{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,26]],"date-time":"2026-04-26T08:14:29Z","timestamp":1777191269738,"version":"3.51.4"},"reference-count":117,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,1,18]],"date-time":"2020-01-18T00:00:00Z","timestamp":1579305600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,18]],"date-time":"2020-01-18T00:00:00Z","timestamp":1579305600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["ACI-1450310"],"award-info":[{"award-number":["ACI-1450310"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-1836650"],"award-info":[{"award-number":["OAC-1836650"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["OAC-1841471"],"award-info":[{"award-number":["OAC-1841471"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["PHY-1505463"],"award-info":[{"award-number":["PHY-1505463"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Comput Softw Big Sci"],"published-print":{"date-parts":[[2020,12]]},"DOI":"10.1007\/s41781-020-0035-2","type":"journal-article","created":{"date-parts":[[2020,1,18]],"date-time":"2020-01-18T05:03:51Z","timestamp":1579323831000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":80,"title":["MadMiner: Machine Learning-Based Inference for Particle Physics"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3344-4209","authenticated-orcid":false,"given":"Johann","family":"Brehmer","sequence":"first","affiliation":[]},{"given":"Felix","family":"Kling","sequence":"additional","affiliation":[]},{"given":"Irina","family":"Espejo","sequence":"additional","affiliation":[]},{"given":"Kyle","family":"Cranmer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,1,18]]},"reference":[{"key":"35_CR1","unstructured":"Brehmer J, Cranmer K, Espejo I, Kling F, Louppe G, Pavez J (2019) Effective LHC measurements with matrix elements and machine learning. arxiv: 1906.01578"},{"key":"35_CR2","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/S0010-4655(00)00243-5","volume":"136","author":"KS Cranmer","year":"2001","unstructured":"Cranmer KS (2001) Kernel estimation in high-energy physics. Comput Phys Commun 136:198","journal-title":"Comput Phys Commun"},{"key":"35_CR3","unstructured":"Cranmer K, Lewis G, Moneta L, Shibata A, Verkerke W (2012) (ROOT) HistFactory: a tool for creating statistical models for use with RooFit and RooStats"},{"key":"35_CR4","unstructured":"Frate M, Cranmer K, Kalia S, Vandenberg-Rodes A, Whiteson D (2017) Modeling smooth backgrounds and generic localized signals with gaussian processes. arxiv: 1709.05681"},{"issue":"4","key":"35_CR5","doi-asserted-by":"crossref","first-page":"1151","DOI":"10.1214\/aos\/1176346785","volume":"12","author":"DB Rubin","year":"1984","unstructured":"Rubin DB (1984) Bayesianly justifiable and relevant frequency calculations for the applied statistician. Ann Statist 12(4):1151","journal-title":"Ann Statist"},{"issue":"4","key":"35_CR6","doi-asserted-by":"crossref","first-page":"2025","DOI":"10.1093\/genetics\/162.4.2025","volume":"162","author":"MA Beaumont","year":"2002","unstructured":"Beaumont MA, Zhang W, Balding DJ (2002) Approximate bayesian computation in population genetics. Genetics 162(4):2025","journal-title":"Genetics"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Alsing J, Wandelt B, Feeney S (2018) Massive optimal data compression and density estimation for scalable, likelihood-free inference in cosmology. arxiv: 1801.01497","DOI":"10.1093\/mnras\/sty819"},{"issue":"8","key":"35_CR8","doi-asserted-by":"crossref","first-page":"083004","DOI":"10.1103\/PhysRevD.97.083004","volume":"97","author":"T Charnock","year":"2018","unstructured":"Charnock T, Lavaux G, Wandelt BD (2018) Automatic physical inference with information maximizing neural networks. Phys. Rev. D 97(8):083004","journal-title":"Phys. Rev. D"},{"issue":"7","key":"35_CR9","first-page":"073002","volume":"D95","author":"J