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Metropolis Monte Carlo simulation is a powerful tool for studying the equilibrium properties of matter. In complex condensed-phase systems, however, it is difficult to design Monte Carlo moves with high acceptance probabilities that also…

Statistical Mechanics · Physics 2014-05-27 Jerome P. Nilmeier , Gavin E. Crooks , David D. L. Minh , John D. Chodera

The current and upcoming generation of Very Large Volume Neutrino Telescopes---collecting unprecedented quantities of neutrino events---can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the…

Data Analysis, Statistics and Probability · Physics 2019-12-05 IceCube Collaboration , M. G. Aartsen , M. Ackermann , J. Adams , J. A. Aguilar , M. Ahlers , M. Ahrens , I. Al Samarai , D. Altmann , K. Andeen , T. Anderson , I. Ansseau , G. Anton , C. Argüelles , T. C. Arlen , J. Auffenberg , S. Axani , H. Bagherpour , X. Bai , A. Balagopal V. , J. P. Barron , I. Bartos , S. W. Barwick , V. Baum , R. Bay , J. J. Beatty , J. Becker Tjus , K. -H. Becker , S. BenZvi , D. Berley , E. Bernardini , D. Z. Besson , G. Binder , D. Bindig , E. Blaufuss , S. Blot , C. Bohm , M. Bohmer , M. Börner , F. Bos , S. Böser , O. Botner , E. Bourbeau , J. Bourbeau , F. Bradascio , J. Braun , M. Brenzke , H. -P. Bretz , S. Bron , J. Brostean-Kaiser , A. Burgman , R. S. Busse , T. Carver , E. Cheung , D. Chirkin , A. Christov , K. Clark , L. Classen , G. H. Collin , J. M. Conrad , P. Coppin , P. Correa , D. F. Cowen , R. Cross , P. Dave , M. Day , J. P. A. M. de André , C. De Clercq , J. J. DeLaunay , H. Dembinski , S. De Ridder , P. Desiati , K. D. de Vries , G. de Wasseige , M. de With , T. DeYoung , J. C. Díaz-Vélez , V. di Lorenzo , H. Dujmovic , J. P. Dumm , M. Dunkman , M. A. DuVernois , E. Dvorak , B. Eberhardt , T. Ehrhardt , B. Eichmann , P. Eller , R. Engel , J. J. Evans , P. A. Evenson , S. Fahey , A. R. Fazely , J. Felde , K. Filimonov , C. Finley , S. Flis , A. Franckowiak , E. Friedman , A. Fritz , T. K. Gaisser , J. Gallagher , A. Gartner , L. Gerhardt , R. Gernhaeuser , K. Ghorbani , W. Giang , T. Glauch , T. Glüsenkamp , A. Goldschmidt , J. G. Gonzalez , D. Grant , Z. Griffith , C. Haack , A. Hallgren , F. Halzen , K. Hanson , J. Haugen , A. Haungs , D. Hebecker , D. Heereman , K. Helbing , R. Hellauer , F. Henningsen , S. Hickford , J. Hignight , G. C. Hill , K. D. Hoffman , B. Hoffmann , R. Hoffmann , T. Hoinka , B. Hokanson-Fasig , K. Holzapfel , K. Hoshina , F. Huang , M. Huber , T. Huber , T. Huege , K. Hultqvist , M. Hünnefeld , R. Hussain , S. In , N. Iovine , A. Ishihara , E. Jacobi , G. S. Japaridze , M. Jeong , K. Jero , B. J. P. Jones , P. Kalaczynski , O. Kalekin , W. Kang , D. Kang , A. Kappes , D. Kappesser , T. Karg , A. Karle , T. Katori , U. Katz , M. Kauer , A. Keivani , J. L. Kelley , A. Kheirandish , J. Kim , M. Kim , T. Kintscher , J. Kiryluk , T. Kittler , S. R. Klein , R. Koirala , H. Kolanoski , L. Köpke , C. Kopper , S. Kopper , J. P. Koschinsky , D. J. Koskinen , M. Kowalski , C. B. Krauss , K. Krings , M. Kroll , G. Krückl , S. Kunwar , N. Kurahashi , T. Kuwabara , A. Kyriacou , M. Labare , J. L. Lanfranchi , M. J. Larson , F. Lauber , D. Lennarz , K. Leonard , M. Lesiak-Bzdak , A. Leszczynska , M. Leuermann , Q. R. Liu , E. Lohfink , J. LoSecco , C. J. Lozano Mariscal , L. Lu , J. Lünemann , W. Luszczak , J. Madsen , G. Maggi , K. B. M. Mahn , S. Mancina , S. Mandalia , S. Marka , Z. Marka , R. Maruyama , K. Mase , R. Maunu , K. Meagher , M. Medici , M. Meier , T. Menne , G. Merino , T. Meures , S. Miarecki , J. Micallef , G. Momenté , T. Montaruli , R. W. Moore , M. Moulai , R. Nahnhauer , P. Nakarmi , U. Naumann , G. Neer , H. Niederhausen , S. C. Nowicki , D. R. Nygren , A. Obertacke Pollmann , M. Oehler , A. Olivas , A. O'Murchadha , E. O'Sullivan , A. Palazzo , T. Palczewski , H. Pandya , D. V. Pankova , L. Papp , P. Peiffer , J. A. Pepper , C. Pérez de los Heros , T. C. Petersen , D. Pieloth , E. Pinat , J. L. Pinfold , M. Plum , P. B. Price , G. T. Przybylski , C. Raab , L. Rädel , M. Rameez , L. Rauch , K. Rawlins , I. C. Rea , R. Reimann , B. Relethford , M. Relich , M. Renschler , E. Resconi , W. Rhode , M. Richman , M. Riegel , S. Robertson , M. Rongen , C. Rott , T. Ruhe , D. Ryckbosch , D. Rysewyk , I. Safa , T. Sälzer , S. E. Sanchez Herrera , A. Sandrock , J. Sandroos , P. Sandstrom , M. Santander , S. Sarkar , S. Sarkar , K. Satalecka , H. Schieler , P. Schlunder , T. Schmidt , A. Schneider , S. Schoenen , S. Schöneberg , F. G. Schröder , L. Schumacher , S. Sclafani , D. Seckel , S. Seunarine , M. H. Shaevitz , J. Soedingrekso , D. Soldin , S. Söldner-Rembold , M. Song , G. M. Spiczak , C. Spiering , J. Stachurska , M. Stamatikos , T. Stanev , A. Stasik , R. Stein , J. Stettner , A. Steuer , T. Stezelberger , R. G. Stokstad , A. Stößl , N. L. Strotjohann , T. Stuttard , G. W. Sullivan , M. Sutherland , I. Taboada , A. Taketa , H. K. M. Tanaka , J. Tatar , F. Tenholt , S. Ter-Antonyan , A. Terliuk , S. Tilav , P. A. Toale , M. N. Tobin , C. Tönnis , S. Toscano , D. Tosi , M. Tselengidou , C. F. Tung , A. Turcati , C. F. Turley , B. Ty , E. Unger , M. Usner , J. Vandenbroucke , W. Van Driessche , D. van Eijk , N. van Eijndhoven , S. Vanheule , J. van Santen , D. Veberic , E. Vogel , M. Vraeghe , C. Walck , A. Wallace , M. Wallraff , F. D. Wandler , N. Wandkowsky , A. Waza , C. Weaver , A. Weindl , M. J. Weiss , C. Wendt , J. Werthebach , S. Westerhoff , B. J. Whelan , K. Wiebe , C. H. Wiebusch , L. Wille , D. R. Williams , L. Wills , M. Wolf , J. Wood , T. R. Wood , E. Woolsey , K. Woschnagg , G. Wrede , S. Wren , D. L. Xu , X. W. Xu , Y. Xu , J. P. Yanez , G. Yodh , S. Yoshida , T. Yuan

