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1 - Books

Modern Fortran Explained, M. Metcalf, J. Reid, M. Cohen, OUP Oxford, 2011. ISBN 9780199601424.
Numerical recipes : the art of scientific computing, William H. Press, Saul A. Teukolsky, William T. Vetterling, Brian P. Flannery, Cambridge University Press, 2007. ISBN 9780521880688.
The R Book, Michael J. Crawley, Wiley, 2007. ISBN 9780470510247.
Pattern Recognition and Machine Learning, C.M. Bishop, Springer, 2006. ISBN 9780387310732.
Numerical Computing with MATLAB, David Kahaner, Cleve Moler, Stephen Nash, SIAM, 2004.
Modern Applied Statistics with S, W. N. Venables, B. D. Ripley, Springer, New York, 2002. ISBN 9780387954578.
Numerical Methods and Software, David Kahaner, Cleve Moler, Stephen Nash, Prentice Hall, 1989.

2 - Reviews

Nested sampling for physical scientists, Greg Ashton et al., Nature 2 (2022) 39, arXiv:2205.15570.
Quantum computing hardware for HEP algorithms and sensing, M. Sohaib Alam et al., arXiv:2204.08605, 2022.
Quantum Networks for High Energy Physics, Andrei Derevianko et al., arXiv:2203.16979, 2022.
Evolution of HEP Processing Frameworks, Christopher D. Jones, Kyle Knoepfel, Paolo Calafiura, Charles Leggett, Vakhtang Tsulaia, arXiv:2203.14345, 2022.
Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges, Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S. Neubauer, Kazuhiro Terao, arXiv:2203.12852, 2022.
Event Generators for High-Energy Physics Experiments, J. M. Campbell et al., arXiv:2203.11110, 2022.
Dark-matter And Neutrino Computation Explored (DANCE) Community Input to Snowmass, Amy Roberts et al., arXiv:2203.08338, 2022.
Machine Learning and Cosmology, Cora Dvorkin et al., arXiv:2203.08056, 2022.
Interpretable machine learning in Physics, Christophe Grojean, Ayan Paul, Zhuoni Qian, Inga Strumke, Nature Rev.Phys. 4 (2022) 284-286, arXiv:2203.08021.
Data Storage for HEP Experiments in the Era of High-Performance Computing, Amit Bashyal, Peter Van Gemmeren, Saba Sehrish, Kyle Knoepfel, Suren Byna, Qiao Kang, arXiv:2203.07885, 2022.
Software and Computing for Small HEP Experiments, Costas Andreopoulos et al., arXiv:2203.07645, 2022.
Solving Simulation Systematics in and with AI/ML, Brett Viren, Jin Huang, Yi Huang, Meifeng Lin, Yihui Ren, Kazuhiro Terao, Dmitrii Torbunov, Haiwang Yu, arXiv:2203.06112, 2022.
Machine Learning in the Search for New Fundamental Physics, Georgia Karagiorgi, Gregor Kasieczka, Scott Kravitz, Benjamin Nachman, David Shih, arXiv:2112.03769, 2021.
Artificial Intelligence and Machine Learning in Nuclear Physics, Amber Boehnlein et al., arXiv:2112.02309, 2021.
A survey of machine learning-based physics event generation, Yasir Alanazi, N. Sato, Pawel Ambrozewicz, Astrid N. Hiller Blin, W. Melnitchouk, Marco Battaglieri, Tianbo Liu, Yaohang Li, arXiv:2106.00643, 2021.
Advances in Machine and Deep Learning for Modeling and Real-time Detection of Multi-Messenger Sources, E. A. Huerta, Zhizhen Zhao, arXiv:2105.06479, 2021.
Modern Machine Learning and Particle Physics, Matthew D. Schwartz, arXiv:2103.12226, 2021.
A Living Review of Machine Learning for Particle Physics, Matthew Feickert, Benjamin Nachman, arXiv:2102.02770, 2021.
Graph Neural Networks for Particle Tracking and Reconstruction, Javier Duarte, Jean-Roch Vlimant, arXiv:2012.01249, 2020. 43 pages, 20 figures. Submitted for review. To appear in 'Artificial Intelligence for Particle Physics', World Scientific Publishing.
Anomaly Detection for Physics Analysis and Less than Supervised Learning, Benjamin Nachman, arXiv:2010.14554, 2020.
Simulation-based inference methods for particle physics, Johann Brehmer, Kyle Cranmer, arXiv:2010.06439, 2020.
Reproducibility and Replication of Experimental Particle Physics Results, Thomas R. Junk, Louis Lyons, arXiv:2009.06864, 2020.
HL-LHC Computing Review: Common Tools and Community Software, HEP Software Foundation et al., arXiv:2008.13636, 2020.
A Review on Machine Learning for Neutrino Experiments, Fernanda Psihas, Micah Groh, Christopher Tunnell, Karl Warburton, Int.J.Mod.Phys. A35 (2020) 2043005, arXiv:2008.01242.
21st Century Statistical and Computational Challenges in Astrophysics, Eric D. Feigelson, Rafael S. de Souza, Emille E. O. Ishida, Gutti Jogesh Babu, Ann.Rev.Stat.App. 8 (2021) 493-517, arXiv:2005.13025.
Quantum Machine Learning in High Energy Physics, Wen Guan, Gabriel Perdue, Arthur Pesah, Maria Schuld, Koji Terashi, Sofia Vallecorsa, Jean-Roch Vlimant, arXiv:2005.08582, 2020.
Machine and Deep Learning Applications in Particle Physics, Dimitri Bourilkov, Int.J.Mod.Phys. A34 (2020) 1930019, arXiv:1912.08245.
A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty, Benjamin Nachman, SciPost Phys. 8 (2020) 090, arXiv:1909.03081.
Supervised deep learning in high energy phenomenology: a mini review, Murat Abdughani, Jie Ren, Lei Wu, Jin Min Yang, Jun Zhao, Commun.Theor.Phys. 71 (2019) 955, arXiv:1905.06047.
Machine learning and the physical sciences, Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, Lenka Zdeborova, Rev.Mod.Phys. 91 (2019) 045002, arXiv:1903.10563.
