From 81c2abf0bfe7dee469b5ec8cc69609b5dda89793 Mon Sep 17 00:00:00 2001 From: Ramon Winterhalder Date: Wed, 6 Nov 2024 14:28:00 +0100 Subject: [PATCH] Update and forward to Claudius --- HEPML.bib | 35 +++++++++++++++++----------------- HEPML.tex | 14 +++++++------- README.md | 8 ++++++++ docs/assets/dark_per_year.png | Bin 15726 -> 15727 bytes docs/assets/per_year.png | Bin 16932 -> 16935 bytes docs/index.md | 8 ++++++++ 6 files changed, 41 insertions(+), 24 deletions(-) diff --git a/HEPML.bib b/HEPML.bib index 44ce6c9..b5cdc10 100644 --- a/HEPML.bib +++ b/HEPML.bib @@ -854,7 +854,7 @@ @article{Gao:2024zdz year = "2024" } -% August 23, 2024* +% August 23, 2024 @article{Grosso:2024wjt, author = "Grosso, Gaia and Letizia, Marco", title = "{Multiple testing for signal-agnostic searches of new physics with machine learning}", @@ -865,7 +865,7 @@ @article{Grosso:2024wjt year = "2024" } -% August 23, 2024* +% August 23, 2024 @article{Kats:2024eaq, author = "Kats, Yevgeny and Ofir, Edo", title = "{From strange-quark tagging to fragmentation tagging with machine learning}", @@ -876,7 +876,7 @@ @article{Kats:2024eaq year = "2024" } -% August 22, 2024* +% August 22, 2024 @article{Zoch:2024eyp, author = "Zoch, Knut and Raine, John Andrew and Sengupta, Debajyoti and Golling, Tobias", title = "{RODEM Jet Datasets}", @@ -887,18 +887,7 @@ @article{Zoch:2024eyp year = "2024" } -% August 22, 2024* -@article{Fanelli:2024zyj, - author = "Fanelli, Duccio and Bindi, Luca and Chicchi, Lorenzo and Pereti, Claudio and Sessoli, Roberta and Tommasini, Simone", - title = "{A short introduction to Neural Networks and their application to Earth and Materials Science Science}", - eprint = "2408.11395", - archivePrefix = "arXiv", - primaryClass = "physics.data-an", - month = "8", - year = "2024" -} - -% August 21, 2024* +% August 21, 2024 @inproceedings{Chung:2024vfg, author = "Chung, Wonyong", title = "{Full Detector Simulation of a Projective Dual-Readout Segmented Crystal Electromagnetic Calorimeter with Precision Timing}", @@ -910,7 +899,7 @@ @inproceedings{Chung:2024vfg year = "2024" } -% August 20, 2024* +% August 20, 2024 @article{Graczyk:2024pjm, author = "Graczyk, Krzysztof M. and Kowal, Beata E. and Ankowski, Artur M. and Banerjee, Rwik Dharmapal and Bonilla, Jose Luis and Prasad, Hemant and Sobczyk, Jan T.", title = "{Electron-nucleus cross sections from transfer learning}", @@ -921,7 +910,7 @@ @article{Graczyk:2024pjm year = "2024" } -% August 19, 2024* +% August 19, 2024 @article{Yu:2024ldv, author = {Yu, Felix J. and Kamp, Nicholas and Arg\"uelles, Carlos A.}, title = "{Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution}", @@ -1011,6 +1000,18 @@ @article{Bose:2024pwc year = "2024" } +% August 02, 2024 +@article{Heimel:2024wph, + author = "Heimel, Theo and Mattelaer, Olivier and Plehn, Tilman and Winterhalder, Ramon", + title = "{Differentiable MadNIS-Lite}", + eprint = "2408.01486", + archivePrefix = "arXiv", + primaryClass = "hep-ph", + reportNumber = "IRMP-CP3-24-23", + month = "8", + year = "2024" +} + % August 02, 2024 @article{Liuti:2024zkc, author = "Liuti, Simonetta and others", diff --git a/HEPML.tex b/HEPML.tex index 0ab2f71..17dca45 100644 --- a/HEPML.tex +++ b/HEPML.tex @@ -45,7 +45,7 @@ \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 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} + \item Datasets~\cite{Kasieczka:2021xcg,Aarrestad:2021oeb,Benato:2021olt,Govorkova:2021hqu,Chen:2021euv,Qu:2022mxj,Eller:2023myr,Rusack:2023pob,Zoch:2024eyp} \end{itemize} \item \textbf{Classification} \\\textit{Given a feature space $x\in\mathbb{R}^n$, a binary classifier is a function $f:\mathbb{R}^n\rightarrow [0,1]$, where $0$ corresponds to features that are more characteristic