From 48afc08cbceee06a000d4dda7dda18e0c6640eec Mon Sep 17 00:00:00 2001 From: Ramon Winterhalder Date: Wed, 6 Nov 2024 14:50:37 +0100 Subject: [PATCH] Fix some trailing curly braces linked to issue 183 --- HEPML.bib | 162 +++++++++++++++++++++++--------------------------- HEPML.tex | 8 +-- README.md | 10 ++-- docs/index.md | 10 ++-- make_md.py | 2 +- 5 files changed, 90 insertions(+), 102 deletions(-) diff --git a/HEPML.bib b/HEPML.bib index b5cdc10..19bbc3a 100644 --- a/HEPML.bib +++ b/HEPML.bib @@ -1224,7 +1224,7 @@ @article{Wu:2024thh author = "Wu, Yifan and Wang, Kun and Zhu, Jingya", title = "{Jet Tagging with More-Interaction Particle Transformer}", eprint = "2407.08682", - doi="10.1088/1674-1137/ad7f3d", + doi= {10.1088/1674-1137/ad7f3d}, archivePrefix = "arXiv", primaryClass = "hep-ph", month = "7", @@ -17095,21 +17095,20 @@ @article{1816035 } %September 8 - -@article{2009.03796, - author="S. Diefenbacher and E. Eren and G. Kasieczka and A. Korol and B. Nachman and D. Shih", - title="{DCTRGAN: Improving the Precision of Generative Models with Reweighting}", - eprint="2009.03796", - archivePrefix = "arXiv", - primaryClass = "hep-ph", - journal = "Journal of Instrumentation", - volume = "15", - pages = "P11004", - doi = {10.1088/1748-0221/15/11/p11004}, - year = "2020", +@article{Diefenbacher:2020rna, + author = "Diefenbacher, Sascha and Eren, Engin and Kasieczka, Gregor and Korol, Anatolii and Nachman, Benjamin and Shih, David", + title = "{DCTRGAN: Improving the Precision of Generative Models with Reweighting}", + eprint = "2009.03796", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + doi = "10.1088/1748-0221/15/11/P11004", + journal = "JINST", + volume = "15", + number = "11", + pages = "P11004", + year = "2020" } - %September 6 @article{1815227, @@ -17530,23 +17529,17 @@ @article{Yu:2020wxu year = "2021" } -@article{1808887, - title="{Graph neural networks in particle physics}", - volume={2}, - ISSN={2632-2153}, - url={http://dx.doi.org/10.1088/2632-2153/abbf9a}, - doi={10.1088/2632-2153/abbf9a}, - number={2}, - journal={Machine Learning: Science and Technology}, - publisher={IOP Publishing}, - author={Shlomi, Jonathan and Battaglia, Peter and Vlimant, Jean-Roch}, - year={2021}, - month={Jan}, - pages={021001}, - eprint = "2007.13681" +@article{Shlomi:2020gdn, + author = "Shlomi, Jonathan and Battaglia, Peter and Vlimant, Jean-Roch", + title = "{Graph Neural Networks in Particle Physics}", + eprint = "2007.13681", + archivePrefix = "arXiv", + primaryClass = "hep-ex", + doi = "10.1088/2632-2153/abbf9a", + month = "7", + year = "2020" } - %July 23 @article{Nachman:2020fff, author="B. Nachman and J. Thaler", @@ -18268,19 +18261,17 @@ @article{Dolen:2016kst } @article{Moult:2017okx, - Archiveprefix = {arXiv}, - Author = {Moult, Ian and Nachman, Benjamin and Neill, Duff}, - Doi = {10.1007/JHEP05(2018)002}, - Eprint = {1710.06859}, - Journal = {JHEP}, - Pages = {002}, - Primaryclass = {hep-ph}, - Slaccitation = {%%CITATION = ARXIV:1710.06859;%%}, - title = "{Convolved Substructure: Analytically Decorrelating Jet Substructure Observables}", - Volume = {05}, - Year = {2018}, - Bdsk-Url-1 = {http://dx.doi.org/10.1007/JHEP05(2018)002} - } + author = "Moult, Ian and Nachman, Benjamin and Neill, Duff", + title = "{Convolved Substructure: Analytically Decorrelating Jet Substructure Observables}", + eprint = "1710.06859", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + doi = "10.1007/JHEP05(2018)002", + journal = "JHEP", + volume = "05", + pages = "002", + year = "2018" +} @article{Sirunyan:2019wwa, author = "{CMS Collaboration}", @@ -18773,36 +18764,33 @@ @misc{sleuth title = "{A Quasi-Model-Independent Search for New High $p_T$ Physics at D0}", url={https://www-d0.fnal.gov/results/publications_talks/thesis/knuteson/thesis.ps}, Note = {Ph.D. thesis, University of California at Berkeley (2000)}} -@article{Abbott:2000fb, - Archiveprefix = {arXiv}, - Author = {Abbott, B. and others}, - Collaboration = {D0}, - Doi = {10.1103/PhysRevD.62.092004}, - Eprint = {hep-ex/0006011}, - Journal = {Phys. Rev.