Review of High-Quality Random Number Generators, Frederick James, Lorenzo Moneta, Comput.Softw.Big Sci. 4 (2020) 2, arXiv:1903.01247.
Deep Learning and its Application to LHC Physics, Dan Guest, Kyle Cranmer, Daniel Whiteson, Ann.Rev.Nucl.Part.Sci. 68 (2018) 161-181, arXiv:1806.11484.
A high-bias, low-variance introduction to Machine Learning for physicists, Pankaj Mehta et al., Phys.Rept. 810 (2019) 1-124, arXiv:1803.08823.
Markov Chain Monte Carlo Methods for Bayesian Data Analysis in Astronomy, Sanjib Sharma, Ann.Rev.Astron.Astrophys. 55 (2017) 213, arXiv:1706.01629.
The Convergence of Markov chain Monte Carlo Methods: From the Metropolis method to Hamiltonian Monte Carlo, Michael Betancourt, arXiv:1706.01520, 2017.
Snowmass 2013 Computing Frontier Storage and Data Management, Michelle Butler, Richard Mount, Mike Hildreth, arXiv:1311.4580, 2013.
Snowmass 2013 Computing Frontier: Networking, Gregory Bell, Michael Ernst, arXiv:1311.2478, 2013.
Introduction to Randomness and Statistics, Alexander K. Hartmann, arXiv:0910.4545, 2009.
An Introduction to Monte Carlo Simulation of Statistical physics Problem, K. P. N. Murthy, arXiv:cond-mat/0104167, 2001.
Probabilistic Inference using Markov Chain Monte Carlo Methods, Radford M. Neal, 1993. Technical Report CRG-TR-93-1.
Probabilistic Inference Using Markov Chain Monte Carlo Methods, Radford M. Neal, 1993.

3 - Reviews - Talks

Software Sustainability & High Energy Physics, Daniel S. Katz et al., arXiv:2010.05102, 2020. 'Sustainable Software in HEP' IRIS-HEP blueprint workshop.
Computational challenges for MC event generation, Andy Buckley, J.Phys.Conf.Ser. 1525 (2020) 012023, arXiv:1908.00167. ACAT 2019.
Machine Learning for New Physics Searches, Raffaele Tito D'Agnolo, arXiv:1809.11150, 2018. CIPANP2018.
Machine Learning in High Energy Physics Community White Paper, Kim Albertsson et al., J.Phys.Conf.Ser. 1085 (2018) 022008, arXiv:1807.02876.
Machine learning challenges in theoretical HEP, Stefano Carrazza, J.Phys.Conf.Ser. 1085 (2018) 022003, arXiv:1711.10840. 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2017).
A brief history of the introduction of generalized ensembles to Markov chain Monte Carlo simulations, Bernd A Berg, Eur.Phys.J.ST 226 (2017) 551-565, arXiv:1612.04270. Conference on Phase Transitions and Critical Phenomena, Coventry, April 6-8, 2016.
Computer tools in particle physics, Avelino Vicente, arXiv:1507.06349, 2015. CINVESTAV, Mexico, 22-26 June, 2015.
TASI 2011: CalcHEP and PYTHIA Tutorials, Kyoungchul Kong, arXiv:1208.0035, 2012.
The Tools and Monte Carlo Working Group Summary Report from the Les Houches 2009 Workshop on TeV Colliders, J. M. Butterworth et al., arXiv:1003.1643, 2010.
Monte Carlo Tools, Torbjorn Sjostrand, arXiv:0911.5286, 2009. 65th Scottish Universities Summer School in Physics: LHC Physics, St Andrews, 16 - 29 August 2009.
Emerging Computing Technologies in High Energy Physics, Amir Farbin, arXiv:0910.3440, 2009. DPF-2009, Detroit, MI, July 2009, eConf C090726.
New trends in modern event generators, Tanju Gleisberg et al., arXiv:0705.4648, 2007. QCD Moriond 2007.
Monte Carlo Generators, Torbjörn Sjöstrand, arXiv:hep-ph/0611247, 2006. 2006 European School of High-Energy Physics, Aronsborg, Sweden, 18 June - 1 July 2006.
The Future of Computation, Apoorva Patel, arXiv:quant-ph/0503068, 2005. Workshop on Quantum Information, Computation and Communication (QICC-2005), IIT Kharagpur, India, February 2005.
Review of Monte Carlo methods for particle multiplicity evaluation, N. Armesto, J. Phys. Conf. Ser. 5 (2005) 219, arXiv:hep-ph/0410161. Focus on MULTIPLICITY: International Workshop on Particle Multiplicity in Relativistic Heavy Ion Collisions, Bari, Italy, June 17th-19th 2004.
Introduction to Monte Carlo methods, Stefan Weinzierl, arXiv:hep-ph/0006269, 2000.

4 - Articles

An Efficient, Scalable IO Framework for Sparse Data: larcv3, Corey Adams, Kazuhiro Terao, Marco Del Tutto, Taritree Wongjirad, arXiv:2209.04023, 2022.
ACHILLES: A novel event generator for electron- and neutrino-nucleus scattering, Joshua Isaacson, William I. Jay, Alessandro Lovato, Pedro A. N. Machado, Noemi Rocco, arXiv:2205.06378, 2022.
HL-LHC Analysis With ROOT, Axel Naumann et al., arXiv:2205.06121, 2022.
Exploring phase space with Nested Sampling, David Yallup, Timo Jansen, Steffen Schumann, Will Handley, Eur.Phys.J.C 82 (2022) 8, arXiv:2205.02030.
EpIC: novel Monte Carlo generator for exclusive processes, E.C. Aschenauer, V. Batozskaya, S. Fazio, K. Gates, H. Moutarde, D. Sokhan, H. Spiesberger, P. Sznajder, K. Tezgin, arXiv:2205.01762, 2022.
The CAFAna framework for neutrino analysis, C. Backhouse, arXiv:2203.13768, 2022.
COSE$\nu$: A Collective Oscillation Simulation Engine for Neutrinos, Manu George, Chun-Yu Lin, Meng-Ru Wu, Tony G. Liu, Zewei Xiong, arXiv:2203.12866, 2022.