of the zeroth class (e.g. background) and $1$ correspond to features that are more characteristic of the one class (e.g. signal). Typically, $f$ will be a function specified by some parameters $w$ (e.g. weights and biases of a neural network) that are determined by minimizing a loss of the form $L[f]=\sum_{i}\ell(f(x_i),y_i)$, where $y_i\in\{0,1\}$ are labels. The function $\ell$ is smaller when $f(x_i)$ and $y_i$ are closer. Two common loss functions are the mean squared error $\ell(x,y)=(x-y)^2$ and the binary cross entropy $\ell(x,y)=y\log(x)+(1-y)\log(1-x)$. Exactly what `more characteristic of' means depends on the loss function used to determine $f$. It is also possible to make a multi-class classifier. A common strategy for the multi-class case is to represent each class as a different basis vector in $\mathbb{R}^{n_\text{classes}}$ and then $f(x)\in[0,1]^{n_\text{classes}}$. In this case, $f(x)$ is usually restricted to have its $n_\text{classes}$ components sum to one and the loss function is typically the cross entropy $\ell(x,y)=\sum_\text{classes $i$} y_i\log(x)$.} @@ -80,17 +80,17 @@ \\\textit{Quark jets tend to be narrower and have fewer particles than gluon jets. This classification task has been a benchmark for many new machine learning models.} \item \textbf{top quark} tagging~\cite{Almeida:2015jua,Stoye:DLPS2017,Kasieczka:2019dbj,Chakraborty:2020yfc,Diefenbacher:2019ezd,Butter:2017cot,Kasieczka:2017nvn,Macaluso:2018tck,Bhattacharya:2020vzu,Lim:2020igi,Dreyer:2020brq,Aguilar-Saavedra:2021rjk,Andrews:2021ejw,Dreyer:2022yom,Ahmed:2022hct,Munoz:2022gjq,Bhattacherjee:2022gjq,Choi:2023slq,Keicher:2023mer,He:2023cfc,Bogatskiy:2023nnw,Shen:2023ofd,Isildak:2023dnf,Sahu:2023uwb,Baron:2023yhw,Bogatskiy:2023fug,Liu:2023dio,Batson:2023ohn,Furuichi:2023vdx,Ngairangbam:2023cps,Cai:2024xnt,Dong:2024xsg} \\\textit{Boosted top quarks form jets that have a three-prong substructure ($t\rightarrow Wb,W\rightarrow q\bar{q}$).} - \item \textbf{strange jets}~\cite{Nakai:2020kuu,Erdmann:2019blf,Erdmann:2020ovh,Subba:2023rpm} + \item \textbf{strange jets}~\cite{Nakai:2020kuu,Erdmann:2019blf,Erdmann:2020ovh,Subba:2023rpm,Kats:2024eaq} \\\textit{Strange quarks have a very similar fragmentation to generic quark and gluon jets, so this is a particularly challenging task.} \item \textbf{$b$-tagging}~\cite{Sirunyan:2017ezt,Guest:2016iqz,Keck:2018lcd,bielkov2020identifying,Bols:2020bkb,ATL-PHYS-PUB-2017-003,ATL-PHYS-PUB-2020-014,Liao:2022ufk,Stein:2023cnt,ATLAS:2023gog,Tamir:2023aiz,VanStroud:2023ggs,Song:2024aka} \\\textit{Due to their long (but not too long) lifetime, the $B$-hadron lifetime is macroscopic and $b$-jet tagging has been one of the earliest adapters of modern machine learning tools.} \item \textbf{Flavor physics}~\cite{1811097,Bahtiyar:2022une,Zhang:2023czx,Nishimura:2023wdu,Smith:2023ssh,Tian:2024yfz,Chang:2024ksq,Co:2024bfl,Malekhosseini:2024eot,Chen:2024epd,Mansouri:2024uwc} \\\textit{This category is for studies related to exclusive particle decays, especially with bottom and charm hadrons.} - \item \textbf{BSM particles and models}~\cite{Datta:2019ndh,Baldi:2014kfa,Chakraborty:2019imr,10.1088/2632-2153/ab9023,1792136,1801423,Chang:2020rtc,Cogollo:2020afo,Grossi:2020orx,Ngairangbam:2020ksz,Englert:2020ntw,Freitas:2020ttd,Khosa:2019kxd,Freitas:2019hbk,Stakia:2021pvp,Arganda:2021azw,Jorge:2021vpo,Ren:2021prq,Barron:2021btf,Yang:2021gge,Alvestad:2021sje,Morais:2021ead,Jung:2021tym,Drees:2021oew,Cornell:2021gut,Vidal:2021oed,Beauchesne:2021qrw,Feng:2021eke,Konar:2022bgc,Badea:2022dzb,Freitas:2022cno,Goodsell:2022beo,Lv:2022pme,Ai:2022qvs,Yang:2022fhw,Alasfar:2022vqw,Barbosa:2022mmw,Chiang:2022lsn,Hall:2022bme,Faucett:2022zie,Bhattacharya:2022kje,Bardhan:2022sif,Bhattacharyya:2022umc,ATLAS:2022ihe,CMS:2022idi,Ballabene:2022fms,ATLAS:2023mcc,Palit:2023dvs,Liu:2023gpt,Pedro:2023sdp,MB:2023edk,Dong:2023nir,Guo:2023jkz,Lu:2023gjk,Flacke:2023eil,Bardhan:2023mia,Aguilar-Saavedra:2023pde,Cremer:2023gne,Esmail:2023axd,Choudhury:2023eje,Bhattacherjee:2023evs,Grefsrud:2023dad,Wang:2023pqx,Zhang:2023ykh,Hammad:2023wme,Hammad:2023sbd,Zhang:2024bld,Ma:2024deu,Jurciukonis:2024hlg,Chiang:2024pho,Birch-Sykes:2024gij,Ahmed:2024iqx,Esmail:2024gdc,Bickendorf:2024ovi,Wojcik:2024lfy} + \item \textbf{BSM particles and models}~\cite{Datta:2019ndh,Baldi:2014kfa,Chakraborty:2019imr,10.1088/2632-2153/ab9023,1792136,1801423,Chang:2020rtc,Cogollo:2020afo,Grossi:2020orx,Ngairangbam:2020ksz,Englert:2020ntw,Freitas:2020ttd,Khosa:2019kxd,Freitas:2019hbk,Stakia:2021pvp,Arganda:2021azw,Jorge:2021vpo,Ren:2021prq,Barron:2021btf,Yang:2021gge,Alvestad:2021sje,Morais:2021ead,Jung:2021tym,Drees:2021oew,Cornell:2021gut,Vidal:2021oed,Beauchesne:2021qrw,Feng:2021eke,Konar:2022bgc,Badea:2022dzb,Freitas:2022cno,Goodsell:2022beo,Lv:2022pme,Ai:2022qvs,Yang:2022fhw,Alasfar:2022vqw,Barbosa:2022mmw,Chiang:2022lsn,Hall:2022bme,Faucett:2022zie,Bhattacharya:2022kje,Bardhan:2022sif,Bhattacharyya:2022umc,ATLAS:2022ihe,CMS:2022idi,Ballabene:2022fms,ATLAS:2023mcc,Palit:2023dvs,Liu:2023gpt,Pedro:2023sdp,MB:2023edk,Dong:2023nir,Guo:2023jkz,Lu:2023gjk,Flacke:2023eil,Bardhan:2023mia,Aguilar-Saavedra:2023pde,Cremer:2023gne,Esmail:2023axd,Choudhury:2023eje,Bhattacherjee:2023evs,Grefsrud:2023dad,Wang:2023pqx,Zhang:2023ykh,Hammad:2023wme,Hammad:2023sbd,Zhang:2024bld,Ma:2024deu,Jurciukonis:2024hlg,Chiang:2024pho,Birch-Sykes:2024gij,Ahmed:2024iqx,Esmail:2024gdc,Bickendorf:2024ovi,Wojcik:2024lfy,Grosso:2024wjt} \\\textit{There are many proposals to train classifiers to enhance the presence of particular new physics models.} \item \textbf{Particle identification}~\cite{deOliveira:2018lqd,Paganini:DLPS2017,Hooberman:DLPS2017,Keck:2018lcd,Belayneh:2019vyx,Qasim:2019otl,Collado:2020fwm,Verma:2021ixg,Graziani:2021vai,Graczykowski:2022zae,Fanelli:2022ifa,Dimitrova:2022uum,Ryzhikov:2022lbu,Kushawaha:2023dms,Wu:2023pzn,Prasad:2023zdd,Lange:2023gbe,Novosel:2023cki,Charan:2023ldg,NA62:2023wzm,Karwowska:2023dhl,Song:2023ceh,Kasak:2023hhr,Ai:2024mkl} \\\textit{This is a generic category for direct particle identification and categorization using various detector technologies. Direct means that the particle directly interacts with the detector (in contrast with $b$-tagging).} - \item \textbf{Neutrino Detectors}~\cite{Aurisano:2016jvx,Acciarri:2016ryt,Hertel:DLPS2017,Adams:2018bvi,Domine:2019zhm,Aiello:2020orq,Adams:2020vlj,Domine:2020tlx,DUNE:2020gpm,DeepLearnPhysics:2020hut,Koh:2020snv,Yu:2020wxu,Psihas:2020pby,alonsomonsalve2020graph,Abratenko:2020pbp,Clerbaux:2020ttg,Liu:2020pzv,Abratenko:2020ocq,Chen:2020zkj,Qian:2021vnh,abbasi2021convolutional,Drielsma:2021jdv,Rossi:2021tjf,Hewes:2021heg,Acciarri:2021oav,Belavin:2021bxb,Maksimovic:2021dmz,Gavrikov:2021ktt,Garcia-Mendez:2021vts,Carloni:2021zbc,MicroBooNE:2021nss,MicroBooNE:2021ojx,Elkarghli:2020owr,DUNE:2022fiy,Lutkus:2022eou,Chappell:2022yxd,Bachlechner:2022cvf,Sogaard:2022qgg,IceCube:2022njh,Bai:2022lbv,Biassoni:2023lih,Yu:2023ehc,Mo:2024dru,Bat:2024gln,Aurisano:2024uvd,IceCube:2024xjj,Cai:2024bpv,Kopp:2024lch} + \item \textbf{Neutrino Detectors}~\cite{Aurisano:2016jvx,Acciarri:2016ryt,Hertel:DLPS2017,Adams:2018bvi,Domine:2019zhm,Aiello:2020orq,Adams:2020vlj,Domine:2020tlx,DUNE:2020gpm,DeepLearnPhysics:2020hut,Koh:2020snv,Yu:2020wxu,Psihas:2020pby,alonsomonsalve2020graph,Abratenko:2020pbp,Clerbaux:2020ttg,Liu:2020pzv,Abratenko:2020ocq,Chen:2020zkj,Qian:2021vnh,abbasi2021convolutional,Drielsma:2021jdv,Rossi:2021tjf,Hewes:2021heg,Acciarri:2021oav,Belavin:2021bxb,Maksimovic:2021dmz,Gavrikov:2021ktt,Garcia-Mendez:2021vts,Carloni:2021zbc,MicroBooNE:2021nss,MicroBooNE:2021ojx,Elkarghli:2020owr,DUNE:2022fiy,Lutkus:2022eou,Chappell:2022yxd,Bachlechner:2022cvf,Sogaard:2022qgg,IceCube:2022njh,Bai:2022lbv,Biassoni:2023lih,Yu:2023ehc,Mo:2024dru,Bat:2024gln,Aurisano:2024uvd,IceCube:2024xjj,Cai:2024bpv,Kopp:2024lch,Yu:2024ldv} \\\textit{Neutrino detectors are very large in order to have a sizable rate of neutrino detection. The entire neutrino interaction can be characterized to distinguish different neutrino flavors.