}, - Pages = {092004}, - Primaryclass = {hep-ex}, - Reportnumber = {FERMILAB-PUB-00-126-E}, - Slaccitation = {%%CITATION = HEP-EX/0006011;%%}, - title = "{Search for new physics in $e\mu X$ data at D\O\ using Sherlock: A quasi model independent search strategy for new physics}", - Volume = {D62}, - Year = {2000}, - Bdsk-Url-1 = {http://dx.doi.org/10.1103/PhysRevD.62.092004}} - -@article{Abbott:2000gx, - Archiveprefix = {arXiv}, - Author = "{D0 Collaboration}", - Doi = {10.1103/PhysRevD.64.012004}, - Eprint = {hep-ex/0011067}, - Journal = {Phys. Rev.}, - Pages = {012004}, - Primaryclass = {hep-ex}, - Reportnumber = {FERMILAB-PUB-00-302-E}, - Slaccitation = {%%CITATION = HEP-EX/0011067;%%}, - title = "{A Quasi model independent search for new physics at large transverse momentum}", - Volume = {D64}, - Year = {2001}, - Bdsk-Url-1 = {http://dx.doi.org/10.1103/PhysRevD.64.012004}} + + @article{D0:2000vuh, + author = "{D0 Collaboration}", + title = "{Search for new physics in e\ensuremath{\mu}X data at D\O{} using SLEUTH: A quasi-model-independent search strategy for new physics}", + eprint = "hep-ex/0006011", + archivePrefix = "arXiv", + reportNumber = "FERMILAB-PUB-00-126-E", + doi = "10.1103/PhysRevD.62.092004", + journal = "Phys. Rev. D", + volume = "62", + pages = "092004", + year = "2000" +} + +@article{D0:2000dnz, + author = "{D0 Collaboration}", + title = "{A Quasi model independent search for new physics at large transverse momentum}", + eprint = "hep-ex/0011067", + archivePrefix = "arXiv", + reportNumber = "FERMILAB-PUB-00-302-E", + doi = "10.1103/PhysRevD.64.012004", + journal = "Phys. Rev. D", + volume = "64", + pages = "012004", + year = "2001" +} + @article{Abbott:2001ke, author = "{D0 Collaboration}", title = "{A quasi-model-independent search for new high $p_T$ physics at D\O}", @@ -18831,20 +18819,20 @@ @article{Aaron:2008aa reportNumber = "DESY-08-173", SLACcitation = "%%CITATION = ARXIV:0901.0507;%%" } -@article{Aktas:2004pz, - Archiveprefix = {arXiv}, - Author = "{H1 Collaboration}", - Doi = {10.1016/j.physletb.2004.09.057}, - Eprint = {hep-ex/0408044}, - Journal = {Phys. Lett.}, - Pages = {14-30}, - Primaryclass = {hep-ex}, - Reportnumber = {DESY-04-140}, - Slaccitation = {%%CITATION = HEP-EX/0408044;%%}, - title = "{A General search for new phenomena in ep scattering at HERA}", - Volume = {B602}, - Year = {2004}, - Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.physletb.2004.09.057}} + +@article{H1:2004rlm, + author = "{H1 Collaboration}", + title = "{A General search for new phenomena in ep scattering at HERA}", + eprint = "hep-ex/0408044", + archivePrefix = "arXiv", + reportNumber = "DESY-04-140", + doi = "10.1016/j.physletb.2004.09.057", + journal = "Phys. Lett. B", + volume = "602", + pages = "14--30", + year = "2004" +} + @phdthesis{Cranmer:2005zn, author = "Cranmer, Kyle S.", title = "{Searching for new physics: Contributions to LEP and the LHC}", @@ -18882,7 +18870,7 @@ @article{Aaltonen:2007ab @article{Aaltonen:2008vt, Archiveprefix = {arXiv}, Author = "{CDF Collaboration}", - Doi = {10.1103/PhysRevD.79.011101}, + Doi = "10.1103/PhysRevD.79.011101", Eprint = {0809.3781}, Journal = {Phys. Rev.}, Pages = {011101}, diff --git a/HEPML.tex b/HEPML.tex index 17dca45..c0d311b 100644 --- a/HEPML.tex +++ b/HEPML.tex @@ -43,7 +43,7 @@ \\\textit{Below are links to many (static) general and specialized reviews. The third bullet contains links to classic papers that applied shallow learning methods many decades before the deep learning revolution.