{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:42:45Z","timestamp":1775608965516,"version":"3.50.1"},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"National Science Foundation","award":["OAC-1940074, OAC-1940209, OAC-1940080"],"award-info":[{"award-number":["OAC-1940074, OAC-1940209, OAC-1940080"]}]},{"name":"National Science Foundation","award":["OAC-1940074, OAC-1940209, OAC-1940080"],"award-info":[{"award-number":["OAC-1940074, OAC-1940209, OAC-1940080"]}]},{"name":"National Science Foundation","award":["OAC-1940074, OAC-1940209, OAC-1940080"],"award-info":[{"award-number":["OAC-1940074, OAC-1940209, OAC-1940080"]}]},{"name":"National Science Foundation","award":["OAC-1940074, OAC-1940209, OAC-1940080"],"award-info":[{"award-number":["OAC-1940074, OAC-1940209, OAC-1940080"]}]},{"name":"National Science Foundation","award":["OAC-1940074, OAC-1940209, OAC-1940080"],"award-info":[{"award-number":["OAC-1940074, OAC-1940209, OAC-1940080"]}]},{"name":"National Science Foundation","award":["OAC-1940074, OAC-1940209, OAC-1940080"],"award-info":[{"award-number":["OAC-1940074, OAC-1940209, OAC-1940080"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EURASIP J. Adv. Signal Process."],"DOI":"10.1186\/s13634-024-01201-8","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T22:57:16Z","timestamp":1736809036000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Role of denoisers in simulation-based inference from graph-structured data: a case study for position inference in an astroparticle detector"],"prefix":"10.1186","volume":"2025","author":[{"given":"Venkat","family":"Roy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aaron","family":"Higuera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shixiao","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christina","family":"Peters","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Waheed U.","family":"Bajwa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christopher D.","family":"Tunnell","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"issue":"2","key":"1201_CR1","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1111\/j.2517-6161.1984.tb01290.x","volume":"46","author":"PJ Diggle","year":"1984","unstructured":"P.J. Diggle, R.J. Gratton, Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society: Series B (Methodological) 46(2), 193\u2013212 (1984)","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"issue":"48","key":"1201_CR2","doi-asserted-by":"publisher","first-page":"30055","DOI":"10.1073\/pnas.1912789117","volume":"117","author":"K Cranmer","year":"2020","unstructured":"K. Cranmer, J. Brehmer, G. Louppe, The frontier of simulation-based inference. Proceedings of the National Academy of Sciences 117(48), 30055\u201330062 (2020)","journal-title":"Proceedings of the National Academy of Sciences"},{"issue":"4","key":"1201_CR3","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1093\/genetics\/162.4.2025","volume":"162","author":"MA Beaumont","year":"2002","unstructured":"M.A. Beaumont, W. Zhang, D.J. Balding, Approximate Bayesian computation in population genetics. Genetics 162(4), 2025\u20132035 (2002)","journal-title":"Genetics"},{"key":"1201_CR4","doi-asserted-by":"crossref","unstructured":"Donald\u00a0B. Rubin, \u201cBayesianly justifiable and relevant frequency calculations for the applied statistician,\u201d The Annals of Statistics, pp. 1151\u20131172, (1984)","DOI":"10.1214\/aos\/1176346785"},{"issue":"4","key":"1201_CR5","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/MSP.2017.2693418","volume":"34","author":"MM Bronstein","year":"2017","unstructured":"M.M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, Geometric deep learning: Going beyond Euclidean data. IEEE Signal Processing Magazine 34(4), 18\u201342 (2017)","journal-title":"IEEE Signal Processing Magazine"},{"issue":"5","key":"1201_CR6","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6633\/ac5754","volume":"85","author":"J Billard","year":"2022","unstructured":"J. Billard, M. Boulay, S. Cebri\u00e1n, L. Covi, G. Fiorillo, A. Green, J. Kopp, B. Majorovits, K. Palladino, F. Petricca et al., Direct detection of dark matter\u2014APPEC committee report. Reports on Progress in Physics 85(5), 056201 (2022)","journal-title":"Reports on Progress in Physics"},{"issue":"3","key":"1201_CR7","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.66.032005","volume":"66","author":"S Yellin","year":"2002","unstructured":"S. Yellin, Finding an upper limit in the presence of an unknown background. Physical Review D 66(3), 032005 (2002)","journal-title":"Physical Review D"},{"key":"1201_CR8","unstructured":"M.U. Gutmann, J.