} \item \textbf{Direct Dark Matter Detectors}~\cite{Ilyasov_2020,Akerib:2020aws,Khosa:2019qgp,Golovatiuk:2021lqn,McDonald:2021hus,Coarasa:2021fpv,Herrero-Garcia:2021goa,Liang:2021nsz,Li:2022tvg,Biassoni:2023lih,XENONCollaboration:2023dar,Ghrear:2024rku,Cerdeno:2024uqt} \\\textit{Dark matter detectors are similar to neutrino detectors, but aim to achieve `zero' background.} @@ -98,7 +98,7 @@ \\\textit{Machine learning is often used in astrophysics and cosmology in different ways than terrestrial particle physics experiments due to a general divide between Bayesian and Frequentist statistics. However, there are many similar tasks and a growing number of proposals designed for one domain that apply to the other. See also https://github.com/georgestein/ml-in-cosmology.} \item \textbf{Tracking}~\cite{Farrell:DLPS2017,Farrell:2018cjr,Amrouche:2019wmx,Ju:2020xty,Akar:2020jti,Shlomi:2020ufi,Choma:2020cry,Siviero:2020tim,Fox:2020hfm,Amrouche:2021tlm,goto2021development,Biscarat:2021dlj,Akar:2021gns,Thais:2021qcb,Ju:2021ayy,Dezoort:2021kfk,Edmonds:2021lzd,Lavrik:2021zgt,Huth:2021zcm,Goncharov:2021wvd,Wang:2022oer,Alonso-Monsalve:2022zlm,Bakina:2022mhs,Akram:2022zmj,Sun:2022bxx,Abidi:2022ogh,Bae:2023eec,Knipfer:2023zrv,Akar:2023zhd,Mieskolainen:2023hkz,Allaire:2023dfg,Allaire:2023llb,Huang:2024voo,Gavalian:2024icb,Guiang:2024qzk,Caron:2024cyo} \\\textit{Charged particle tracking is a challenging pattern recognition task. This category is for various classification tasks associated with tracking, such as seed selection.} - \item \textbf{Heavy Ions / Nuclear Physics}~\cite{Pang:2016vdc,Chien:2018dfn,Du:2020pmp,Du:2019civ,Mallick:2021wop,Nagu:2021zho,Zhao:2021yjo,Sombillo:2021ifs,Zhou:2021bvw,Apolinario:2021olp,Brown:2021upr,Du:2021pqa,Kuttan:2021npg,Huang:2021iux,Shokr:2021ouh,He:2021uko,Habashy:2021orz,Zepeda:2021tzp,Mishra:2021eqb,Ng:2021ibr,Habashy:2021qku,Biro:2021zgm,Lai:2021ckt,Du:2021qwv,Du:2021brx,Xiang:2021ssj,Soma:2022qnv,Rahman:2022tfq,Boglione:2022gpv,Liyanage:2022byj,Liu:2022hzd,Fanelli:2022kro,Chen:2022shj,Saha:2022skj,Lee:2022kdn,Biro:2022zhl,Zhang:2022hjh,Yang:2022eag,Rigo:2022ces,Yang:2022rlw,Munoz:2022slm,Goriely:2022upe,Mallick:2022alr,Fore:2022ljl,Steffanic:2023cyx,Mallick:2023vgi,He:2023urp,Xu:2023fbs,Kanwar:2023otc,Mumpower:2023lch,Escher:2023oyy,Hirvonen:2023lqy,Biro:2023kyx,He:2023zin,Zhou:2023pti,CrispimRomao:2023ssj,Basak:2023wzq,Shi:2023xfz,Soleymaninia:2023dds,Lin:2023bmy,Dellen:2023avd,AlHammal:2023svo,Wang:2023muv,Wang:2023kcg,Ai:2023azx,Yiu:2023ido,Karmakar:2023mhy,Lasseri:2023dhi,Yoshida:2023wrb,Liu:2023xgl,Hizawa:2023plv,Wen:2023oju,Allaire:2023fgp,Bedaque:2023udu,Lay:2023boz,Wang:2024gjz,Mengel:2024fcl,Hirvonen:2024ycx,Goswami:2024xrx,Santos:2024bqr,Hirvonen:2024zne,Liuti:2024zkc,JETSCAPE:2024cqe} + \item \textbf{Heavy Ions / Nuclear