} \begin{itemize} \item Modern reviews~\cite{Larkoski:2017jix,Guest:2018yhq,Albertsson:2018maf,Radovic:2018dip,Carleo:2019ptp,Bourilkov:2019yoi,Schwartz:2021ftp,Karagiorgi:2021ngt,Boehnlein:2021eym,Shanahan:2022ifi} - \item Specialized reviews~\cite{Kasieczka:2019dbj,1807719,1808887,Psihas:2020pby,Butter:2020tvl,Forte:2020yip,Brehmer:2020cvb,Nachman:2020ccu,Duarte:2020ngm,Vlimant:2020enz,Cranmer:2019eaq,Rousseau:2020rnz,Kagan:2020yrm,Guan:2020bdl,deLima:2021fwm,Alanazi:2021grv,Baldi:2022okj,Viren:2022qon,Bogatskiy:2022hub,Butter:2022rso,Dvorkin:2022pwo,Adelmann:2022ozp,Thais:2022iok,Harris:2022qtm,Coadou:2022nsh,Benelli:2022sqn,Chen:2022pzc,Plehn:2022ftl,Cheng:2022idp,Huerta:2022kgj,Huber:2022lpm,Zhou:2023pti,DeZoort:2023vrm,Du:2023qst,Allaire:2023fgp,Hashemi:2023rgo,Belis:2023mqs,Araz:2023mda,Gooding:2024wpi,Kheddar:2024osf,Bardhan:2024zla,Mondal:2024nsa,Huetsch:2024quz,Ahmad:2024dql,Barman:2024wfx,Larkoski:2024uoc,Halverson:2024hax} + \item Specialized reviews~\cite{Kasieczka:2019dbj,1807719,Shlomi:2020gdn,Psihas:2020pby,Butter:2020tvl,Forte:2020yip,Brehmer:2020cvb,Nachman:2020ccu,Duarte:2020ngm,Vlimant:2020enz,Cranmer:2019eaq,Rousseau:2020rnz,Kagan:2020yrm,Guan:2020bdl,deLima:2021fwm,Alanazi:2021grv,Baldi:2022okj,Viren:2022qon,Bogatskiy:2022hub,Butter:2022rso,Dvorkin:2022pwo,Adelmann:2022ozp,Thais:2022iok,Harris:2022qtm,Coadou:2022nsh,Benelli:2022sqn,Chen:2022pzc,Plehn:2022ftl,Cheng:2022idp,Huerta:2022kgj,Huber:2022lpm,Zhou:2023pti,DeZoort:2023vrm,Du:2023qst,Allaire:2023fgp,Hashemi:2023rgo,Belis:2023mqs,Araz:2023mda,Gooding:2024wpi,Kheddar:2024osf,Bardhan:2024zla,Mondal:2024nsa,Huetsch:2024quz,Ahmad:2024dql,Barman:2024wfx,Larkoski:2024uoc,Halverson:2024hax} \item Classical papers~\cite{Denby:1987rk,Lonnblad:1990bi} \item Datasets~\cite{Kasieczka:2021xcg,Aarrestad:2021oeb,Benato:2021olt,Govorkova:2021hqu,Chen:2021euv,Qu:2022mxj,Eller:2023myr,Rusack:2023pob,Zoch:2024eyp} \end{itemize} @@ -63,7 +63,7 @@ \\\textit{Data that have a variable with a particular order may be represented as a sequence. Recurrent neural networks are natural tools for processing sequence data. } \item \textbf{Trees}~\cite{Louppe:2017ipp,Cheng:2017rdo,Jercic:2021bfc,Dutta:2023jbz,Belfkir:2023vpo,Finke:2023ltw,Matousek:2024vpa,Choudhury:2024crp} \\\textit{Recursive neural networks are natural tools for processing data in a tree structure.} - \item \textbf{Graphs}~\cite{Henrion:DLPS2017,Ju:2020xty,Abdughani:2018wrw,Martinez:2018fwc,Ren:2019xhp,Moreno:2019bmu,Qasim:2019otl,Chakraborty:2019imr,DiBello:2020bas,Chakraborty:2020yfc,1797439,1801423,1808887,Iiyama:2020wap,1811770,Choma:2020cry,alonsomonsalve2020graph,guo2020boosted,Heintz:2020soy,Verma:2020gnq,Dreyer:2020brq,Qian:2021vnh,Pata:2021oez,Biscarat:2021dlj,Rossi:2021tjf,Hewes:2021heg,Thais:2021qcb,Dezoort:2021kfk,Verma:2021ceh,Hariri:2021clz,Belavin:2021bxb,Atkinson:2021nlt,Konar:2021zdg,Atkinson:2021jnj,Tsan:2021brw,Elabd:2021lgo,Pata:2022wam,Gong:2022lye,Qasim:2022rww,Ma:2022bvt,Bogatskiy:2022czk,Builtjes:2022usj,DiBello:2022iwf,Mokhtar:2022pwm,Huang:2023ssr,Forestano:2023fpj,Anisha:2023xmh,Ehrke:2023cpn,Murnane:2023kfm,Yu:2023juh,Neu:2023sfh,Wang:2023cac,McEneaney:2023vwp,Liu:2023siw,GarciaPardinas:2023pmx,Duperrin:2023elp,BelleII:2023egc,Holmberg:2023rfr,Bhattacherjee:2023evs,Murnane:2023ksa,Konar:2023ptv,Chatterjee:2024pbp,Heinrich:2024tdf,Mo:2024dru,Lu:2024qrc,Birch-Sykes:2024gij,Belle-II:2024lwr,Pfeffer:2024tjl,Aurisano:2024uvd,Kobylianskii:2024sup,Aamir:2024lpz,Soybelman:2024mbv,Correia:2024ogc,Calafiura:2024qhv} + \item \textbf{Graphs}~\cite{Henrion:DLPS2017,Ju:2020xty,Abdughani:2018wrw,Martinez:2018fwc,Ren:2019xhp,Moreno:2019bmu,Qasim:2019otl,Chakraborty:2019imr,DiBello:2020bas,Chakraborty:2020yfc,1797439,1801423,Shlomi:2020gdn,Iiyama:2020wap,1811770,Choma:2020cry,alonsomonsalve2020graph,guo2020boosted,Heintz:2020soy,Verma:2020gnq,Dreyer:2020brq,Qian:2021vnh,Pata:2021oez,Biscarat:2021dlj,Rossi:2021tjf,Hewes:2021heg,Thais:2021qcb,Dezoort:2021kfk,Verma:2021ceh,Hariri:2021clz,Belavin:2021bxb,Atkinson:2021nlt,Konar:2021zdg,Atkinson:2021jnj,Tsan:2021brw,Elabd:2021lgo,Pata:2022wam,Gong:2022lye,Qasim:2022rww,Ma:2022bvt,Bogatskiy:2022czk,Builtjes:2022usj,DiBello:2022iwf,Mokhtar:2022pwm,Huang:2023ssr,Forestano:2023fpj,Anisha:2023xmh,Ehrke:2023cpn,Murnane:2023kfm,Yu:2023juh,Neu:2023sfh,Wang:2023cac,McEneaney:2023vwp,Liu:2023siw,GarciaPardinas:2023pmx,Duperrin:2023elp,BelleII:2023egc,Holmberg:2023rfr,Bhattacherjee:2023evs,Murnane:2023ksa,Konar:2023ptv,Chatterjee:2024pbp,Heinrich:2024tdf,Mo:2024dru,Lu:2024qrc,Birch-Sykes:2024gij,Belle-II:2024lwr,Pfeffer:2024tjl,Aurisano:2024uvd,Kobylianskii:2024sup,Aamir:2024lpz,Soybelman:2024mbv,Correia:2024ogc,Calafiura:2024qhv} \\\textit{A graph is a collection of nodes and edges. Graph neural networks are natural tools for processing data in a tree structure.} \item \textbf{Sets (point clouds)}~\cite{Komiske:2018cqr,Qu:2019gqs,Mikuni:2020wpr,Shlomi:2020ufi,Dolan:2020qkr,Fenton:2020woz,Lee:2020qil,collado2021learning,Mikuni:2021pou,Shmakov:2021qdz,Shimmin:2021pkm,ATL-PHYS-PUB-2020-014,Qu:2022mxj,Kach:2022uzq,Onyisi:2022hdh,Athanasakos:2023fhq,Kach:2023rqw,Badea:2023jdb,Buhmann:2023zgc,Acosta:2023nuw,Mondal:2023law,Hammad:2023sbd,Odagiu:2024bkp,Gambhir:2024dtf} \\\textit{A point cloud is a (potentially variable-size) set of points in space. Sets are distinguished from sequences in that there is no particular order (i.e. permutation invariance). Sets can also be viewed as graphs without edges and so graph methods that can parse variable-length inputs may also be appropriate for set learning, although there are other methods as well.} @@ -165,7 +165,7 @@ \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{deOliveira:2017pjk,Paganini:2017hrr,Paganini:2017dwg,Alonso-Monsalve:2018aqs,Butter:2019eyo,Martinez:2019jlu,Bellagente:2019uyp,Vallecorsa:2019ked,SHiP:2019gcl,Carrazza:2019cnt,Butter:2019cae,Lin:2019htn,DiSipio:2019imz,Hashemi:2019fkn,Chekalina:2018hxi,ATL-SOFT-PUB-2018-001,Zhou:2018ill,Carminati:2018khv,Vallecorsa:2018zco,Datta:2018mwd,Musella:2018rdi,Erdmann:2018kuh,Deja:2019vcv,Derkach:2019qfk,Erbin:2018csv,Erdmann:2018jxd,Urban:2018tqv,Oliveira:DLPS2017,deOliveira:2017rwa,Farrell:2019fsm,Hooberman:DLPS2017,Belayneh:2019vyx,Wang:2020tap,buhmann2020getting,Alanazi:2020jod,2009.03796,2008.06545,Kansal:2020svm,Maevskiy:2020ank,Lai:2020byl,Choi:2021sku,Rehm:2021zow,Rehm:2021zoz,Carrazza:2021hny,Rehm:2021qwm,Lebese:2021foi,Winterhalder:2021ave,Kansal:2021cqp,NEURIPS2020_a878dbeb,Khattak:2021ndw,Mu:2021nno,Li:2021cbp,Bravo-Prieto:2021ehz,Anderlini:2021qpm,Chisholm:2021pdn,Desai:2021wbb,Buhmann:2021caf,Bieringer:2022cbs,Ghosh:2022zdz,Anderlini:2022ckd,Ratnikov:2022hge,Rogachev:2022hjg,ATLAS:2022jhk,Anderlini:2022hgm,Buhmann:2023pmh,Yue:2023uva,Hashemi:2023ruu,EXO:2023pkl,Diefenbacher:2023prl,Chan:2023ume,Dubinski:2023fsy,Alghamdi:2023emm,Barbetti:2023bvi,Erdmann:2023ngr,FaucciGiannelli:2023fow,Scham:2023cwn,Scham:2023usu,Chan:2023icm,Dooney:2024pvt,Wojnar:2024cbn,Kach:2024yxi} + \item \textbf{GANs}~\cite{deOliveira:2017pjk,Paganini:2017hrr,Paganini:2017dwg,Alonso-Monsalve:2018aqs,Butter:2019eyo,Martinez:2019jlu,Bellagente:2019uyp,Vallecorsa:2019ked,SHiP:2019gcl,Carrazza:2019cnt,Butter:2019cae,Lin:2019htn,DiSipio:2019imz,Hashemi:2019fkn,Chekalina:2018hxi,ATL-SOFT-PUB-2018-001,Zhou:2018ill,Carminati:2018khv,Vallecorsa:2018zco,Datta:2018mwd,Musella:2018rdi,Erdmann:2018kuh,Deja:2019vcv,Derkach:2019qfk,Erbin:2018csv,Erdmann:2018jxd,Urban:2018tqv,Oliveira:DLPS2017,deOliveira:2017rwa,Farrell:2019fsm,Hooberman:DLPS2017,Belayneh:2019vyx,Wang:2020tap,buhmann2020getting,Alanazi:2020jod,Diefenbacher:2020rna,2008.06545,Kansal:2020svm,Maevskiy:2020ank,Lai:2020byl,Choi:2021sku,Rehm:2021zow,Rehm:2021zoz,Carrazza:2021hny,Rehm:2021qwm,Lebese:2021foi,Winterhalder:2021ave,Kansal:2021cqp,NEURIPS2020_a878dbeb,Khattak:2021ndw,Mu:2021nno,Li:2021cbp,Bravo-Prieto:2021ehz,Anderlini:2021qpm,Chisholm:2021pdn,Desai:2021wbb,Buhmann:2021caf,Bieringer:2022cbs,Ghosh:2022zdz,Anderlini:2022ckd,Ratnikov:2022hge,Rogachev:2022hjg,ATLAS:2022jhk,Anderlini:2022hgm,Buhmann:2023pmh,Yue:2023uva,Hashemi:2023ruu,EXO:2023pkl,Diefenbacher:2023prl,Chan:2023ume,Dubinski:2023fsy,Alghamdi:2023emm,Barbetti:2023bvi,Erdmann:2023ngr,FaucciGiannelli:2023fow,Scham:2023cwn,Scham:2023usu,Chan:2023icm,Dooney:2024pvt,Wojnar:2024cbn,Kach:2024yxi} \\\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{Monk:2018zsb,ATL-SOFT-PUB-2018-001,Cheng:2020dal,1816035,Howard:2021pos,Buhmann:2021lxj,Bortolato:2021zic,deja2020endtoend,Hariri:2021clz,Fanelli:2019qaq,Collins:2021pld,Orzari:2021suh,Jawahar:2021vyu,Tsan:2021brw,Buhmann:2021caf,Touranakou:2022qrp,Ilten:2022jfm,Collins:2022qpr,AbhishekAbhishek:2022wby,Cresswell:2022tof,Roche:2023int,Anzalone:2023ugq,Lasseri:2023dhi,Chekanov:2023uot,Zhang:2023khv,Hoque:2023zjt,Kuh:2024lgx,Liu:2024kvv} \\\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.} @@ -197,7 +197,7 @@ \\\textit{This can also be viewed as a regression problem, but there the goal is typically to do maximum likelihood estimation in contrast to directly minimizing the mean squared error between a function and the target.} \item \textbf{Unfolding}~\cite{Mieskolainen:2018fhf,Andreassen:2019cjw,Datta:2018mwd,Bellagente:2019uyp,Gagunashvili:2010zw,Glazov:2017vni,Martschei:2012pr,Lindemann:1995ut,Zech2003BinningFreeUB,1800956,Vandegar:2020yvw,Howard:2021pos,Baron:2021vvl,Andreassen:2021zzk,Komiske:2021vym,H1:2021wkz,Arratia:2021otl,Wong:2021zvv,Arratia:2022wny,Backes:2022vmn,Chan:2023tbf,Shmakov:2023kjj,Shmakov:2024gkd,Huetsch:2024quz,Desai:2024kpd} \\\textit{This is the task of removing detector distortions. In contrast to parameter estimation, the goal is not to infer model parameters, but instead, the undistorted phase space probability density. This is often also called deconvolution.} - \item \textbf{Domain adaptation}~\cite{Rogozhnikov:2016bdp,Andreassen:2019nnm,Cranmer:2015bka,2009.03796,Nachman:2021opi,Camaiani:2022kul,Schreck:2023pzs,Algren:2023qnb,Zhao:2024ely,Kelleher:2024rmb,Kelleher:2024jsh} + \item \textbf{Domain adaptation}~\cite{Rogozhnikov:2016bdp,Andreassen:2019nnm,Cranmer:2015bka,Diefenbacher:2020rna,Nachman:2021opi,Camaiani:2022kul,Schreck:2023pzs,Algren:2023qnb,Zhao:2024ely,Kelleher:2024rmb,Kelleher:2024jsh} \\\textit{Morphing simulations to look like data is a form of domain adaptation.} \item \textbf{BSM}~\cite{Andreassen:2020nkr,Hollingsworth:2020kjg,Brehmer:2018kdj,Brehmer:2018eca,Brehmer:2018hga,Brehmer:2019xox,Romao:2020ojy,deSouza:2022uhk,GomezAmbrosio:2022mpm,Castro:2022zpq,Anisha:2023xmh,Dennis:2023kfe,vanBeekveld:2023ney,Chhibra:2023tyf,Mandal:2023mck,Franz:2023gic,Arganda:2023qni,Barman:2024xlc,vanBeekveld:2024cby,Bhattacharya:2024sxl,Catena:2024fjn,Ahmed:2024oxg,Baruah:2024gwy,Choudhury:2024mox,Ahmed:2024uaz,Hammad:2024hhm,Schofbeck:2024zjo} \\\textit{This category is for parameter estimation when the parameter is the signal strength of new physics.} diff --git a/README.md b/README.md index 43d4842..855e88d 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [The Machine Learning Landscape of Top Taggers](https://arxiv.org/abs/1902.09914) [[DOI](https://doi.org/10.21468/SciPostPhys.7.1.014)] (2019) * [Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review](https://arxiv.org/abs/2007.09121) (2020) -* [Graph neural networks in particle physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/{10.1088/2632-2153/abbf9a)] (2020) +* [Graph Neural Networks in Particle Physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/10.1088/2632-2153/abbf9a)] (2020) * [A