Data and Analysis Preservation, Recasting, and Reinterpretation, Stephen Bailey et al., arXiv:2203.10057, 2022.
Portability: A Necessary Approach for Future Scientific Software, Meghna Bhattacharya et al., arXiv:2203.09945, 2022.
Analysis Description Language: A DSL for HEP Analysis, Harrison B. Prosper, Sezen Sekmen, Gokhan Unel, arXiv:2203.09886, 2022.
Basic Elements for Simulations of Standard Model Physics with Quantum Annealers: Multigrid and Clock States, Marc Illa, Martin J. Savage, arXiv:2202.12340, 2022.
nuSQuIDS: A toolbox for neutrino propagation, Carlos A. Arguelles, Jordi Salvado, Christopher N. Weaver, Comput.Phys.Commun. 277 (2022) 108346, arXiv:2112.13804.
Home on the (HEALPix) Range: Fast All-Sky Geometry and Image Arithmetic in a Relational Database for Multi-Messenger Astronomy Brokers, Leo P. Singer, B. Parazin, Michael W. Coughlin, Joshua S. Bloom, Arien Crellin-Quick, Daniel A. Goldstein, Stefan van der Walt, Astron.J. 163 (2022) 209, arXiv:2112.06947.
EOS - A Software for Flavor Physics Phenomenology, Danny van Dyk et al., Eur.Phys.J.C 82 (2022) 569, arXiv:2111.15428.
On-line computing challenges: detector and readout requirements, Richard Brenner, Christos Leonidopoulos, Eur.Phys.J.Plus 136 (2021) 1198, arXiv:2111.04168.
Nested sampling for frequentist computation: fast estimation of small $p$-values, Andrew Fowlie, Sebastian Hoof, Will Handley, Phys.Rev.Lett. 128 (2022) 021801, arXiv:2105.13923.
Geant4Reweight: a framework for evaluating and propagating hadronic interaction uncertainties in GEANT4, J. Calcutt, C. Thorpe, K. Mahn, Laura Fields, JINST 16 (2021) P08042, arXiv:2105.01744.
An Error Analysis Toolkit for Binned Counting Experiments, B. Messerly et al. (MINERvA), EPJ Web Conf. 251 (2021) 03046, arXiv:2103.08677.
RGBeta: A Mathematica Package for the Evaluation of Renormalization Group $ \beta$-Functions, Anders Eller Thomsen, Eur.Phys.J. C81 (2021) 408, arXiv:2101.08265.
Adaptive Multidimensional Integration: VEGAS Enhanced, G. Peter Lepage, J.Comput.Phys. 439 (2021) 110386, arXiv:2009.05112.
BAT.jl - A Julia-based tool for Bayesian inference, Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kroninger, Salvatore La Cagnina, Lars Rohrig, Lolian Shtembari, arXiv:2008.03132, 2020.
DiffExp, a Mathematica package for computing Feynman integrals in terms of one-dimensional series expansions, Martijn Hidding, Comput.Phys.Commun. 269 (2021) 108125, arXiv:2006.05510.
gSeaGen: the KM3NeT GENIE-based code for neutrino telescopes, Sebastiano Aiello et al., Comput.Phys.Commun. 256 (2020) 107477, arXiv:2003.14040.
FeynMaster: a plethora of Feynman tools, Duarte Fontes, Jorge C. Romao, Comput.Phys.Commun. 256 (2020) 107311, arXiv:1909.05876.
A new Monte Carlo-based fitting method, Paolo Pedroni, Stefano Sconfietti, J.Phys. G47 (2020) 054001, arXiv:1909.03885.
FPGA-accelerated machine learning inference as a service for particle physics computing, Javier Duarte et al., Comput.Softw.Big Sci. 3 (2019) 13, arXiv:1904.08986.
TURTLE: A C library for an optimistic stepping through a topography, Valentin Niess, Anne Barnoud, Cristina Carloganu, Olivier Martineau-Huynh, Comput.Phys.Commun. (2019) 106952, arXiv:1904.03435.
SmeftFR - Feynman rules generator for the Standard Model Effective Field Theory, A. Dedes, M. Paraskevas, J. Rosiek, K. Suxho, L. Trifyllis, Comput.Phys.Commun. 247 (2020) 106931, arXiv:1904.03204.
FIRE6: Feynman Integral REduction with Modular Arithmetic, A.V. Smirnov, F.S. Chuharev, Comput.Phys.Commun. 247 (2020) 106877, arXiv:1901.07808.
Architecture of Distributed Data Storage for Astroparticle Physics, Alexander Kryukov, Andrey Demichev, arXiv:1811.02403, 2018.
flavio: a Python package for flavour and precision phenomenology in the Standard Model and beyond, David M. Straub, arXiv:1810.08132, 2018.
MadDM v.3.0: a Comprehensive Tool for Dark Matter Studies, Federico Ambrogi et al., Phys.Dark Univ. 24 (2019) 100249, arXiv:1804.00044.
Computational Techniques for the Analysis of Small Signals in High-Statistics Neutrino Oscillation Experiments, M. G. Aartsen et al. (IceCube-Gen2), Nucl.Instrum.Meth. A977 (2020) 164332, arXiv:1803.05390.
swordfish: Efficient Forecasting of New Physics Searches without Monte Carlo, Thomas D. P. Edwards, Christoph Weniger, arXiv:1712.05401, 2017.
MatchingTools: a Python library for symbolic effective field theory calculations, Juan C. Criado, Comput.Phys.Commun. 227 (2018) 42-50, arXiv:1710.06445.
The SMEFTsim package, theory and tools, Ilaria Brivio, Yun Jiang, Michael Trott, JHEP 1712 (2017) 070, arXiv:1709.06492.
GAMBIT: The Global and Modular Beyond-the-Standard-Model Inference Tool, Peter Athron et al. (GAMBIT), Eur.Phys.J. C77 (2017) 784, arXiv:1705.07908.
Kira - A Feynman Integral Reduction Program, Philipp Maierhoefer, Johann Usovitsch, Peter Uwer, Comput.Phys.Commun. 230 (2018) 99-112, arXiv:1705.05610.