Physics}~\cite{Pang:2016vdc,Chien:2018dfn,Du:2020pmp,Du:2019civ,Mallick:2021wop,Nagu:2021zho,Zhao:2021yjo,Sombillo:2021ifs,Zhou:2021bvw,Apolinario:2021olp,Brown:2021upr,Du:2021pqa,Kuttan:2021npg,Huang:2021iux,Shokr:2021ouh,He:2021uko,Habashy:2021orz,Zepeda:2021tzp,Mishra:2021eqb,Ng:2021ibr,Habashy:2021qku,Biro:2021zgm,Lai:2021ckt,Du:2021qwv,Du:2021brx,Xiang:2021ssj,Soma:2022qnv,Rahman:2022tfq,Boglione:2022gpv,Liyanage:2022byj,Liu:2022hzd,Fanelli:2022kro,Chen:2022shj,Saha:2022skj,Lee:2022kdn,Biro:2022zhl,Zhang:2022hjh,Yang:2022eag,Rigo:2022ces,Yang:2022rlw,Munoz:2022slm,Goriely:2022upe,Mallick:2022alr,Fore:2022ljl,Steffanic:2023cyx,Mallick:2023vgi,He:2023urp,Xu:2023fbs,Kanwar:2023otc,Mumpower:2023lch,Escher:2023oyy,Hirvonen:2023lqy,Biro:2023kyx,He:2023zin,Zhou:2023pti,CrispimRomao:2023ssj,Basak:2023wzq,Shi:2023xfz,Soleymaninia:2023dds,Lin:2023bmy,Dellen:2023avd,AlHammal:2023svo,Wang:2023muv,Wang:2023kcg,Ai:2023azx,Yiu:2023ido,Karmakar:2023mhy,Lasseri:2023dhi,Yoshida:2023wrb,Liu:2023xgl,Hizawa:2023plv,Wen:2023oju,Allaire:2023fgp,Bedaque:2023udu,Lay:2023boz,Wang:2024gjz,Mengel:2024fcl,Hirvonen:2024ycx,Goswami:2024xrx,Santos:2024bqr,Hirvonen:2024zne,Liuti:2024zkc,JETSCAPE:2024cqe,Graczyk:2024pjm} \\\textit{Many tools in high energy nuclear physics are similar to high energy particle physics. The physics target of these studies are to understand collective properties of the strong force.} \end{itemize} \item \textbf{Learning strategies} @@ -169,7 +169,7 @@ \\\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.} - \item \textbf{(Continuous) Normalizing flows}~\cite{Albergo:2019eim,1800956,Kanwar:2003.06413,Brehmer:2020vwc,Bothmann:2020ywa,Gao:2020zvv,Gao:2020vdv,Nachman:2020lpy,Choi:2020bnf,Lu:2020npg,Bieringer:2020tnw,Hollingsworth:2021sii,Winterhalder:2021ave,Krause:2021ilc,Hackett:2021idh,Menary:2021tjg,Hallin:2021wme,NEURIPS2020_a878dbeb,Vandegar:2020yvw,Jawahar:2021vyu,Bister:2021arb,Krause:2021wez,Butter:2021csz,Winterhalder:2021ngy,Butter:2022lkf,Verheyen:2022tov,Leigh:2022lpn,Chen:2022ytr,Albandea:2022fky,Krause:2022jna,Cresswell:2022tof,Kach:2022qnf,Kach:2022uzq,Dolan:2022ikg,Backes:2022vmn,Heimel:2022wyj,Albandea:2023wgd,Rousselot:2023pcj,Diefenbacher:2023vsw,Nicoli:2023qsl,R:2023dcr,Nachman:2023clf,Raine:2023fko,Golling:2023yjq,Wen:2023oju,Xu:2023xdc,Singha:2023xxq,Buckley:2023rez,Pang:2023wfx,Golling:2023mqx,Reyes-Gonzalez:2023oei,Bickendorf:2023nej,Finke:2023ltw,Bright-Thonney:2023sqf,Albandea:2023ais,Pham:2023bnl,Gavranovic:2023oam,Heimel:2023ngj,Bierlich:2023zzd,ElBaz:2023ijr,Ernst:2023qvn,Krause:2023uww,Kanwar:2024ujc,Deutschmann:2024lml,Kelleher:2024rmb,Vaselli:2024vrx,Kelleher:2024jsh,Schnake:2024mip,Daumann:2024kfd,Abbott:2024knk,Bai:2024pii,Du:2024gbp,Favaro:2024rle,Buss:2024orz,Dreyer:2024bhs,Quetant:2024ftg} + \item \textbf{(Continuous) Normalizing flows}~\cite{Albergo:2019eim,1800956,Kanwar:2003.06413,Brehmer:2020vwc,Bothmann:2020ywa,Gao:2020zvv,Gao:2020vdv,Nachman:2020lpy,Choi:2020bnf,Lu:2020npg,Bieringer:2020tnw,Hollingsworth:2021sii,Winterhalder:2021ave,Krause:2021ilc,Hackett:2021idh,Menary:2021tjg,Hallin:2021wme,NEURIPS2020_a878dbeb,Vandegar:2020yvw,Jawahar:2021vyu,Bister:2021arb,Krause:2021wez,Butter:2021csz,Winterhalder:2021ngy,Butter:2022lkf,Verheyen:2022tov,Leigh:2022lpn,Chen:2022ytr,Albandea:2022fky,Krause:2022jna,Cresswell:2022tof,Kach:2022qnf,Kach:2022uzq,Dolan:2022ikg,Backes:2022vmn,Heimel:2022wyj,Albandea:2023wgd,Rousselot:2023pcj,Diefenbacher:2023vsw,Nicoli:2023qsl,R:2023dcr,Nachman:2023clf,Raine:2023fko,Golling:2023yjq,Wen:2023oju,Xu:2023xdc,Singha:2023xxq,Buckley:2023rez,Pang:2023wfx,Golling:2023mqx,Reyes-Gonzalez:2023oei,Bickendorf:2023nej,Finke:2023ltw,Bright-Thonney:2023sqf,Albandea:2023ais,Pham:2023bnl,Gavranovic:2023oam,Heimel:2023ngj,Bierlich:2023zzd,ElBaz:2023ijr,Ernst:2023qvn,Krause:2023uww,Kanwar:2024ujc,Deutschmann:2024lml,Kelleher:2024rmb,Vaselli:2024vrx,Kelleher:2024jsh,Schnake:2024mip,Daumann:2024kfd,Abbott:2024knk,Bai:2024pii,Du:2024gbp,Favaro:2024rle,Buss:2024orz,Dreyer:2024bhs,Quetant:2024ftg,Heimel:2024wph} \\\textit{Normalizing flows~\cite{pmlr-v37-rezende15} learn $p(x)$ explicitly by starting with a simple probability density and then applying a series of bijective transformations with tractable Jacobians.