Add CALPAGAN arXiv:2401.02248

HEPML.bib

@@ -1312,6 +1312,21 @@ @article{Fanelli:2024wrj
     year = "2024"
 }
 
+% July 10, 2024
+@article{Simsek:2024zhj,
+    author = "Simsek, Ebru and Isildak, Bora and Dogru, Anil and Aydogan, Reyhan and Bayrak, Aydogan Burak and Ertekin, Seyda",
+    title = "{CALPAGAN: Calorimetry for Particles Using Generative Adversarial Networks}",
+    eprint = "2401.02248",
+    archivePrefix = "arXiv",
+    primaryClass = "hep-ex",
+    doi = "10.1093/ptep/ptae106",
+    journal = "PTEP",
+    volume = "2024",
+    number = "8",
+    month = "7",
+    year = "2024"
+}
+
 % July 10, 2024
 @article{Tani:2024qzm,
     author = "Tani, Laurits and Seeba, Nalong-Norman and Vanaveski, Hardi and Pata, Joosep and Lange, Torben",

HEPML.tex

@@ -167,7 +167,7 @@
 \item \textbf{Generative models / density estimation}
 \\\textit{The goal of generative modeling is to learn (explicitly or implicitly) a probability density $p(x)$ for the features $x\in\mathbb{R}^n$.  This task is usually unsupervised (no labels).}
 	\begin{itemize}
-		\item \textbf{GANs}~\cite{Krause:2024avx,Kach:2024yxi,Wojnar:2024cbn,Dooney:2024pvt,Chan:2023icm,Scham:2023usu,Scham:2023cwn,FaucciGiannelli:2023fow,Erdmann:2023ngr,Barbetti:2023bvi,Alghamdi:2023emm,Dubinski:2023fsy,Chan:2023ume,Diefenbacher:2023prl,EXO:2023pkl,Hashemi:2023ruu,Yue:2023uva,Buhmann:2023pmh,Anderlini:2022hgm,ATLAS:2022jhk,Rogachev:2022hjg,Ratnikov:2022hge,Anderlini:2022ckd,Ghosh:2022zdz,Bieringer:2022cbs,Buhmann:2021caf,Desai:2021wbb,Chisholm:2021pdn,Anderlini:2021qpm,Bravo-Prieto:2021ehz,Li:2021cbp,Mu:2021nno,Khattak:2021ndw,NEURIPS2020_a878dbeb,Kansal:2021cqp,Winterhalder:2021ave,Lebese:2021foi,Rehm:2021qwm,Carrazza:2021hny,Rehm:2021zoz,Rehm:2021zow,Choi:2021sku,Lai:2020byl,Maevskiy:2020ank,Kansal:2020svm,2008.06545,Diefenbacher:2020rna,Alanazi:2020jod,buhmann2020getting,Wang:2020tap,Belayneh:2019vyx,Hooberman:DLPS2017,Farrell:2019fsm,deOliveira:2017rwa,Oliveira:DLPS2017,Urban:2018tqv,Erdmann:2018jxd,Erbin:2018csv,Derkach:2019qfk,Deja:2019vcv,Erdmann:2018kuh,Musella:2018rdi,Datta:2018mwd,Vallecorsa:2018zco,Carminati:2018khv,Zhou:2018ill,ATL-SOFT-PUB-2018-001,Chekalina:2018hxi,Hashemi:2019fkn,DiSipio:2019imz,Lin:2019htn,Butter:2019cae,Carrazza:2019cnt,SHiP:2019gcl,Vallecorsa:2019ked,Bellagente:2019uyp,Martinez:2019jlu,Butter:2019eyo,Alonso-Monsalve:2018aqs,Paganini:2017dwg,Paganini:2017hrr,deOliveira:2017pjk}
+		\item \textbf{GANs}~\cite{Krause:2024avx,Kach:2024yxi,Wojnar:2024cbn,Dooney:2024pvt,Simsek:2024zhj,Chan:2023icm,Scham:2023usu,Scham:2023cwn,FaucciGiannelli:2023fow,Erdmann:2023ngr,Barbetti:2023bvi,Alghamdi:2023emm,Dubinski:2023fsy,Chan:2023ume,Diefenbacher:2023prl,EXO:2023pkl,Hashemi:2023ruu,Yue:2023uva,Buhmann:2023pmh,Anderlini:2022hgm,ATLAS:2022jhk,Rogachev:2022hjg,Ratnikov:2022hge,Anderlini:2022ckd,Ghosh:2022zdz,Bieringer:2022cbs,Buhmann:2021caf,Desai:2021wbb,Chisholm:2021pdn,Anderlini:2021qpm,Bravo-Prieto:2021ehz,Li:2021cbp,Mu:2021nno,Khattak:2021ndw,NEURIPS2020_a878dbeb,Kansal:2021cqp,Winterhalder:2021ave,Lebese:2021foi,Rehm:2021qwm,Carrazza:2021hny,Rehm:2021zoz,Rehm:2021zow,Choi:2021sku,Lai:2020byl,Maevskiy:2020ank,Kansal:2020svm,2008.06545,Diefenbacher:2020rna,Alanazi:2020jod,buhmann2020getting,Wang:2020tap,Belayneh:2019vyx,Hooberman:DLPS2017,Farrell:2019fsm,deOliveira:2017rwa,Oliveira:DLPS2017,Urban:2018tqv,Erdmann:2018jxd,Erbin:2018csv,Derkach:2019qfk,Deja:2019vcv,Erdmann:2018kuh,Musella:2018rdi,Datta:2018mwd,Vallecorsa:2018zco,Carminati:2018khv,Zhou:2018ill,ATL-SOFT-PUB-2018-001,Chekalina:2018hxi,Hashemi:2019fkn,DiSipio:2019imz,Lin:2019htn,Butter:2019cae,Carrazza:2019cnt,SHiP:2019gcl,Vallecorsa:2019ked,Bellagente:2019uyp,Martinez:2019jlu,Butter:2019eyo,Alonso-Monsalve:2018aqs,Paganini:2017dwg,Paganini:2017hrr,deOliveira:2017pjk}
 		\\\textit{Generative Adversarial Networks~\cite{Goodfellow:2014upx} learn $p(x)$ implicitly through the minimax optimization of two networks: one that maps noise to structure $G(z)$ and one a classifier (called the discriminator) that learns to distinguish examples generated from $G(z)$ and those generated from the target process.  When the discriminator is maximally `confused', then the generator is effectively mimicking $p(x)$.}
 		\item \textbf{(Variational) Autoencoders}~\cite{Smith:2024lxz,Krause:2024avx,Liu:2024kvv,Kuh:2024lgx,Hoque:2023zjt,Zhang:2023khv,Chekanov:2023uot,Lasseri:2023dhi,Anzalone:2023ugq,Roche:2023int,Cresswell:2022tof,AbhishekAbhishek:2022wby,Collins:2022qpr,Ilten:2022jfm,Touranakou:2022qrp,Buhmann:2021caf,Tsan:2021brw,Jawahar:2021vyu,Orzari:2021suh,Collins:2021pld,Fanelli:2019qaq,Hariri:2021clz,deja2020endtoend,Bortolato:2021zic,Buhmann:2021lxj,Howard:2021pos,1816035,Cheng:2020dal,ATL-SOFT-PUB-2018-001,Monk:2018zsb}
 		\\\textit{An autoencoder consists of two functions: one that maps $x$ into a latent space $z$ (encoder) and a second one that maps the latent space back into the original space (decoder).  The encoder and decoder are simultaneously trained so that their composition is nearly the identity.  When the latent space has a well-defined probability density (as in variational autoencoders), then one can sample from the autoencoder by applying the detector to a randomly chosen element of the latent space.}