NUISANCE: a neutrino cross-section generator tuning and comparison framework, P. Stowell et al., JINST 12 (2017) P01016, arXiv:1612.07393.
FormTracer - A Mathematica Tracing Package Using FORM, Anton K. Cyrol, Mario Mitter, Nils Strodthoff, Comput.Phys.Commun. C219 (2017) 346-352, arXiv:1610.09331.
RECOLA: REcursive Computation of One-Loop Amplitudes, Stefano Actis et al., Comput.Phys.Commun. 214 (2017) 140-173, arXiv:1605.01090.
ASCR/HEP Exascale Requirements Review Report, Salman Habib et al., arXiv:1603.09303, 2016.
mr: a C++ library for the matching and running of the Standard Model parameters, Bernd A. Kniehl, Andrey F. Pikelner, Oleg L. Veretin, Comput.Phys.Commun. 206 (2016) 84-96, arXiv:1601.08143.
Parameterized Machine Learning for High-Energy Physics, Pierre Baldi, Kyle Cranmer, Taylor Faucett, Peter Sadowski, Daniel Whiteson, Eur.Phys.J. C76 (2016) 235, arXiv:1601.07913.
TikZ-Feynman: Feynman diagrams with TikZ, Joshua Ellis, Comput.Phys.Commun. 210 (2017) 103-123, arXiv:1601.05437.
Performance and optimization of support vector machines in high-energy physics classification problems, Mehmet Ozgur Sahin, Dirk Krucker, Isabell-Alissandra Melzer-Pellmann, Nucl.Instrum.Meth. A838 (2016) 137-146, arXiv:1601.02809.
FaRe: a Mathematica package for tensor reduction of Feynman integrals, Michele Re Fiorentin, Int. J. Mod. Phys. C27 (2015) 1650027, arXiv:1507.03527.
Package-X: A Mathematica package for the analytic calculation of one-loop integrals, Hiren H. Patel, Comput. Phys. Commun. 197 (2015) 276-290, arXiv:1503.01469.
How to speed up R code: an introduction, Nathan Uyttendaele, arXiv:1503.00855, 2015.
A Simple Quantum Integro-Differential Solver (SQuIDS), Carlos Alberto Arguelles Delgado, Jordi Salvado, Christopher N. Weaver, Comput. Phys. Commun. 196 (2015) 569-591, arXiv:1412.3832.
HistFitter software framework for statistical data analysis, M. Baak et al., Eur.Phys.J. C75 (2015) 153, arXiv:1410.1280.
GoSam-2.0: a tool for automated one-loop calculations within the Standard Model and beyond, G. Cullen et al., Eur.Phys.J. C74 (2014) 3001, arXiv:1404.7096.
r-Java 2.0: the astrophysics, M. Kostka, N. Koning, Z. Shand, R. Ouyed, P. Jaikumar, arXiv:1402.3824, 2014.
Cosmo++: An Object-Oriented C++ Library for Cosmology, Grigor Aslanyan, Comput.Phys.Commun. 185 (2014) 3215-3227, arXiv:1312.4961.
Accelerated Event-by-Event Neutrino Oscillation Reweighting with Matter Effects on a GPU, R. G. Calland, A. C. Kaboth, D. Payne, JINST 9 (2014) 04016, arXiv:1311.7579.
jQuery.Feyn: Drawing Feynman Diagrams with SVG, Zan Pan, arXiv:1311.6712, 2013.
Herwig++ 2.7 Release Note, J. Bellm et al., arXiv:1310.6877, 2013.
FeynRules 2.0 - A complete toolbox for tree-level phenomenology, Adam Alloul, Neil D. Christensen, Celine Degrande, Claude Duhr, Benjamin Fuks, Comput.Phys.Commun. 185 (2014) 2250-2300, arXiv:1310.1921.
Porting Large HPC Applications to GPU Clusters: The Codes GENE and VERTEX, Tilman Dannert, Andreas Marek, Markus Rampp, arXiv:1310.1485, 2013.
MadGraph 5 : Going Beyond, Johan Alwall, Michel Herquet, Fabio Maltoni, Olivier Mattelaer, Tim Stelzer, JHEP 06 (2011) 128, arXiv:1106.0522.
CHIWEI: A code of goodness of fit tests for weighted and unweighed histograms, Nikolai Gagunashvili, Comput.Phys.Commun. 183 (2012) 418-421, arXiv:1104.3733.
Mathematica with ROOT, Ken Hsieh, Thomas G. Throwe, Sebastian White, arXiv:1102.5068, 2011.
Evolution Strategies for Cosmology: A Comparison of Nested Sampling Methods, M. Axiak, T. D. Kitching, J. I. van Hemert, arXiv:1101.0717, 2011.
R2SM: a package for the analytic computation of the R2 Rational terms in the Standard Model of the Electroweak interactions, M.V. Garzelli, I. Malamos, Eur. Phys. J. C71 (2011) 1605, arXiv:1010.1248.
OPUCEM: A Library with Error Checking Mechanism for Computing Oblique Parameters, Ozgur Cobanoglu, Erkcan Ozcan, Saleh Sultansoy, Gokhan Unel, Comput. Phys. Commun. 182 (2011) 1732-1743, arXiv:1005.2784.
Data Preservation in High Energy Physics, Dphep Study Group (DPHEP Study Group), Eur. Phys. J. C68 (2009) 183-195, arXiv:0912.0255.
Limits, discovery and cut optimization for a Poisson process with uncertainty in background and signal efficiency: TRolke 2.0, J. Lundberg, J. Conrad, W. Rolke, A. Lopez, Comput. Phys. Commun. 181 (2010) 683-686, arXiv:0907.3450.
The GENIE Neutrino Monte Carlo Generator, C. Andreopoulos et al., Nucl. Instrum. Meth. A614 (2010) 87-104, arXiv:0905.2517.
Neutrino oscillation parameter sampling with MonteCUBES, Mattias Blennow, Enrique Fernandez-Martinez, Comput. Phys. Commun. 181 (2010) 227-231, arXiv:0903.3985.