} \item \textbf{Diffusion Models}~\cite{Mikuni:2022xry,Leigh:2023toe,Mikuni:2023dvk,Shmakov:2023kjj,Buhmann:2023bwk,Butter:2023fov,Mikuni:2023tok,Acosta:2023zik,Leigh:2023zle,Imani:2023blb,Amram:2023onf,Diefenbacher:2023flw,Cotler:2023lem,Diefenbacher:2023wec,Mikuni:2023tqg,Hunt-Smith:2023ccp,Buhmann:2023kdg,Buhmann:2023zgc,Buhmann:2023acn,Devlin:2023jzp,Heimel:2023ngj,Wang:2023sry,Butter:2023ira,Sengupta:2023vtm,Jiang:2024ohg,Kobylianskii:2024ijw,Vaselli:2024vrx,Jiang:2024bwr,Kobylianskii:2024sup,Favaro:2024rle,Kita:2024nnw,Quetant:2024ftg,Wojnar:2024cbn} \\\textit{These approaches learn the gradient of the density instead of the density directly.} @@ -201,7 +201,7 @@ \\\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.} - \item \textbf{Differentiable Simulation}~\cite{Heinrich:2022xfa,MODE:2022znx,Nachman:2022jbj,Lei:2022dvn,Napolitano:2023jhg,Shenoy:2023ros,Kagan:2023gxz,Aehle:2023wwi,Smith:2023ssh,BarhamAlzas:2024ggt} + \item \textbf{Differentiable Simulation}~\cite{Heinrich:2022xfa,MODE:2022znx,Nachman:2022jbj,Lei:2022dvn,Napolitano:2023jhg,Shenoy:2023ros,Kagan:2023gxz,Aehle:2023wwi,Smith:2023ssh,BarhamAlzas:2024ggt,Heimel:2024wph,Chung:2024vfg} \\\textit{Coding up a simulation using a differentiable programming language like TensorFlow, PyTorch, or JAX.} \end{itemize} \item \textbf{Uncertainty Quantification} diff --git a/README.md b/README.md index 554bdd8..43d4842 100644 --- a/README.md +++ b/README.md @@ -90,6 +90,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Particle Transformer for Jet Tagging](https://arxiv.org/abs/2202.03772) (2022) * [Public Kaggle Competition ''IceCube -- Neutrinos in Deep Ice''](https://arxiv.org/abs/2307.15289) (2023) * [Electron Energy Regression in the CMS High-Granularity Calorimeter Prototype](https://arxiv.org/abs/2309.06582) (2023) +* [RODEM Jet Datasets](https://arxiv.org/abs/2408.11616) (2024) ## Classification ### Parameterized classifiers @@ -382,6 +383,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [A tagger for strange jets based on tracking information using long short-term memory](https://arxiv.org/abs/1907.07505) [[DOI](https://doi.org/10.1088/1748-0221/15/01/P01021)] (2019) * [Maximum performance of strange-jet tagging at hadron colliders](https://arxiv.org/abs/2011.10736) [[DOI](https://doi.org/10.1088/1748-0221/16/08/P08039)] (2020) * [Study of anomalous $W^-W^+\gamma/Z$ couplings using polarizations and spin correlations in $e^-e^+\to W^-W^+$ with polarized beams](https://arxiv.org/abs/2305.15106) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12292-2)] (2023) +* [From strange-quark tagging to fragmentation tagging with machine learning](https://arxiv.org/abs/2408.12377) (2024) #### $b$-tagging @@ -490,6 +492,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Boosting probes of CP violation in the top Yukawa coupling with Deep Learning](https://arxiv.org/abs/2405.16499) (2024) * [Learning to see R-parity violating scalar top decays](https://arxiv.org/abs/2406.03096) [[DOI](https://doi.org/10.1103/PhysRevD.110.056006)] (2024) * [Graph Reinforcement Learning for Exploring BSM Model Spaces](https://arxiv.org/abs/2407.07203) (2024) +* [Multiple testing for signal-agnostic searches of new physics with machine learning](https://arxiv.org/abs/2408.12296) (2024) #### Particle identification @@ -568,6 +571,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore](https://arxiv.org/abs/2405.02163) (2024) * [RELICS: a REactor neutrino LIquid xenon Coherent elastic Scattering experiment](https://arxiv.org/abs/2405.05554) [[DOI](https://doi.org/10.1103/PhysRevD.110.072011)] (2024) * [Improving Neutrino Energy