Brehmer","year":"2017","unstructured":"Brehmer J, Cranmer K, Kling F, Plehn T (2017) Better Higgs boson measurements through information geometry. Phys Rev D95(7):073002","journal-title":"Phys Rev"},{"issue":"9","key":"35_CR10","first-page":"095017","volume":"D97","author":"J Brehmer","year":"2018","unstructured":"Brehmer J, Kling F, Plehn T, Tait TMP (2018) Better Higgs-CP tests through information geometry. Phys Rev D97(9):095017","journal-title":"Phys Rev"},{"key":"35_CR11","doi-asserted-by":"crossref","first-page":"4126","DOI":"10.1143\/JPSJ.57.4126","volume":"57","author":"K Kondo","year":"1988","unstructured":"Kondo K (1988) Dynamical likelihood method for reconstruction of events with missing momentum. I. Method and toy models. J Phys Soc Jpn 57:4126","journal-title":"J Phys Soc Jpn"},{"key":"35_CR12","doi-asserted-by":"crossref","first-page":"638","DOI":"10.1038\/nature02589","volume":"429","author":"VM Abazov","year":"2004","unstructured":"Abazov VM et al (2004) A precision measurement of the mass of the top quark. Nature 429:638 (DO)","journal-title":"Nature"},{"key":"35_CR13","first-page":"025","volume":"CHARGED2008","author":"P Artoisenet","year":"2008","unstructured":"Artoisenet P, Mattelaer O (2008) MadWeight: automatic event reweighting with matrix elements. PoS CHARGED2008:025","journal-title":"PoS"},{"key":"35_CR14","first-page":"075022","volume":"D81","author":"Y Gao","year":"2010","unstructured":"Gao Y, Gritsan AV, Guo Z, Melnikov K, Schulze M, Tran NV (2010) Spin determination of single-produced resonances at hadron colliders. Phys Rev D81:075022","journal-title":"Phys Rev"},{"key":"35_CR15","first-page":"074010","volume":"D83","author":"J Alwall","year":"2011","unstructured":"Alwall J, Freitas A, Mattelaer O (2011) The matrix element method and QCD radiation. Phys Rev D83:074010","journal-title":"Phys Rev"},{"key":"35_CR16","first-page":"095031","volume":"D86","author":"S Bolognesi","year":"2012","unstructured":"Bolognesi S, Gao Y, Gritsan AV et al (2012) On the spin and parity of a single-produced resonance at the LHC. Phys Rev D86:095031","journal-title":"Phys Rev"},{"issue":"5","key":"35_CR17","first-page":"055006","volume":"D87","author":"P Avery","year":"2013","unstructured":"Avery P et al (2013) Precision studies of the Higgs boson decay channel $$H \\rightarrow ZZ \\rightarrow 4l$$ with MEKD. Phys Rev D87(5):055006","journal-title":"Phys Rev"},{"issue":"1","key":"35_CR18","first-page":"015019","volume":"D87","author":"JR Andersen","year":"2013","unstructured":"Andersen JR, Englert C, Spannowsky M (2013) Extracting precise Higgs couplings by using the matrix element method. Phys Rev D87(1):015019","journal-title":"Phys Rev"},{"issue":"7","key":"35_CR19","first-page":"073005","volume":"D87","author":"JM Campbell","year":"2013","unstructured":"Campbell JM, Ellis RK, Giele WT, Williams C (2013) Finding the Higgs boson in decays to $$Z \\gamma$$ using the matrix element method at Next-to-Leading Order. Phys Rev D87(7):073005","journal-title":"Phys Rev"},{"issue":"9","key":"35_CR20","doi-asserted-by":"crossref","first-page":"091802","DOI":"10.1103\/PhysRevLett.111.091802","volume":"111","author":"P Artoisenet","year":"2013","unstructured":"Artoisenet P, de Aquino P, Maltoni F, Mattelaer O (2013) Unravelling $$t\\overline{t}h$$ via the Matrix Element Method. Phys Rev Lett 111(9):091802","journal-title":"Phys Rev Lett"},{"key":"35_CR21","unstructured":"Gainer JS, Lykken J, Matchev KT, Mrenna S, Park M (2013) The matrix element method: past, present, and future. In: Proceedings of community summer study on the future of U.S. particle physics: snowmass on the Mississippi (CSS2013): Minneapolis, MN, USA, July 29\u2013August 6 2013. arxiv: 1307.3546"},{"key":"35_CR22","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.cpc.2015.02.020","volume":"192","author":"D Schouten","year":"2015","unstructured":"Schouten D, DeAbreu A, Stelzer B (2015) Accelerated matrix element method with parallel computing. Comput Phys Commun 192:54","journal-title":"Comput Phys Commun"},{"key":"35_CR23","doi-asserted-by":"crossref","first-page":"083","DOI":"10.1007\/JHEP09(2015)083","volume":"09","author":"T Martini","year":"2015","unstructured":"Martini T, Uwer P (2015) Extending the matrix element method beyond the born approximation: calculating event weights at next-to-leading order accuracy. JHEP 09:083","journal-title":"JHEP"},{"issue":"5","key":"35_CR24","first-page":"055023","volume":"D94","author":"AV Gritsan","year":"2016","unstructured":"Gritsan AV, R\u00f6ntsch R, Schulze M, Xiao M (2016) Constraining anomalous Higgs boson couplings to the heavy flavor fermions using matrix element techniques. Phys Rev D94(5):055023","journal-title":"Phys Rev"},{"key":"35_CR25","unstructured":"Martini T, Uwer P (2017) The Matrix Element Method at next-to-leading order QCD for hadronic collisions: single top-quark production at the LHC as an example application. arxiv: 1712.04527"},{"key":"35_CR26","unstructured":"Kraus M, Martini T, Uwer P (2019) Predicting event weights at next-to-leading order QCD for jet events defined by $$2\\rightarrow 1$$ jet algorithms. arxiv: 1901.08008"},{"key":"35_CR27","first-page":"2405","volume":"D45","author":"D Atwood","year":"1992","unstructured":"Atwood D, Soni A (1992) Analysis for magnetic moment and electric dipole moment form-factors of the top quark via $$e^+ e^- \\rightarrow t \\bar{t}$$. Phys Rev D45:2405","journal-title":"Phys Rev"},{"key":"35_CR28","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/0370-2693(93)90101-M","volume":"B306","author":"M Davier","year":"1993","unstructured":"Davier M, Duflot L, Le Diberder F, Rouge A (1993) The Optimal method for the measurement of tau polarization. Phys Lett B306:411","journal-title":"Phys Lett"},{"key":"35_CR29","first-page":"397","volume":"C62","author":"M Diehl","year":"1994","unstructured":"Diehl M, Nachtmann O (1994) Optimal observables for the measurement of three gauge boson couplings in $$e^+ e^- \\rightarrow W^+ W^-$$. Z Phys C62:397","journal-title":"Z Phys"},{"key":"35_CR30","first-page":"074002","volume":"D84","author":"DE Soper","year":"2011","unstructured":"Soper DE, Spannowsky M (2011) Finding physics signals with shower deconstruction. Phys Rev D84:074002","journal-title":"Phys Rev"},{"key":"35_CR31","first-page":"054012","volume":"D87","author":"DE Soper","year":"2013","unstructured":"Soper DE, Spannowsky M (2013) Finding top quarks with shower deconstruction. Phys Rev D87:054012","journal-title":"Phys Rev"},{"issue":"9","key":"35_CR32","first-page":"094005","volume":"D89","author":"DE Soper","year":"2014","unstructured":"Soper DE, Spannowsky M (2014) Finding physics signals with event deconstruction. Phys Rev D89(9):094005","journal-title":"Phys Rev"},{"key":"35_CR33","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.physletb.2016.02.074","volume":"B756","author":"C Englert","year":"2016","unstructured":"Englert C, Mattelaer O, Spannowsky M (2016) Measuring the Higgs-bottom coupling in weak boson fusion. Phys Lett B756:103","journal-title":"Phys Lett"},{"key":"35_CR34","unstructured":"Fan Y, Nott DJ, Sisson SA (2012) Approximate Bayesian computation via regression density estimation. ArXiv e-prints arxiv: 1212.1479"},{"key":"35_CR35","unstructured":"Dinh L, Krueger D, Bengio Y (2014) NICE: Non-linear Independent Components Estimation. ArXiv e-prints arxiv: 1410.8516"},{"key":"35_CR36","unstructured":"Germain M, Gregor K, Murray I, Larochelle H (2015) MADE: masked autoencoder for distribution estimation. ArXiv e-prints arxiv: 1502.03509"},{"key":"35_CR37","unstructured":"Cranmer K, Pavez J, Louppe G (2015) Approximating likelihood ratios with calibrated discriminative classifiers. arxiv: 1506.02169"},{"key":"35_CR38","doi-asserted-by":"crossref","unstructured":"Cranmer K, Louppe G (2016) Unifying generative models and exact likelihood-free inference with conditional bijections. J. Brief Ideas","DOI":"10.21105\/joss.00011"},{"issue":"1","key":"35_CR39","doi-asserted-by":"crossref","first-page":"11","DOI":"10.21105\/joss.00011","volume":"1","author":"G Louppe","year":"2016","unstructured":"Louppe G, Cranmer K, Pavez J (2016) carl: a likelihood-free inference toolbox. J Open Source Softw 1(1):11","journal-title":"J Open Source Softw"},{"key":"35_CR40","unstructured":"Dinh L, Sohl-Dickstein J, Bengio S (2016) Density estimation using Real NVP. ArXiv e-prints arxiv: 1605.08803"},{"key":"35_CR41","unstructured":"Papamakarios G, Murray I (2016) Fast $$\\epsilon$$-free inference of simulation models with Bayesian conditional density estimation. arXiv e-prints arXiv:1605.06376"},{"key":"35_CR42","unstructured":"Dutta R, Corander J, Kaski S, Gutmann MU (2016) Likelihood-free inference by ratio estimation. ArXiv e-prints arxiv: 1611.10242"},{"key":"35_CR43","unstructured":"Uria B, C\u00f4t\u00e9 M-A, Gregor K, Murray I, Larochelle H (2016) Neural autoregressive distribution estimation. ArXiv e-prints arxiv: 1605.02226"},{"key":"35_CR44","unstructured":"Gutmann MU, Dutta R, Kaski S, Corander J (2017) Likelihood-free inference via classification. Stat Comput 1\u201315"},{"key":"35_CR45","unstructured":"Tran D, Ranganath R, Blei DM (2017) Hierarchical implicit models and likelihood-free variational inference. ArXiv e-prints arxiv: 1702.08896"},{"key":"35_CR46","unstructured":"Louppe G, Cranmer K (2017) Adversarial variational optimization of non-differentiable simulators. ArXiv e-prints arxiv: 1707.07113"},{"key":"35_CR47","unstructured":"Papamakarios G, Pavlakou T, Murray I (2017) Masked autoregressive flow for density estimation. ArXiv e-prints arxiv: 1705.07057"},{"key":"35_CR48","unstructured":"Lueckmann J-M, Goncalves PJ, Bassetto G, \u00d6cal K, Nonnenmacher M, Macke JH (2017) Flexible statistical inference for mechanistic models of neural dynamics. arXiv e-prints arXiv:1711.01861"},{"key":"35_CR49","unstructured":"Huang C-W, Krueger D, Lacoste A, Courville A (2018) Neural autoregressive flows. ArXiv e-prints arxiv: 1804.00779"},{"key":"35_CR50","unstructured":"Papamakarios G, Sterratt DC, Murray I (2018) Sequential neural likelihood: fast likelihood-free inference with autoregressive flows. ArXiv e-prints arxiv: 1805.07226"},{"key":"35_CR51","unstructured":"Lueckmann J-M, Bassetto G, Karaletsos T, Macke JH (2018) Likelihood-free inference with emulator networks. arXiv e-prints arXiv:1805.09294"},{"key":"35_CR52","unstructured":"Chen TQ, Rubanova Y, Bettencourt J, Duvenaud DK (2018) Neural ordinary differential equations. CoRR arxiv: abs\/1806.07366"},{"key":"35_CR53","unstructured":"Kingma DP, Dhariwal P (2018) Glow: generative flow with invertible 1x1 convolutions. arXiv e-prints arXiv:1807.03039,"},{"key":"35_CR54","unstructured":"Grathwohl W, Chen RTQ, Bettencourt J, Sutskever I, Duvenaud D (2018) FFJORD: free-form continuous dynamics for scalable reversible generative models. ArXiv e-prints arxiv: 1810.01367"},{"key":"35_CR55","unstructured":"Dinev T, Gutmann MU (2018) Dynamic likelihood-free inference via ratio estimation (DIRE). arXiv e-prints arXiv:1810.09899"},{"key":"35_CR56","unstructured":"Hermans J, Begy V, Louppe G (2019) Likelihood-free MCMC with approximate likelihood ratios. arxiv: 1903.04057"},{"key":"35_CR57","doi-asserted-by":"crossref","unstructured":"Alsing J, Charnock T, Feeney S, Wandelt B (2019) Fast likelihood-free cosmology with neural density estimators and active learning. arxiv: 1903.00007","DOI":"10.1093\/mnras\/stz1960"},{"key":"35_CR58","unstructured":"Greenberg DS, Nonnenmacher M, Macke JH (2019) Automatic posterior transformation for likelihood-free inference. arXiv e-prints arXiv:1905.07488"},{"key":"35_CR59","unstructured":"Brehmer J, Louppe G, Pavez J, Cranmer K (2018) Mining gold