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162 changes: 75 additions & 87 deletions HEPML.bib
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Expand Up @@ -1212,7 +1212,7 @@ @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",
Expand Down Expand Up @@ -17083,21 +17083,20 @@ @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,
Expand Down Expand Up @@ -17518,23 +17517,17 @@ @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",
Expand Down Expand Up @@ -18256,19 +18249,17 @@ @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}",
Expand Down Expand Up @@ -18761,36 +18752,33 @@ @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}",
Expand Down Expand Up @@ -18819,20 +18807,20 @@ @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}",
Expand Down Expand Up @@ -18870,7 +18858,7 @@ @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},
Expand Down
8 changes: 4 additions & 4 deletions HEPML.tex
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Expand Up @@ -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}
Expand All @@ -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.}
Expand Down Expand Up @@ -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.}
Expand Down Expand Up @@ -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.}
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