Review on Machine Learning for Neutrino Experiments](https://arxiv.org/abs/2008.01242) [[DOI](https://doi.org/10.1142/S0217751X20430058)] (2020) * [Generative Networks for LHC events](https://arxiv.org/abs/2008.08558) (2020) * [Parton distribution functions](https://arxiv.org/abs/2008.12305) (2020) @@ -173,7 +173,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions](https://arxiv.org/abs/2003.11787) [[DOI](https://doi.org/10.1007/JHEP07(2020)111)] (2020) * [Probing triple Higgs coupling with machine learning at the LHC](https://arxiv.org/abs/2005.11086) [[DOI](https://doi.org/10.1103/PhysRevD.104.056003)] (2020) * [Casting a graph net to catch dark showers](https://arxiv.org/abs/2006.08639) [[DOI](https://doi.org/10.21468/SciPostPhys.10.2.046)] (2020) -* [Graph neural networks in particle physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/{10.1088/2632-2153/abbf9a)] (2020) +* [Graph Neural Networks in Particle Physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/10.1088/2632-2153/abbf9a)] (2020) * [Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics](https://arxiv.org/abs/2008.03601) [[DOI](https://doi.org/10.3389/fdata.2020.598927)] (2020) * [Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons](https://arxiv.org/abs/2008.06064) [[DOI](https://doi.org/10.1103/PhysRevD.102.075014)] (2020) * [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://arxiv.org/abs/2007.00149) (2020) @@ -1285,7 +1285,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Learning to Pivot with Adversarial Networks](https://arxiv.org/abs/1611.01046) [[url](https://papers.nips.cc/paper/2017/hash/48ab2f9b45957ab574cf005eb8a76760-Abstract.html)] (2016) * [Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure](https://arxiv.org/abs/1603.00027) [[DOI](https://doi.org/10.1007/JHEP05(2016)156)] (2016) -* Convolved Substructure: Analytically Decorrelating Jet Substructure Observables +* [Convolved Substructure: Analytically Decorrelating Jet Substructure Observables](https://arxiv.org/abs/1710.06859) [[DOI](https://doi.org/10.1007/JHEP05(2018)002)] (2017) * [uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers](https://arxiv.org/abs/1305.7248) [[DOI](https://doi.org/10.1088/1748-0221/8/12/P12013)] (2013) * [Decorrelated Jet Substructure Tagging using Adversarial Neural Networks](https://arxiv.org/abs/1703.03507) [[DOI](https://doi.org/10.1103/PhysRevD.96.074034)] (2017) * [Mass Agnostic Jet Taggers](https://arxiv.org/abs/1908.08959) [[DOI](https://doi.org/10.21468/SciPostPhys.8.1.011)] (2019) @@ -1345,7 +1345,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [A Novel Scenario in the Semi-constrained NMSSM](https://arxiv.org/abs/2002.05554) [[DOI](https://doi.org/10.1007/JHEP06(2020)078)] (2020) * [Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed](https://arxiv.org/abs/2005.05334) [[DOI](https://doi.org/10.1007/s41781-021-00056-0)] (2020) * [AI-based Monte Carlo event generator for electron-proton scattering](https://arxiv.org/abs/2008.03151) [[DOI](https://doi.org/10.1103/PhysRevD.106.096002)] (2020) -* [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/{10.1088/1748-0221/15/11/p11004)] (2020) +* [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/10.1088/1748-0221/15/11/P11004)] (2020) * [GANplifying Event Samples](https://arxiv.org/abs/2008.06545) [[DOI](https://doi.org/10.21468/SciPostPhys.10.6.139)] (2020) * [Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics](https://arxiv.org/abs/2012.00173) (2020) * [Simulating the time projection chamber responses at the MPD detector using generative adversarial networks](https://arxiv.org/abs/2012.04595) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09366-4)] (2020) @@ -1807,7 +1807,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Reweighting with