from implicit models to improve likelihood-free inference. arxiv: 1805.12244"},{"issue":"11","key":"35_CR60","doi-asserted-by":"crossref","first-page":"111801","DOI":"10.1103\/PhysRevLett.121.111801","volume":"121","author":"J Brehmer","year":"2018","unstructured":"Brehmer J, Cranmer K, Louppe G, Pavez J (2018) Constraining effective field theories with machine learning. Phys Rev Lett 121(11):111801","journal-title":"Phys Rev Lett"},{"issue":"5","key":"35_CR61","doi-asserted-by":"crossref","first-page":"052004","DOI":"10.1103\/PhysRevD.98.052004","volume":"98","author":"J Brehmer","year":"2018","unstructured":"Brehmer J, Cranmer K, Louppe G, Pavez J (2018) A guide to constraining effective field theories with machine learning. Phys Rev D 98(5):052004","journal-title":"Phys Rev D"},{"key":"35_CR62","unstructured":"Stoye M, Brehmer J, Louppe G, Pavez J, Cranmer K (2018) Likelihood-free inference with an improved cross-entropy estimator. arxiv: 1808.00973"},{"key":"35_CR63","doi-asserted-by":"crossref","first-page":"079","DOI":"10.1007\/JHEP07(2014)079","volume":"07","author":"J Alwall","year":"2014","unstructured":"Alwall J, Frederix R, Frixione S et al (2014) The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations. JHEP 07:079","journal-title":"JHEP"},{"key":"35_CR64","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1016\/j.cpc.2008.01.036","volume":"178","author":"T Sjostrand","year":"2008","unstructured":"Sjostrand T, Mrenna S, Skands PZ (2008) A Brief Introduction to PYTHIA 8.1. Comput Phys Commun 178:852","journal-title":"Comput Phys Commun"},{"key":"35_CR65","doi-asserted-by":"crossref","first-page":"057","DOI":"10.1007\/JHEP02(2014)057","volume":"02","author":"J de Favereau","year":"2014","unstructured":"de Favereau J, Delaere C, Demin P et al (2014) (DELPHES 3): DELPHES 3, A modular framework for fast simulation of a generic collider experiment. JHEP 02:057","journal-title":"JHEP"},{"key":"35_CR66","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/S0168-9002(03)01368-8","volume":"A506","author":"S Agostinelli","year":"2003","unstructured":"Agostinelli S et al (2003) (GEANT4): GEANT4: A Simulation toolkit. Nucl. Instrum. Meth. A506:250","journal-title":"Nucl. Instrum. Meth."},{"key":"35_CR67","unstructured":"Cranmer K Practical Statistics for the LHC. In Proceedings, 2011 European School of High-Energy Physics (ESHEP 2011): Cheile Gradistei, Romania, September 7\u201320, 2011, pp 267-308, 2015. [247(2015)] arxiv: 1503.07622"},{"issue":"5","key":"35_CR68","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1140\/epjc\/s10052-016-4099-4","volume":"C76","author":"P Baldi","year":"2016","unstructured":"Baldi P, Cranmer K, Faucett T, Sadowski P, Whiteson D (2016) Parameterized neural networks for high-energy physics. Eur Phys J C76(5):235","journal-title":"Eur Phys J"},{"issue":"1","key":"35_CR69","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1214\/aoms\/1177732360","volume":"9","author":"SS Wilks","year":"1938","unstructured":"Wilks SS (1938) The large-sample distribution of the likelihood ratio for testing composite hypotheses. Ann Math Stat 9(1):60","journal-title":"Ann Math Stat"},{"issue":"3","key":"35_CR70","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1090\/S0002-9947-1943-0012401-3","volume":"54","author":"A Wald","year":"1943","unstructured":"Wald A (1943) Tests of statistical hypotheses concerning several parameters when the number of observations is large. Trans Am Math Soc 54(3):426","journal-title":"Trans Am Math Soc"},{"key":"35_CR71","doi-asserted-by":"crossref","first-page":"1554","DOI":"10.1140\/epjc\/s10052-011-1554-0","volume":"71","author":"G Cowan","year":"2011","unstructured":"Cowan G, Cranmer K, Gross E, Vitells O (2011) Asymptotic formulae for likelihood-based tests of new physics. Eur Phys J C 71:1554 (Erratum: Eur Phys J C73:2501\u20132013)","journal-title":"Eur Phys J C"},{"issue":"1","key":"35_CR72","doi-asserted-by":"crossref","first-page":"L60","DOI":"10.1093\/mnrasl\/sly029","volume":"476","author":"J Alsing","year":"2018","unstructured":"Alsing J, Wandelt B (2018) Generalized massive optimal data compression. Mon Not R Astron So. 476(1):L60","journal-title":"Mon Not R Astron So."