Reconstruction with Machine Learning](https://arxiv.org/abs/2405.15867) (2024) +* [Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution](https://arxiv.org/abs/2408.08474) (2024) #### Direct Dark Matter Detectors @@ -758,6 +762,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions](https://arxiv.org/abs/2407.01338) [[DOI](https://doi.org/10.1103/PhysRevC.110.034911)] (2024) * [AI for Nuclear Physics: the EXCLAIM project](https://arxiv.org/abs/2408.00163) (2024) * [Bayesian Inference analysis of jet quenching using inclusive jet and hadron suppression measurements](https://arxiv.org/abs/2408.08247) (2024) +* [Electron-nucleus cross sections from transfer learning](https://arxiv.org/abs/2408.09936) (2024) ### Learning strategies @@ -1496,6 +1501,7 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows](https://arxiv.org/abs/2405.20407) [[DOI](https://doi.org/10.1088/1748-0221/19/09/P09003)] (2024) * [Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction](https://arxiv.org/abs/2406.01620) (2024) * [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) [[DOI](https://doi.org/10.1103/PhysRevD.110.076023)] (2024) +* [Differentiable MadNIS-Lite](https://arxiv.org/abs/2408.01486) (2024) ### Diffusion Models @@ -1852,6 +1858,8 @@ This review was built with the help of the HEP-ML community, the [INSPIRE REST A * [Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming](https://arxiv.org/abs/2310.05673) (2023) * [Differentiable Vertex Fitting for Jet Flavour Tagging](https://arxiv.org/abs/2310.12804) [[DOI](https://doi.org/10.1103/PhysRevD.110.052010)] (2023) * [Differentiable nuclear deexcitation simulation for low energy neutrino physics](https://arxiv.org/abs/2404.00180) (2024) +* [Differentiable MadNIS-Lite](https://arxiv.org/abs/2408.01486) (2024) +* [Full Detector Simulation of a Projective Dual-Readout Segmented Crystal Electromagnetic Calorimeter with Precision Timing](https://arxiv.org/abs/2408.11027) (2024) ## Uncertainty Quantification ### Interpretability diff --git a/docs/assets/dark_per_year.png b/docs/assets/dark_per_year.png index b2b9a4cceded1bf6af56a288aa7797dda193c8ae..034810cb5977d7f60be120fcc53ea29f19071dc9 100644 GIT binary patch literal 15727 zcmd73WmKDM*Djh6q)?m+#U)s=QnXN@l2Qm(pg`~x3Y4J1ouqWN2~vtXv=n!WdsszQ z(S>_qL2-8pavs)t-}n2@*<1Om~hsls$Y zAQA)!1pai59Jt~t&5;27d*J>I;jZUwp!)f%Mv%H74m+1Y?Nm5K& zOj3mFjk~*xo4mNV<9{9yb9S{A?-8yY00yCSQ8jV{f#_ac{eg3pbL>E%hf`{>$1lB8 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zq~F+18=3odXdXeGrGBF>F)K585fa}<+8**?+@F$O_T!C_z^xFB1s+AKh2PZEmI94Nb{lw4xufDpE_o&Lf1G0W=oT~*OL*;ugkXyx@) zH>2lPZxS%s>_;L|@{+f$wGBPFF2pBv!mHL=yblE4b9KA&?|NWTwhA@j)Lfci>0S>g!a%E+hH%AI5CkU<)ss^f}>BB5C zBbUhX@WrH63vf!{B-R3Z{<(TbZysI>m18Wja8nV>KlKqjU90^H#x!1VnkJ9Ti_gbL zp{)<;@D6i{+1svNpdzUY;N+h(K^)`ao{`c1<=^2bfhnDTtq`;$)nqThV_>t)R&W;} XxfndHqxAfQfIk?06TOnl*n9sAm&OYx diff --git a/docs/index.md b/docs/index.md index ba9a07d..068d907 100644 --- a/docs/index.md +++ b/docs/index.md @@ -132,6 +132,7 @@ const expandElements = shouldExpand => { * [Particle Transformer for Jet Tagging](https://arxiv.org/abs/2202.03772) (2022) * [Public Kaggle Competition ''IceCube -- Neutrinos in Deep Ice''](https://arxiv.org/abs/2307.15289) (2023) * [Electron Energy Regression in the CMS High-Granularity Calorimeter Prototype](https://arxiv.org/abs/2309.06582) (2023) + * [RODEM Jet Datasets](https://arxiv.org/abs/2408.11616) (2024) ## Classification @@ -439,6 +440,7 @@ const expandElements = shouldExpand => { * [A tagger for strange jets based on tracking information using long short-term memory](https://arxiv.org/abs/1907.07505) [[DOI](https://doi.org/10.1088/1748-0221/15/01/P01021)] (2019) * [Maximum performance of strange-jet tagging at hadron