Boosted Decision Trees](https://arxiv.org/abs/1608.05806) [[DOI](https://doi.org/10.1088/1742-6596/762/1/012036)] (2016) * [Neural Networks for Full Phase-space Reweighting and Parameter Tuning](https://arxiv.org/abs/1907.08209) [[DOI](https://doi.org/10.1103/PhysRevD.101.091901)] (2019) * [Approximating Likelihood Ratios with Calibrated Discriminative Classifiers](https://arxiv.org/abs/1506.02169) (2015) -* [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/{10.1088/1748-0221/15/11/p11004)] (2020) +* [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/10.1088/1748-0221/15/11/P11004)] (2020) * [Neural Conditional Reweighting](https://arxiv.org/abs/2107.08979) [[DOI](https://doi.org/10.1103/PhysRevD.105.076015)] (2021) * [Model independent measurements of Standard Model cross sections with Domain Adaptation](https://arxiv.org/abs/2207.09293) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10871-3)] (2022) * [Mimicking non-ideal instrument behavior for hologram processing using neural style translation](https://arxiv.org/abs/2301.02757) [[DOI](https://doi.org/10.1364/OE.486741)] (2023) diff --git a/docs/index.md b/docs/index.md index 068d907..a4673d3 100644 --- a/docs/index.md +++ b/docs/index.md @@ -61,7 +61,7 @@ const expandElements = shouldExpand => { * [The Machine Learning Landscape of Top Taggers](https://arxiv.org/abs/1902.09914) [[DOI](https://doi.org/10.21468/SciPostPhys.7.1.014)] (2019) * [Dealing with Nuisance Parameters using Machine Learning in High Energy Physics: a Review](https://arxiv.org/abs/2007.09121) (2020) - * [Graph neural networks in particle physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/{10.1088/2632-2153/abbf9a)] (2020) + * [Graph Neural Networks in Particle Physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/10.1088/2632-2153/abbf9a)] (2020) * [A Review on Machine Learning for Neutrino Experiments](https://arxiv.org/abs/2008.01242) [[DOI](https://doi.org/10.1142/S0217751X20430058)] (2020) * [Generative Networks for LHC events](https://arxiv.org/abs/2008.08558) (2020) * [Parton distribution functions](https://arxiv.org/abs/2008.12305) (2020) @@ -225,7 +225,7 @@ const expandElements = shouldExpand => { * [Neural Network-based Top Tagger with Two-Point Energy Correlations and Geometry of Soft Emissions](https://arxiv.org/abs/2003.11787) [[DOI](https://doi.org/10.1007/JHEP07(2020)111)] (2020) * [Probing triple Higgs coupling with machine learning at the LHC](https://arxiv.org/abs/2005.11086) [[DOI](https://doi.org/10.1103/PhysRevD.104.056003)] (2020) * [Casting a graph net to catch dark showers](https://arxiv.org/abs/2006.08639) [[DOI](https://doi.org/10.21468/SciPostPhys.10.2.046)] (2020) - * [Graph neural networks in particle physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/{10.1088/2632-2153/abbf9a)] (2020) + * [Graph Neural Networks in Particle Physics](https://arxiv.org/abs/2007.13681) [[DOI](https://doi.org/10.1088/2632-2153/abbf9a)] (2020) * [Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics](https://arxiv.org/abs/2008.03601) [[DOI](https://doi.org/10.3389/fdata.2020.598927)] (2020) * [Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons](https://arxiv.org/abs/2008.06064) [[DOI](https://doi.org/10.1103/PhysRevD.102.075014)] (2020) * [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://arxiv.org/abs/2007.00149) (2020) @@ -1408,7 +1408,7 @@ const expandElements = shouldExpand => { * [Learning to Pivot with Adversarial Networks](https://arxiv.org/abs/1611.01046) [[url](https://papers.nips.cc/paper/2017/hash/48ab2f9b45957ab574cf005eb8a76760-Abstract.html)] (2016) * [Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure](https://arxiv.org/abs/1603.00027) [[DOI](https://doi.org/10.1007/JHEP05(2016)156)] (2016) - * Convolved Substructure: Analytically Decorrelating Jet