},{"issue":"6","key":"35_CR73","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1176343282","volume":"3","author":"B Efron","year":"1975","unstructured":"Efron B (1975) Defining the curvature of a statistical problem (with applications to second order efficiency). Ann Stat 3(6):1189","journal-title":"Ann Stat"},{"issue":"2","key":"35_CR74","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1214\/aos\/1176345779","volume":"10","author":"S-I Amari","year":"1982","unstructured":"Amari S-I (1982) Differential geometry of curved exponential families-curvatures and information loss. Ann Statist 10(2):357","journal-title":"Ann Statist"},{"key":"35_CR75","unstructured":"Brehmer J (2017) New ideas for effective higgs measurements. Ph.D. thesis, U. Heidelberg (main) http:\/\/www.thphys.uni-heidelberg.de\/~plehn\/includes\/theses\/brehmer_d.pdf"},{"key":"35_CR76","first-page":"81","volume":"37","author":"C Radhakrishna Rao","year":"1945","unstructured":"Radhakrishna Rao C (1945) Information and the accuracy attainable in the estimation of statistical parameters. Bull Calcutta Math Soc 37:81","journal-title":"Bull Calcutta Math Soc"},{"key":"35_CR77","unstructured":"Cram\u00e9r H (1946) Mathematical methods of statistics. Princeton University Press, ISBN 0691080046"},{"issue":"02","key":"35_CR78","doi-asserted-by":"crossref","first-page":"021","DOI":"10.1088\/1475-7516\/2018\/02\/021","volume":"1802","author":"TDP Edwards","year":"2018","unstructured":"Edwards TDP, Weniger C (2018) A fresh approach to forecasting in astroparticle physics and dark matter searches. JCAP 1802(02):021","journal-title":"JCAP"},{"key":"35_CR79","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1016\/j.cpc.2012.01.022","volume":"183","author":"C Degrande","year":"2012","unstructured":"Degrande C, Duhr C, Fuks B, Grellscheid D, Mattelaer O, Reiter T (2012) UFO\u2014The Universal FeynRules Output. Comput Phys Commun 183:1201","journal-title":"Comput Phys Commun"},{"issue":"12","key":"35_CR80","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1140\/epjc\/s10052-016-4533-7","volume":"C76","author":"O Mattelaer","year":"2016","unstructured":"Mattelaer O (2016) On the maximal use of Monte Carlo samples: re-weighting events at NLO accuracy. Eur Phys J C76(12):674","journal-title":"Eur Phys J"},{"key":"35_CR81","unstructured":"Aad G et\u00a0al (2015) A morphing technique for signal modelling in a multidimensional space of coupling parameters. Physics note ATL-PHYS-PUB-2015-047. http:\/\/cds.cern.ch\/record\/2066980 (ATLAS)"},{"key":"35_CR82","doi-asserted-by":"crossref","unstructured":"Alsing J, Wandelt B (2019) Nuisance hardened data compression for fast likelihood-free inference. arxiv: 1903.01473","DOI":"10.1093\/mnras\/stz1900"},{"key":"35_CR83","doi-asserted-by":"publisher","unstructured":"Lukas M Feickert, Stark G, Turra R, Forde J (2018) diana-hep\/pyhf v0.0.15 https:\/\/doi.org\/10.5281\/zenodo.1464139","DOI":"10.5281\/zenodo.1464139"},{"key":"35_CR84","doi-asserted-by":"crossref","first-page":"099","DOI":"10.1007\/JHEP02(2012)099","volume":"02","author":"R Frederix","year":"2012","unstructured":"Frederix R, Frixione S, Hirschi V, Maltoni F, Pittau R, Torrielli P (2012) Four-lepton production at hadron colliders: aMC@NLO predictions with theoretical uncertainties. JHEP 02:099","journal-title":"JHEP"},{"key":"35_CR85","unstructured":"Paszke A, Gross S, Chintala S et\u00a0al. (2017) Automatic differentiation in pytorch. In: NIPS-W"},{"issue":"1","key":"35_CR86","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","volume":"12","author":"N