Markovian Monte Carlo program EvolFMC v.2 for solving QCD evolution equations, S.Jadach, W.Placzek, M.Skrzypek, P.Stoklosa, Comput. Phys. Commun. 181 (2010) 393-412, arXiv:0812.3299.
JaxoDraw: A graphical user interface for drawing Feynman diagrams. Version 2.0 release notes, D. Binosi, J. Collins, C. Kaufhold, L.Theussl, Comput. Phys. Commun. 180 (2009) 1709-1715, arXiv:0811.4113.
FeynRules - Feynman rules made easy, Neil D. Christensen, Claude Duhr, Comput. Phys. Commun. 180 (2009) 1614-1641, arXiv:0806.4194.
Herwig++ Physics and Manual, M. Bahr et al., Eur. Phys. J. C58 (2008) 639-707, arXiv:0803.0883.
Sky in Google Earth: The Next Frontier in Astronomical Data Discovery and Visualization, Ryan Scranton et al., arXiv:0709.0752, 2007.
BRIDGE: Branching Ratio Inquiry/Decay Generated Events, Patrick Meade, Matthew Reece, arXiv:hep-ph/0703031, 2007.
New features in the simulation of neutrino oscillation experiments with GLoBES 3.0, Patrick Huber et al., Comput. Phys. Commun. 177 (2007) 432-438, arXiv:hep-ph/0701187.
A Mathematica Interface for FormCalc-generated Code, T. Hahn, Comput. Phys. Commun. 178 (2008) 217-221, arXiv:hep-ph/0611273.
Efficient numerical diagonalization of hermitian 3x3 matrices, Joachim Kopp, Int.J.Mod.Phys. C19 (2008) 523-548, arXiv:physics/0610206.
Herwig++ 2.0 Release Note, S. Gieseke et al., arXiv:hep-ph/0609306, 2006.
Routines for the diagonalization of complex matrices, T. Hahn, arXiv:physics/0607103, 2006.
CRPropa: A Numerical Tool for the Propagation of UHE Cosmic Rays, Gamma-rays and Neutrinos, E. Armengaud, G. Sigl, T. Beau, F. Miniati, Astropart. Phys. 28 (2007) 463-471, arXiv:astro-ph/0603675.
PYTHIA 6.4 Physics and Manual, Torbjorn Sjostrand, Stephen Mrenna, Peter Skands, JHEP 05 (2006) 026, arXiv:hep-ph/0603175.
GalaxyCount: a JAVA calculator of galaxy counts and variances in magnitude-limited wide-field surveys down to B=28 mag, S.C. Ellis, J. Bland-Hawthorn, Mon. Not. Roy. Astron. Soc. 377 (2007) 815-828, arXiv:astro-ph/0602573.
NMHDECAY 2.0: An updated program for Sparticle masses, Higgs masses, couplings and decay widths in the NMSSM, Ulrich Ellwanger, Cyril Hugonie, Comput. Phys. Commun. 175 (2006) 290-303, arXiv:hep-ph/0508022.
ZFITTER: a semi-analytical program for fermion pair production in e+e- annihilation, from version 6.21 to version 6.42, A. B. Arbuzov et al., Comput. Phys. Commun. 174 (2006) 728, arXiv:hep-ph/0507146.
StatPatternRecognition: A C++ Package for Statistical Analysis of High Energy Physics Data, I. Narsky, arXiv:physics/0507143, 2005.
HypExp, a Mathematica package for expanding hypergeometric functions around integer-valued parameters, T. Huber, D. Maitre, Comput. Phys. Commun. 175 (2006) 122-144, arXiv:hep-ph/0507094.
Package for Calculations and Simplifications of Expressions with Dirac Matrixes (MatrixExp), V. A. Poghosyan, Comput. Phys. Commun. 170 (2005) 287, arXiv:hep-ph/0507080.
TaylUR, an arbitrary-order automatic differentiation package for Fortran 95, G.M. von Hippel, Comput. Phys. Commun. 174 (2006) 569, arXiv:physics/0506222.
LevelScheme: A level scheme drawing and scientific figure preparation system for Mathematica, M. A. Caprio, Comput. Phys. Commun. 171 (2005) 107, arXiv:physics/0505065.
Therminator: Thermal heavy-Ion generator, Adam Kisiel, Tomasz Taluc, Wojciech Broniowski, Wojciech Florkowski, Comput. Phys. Commun. 174 (2006) 669, arXiv:nucl-th/0504047.
TeXmacs-maxima interface, Andrey G. Grozin, arXiv:cs/0504039, 2005.
BCVEGPY2.0: A upgrade version of the generator BCVEGPY with an addendum about hadroproduction of the P-wave B_c states, Chao-Hsi Chang, Jian-Xiong Wang, Xing-Gang Wu, Comput. Phys. Commun. 174 (2006) 241, arXiv:hep-ph/0504017.
Apacic++ 2.0, A Parton Cascade In C++, F. Krauss, A. Schaelicke, G. Soff, Comput. Phys. Commun. 174 (2006) 876, arXiv:hep-ph/0503087.
Application of Genetic Programming to High Energy Physics Event Selection, J. M. Link (FOCUS), Nucl. Instrum. Meth. A551 (2005) 504, arXiv:hep-ex/0503007.
Automated computation of one-loop integrals in massless theories, Andre van Hameren, Jens Vollinga, Stefan Weinzierl, Eur. Phys. J. C41 (2005) 361, arXiv:hep-ph/0502165.
A JavaScript Passive Evolution Calculator, Pieter G. van Dokkum, Marijn Franx, Astrophys. J. 553 (2001) 90, arXiv:astro-ph/0501236.
CalcHEP 3.2: MSSM, structure functions, event generation, batchs, and generation of matrix elements for other packages, A. Pukhov, arXiv:hep-ph/0412191, 2004.
Fittino, a program for determining MSSM parameters from collider observables using an iterative method, Philip Bechtle, Klaus Desch, Peter Wienemann, Comput. Phys. Commun. 174 (2006) 47, arXiv:hep-ph/0412012.
MILOU : a Monte-Carlo for Deeply Virtual Compton Scattering, E. Perez, L. Schoeffel, L. Favart, arXiv:hep-ph/0411389, 2004.
