixture models where the coefficients in front of the constituent densities as well as the density parameters (e.g. mean and variances of Gaussians) are parameterized by neural networks.}
 		\item \textbf{Phase space generation}~\cite{Deutschmann:2024lml,Calisto:2023vmm,Singh:2023yvj,Renteria-Estrada:2023buo,Heimel:2022wyj,Jinno:2022sbr,Maitre:2022xle,Yoon:2020zmb,Danziger:2021eeg,Backes:2020vka,Verheyen:2020bjw,Chen:2020nfb,Nachman:2020fff,Carrazza:2020rdn,Klimek:2018mza,Gao:2020vdv,Gao:2020zvv,Bothmann:2020ywa,Bendavid:2017zhk}
 		\\\textit{Monte Carlo event generators integrate over a phase space that needs to be generated efficiently and this can be aided by machine learning methods.}

README.md

@@ -1650,6 +1650,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 
 * [Mapping QGP properties in Pb--Pb and Xe--Xe collisions at the LHC](https://arxiv.org/abs/2308.16722) [[DOI](https://doi.org/10.1103/PhysRevC.108.064908)] (2023)
 * [Geometry-aware Autoregressive Models for Calorimeter Shower Simulations](https://arxiv.org/abs/2212.08233) (2022)
+* [Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks](https://arxiv.org/abs/2202.01276) [[DOI](https://doi.org/10.3389/fdata.2022.868333)] (2022)
 * [A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme](https://arxiv.org/abs/2110.10259) [[DOI](https://doi.org/10.1088/1748-0221/17/02/P02018)] (2021)
 * [Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples](https://arxiv.org/abs/2103.13416) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09469-y)] (2021)
 * [Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning](https://arxiv.org/abs/2010.01835) (2020)

docs/index.md

@@ -1811,6 +1811,7 @@ const expandElements = shouldExpand => {
 
     * [Mapping QGP properties in Pb--Pb and Xe--Xe collisions at the LHC](https://arxiv.org/abs/2308.16722) [[DOI](https://doi.org/10.1103/PhysRevC.108.064908)] (2023)
     * [Geometry-aware Autoregressive Models for Calorimeter Shower Simulations](https://arxiv.org/abs/2212.08233) (2022)
+    * [Maximum Likelihood Reconstruction of Water Cherenkov Events With Deep Generative Neural Networks](https://arxiv.org/abs/2202.01276) [[DOI](https://doi.org/10.3389/fdata.2022.868333)] (2022)
     * [A Neural-Network-defined Gaussian Mixture Model for particle identification applied to the LHCb fixed-target programme](https://arxiv.org/abs/2110.10259) [[DOI](https://doi.org/10.1088/1748-0221/17/02/P02018)] (2021)
     * [Mixture Density Network Estimation of Continuous Variable Maximum Likelihood Using Discrete Training Samples](https://arxiv.org/abs/2103.13416) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09469-y)] (2021)
     * [Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning](https://arxiv.org/abs/2010.01835) (2020)