Qian","year":"1999","unstructured":"Qian N (1999) On the momentum term in gradient descent learning algorithms. Neural Netw 12(1):145","journal-title":"Neural Netw"},{"key":"35_CR87","unstructured":"Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv e-prints arXiv:1412.6980"},{"key":"35_CR88","unstructured":"Reddi SJ, Kale S, Kumar S (2018) On the convergence of adam and beyond. In: International conference on learning representations"},{"key":"35_CR89","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2016) Simple and scalable predictive uncertainty estimation using deep ensembles. arXiv e-prints arXiv:1612.01474"},{"key":"35_CR90","doi-asserted-by":"publisher","unstructured":"Brehmer J, Kling F, Espejo I, Cranmer K (2019) MadMiner code repository. https:\/\/doi.org\/10.5281\/zenodo.1489147","DOI":"10.5281\/zenodo.1489147"},{"key":"35_CR91","unstructured":"Brehmer J, Kling F, Espejo I, Cranmer K (2019) MadMiner technical documentation. https:\/\/madminer.readthedocs.io\/en\/latest\/"},{"key":"35_CR92","unstructured":"Espejo I, Brehmer J, Cranmer K (2019) MadMiner Docker repositories. https:\/\/hub.docker.com\/u\/madminertool"},{"key":"35_CR93","unstructured":"\u0160imko T, Heinrich L, Hirvonsalo H, Kousidis D, Rodr\u00edguez D (2018) REANA: a system for reusable research data analyses. Technical Report CERN-IT-2018-003, CERN, Geneva. https:\/\/cds.cern.ch\/record\/2652340"},{"key":"35_CR94","unstructured":"Espejo I, Brehmer J, Kling F, Cranmer K (2019) MadMiner Reana deployment. https:\/\/github.com\/irinaespejo\/workflow-madminer"},{"key":"35_CR95","unstructured":"The HDF Group: Hierarchical data format version 5, 2000\u20132010. http:\/\/www.hdfgroup.org\/HDF5"},{"key":"35_CR96","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/S0010-4655(00)00189-2","volume":"134","author":"M Dobbs","year":"2001","unstructured":"Dobbs M, Hansen JB (2001) The HepMC C++ Monte Carlo event record for High Energy Physics. Comput Phys Commun 134:41","journal-title":"Comput Phys Commun"},{"key":"35_CR97","doi-asserted-by":"publisher","unstructured":"Rodrigues E, Marinangeli M, Pollack B et\u00a0al (2019) scikit-hep\/scikit-hep: scikit-hep-0.5.1 https:\/\/doi.org\/10.5281\/zenodo.3234683","DOI":"10.5281\/zenodo.3234683"},{"key":"35_CR98","unstructured":"Oliphant T (2006): NumPy: A guide to NumPy. USA: Trelgol Publishing. http:\/\/www.numpy.org\/"},{"key":"35_CR99","doi-asserted-by":"crossref","first-page":"023001","DOI":"10.1088\/0954-3899\/43\/2\/023001","volume":"G43","author":"J Butterworth","year":"2016","unstructured":"Butterworth J et al (2016) PDF4LHC recommendations for LHC Run II. J Phys G43:023001","journal-title":"J Phys"},{"key":"35_CR100","unstructured":"de Florian D et al, (LHC Higgs Cross Section Working Group) (2016) Handbook of LHC Higgs cross sections: 4. Deciphering the Nature of the Higgs Sector arXiv:1610:07922"},{"key":"35_CR101","doi-asserted-by":"crossref","first-page":"045","DOI":"10.1088\/1126-6708\/2007\/06\/045","volume":"06","author":"GF Giudice","year":"2007","unstructured":"Giudice GF, Grojean C, Pomarol A, Rattazzi R (2007) The strongly-interacting light Higgs. JHEP 06:045","journal-title":"JHEP"},{"key":"35_CR102","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/JHEP04(2014)110","volume":"04","author":"A Alloul","year":"2014","unstructured":"Alloul A, Fuks B, Sanz V (2014) Phenomenology of the Higgs Effective Lagrangian via FEYNRULES. JHEP 04:110","journal-title":"JHEP"},{"key":"35_CR103","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/JHEP10(2016)123","volume":"10","author":"F Maltoni","year":"2016","unstructured":"Maltoni F, Vryonidou E, Zhang C (2016) Higgs production in association with a top-antitop pair in the standard model effective field theory at NLO in QCD. JHEP 10:123","journal-title":"JHEP"},{"key":"35_CR104","unstructured":"Cepeda M, et\u00a0al (Physics of the HL-LHC Working Group) (2019) Higgs