Substructure Observables + * [Convolved Substructure: Analytically Decorrelating Jet Substructure Observables](https://arxiv.org/abs/1710.06859) [[DOI](https://doi.org/10.1007/JHEP05(2018)002)] (2017) * [uBoost: A boosting method for producing uniform selection efficiencies from multivariate classifiers](https://arxiv.org/abs/1305.7248) [[DOI](https://doi.org/10.1088/1748-0221/8/12/P12013)] (2013) * [Decorrelated Jet Substructure Tagging using Adversarial Neural Networks](https://arxiv.org/abs/1703.03507) [[DOI](https://doi.org/10.1103/PhysRevD.96.074034)] (2017) * [Mass Agnostic Jet Taggers](https://arxiv.org/abs/1908.08959) [[DOI](https://doi.org/10.21468/SciPostPhys.8.1.011)] (2019) @@ -1473,7 +1473,7 @@ const expandElements = shouldExpand => { * [A Novel Scenario in the Semi-constrained NMSSM](https://arxiv.org/abs/2002.05554) [[DOI](https://doi.org/10.1007/JHEP06(2020)078)] (2020) * [Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed](https://arxiv.org/abs/2005.05334) [[DOI](https://doi.org/10.1007/s41781-021-00056-0)] (2020) * [AI-based Monte Carlo event generator for electron-proton scattering](https://arxiv.org/abs/2008.03151) [[DOI](https://doi.org/10.1103/PhysRevD.106.096002)] (2020) - * [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/{10.1088/1748-0221/15/11/p11004)] (2020) + * [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/10.1088/1748-0221/15/11/P11004)] (2020) * [GANplifying Event Samples](https://arxiv.org/abs/2008.06545) [[DOI](https://doi.org/10.21468/SciPostPhys.10.6.139)] (2020) * [Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics](https://arxiv.org/abs/2012.00173) (2020) * [Simulating the time projection chamber responses at the MPD detector using generative adversarial networks](https://arxiv.org/abs/2012.04595) [[DOI](https://doi.org/10.1140/epjc/s10052-021-09366-4)] (2020) @@ -2001,7 +2001,7 @@ const expandElements = shouldExpand => { * [Reweighting with Boosted Decision Trees](https://arxiv.org/abs/1608.05806) [[DOI](https://doi.org/10.1088/1742-6596/762/1/012036)] (2016) * [Neural Networks for Full Phase-space Reweighting and Parameter Tuning](https://arxiv.org/abs/1907.08209) [[DOI](https://doi.org/10.1103/PhysRevD.101.091901)] (2019) * [Approximating Likelihood Ratios with Calibrated Discriminative Classifiers](https://arxiv.org/abs/1506.02169) (2015) - * [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/{10.1088/1748-0221/15/11/p11004)] (2020) + * [DCTRGAN: Improving the Precision of Generative Models with Reweighting](https://arxiv.org/abs/2009.03796) [[DOI](https://doi.org/10.1088/1748-0221/15/11/P11004)] (2020) * [Neural Conditional Reweighting](https://arxiv.org/abs/2107.08979) [[DOI](https://doi.org/10.1103/PhysRevD.105.076015)] (2021) * [Model independent measurements of Standard Model cross sections with Domain Adaptation](https://arxiv.org/abs/2207.09293) [[DOI](https://doi.org/10.1140/epjc/s10052-022-10871-3)] (2022) * [Mimicking non-ideal instrument behavior for hologram processing using neural style translation](https://arxiv.org/abs/2301.02757) [[DOI](https://doi.org/10.1364/OE.486741)] (2023) diff --git a/make_md.py b/make_md.py index 3cf96f1..7a79381 100644 --- a/make_md.py +++ b/make_md.py @@ -128,7 +128,7 @@ def convert_from_bib(myline): myentry_dict["year"] = entry_cleaned.split("year")[1].split("=")[1].split("\n")[0].replace("\"","").replace("{","").replace("}","").replace(",","") pass elif "doi" in first_entry: - myentry_dict["doi"] = entry_cleaned.split("doi")[1].split("=")[1].split("\n")[0].replace("\"","").replace(",","").replace("\'","").replace(" ","") + myentry_dict["doi"] = entry_cleaned.split("doi")[1].split("=")[1].split("\n")[0].replace("\"","").replace(",","").replace("\'","").replace(" ","").replace("{","") elif "url" in first_entry: if "@" in first_entry: continue