LSJK - a C++ library for arbitrary-precision numeric evaluation of the generalized log-sine functions, M.Yu. Kalmykov, A. Sheplyakov, Comput. Phys. Commun. 172 (2005) 45, arXiv:hep-ph/0411100.
The Cyborg astrobiologist: First field experience, Patrick C. McGuire, Jens Ormo, Enrique Diaz Martinez, Jose Antonio Rodriguez Manfredi, Javier Gomez Elvira, Helge Ritter, Markus Oesker, Joerg Ontrup, Int. J. Astrobiol. 3 (2004) 189-207, arXiv:cs/0410071.
Computer Simulations of High Energy Physics, Philip Stephens, arXiv:hep-ph/0408363, 2004. Ph.D. thesis, 252 pages.
Adaptive scanning - a proposal how to scan theoretical predictions over a multi-dimensional parameter space efficiently, Oliver Brein, Comput. Phys. Commun. 170 (2005) 42, arXiv:hep-ph/0407340.
Simulation of long-baseline neutrino oscillation experiments with GLoBES, P. Huber, M. Lindner, W. Winter, Comput. Phys. Commun. 167 (2005) 195, arXiv:hep-ph/0407333.
NMHDECAY: A Fortran Code for the Higgs Masses, Couplings and Decay Widths in the NMSSM, Ulrich Ellwanger, John F. Gunion, Cyril Hugonie, JHEP 0502 (2005) 066, arXiv:hep-ph/0406215.
DarkSUSY: Computing Supersymmetric Dark Matter Properties Numerically, P. Gondolo et al., JCAP 0407 (2004) 008, arXiv:astro-ph/0406204.
The Fraunhofer Quantum Computing Portal - - A web-based Simulator of Quantum Computing Processes, Helge Rose' et al., arXiv:quant-ph/0406089, 2004.
Q-circuit Tutorial, Bryan Eastin, Steven T. Flammia, arXiv:quant-ph/0406003, 2004.
micrOMEGAs: Version 1.3, G. Belanger, F. Boudjema, A. Pukhov, A. Semenov, Comput. Phys. Commun. 174 (2006) 577, arXiv:hep-ph/0405253.
Program to calculate pure angular momentum coefficients in jj-coupling, G. Gaigalas, S. Fritzsche, I.P. Grant, J. Phys. A39 (2006) R315-R392, arXiv:physics/0405129.
Calculation of reduced coefficients and matrix elements in jj-coupling, G. Gaigalas, S. Fritzsche, Phys. Rev. D70 (2004) 111901, arXiv:physics/0405128.
Secondly quantized multi-configurational approach for atomic databases, G. Gaigalas, Z. Rudzikas, arXiv:physics/0405076, 2004.
The library of subroutines for calculation of matrix elements of two-particle operators for many-electron atoms, G. Gaigalas, JHEP 0701 (2007) 080, arXiv:physics/0405072.
The library of subroutines for calculating standard quantities in atomic structure theory, G. Gaigalas (BaBar), Phys. Rev. Lett. 93 (2004) 091802, arXiv:physics/0405071.
LCG Monte-Carlo Data Base, P. Bartalini et al., arXiv:hep-ph/0404241, 2004.
BRANECODE: A Program for Simulations of Braneworld Dynamics, Johannes Martin et al., Comput. Phys. Commun. 171 (2005) 69, arXiv:hep-ph/0404141.
Two programs for studying stellar evolution and nuclear astrophysics, Theodore Liolios, Bulg.Astron.J. 11 (2009) 41, arXiv:astro-ph/0404070.
Cuba - a library for multidimensional numerical integration, T. Hahn, Comput. Phys. Commun. 168 (2013) 78, arXiv:hep-ph/0404043.
SuperCosmology, Renata Kallosh, Sergey Prokushkin, arXiv:hep-th/0403060, 2004.
The MC@NLO 2.3 Event Generator, S. Frixione, B.R. Webber, arXiv:hep-ph/0402116, 2004.
The CAVES project: Exploring virtual data concepts for data analysis, Dimitri Bourilkov, arXiv:physics/0401007, 2004.
ISAJET 7.69: A Monte Carlo Event Generator for $pp$, $\bar pp$, and $e^+e^-$ Reactions, Howard Baer, Frank E. Paige, Serban D. Protopescu, Xerxes Tata, arXiv:hep-ph/0312045, 2003.
CMBfit: Rapid WMAP likelihood calculations with normal parameters, Havard B. Sandvik, Max Tegmark, Xiaomin Wang, Matias Zaldarriaga, Phys. Rev. D69 (2004) 063005, arXiv:astro-ph/0311544.
Reconstructing the primordial power spectrum - a new algorithm, Steen Hannestad, JCAP 0404 (2004) 002, arXiv:astro-ph/0311491.
Parallel Computation of Feynman diagrams with DIANA, M. Tentyukov, J. Fleischer, Comput. Phys. Commun. 160 (2004) 167, arXiv:hep-ph/0311111.
An Algorithm for optimal partitioning of data on an interval, Brad Jackson, Jeffrey D. Scargle, David Barnes, Sundararajan Arabhi, Alina Alt, Peter Gioumousis, Elyus Gwin, Paungkaew Sangtrakulcharoen, Linda Tan, Tun Tao Tsai, IEEE Signal Process.Lett. 12 (2005) 105, arXiv:math/0309285.
Code generation (automatized programming) of symbolic formulae for helicity amplitudes, P. Cherzor, arXiv:hep-ph/0309251, 2003.
The MC@NLO 2.2 Event Generator, S. Frixione, B. R. Webber, arXiv:hep-ph/0309186, 2003.
JaxoDraw: A graphical user interface for drawing Feynman diagrams, D. Binosi, L. Theussl, Comput. Phys. Commun. 161 (2004) 76, arXiv:hep-ph/0309015.
PYTHIA 6.3 Physics and Manual, T. Sjostrand, L. Lonnblad, S. Mrenna, P. Skands, arXiv:hep-ph/0308153, 2003.