colliders](https://arxiv.org/abs/2011.10736) [[DOI](https://doi.org/10.1088/1748-0221/16/08/P08039)] (2020) * [Study of anomalous $W^-W^+\gamma/Z$ couplings using polarizations and spin correlations in $e^-e^+\to W^-W^+$ with polarized beams](https://arxiv.org/abs/2305.15106) [[DOI](https://doi.org/10.1140/epjc/s10052-023-12292-2)] (2023) + * [From strange-quark tagging to fragmentation tagging with machine learning](https://arxiv.org/abs/2408.12377) (2024) #### $b$-tagging @@ -547,6 +549,7 @@ const expandElements = shouldExpand => { * [Boosting probes of CP violation in the top Yukawa coupling with Deep Learning](https://arxiv.org/abs/2405.16499) (2024) * [Learning to see R-parity violating scalar top decays](https://arxiv.org/abs/2406.03096) [[DOI](https://doi.org/10.1103/PhysRevD.110.056006)] (2024) * [Graph Reinforcement Learning for Exploring BSM Model Spaces](https://arxiv.org/abs/2407.07203) (2024) + * [Multiple testing for signal-agnostic searches of new physics with machine learning](https://arxiv.org/abs/2408.12296) (2024) #### Particle identification @@ -625,6 +628,7 @@ const expandElements = shouldExpand => { * [Measurement of atmospheric neutrino oscillation parameters using convolutional neural networks with 9.3 years of data in IceCube DeepCore](https://arxiv.org/abs/2405.02163) (2024) * [RELICS: a REactor neutrino LIquid xenon Coherent elastic Scattering experiment](https://arxiv.org/abs/2405.05554) [[DOI](https://doi.org/10.1103/PhysRevD.110.072011)] (2024) * [Improving Neutrino Energy Reconstruction with Machine Learning](https://arxiv.org/abs/2405.15867) (2024) + * [Enhancing Events in Neutrino Telescopes through Deep Learning-Driven Super-Resolution](https://arxiv.org/abs/2408.08474) (2024) #### Direct Dark Matter Detectors @@ -815,6 +819,7 @@ const expandElements = shouldExpand => { * [Effects of saturation and fluctuating hotspots for flow observables in ultrarelativistic heavy-ion collisions](https://arxiv.org/abs/2407.01338) [[DOI](https://doi.org/10.1103/PhysRevC.110.034911)] (2024) * [AI for Nuclear Physics: the EXCLAIM project](https://arxiv.org/abs/2408.00163) (2024) * [Bayesian Inference analysis of jet quenching using inclusive jet and hadron suppression measurements](https://arxiv.org/abs/2408.08247) (2024) + * [Electron-nucleus cross sections from transfer learning](https://arxiv.org/abs/2408.09936) (2024) ??? example "Learning strategies" @@ -1634,6 +1639,7 @@ const expandElements = shouldExpand => { * [Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows](https://arxiv.org/abs/2405.20407) [[DOI](https://doi.org/10.1088/1748-0221/19/09/P09003)] (2024) * [Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction](https://arxiv.org/abs/2406.01620) (2024) * [PIPPIN: Generating variable length full events from partons](https://arxiv.org/abs/2406.13074) [[DOI](https://doi.org/10.1103/PhysRevD.110.076023)] (2024) + * [Differentiable MadNIS-Lite](https://arxiv.org/abs/2408.01486) (2024) ??? example "Diffusion Models" @@ -2056,6 +2062,8 @@ const expandElements = shouldExpand => { * [Progress in End-to-End Optimization of Detectors for Fundamental Physics with Differentiable Programming](https://arxiv.org/abs/2310.05673) (2023) * [Differentiable Vertex Fitting for Jet Flavour Tagging](https://arxiv.org/abs/2310.12804) [[DOI](https://doi.org/10.1103/PhysRevD.110.052010)] (2023) * [Differentiable nuclear deexcitation simulation for low energy neutrino physics](https://arxiv.org/abs/2404.00180) (2024) + * [Differentiable MadNIS-Lite](https://arxiv.org/abs/2408.01486) (2024) + * [Full Detector Simulation of a Projective Dual-Readout Segmented Crystal Electromagnetic Calorimeter with Precision Timing](https://arxiv.org/abs/2408.11027) (2024) ## Uncertainty Quantification