\u00a0Corander et\u00a0al., \u201cBayesian optimization for likelihood-free inference of simulator-based statistical models,\u201d Journal of Machine Learning Research, (2016)"},{"key":"1201_CR9","doi-asserted-by":"crossref","unstructured":"A.D. Gordon, T.A. Henzinger, A.V. Nori, S.K. Rajamani, \u201cProbabilistic programming,\u201d in Future of Software Engineering Proceedings, pp. 167\u2013181. Association for Computing Machinery, New York, NY., (2014)","DOI":"10.1145\/2593882.2593900"},{"key":"1201_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2020.103647","volume":"134","author":"T Shan","year":"2020","unstructured":"T. Shan, J. Wang, F. Chen, P. Szenher, B. Englot, Simulation-based LIDAR super-resolution for ground vehicles. Robotics and Autonomous Systems 134, 103647 (2020)","journal-title":"Robotics and Autonomous Systems"},{"key":"1201_CR11","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1109\/TCI.2022.3176536","volume":"8","author":"S Mohan","year":"2022","unstructured":"S. Mohan, R. Manzorro, J.L. Vincent, B. Tang, D.Y. Sheth, E.P. Simoncelli, D.S. Matteson, P.A. Crozier, C. Fernandez-Granda, Deep denoising for scientific discovery: A case study in electron microscopy. IEEE Transactions on Computational Imaging 8, 585\u2013597 (2022)","journal-title":"IEEE Transactions on Computational Imaging"},{"issue":"3","key":"1201_CR12","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","volume":"30","author":"DI Shuman","year":"2013","unstructured":"D.I. Shuman, S.K. Narang, P. Frossard, A. Ortega, P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Processing Magazine 30(3), 83\u201398 (2013)","journal-title":"IEEE Signal Processing Magazine"},{"issue":"5","key":"1201_CR13","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1109\/JPROC.2018.2820126","volume":"106","author":"A Ortega","year":"2018","unstructured":"A. Ortega, P. Frossard, J. Kova\u010devi\u0107, J.M.F. Moura, P. Vandergheynst, Graph signal processing: Overview, challenges, and applications. Proceedings of the IEEE 106(5), 808\u2013828 (2018)","journal-title":"Proceedings of the IEEE"},{"issue":"3","key":"1201_CR14","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MSP.2018.2890143","volume":"36","author":"G Mateos","year":"2019","unstructured":"G. Mateos, S. Segarra, A.G. Marques, A. Ribeiro, Connecting the dots: Identifying network structure via graph signal processing. IEEE Signal Processing Magazine 36(3), 16\u201343 (2019)","journal-title":"IEEE Signal Processing Magazine"},{"key":"1201_CR15","unstructured":"P.W. Battaglia, J.B. Hamrick, V.\u00a0Bapst, A.\u00a0Sanchez-Gonzalez, V.\u00a0Zambaldi, M.\u00a0Malinowski, A.\u00a0Tacchetti, D.\u00a0Raposo, A.\u00a0Santoro, R.\u00a0Faulkner et\u00a0al., \u201cRelational inductive biases, deep learning, and graph networks,\u201d arXiv preprint arXiv:1806.01261, (2018)"},{"key":"1201_CR16","unstructured":"V.\u00a0Kalofolias, N.\u00a0Perraudin, \u201cLarge scale graph learning from smooth signals,\u201d arXiv preprint arXiv:1710.05654, (2017)"},{"issue":"3","key":"1201_CR17","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1109\/TSIPN.2017.2731051","volume":"3","author":"S Segarra","year":"2017","unstructured":"S. Segarra, A.G. Marques, G. Mateos, A. Ribeiro, Network topology inference from spectral templates. IEEE Transactions on Signal and Information Processing over Networks 3(3), 467\u2013483 (2017)","journal-title":"IEEE Transactions on Signal and Information Processing over Networks"},{"key":"1201_CR18","doi-asserted-by":"crossref","unstructured":"S.\u00a0Chen, A.\u00a0Sandryhaila, J.\u00a0MF Moura, J.\u00a0Kovacevic, \u201cSignal denoising on graphs via graph filtering,\u201d in Proc. 2014 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, pp. 872\u2013876 (2014)","DOI":"10.1109\/GlobalSIP.2014.7032244"},{"key":"1201_CR19","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Yazaki, Y.\u00a0Tanaka, S.H. Chan, \u201cInterpolation and denoising of graph signals using plug-and-play ADMM,\u201d in Proc. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5431\u20135435 (2019)","DOI":"10.1109\/ICASSP.2019.8682282"},{"issue":"23","key":"1201_CR20","doi-asserted-by":"publisher","first-page":"6160","DOI":"10.1109\/TSP.2016.2602809","volume":"64","author":"X Dong","year":"2016","unstructured":"X. Dong, D. Thanou, P. Frossard, P. Vandergheynst, Learning Laplacian matrix in smooth graph signal representations. IEEE Transactions on Signal Processing 64(23), 6160\u20136173 (2016)","journal-title":"IEEE Transactions on Signal Processing"},{"key":"1201_CR21","doi-asserted-by":"crossref","unstructured":"S.P. Chepuri, S.\u00a0Liu, G.\u00a0Leus, A.O. Hero, \u201cLearning sparse graphs under smoothness prior,\u201d in Proc. 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6508\u20136512 (2017)","DOI":"10.1109\/ICASSP.2017.7953410"},{"issue":"3","key":"1201_CR22","doi-asserted-by":"publisher","first-page":"1171","DOI":"10.1214\/009053607000000677","volume":"36","author":"T Hofmann","year":"2008","unstructured":"T. Hofmann, B. Sch\u00f6lkopf, A.J. Smola, Kernel methods in machine learning. The Annals of Statistics 36(3), 1171\u20131220 (2008)","journal-title":"The Annals of Statistics"},{"issue":"6","key":"1201_CR23","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1109\/TIP.2010.2042995","volume":"19","author":"P Bouboulis","year":"2010","unstructured":"P. Bouboulis, K. Slavakis, S. Theodoridis, Adaptive kernel-based image denoising employing semi-parametric regularization. IEEE Transactions on Image Processing 19(6), 1465\u20131479 (2010)","journal-title":"IEEE Transactions on Image Processing"},{"key":"1201_CR24","doi-asserted-by":"crossref","unstructured":"A.J. Smola, R.\u00a0Kondor, \u201cKernels and regularization on graphs,\u201d in Learning Theory and Kernel Machines, pp. 144\u2013158. Springer, (2003)","DOI":"10.1007\/978-3-540-45167-9_12"},{"issue":"3","key":"1201_CR25","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1109\/TSP.2016.2620116","volume":"65","author":"D Romero","year":"2016","unstructured":"D. Romero, M. Ma, G.B. Giannakis, Kernel-based reconstruction of graph signals. IEEE Transactions on Signal Processing 65(3), 764\u2013778 (2016)","journal-title":"IEEE Transactions on Signal Processing"},{"key":"1201_CR26","doi-asserted-by":"crossref","unstructured":"R.\u00a0Kondor, J.P. Vert, \u201cDiffusion kernels,\u201d Kernel Methods in Computational Biology, pp. 171\u2013192, (2004)","DOI":"10.7551\/mitpress\/4057.003.0011"},{"key":"1201_CR27","unstructured":"J.\u00a0Lafferty, G.\u00a0Lebanon, T.\u00a0Jaakkola, \u201cDiffusion kernels on statistical manifolds.,\u201d Journal of Machine Learning Research, vol. 6, no. 1, (2005)"},{"issue":"12","key":"1201_CR28","first-page":"1","volume":"77","author":"E Aprile","year":"2017","unstructured":"E. Aprile, J. Aalbers, F. Agostini, M. Alfonsi, F.D. Amaro, M. Anthony, B. Antunes, F. Arneodo, M. Balata, P. Barrow et al., The XENON1T dark matter experiment. The European Physical Journal C 77(12), 1\u201323 (2017)","journal-title":"The European Physical Journal C"},{"issue":"25","key":"1201_CR29","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.123.251801","volume":"123","author":"E Aprile","year":"2019","unstructured":"E. Aprile et al., Light dark matter search with ionization signals in XENON1T. Physical Review Letters 123(25), 251801 (2019)","journal-title":"Physical Review Letters"},{"key":"1201_CR30","doi-asserted-by":"crossref","unstructured":"S.\u00a0Liang, A.\u00a0Higuera, C.\u00a0Peters, V.\u00a0Roy, W.U. Bajwa, H.\u00a0Shatkay, C.D. Tunnell, \u201cDomain-informed neural networks for interaction localization within astroparticle experiments,\u201d Frontiers in Artificial Intelligence, vol. 5, (2022)","DOI":"10.3389\/frai.2022.832909"},{"issue":"12","key":"1201_CR31","doi-asserted-by":"publisher","first-page":"881","DOI":"10.1140\/epjc\/s10052-017-5326-3","volume":"77","author":"E Aprile","year":"2017","unstructured":"E. Aprile et al., The XENON1T Dark Matter Experiment. Eur. Phys. J. C 77(12), 881 (2017)","journal-title":"Eur. Phys. J. C"},{"issue":"11","key":"1201_CR32","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.121.111302","volume":"121","author":"E Aprile","year":"2018","unstructured":"E. Aprile et al., Dark Matter Search Results from a One Ton-Year Exposure of XENON1T. Phys. Rev. Lett. 121(11), 111302 (2018)","journal-title":"Phys. Rev. Lett."