Super computers in astrophysics and High Performance simulations of self-gravitating systems, R. Capuzzo-Dolcetta, P. Di Matteo, P. Miocchi, Mem. Soc. Ast. It. 73 (2002) 23, arXiv:astro-ph/0307313. SAIt 2003 national meeting (Trieste, Italy, 14 - 17 aprile 2003).
Data Management and Mining in Astrophysical Databases, M. Frailis, A. De Angelis, V. Roberto, arXiv:cs.db/0307032, 2003.
Automatic classification using selforganizing neural networks in astrophysical experiments, Praveen Boinee, Alessandro De Angelis, Edoardo Milotti, arXiv:cs/0307031, 2003. 'Science with the New Generation of High Energy Gamma-ray Experiments' (Perugia, Italy, May 2003). Forum, Udine 2003.
How to derive and compute 1,648 diagrams, Caleb C. Cannon, Andrei Derevianko, arXiv:physics/0306099, 2003.
MatlabMPI, Jeremy Kepner, Stan Ahalt, J.Parallel Distrib.Comput. (2003), arXiv:astro-ph/0305090.
Tables of convolution integrals, A.B. Arbuzov, arXiv:hep-ph/0304063, 2003.
gTybalt - a free computer algebra system, Stefan Weinzierl, Comput.Phys.Commun. 156 (2004) 180-198,
Tree-Particle-Mesh: an adaptive, efficient, and parallel code for collisionless cosmological simulation, Paul Bode, Jeremiah P. Ostriker, Astrophys. J. Supp. 145 (2003) 1, arXiv:astro-ph/0302065.
SPheno, a program for calculating supersymmetric spectra, SUSY particle decays and SUSY particle production at e+ e- colliders, Werner Porod, Comput. Phys. Commun. 153 (2003) 275, arXiv:hep-ph/0301101.
SuSpect: a Fortran Code for the Supersymmetric and Higgs Particle Spectrum in the MSSM, Abdelhak Djouadi, Jean-Loic Kneur, Gilbert Moultaka, Comput. Phys. Commun. 176 (2007) 426-455, arXiv:hep-ph/0211331.
Calculating two- and three-body decays with FeynArts and FormCalc, Michael Klasen, Int. J. Mod. Phys. C14 (2003) 1273, arXiv:hep-ph/0210426.
JetWeb: A WWW Interface and Database for Monte Carlo Tuning and Validation, J. M. Butterworth, S. Butterworth, Comput. Phys. Commun. 153 (2003) 164, arXiv:hep-ph/0210404.
A Simplex Method for Function Minimization, J. A. Nelder, R. Mead, The Computer Journal 7 (1965) 308-313.

5 - Articles - Talks

Towards enhanced databases for High Energy Physics, Andrea Ceccarelli, Andrea Cioni, Maria Vittoria Garzelli, Piergiulio Lenzi, Laura Redapi, arXiv:1907.11772, 2019. XXVII International Workshop on Deep Inelastic Scattering and Related Subjects, Torino Italy, 8 - 12 April 2019.
The Scikit-HEP Project, Eduardo Rodrigues, EPJ Web Conf. 214 (2019) 06005, arXiv:1905.00002. 23rd International Conference on Computing in High Energy and Nuclear Physics (CHEP 2018), 9-13 July 2018.
HEPData: a repository for high energy physics data, Eamonn Maguire, Lukas Heinrich, Graeme Watt, J.Phys.Conf.Ser. 898 (2017) 102006, arXiv:1704.05473. 22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, 10-14 October 2016, San Francisco.
FORM, Diagrams and Topologies, Franz Herzog, Ben Ruijl, Takahiro Ueda, J.A.M. Vermaseren, Andreas Vogt, PoS LL2016 (2016) 073, arXiv:1608.01834. Loops $\text{\&}$ Legs 2016, Leipzig (Germany), April 2016.
The NIFTY way of Bayesian signal inference, Marco Selig, arXiv:1412.7160, 2014. 33rd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering (MaxEnt 2013).
A comparison of Monte Carlo generators, Tomasz Golan, AIP Conf.Proc. 1663 (2015) 030003, arXiv:1402.1608. NUINT12: Eighth International Workshop on Neutrino-Nucleus Interactions in the Few-GeV Region, October 22-27, 2012, Rio de Janeiro, Brasil.
Snowmass 2013 Computing Frontier: Intensity Frontier, B. Rebel, M. C. Sanchez, S. Wolbers, arXiv:1310.6964, 2013.
FormCalc 8: Better Algebra and Vectorization, B. Chokoufe Nejad, T. Hahn, J.-N. Lang, E. Mirabella, J. Phys. Conf. Ser. 523 (2014) 012050, arXiv:1310.0274. ACAT 2013, Beijing, China, 16-21 May 2013.
Data Preservation in High Energy Physics, Roman Kogler, David M. South, Michael Steder, J. Phys. Conf. Ser. 368 (2012) 012026, arXiv:1111.2788. ACAT 2011.
Data Preservation in High Energy Physics - why, how and when?, Siegfried Bethke, Nucl. Phys. B, Proc. Suppl. 207-208 2010 (2010) 156-159, arXiv:1009.3763. QCD10, Montpellier, France, June 2010.
HepData reloaded: reinventing the HEP data archive, Andy Buckley, Mike Whalley, PoS ACAT2010 (2010) 067, arXiv:1006.0517. 13th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2010), February 22-27, 2010, Jaipur, India.
FLUKA as a new high energy cosmic ray generator, Giuseppe Battistoni, Annarita Margiotta, Silvia Muraro, Maximiliano Sioli, Nucl. Instrum. Meth. A626-627 (2011) S191-S192, arXiv:1002.4655. Very Large Volume neutrino Telescope Workshop 2009 - VLVnT09, Athens, October 2009.
Data Preservation at LEP, Andre G. Holzner et al., arXiv:0912.1803, 2009. First Workshop on Data Preservation and Long Term Analysis in HEP.
Running Nuwro, Cezary Juszczak, Acta Phys. Polon. B40 (2009) 2507-2512, arXiv:0909.1492. 45th Winter School in Theoretical Physics 'Neutrino Interactions: from Theory to Monte Carlo Simulations', Ladek-Zdroj, Poland, February 2-11, 2009.