physics at the HL-LHC and HE-LHC. arxiv: 1902.00134"},{"issue":"5","key":"35_CR105","first-page":"054002","volume":"D89","author":"T Plehn","year":"2014","unstructured":"Plehn T, Schichtel P, Wiegand D (2014) Where boosted significances come from. Phys Rev D89(5):054002","journal-title":"Phys Rev"},{"issue":"3","key":"35_CR106","first-page":"035026","volume":"D95","author":"F Kling","year":"2017","unstructured":"Kling F, Plehn T, Schichtel P (2017) Maximizing the significance in Higgs boson pair analyses. Phys Rev D95(3):035026","journal-title":"Phys Rev"},{"issue":"11","key":"35_CR107","first-page":"113004","volume":"D97","author":"D Gon\u00e7alves","year":"2018","unstructured":"Gon\u00e7alves D, Han T, Kling F, Plehn T, Takeuchi M (2018) Higgs boson pair production at future hadron colliders: From kinematics to dynamics. Phys Rev D97(11):113004","journal-title":"Phys Rev"},{"key":"35_CR108","first-page":"239","volume":"2014","author":"D Merkel","year":"2014","unstructured":"Merkel D (2014) Docker: Lightweight linux containers for consistent development and deployment. Linux J 2014:239","journal-title":"Linux J"},{"key":"35_CR109","unstructured":"Kluyver T, Ragan-Kelley B, P\u00e9rez F et\u00a0al. (2016) Jupyter notebooks\u2014a publishing format for reproducible computational workflows. In: ELPUB"},{"issue":"3","key":"35_CR110","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1109\/MCSE.2007.55","volume":"9","author":"JD Hunter","year":"2007","unstructured":"Hunter JD (2007) Matplotlib: A 2d graphics environment. Comput Sci Eng 9(3):90","journal-title":"Comput Sci Eng"},{"key":"35_CR111","doi-asserted-by":"publisher","unstructured":"Lukas: lukasheinrich\/pylhe v0.0.4, 2018. https:\/\/doi.org\/10.5281\/zenodo.1217032","DOI":"10.5281\/zenodo.1217032"},{"key":"35_CR112","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.cpc.2015.01.024","volume":"191","author":"T Sjstrand","year":"2015","unstructured":"Sjstrand T, Ask S, Christiansen JR et al (2015) An Introduction to PYTHIA 8.2. Comput Phys Commun 191:159","journal-title":"Comput Phys Commun"},{"key":"35_CR113","unstructured":"Van Rossum G, Drake FL Jr (1995) Python tutorial. Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands"},{"key":"35_CR114","unstructured":"Rodrigues E (2019) The Scikit-HEP Project. In: 23rd International conference on computing in high energy and nuclear physics (CHEP 2018) Sofia, Bulgaria, 9\u201313 July 2018. arxiv: 1905.00002"},{"key":"35_CR115","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825","journal-title":"J Mach Learn Res"},{"key":"35_CR116","doi-asserted-by":"publisher","unstructured":"Pivarski J, Das P, Smirnov D et\u00a0al. (2019) scikit-hep\/uproot: 3.7.2. https:\/\/doi.org\/10.5281\/zenodo.3256257","DOI":"10.5281\/zenodo.3256257"},{"key":"35_CR117","doi-asserted-by":"publisher","unstructured":"Heinrich L, Cranmer K (2017) diana-hep\/yadage v0.12.13. https:\/\/doi.org\/10.5281\/zenodo.1001816","DOI":"10.5281\/zenodo.1001816"}],"container-title":["Computing and Software for Big Science"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41781-020-0035-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s41781-020-0035-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s41781-020-0035-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,11]],"date-time":"2022-10-11T21:37:28Z","timestamp":1665524248000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s41781-020-0035-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,18]]},"references-count":117,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["35"],"URL":"https:\/\/doi.org\/10.1007\/s41781-020-0035-2","relation":{},"ISSN":["2510-2036","2510-2044"],"issn-type":[{"value":"2510-2036","type":"print"},{"value":"2510-2044","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,18]]},"assertion":[{"value":"8 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"3"}}