README.md

@@ -1387,6 +1387,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A
 * [Pay Attention To Mean Fields For Point Cloud Generation](https://arxiv.org/abs/2408.04997) (2024)
 * [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704) (2024)
 * [cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation](https://arxiv.org/abs/2401.16356) [[DOI](https://doi.org/10.1103/PhysRevD.110.022004)] (2024)
+* [CALPAGAN: Calorimetry for Particles Using Generative Adversarial Networks](https://arxiv.org/abs/2401.02248) [[DOI](https://doi.org/10.1093/ptep/ptae106)] (2024)
 * [Integrating Particle Flavor into Deep Learning Models for Hadronization](https://arxiv.org/abs/2312.08453) (2023)
 * [DeepTreeGANv2: Iterative Pooling of Point Clouds](https://arxiv.org/abs/2312.00042) (2023)
 * [DeepTreeGAN: Fast Generation of High Dimensional Point Clouds](https://arxiv.org/abs/2311.12616) [[DOI](https://doi.org/10.1051/epjconf/202429509010)] (2023)

docs/index.md

@@ -1518,6 +1518,7 @@ const expandElements = shouldExpand => {
     * [Pay Attention To Mean Fields For Point Cloud Generation](https://arxiv.org/abs/2408.04997) (2024)
     * [Applying generative neural networks for fast simulations of the ALICE (CERN) experiment](https://arxiv.org/abs/2407.16704) (2024)
     * [cDVGAN: One Flexible Model for Multi-class Gravitational Wave Signal and Glitch Generation](https://arxiv.org/abs/2401.16356) [[DOI](https://doi.org/10.1103/PhysRevD.110.022004)] (2024)
+    * [CALPAGAN: Calorimetry for Particles Using Generative Adversarial Networks](https://arxiv.org/abs/2401.02248) [[DOI](https://doi.org/10.1093/ptep/ptae106)] (2024)
     * [Integrating Particle Flavor into Deep Learning Models for Hadronization](https://arxiv.org/abs/2312.08453) (2023)
     * [DeepTreeGANv2: Iterative Pooling of Point Clouds](https://arxiv.org/abs/2312.00042) (2023)
     * [DeepTreeGAN: Fast Generation of High Dimensional Point Clouds](https://arxiv.org/abs/2311.12616) [[DOI](https://doi.org/10.1051/epjconf/202429509010)] (2023)