A simple C++ class structure for construction of a Monte Carlo event generators which can produce unweighted events within relativistic phase space is presented. The generator is self-adapting to the provided matrix element and acceptance…

High Energy Physics - Phenomenology · Physics 2018-12-18 R. A. Kycia , J. Chwastowski , R. Staszewski , J. Turnau

The AcerMC Monte Carlo generator is dedicated to the generation of Standard Model background processes which were recognised as critical for the searches at LHC, and generation of which was either unavailable or not straightforward so far.…

High Energy Physics - Phenomenology · Physics 2012-11-15 Borut Paul Kersevan , Elzbieta Richter-Was

We present a next generation of multi-particle Monte Carlo (MC) Event generators for LHC and ILC for the MSSM, namely the three program packages Madgraph/MadEvent, WHiZard/O'Mega and Sherpa/Amegic++. The interesting but difficult…

High Energy Physics - Phenomenology · Physics 2014-11-18 J. Reuter , K. Hagiwara , W. Kilian , F. Krauss , T. Ohl , T. Plehn , D. Rainwater , S. Schumann

We develop a biased Monte Carlo algorithm to measure probabilities of rare events in cluster-cluster aggregation for arbitrary collision kernels. Given a trajectory with a fixed number of collisions, the algorithm modifies both the waiting…

Statistical Mechanics · Physics 2023-05-24 Rahul Dandekar , R. Rajesh , V. Subashri , Oleg Zaboronski

A general purpose, self-adapting, Monte Carlo (MC) event generator (simulator) is described. The high efficiency of the MC, that is small maximum weight or variance of the MC weight is achieved by means of dividing the integration domain…

Computational Physics · Physics 2009-11-07 S. Jadach

Monte Carlo Event Generators are important tools for the understanding of physics at particle colliders like the LHC. In order to best predict a wide variety of observables, the optimization of parameters in the Event Generators based on…

High Energy Physics - Phenomenology · Physics 2020-02-19 Johannes Bellm , Leif Gellersen

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High…

Data Analysis, Statistics and Probability · Physics 2019-10-02 Viktoria Chekalina , Elena Orlova , Fedor Ratnikov , Dmitry Ulyanov , Andrey Ustyuzhanin , Egor Zakharov

The particle Gibbs sampler is a Markov chain Monte Carlo (MCMC) algorithm to sample from the full posterior distribution of a state-space model. It does so by executing Gibbs sampling steps on an extended target distribution defined on the…

Computation · Statistics 2015-07-29 Nicolas Chopin , Sumeetpal S. Singh

The new monte-carlo generator of heavy ion collisions, DCM-SMM, based on Dubna Cascade Model (DCM-QGSM) and Statistical Multifragmentation Model (SMM) is described. The model aimed to generate particle--nucleus and nucleus--nucleus…

Nuclear Theory · Physics 2020-07-15 M. Baznat , A. Botvina , G. Musulmanbekov , V. Toneev , V. Zhezher

McMule, a Monte Carlo for MUons and other LEptons, implements many major QED processes at NNLO (eg. $ee\to ee$, $e\mu\to e\mu$, $ee\to\mu\mu$, $\ell p\to\ell p$, $\mu\to\nu\bar\nu e$) including effects from the lepton masses, making it…

High Energy Physics - Phenomenology · Physics 2025-01-08 Yannick Ulrich

Recently the collider physics community has seen significant advances in the formalisms and implementations of event generators. This review is a primer of the methods commonly used for the simulation of high energy physics events at…

Ultra-peripheral collisions (UPCs) of heavy ions can be used as a clean environment to study two-photon induced interactions such as dilepton pair photoproduction. Recently, precise data on lepton pair production in UPCs were obtained by…

High Energy Physics - Phenomenology · Physics 2022-05-18 Nazar Burmasov , Evgeny Kryshen , Paul Buehler , Roman Lavicka

Sampling minimum energy grain boundary (GB) structures in the five-dimensional crystallographic phase space can provide much-needed insight into how GB crystallography affects various interfacial properties. However, the complexity and…

Materials Science · Physics 2018-09-10 Arash Dehghan Banadaki , Mark A. Tschopp , Srikanth Patala

Intractable generative models are models for which the likelihood is unavailable but sampling is possible. Most approaches to parameter inference in this setting require the computation of some discrepancy between the data and the…

Computation · Statistics 2022-07-05 Ziang Niu , Johanna Meier , François-Xavier Briol

We present a Monte-Carlo event generator for simulating chargino pair-production at the International Linear Collider (ILC) at next-to-leading order in the electroweak couplings. By properly resumming photons in the soft and collinear…

High Energy Physics - Phenomenology · Physics 2008-11-26 W. Kilian , J. Reuter , T. Robens

CASCADE is a full hadron level Monte Carlo event generator for e-p, gamma-p and p-p_bar processes, which uses the CCFM evolution equation for the initial state cascade in a backward evolution approach supplemented with off-shell matrix…

High Energy Physics - Phenomenology · Physics 2009-11-07 H. Jung

Monte Carlo Event Generators are tools for simulating outcomes of high-energy collisions and particle production in High Energy Physics (HEP), such as those conducted at the Large Hadron Collider (LHC). Two of the most widely used…

High Energy Physics - Experiment · Physics 2025-11-05 Saliha Bashir , Agnieszka Obłąkowska-Mucha , Gloria Corti

In statistical analysis, Monte Carlo (MC) stands as a classical numerical integration method. When encountering challenging sample problem, Markov chain Monte Carlo (MCMC) is a commonly employed method. However, the MCMC estimator is biased…

Numerical Analysis · Mathematics 2024-11-05 Jiarui Du , Zhijian He