},{"key":"1201_CR33","unstructured":"P. Gaemers et\u00a0al., \u201cXENONnt\/wfsim,\u201d (2021)"},{"issue":"6","key":"1201_CR34","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1109\/JSTSP.2017.2726975","volume":"11","author":"HE Egilmez","year":"2017","unstructured":"H.E. Egilmez, E. Pavez, A. Ortega, Graph learning from data under laplacian and structural constraints. IEEE Journal of Selected Topics in Signal Processing 11(6), 825\u2013841 (2017)","journal-title":"IEEE Journal of Selected Topics in Signal Processing"},{"key":"1201_CR35","unstructured":"V.\u00a0Kalofolias, \u201cHow to learn a graph from smooth signals,\u201d in Artificial Intelligence and Statistics. PMLR, pp. 920\u2013929 (2016)"},{"key":"1201_CR36","doi-asserted-by":"publisher","first-page":"1143","DOI":"10.1109\/JSTARS.2020.2979801","volume":"13","author":"L Zhuang","year":"2020","unstructured":"L. Zhuang, M.K. Ng, Hyperspectral mixed noise removal by $$\\ell _1$$-norm-based subspace representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, 1143\u20131157 (2020)","journal-title":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing"},{"issue":"1","key":"1201_CR37","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1137\/0917016","volume":"17","author":"CR Vogel","year":"1996","unstructured":"C.R. Vogel, M.E. Oman, Iterative methods for total variation denoising. SIAM Journal on Scientific Computing 17(1), 227\u2013238 (1996)","journal-title":"SIAM Journal on Scientific Computing"},{"issue":"1","key":"1201_CR38","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1080\/00031305.1975.10479105","volume":"29","author":"DW Marquardt","year":"1975","unstructured":"D.W. Marquardt, R.D. Snee, Ridge regression in practice. The American Statistician 29(1), 3\u201320 (1975)","journal-title":"The American Statistician"},{"issue":"1","key":"1201_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1029-242X-2014-316","volume":"2014","author":"J Li","year":"2014","unstructured":"J. Li, J.M. Guo, W.C. Shiu, Bounds on normalized laplacian eigenvalues of graphs. Journal of Inequalities and Applications 2014(1), 1\u20138 (2014)","journal-title":"Journal of Inequalities and Applications"},{"key":"1201_CR40","doi-asserted-by":"crossref","unstructured":"S.\u00a0Bates, T.\u00a0Hastie, R.\u00a0Tibshirani, \u201cCross-validation: What does it estimate and how well does it do it?,\u201d Journal of the American Statistical Association, pp. 1\u201312, (2023)","DOI":"10.1080\/01621459.2023.2197686"},{"issue":"2","key":"1201_CR41","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1080\/00401706.1979.10489751","volume":"21","author":"GH Golub","year":"1979","unstructured":"G.H. Golub, Michael Heath, and Grace Wahba, \u201cGeneralized cross-validation as a method for choosing a good ridge parameter,\u2019\u2019. Technometrics 21(2), 215\u2013223 (1979)","journal-title":"Technometrics"},{"key":"1201_CR42","unstructured":"M.\u00a0Grant, S.\u00a0Boyd, \u201cCVX: Matlab software for disciplined convex programming, version 2.1,\u201d http:\/\/cvxr.com\/cvx, Mar. (2014)"},{"key":"1201_CR43","unstructured":"D.P. Kingma, J.\u00a0Ba, \u201cAdam: A method for stochastic optimization,\u201d arXiv preprint arXiv:1412.6980, (2014)"},{"issue":"5","key":"1201_CR44","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevD.100.052014","volume":"100","author":"E Aprile","year":"2019","unstructured":"E. Aprile, J. Aalbers, F. Agostini, M. Alfonsi, L. Althueser, F.D. Amaro, V.C. Antochi, F. Arneodo, L. Baudis, B. Bauermeister et al., Xenon1t dark matter data analysis: Signal reconstruction, calibration, and event selection. Physical review D 100(5), 052014 (2019)","journal-title":"Physical review D"}],"container-title":["EURASIP Journal on Advances in Signal Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-024-01201-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13634-024-01201-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13634-024-01201-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T22:57:21Z","timestamp":1736809041000},"score":1,"resource":{"primary":{"URL":"https:\/\/asp-eurasipjournals.springeropen.com\/articles\/10.1186\/s13634-024-01201-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,13]]},"references-count":44,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1201"],"URL":"https:\/\/doi.org\/10.1186\/s13634-024-01201-8","relation":{},"ISSN":["1687-6180"],"issn-type":[{"value":"1687-6180","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,13]]},"assertion":[{"value":"29 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1"}}