MUPAGE: a fast atmospheric MUon GEnerator for neutrino telescopes based on PArametric formulas, G. Carminati et al., arXiv:0907.5563, 2009. 31st ICRC, Lodz, Poland, 2009.
Data Preservation and Long Term Analysis in High Energy Physics, David M. South, arXiv:0907.1586, 2009. 44th Rencontres de Moriond on QCD and High Energy Interactions, La Thuile, Valle d'Aosta, Italy, 14-21 Mar 2009.
RooStatsCms: a tool for analyses modelling, combination and statistical studies, D. Piparo, Dr. Gregory Schott, Prof. G. Quast, Nucl. Phys. Proc. Suppl. 197 (2009) 95-98, arXiv:0812.2217. 11th Topical Seminar on Innovative Particle and Radiation Detectors.
Herwig++, Martyn Gigg, Peter Richardson, arXiv:0706.2921, 2007. 42nd Rencontres de Moriond on QCD and Hadronic Interactions, La Thuile, Italy, 17-24 Mar 2007.
GiNaC - Symbolic computation with C++, Jens Vollinga, Nucl. Instrum. Meth. A559 (2006) 282, arXiv:hep-ph/0510057. X International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2005, DESY-Zeuthen, Germany, 22-27 May 2005.
Precision Higgs Masses with FeynHiggs 2.2, T. Hahn, S. Heinemeyer, W. Hollik, G. Weiglein, eConf C050318 (2005) 0106, arXiv:hep-ph/0507009. 2005 International Linear Collider Workshop (LCWS 2005), Stanford, California, 18-22 Mar 2005.
New Developments in FormCalc 4.1, T. Hahn, eConf C050318 (2005) 0604, arXiv:hep-ph/0506201. 2005 International Linear Collider Workshop (LCWS 2005), Stanford, California, 18-22 Mar 2005.
Virtual Astronomy, Information Technology, and the New Scientific Methodology, S. G. Djorgovski, IEEE Proc. (2005), arXiv:astro-ph/0504651. IEEE Proc. of CAMP05, "Computer Architectures for Machine Perception".
The CEDAR Project, J. M. Butterworth et al., arXiv:hep-ph/0412139, 2004. Computing in High-Energy Physics (CHEP'04), Interlaken, Switzerland, 27th September - 1st October 2004.
TAUOLA as tau Monte Carlo for future applications, Z. Was, P. Golonka, Nucl. Phys. Proc. Suppl. 144 (2005) 88, arXiv:hep-ph/0411377. International workshop on Tau Lepton Physics, TAU04 Nara, Japan September 14-17,2004.
Multi-Terabyte EIDE Disk Arrays running Linux RAID5, D. A. Sanders et al., arXiv:physics/0411188, 2004. 2004 Computing in High Energy and Nuclear Physics (CHEP04), Interlaken, Switzerland, 27th September - 1st October 2004.
The Inconstancy of the Fundamental Physical Constants: Computational Status, V.V. Ezhela, Yu.V. Kuyanov, V.N. Larin, A.S. Siver, arXiv:physics/0409117, 2004. 'Conference on Fundamental Symmetries and Fundamental Constants' (Trieste, 2004).
Automated calculations for massive fermion production with aITALC, A. Lorca, T. Riemann, Nucl. Phys. Proc. Suppl. 135 (2004) 328, arXiv:hep-ph/0407149. 'Loops and Legs in Quantum Field Theory 2004', Zinnowitz, Usedom Island, Germany, April 2004.
Cosmological Markov Chain Monte Carlo simulation with Cmbeasy, Christian M. Mueller, arXiv:astro-ph/0406206, 2004. XXXIX Rencontres de Moriond 'Exploring the Universe'.
Formulae for a numerical computation of one-loop tensor integrals, R. Pittau, arXiv:hep-ph/0406105, 2004. LCWS2004, Paris, France, April 2004.
Les Houches Guidebook to Monte Carlo Generators for Hadron Collider Physics, H. Baer et al., arXiv:hep-ph/0403045, 2004. Workshop 'Physics at TeV Colliders', Les Houches, France, May 2003.
The BABAYAGA event generator, C.M. Carloni Calame, G. Montagna, O. Nicrosini, F. Piccinini, Nucl. Phys. Proc. Suppl. 131 (2004) 48, arXiv:hep-ph/0312014. 'SIGHAD03 - Worskhop on Hadronic Cross Section at Low Energy', Pisa, Italy, 8 -10 October, 2003.
Event Generator for Particle Production in High-Energy Collisions, A. Schaelicke et al., Prog. Part. Nucl. Phys. 53 (2004) 329, arXiv:hep-ph/0311270. 25th Course of the International School on Nuclear Physics.
The physics models of FLUKA: status and recent development, A. Fasso' et al., eConf C0303241 (2003) MOMT005, arXiv:hep-ph/0306267. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
The FLUKA code: present applications and future developments, A. Fasso' et al., eConf C0303241 (2003) MOMT004, arXiv:physics/0306162. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
The BTeV Software Tutorial Suite, Robert K. Kutschke (BTeV), eConf C0303241 (2003) THLT006, arXiv:physics/0306107. CHEP03.
Physics Analysis Expert PAX: First Applications, M. Erdmann et al., eConf C0303241 (2003) THLT008, arXiv:physics/0306085. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
A Review Of Two Novel Numerical Methods in QFT, R. Easther, D. D. Ferrante, G. S. Guralnik, D. Petrov, arXiv:hep-lat/0306038, 2003. Seventh Workshop on Quantum Chromodynamics, 6-10 January 2003.
DIAL: Distributed Interactive Analysis of Large Datasets, D. L. Adams, eConf C0303241 (2003) TULT005, arXiv:hep-ph/0305093. CHEP03.
The Event as an Object-Relational Database: Avoiding the Dependency Nightmare, C. D. Jones, eConf C0303241 (2003) MOJT011, arXiv:hep-ex/0305091. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
Reconstruction and Analysis on Demand: A Success Story, C. D. Jones, eConf C0303241 (2003) THJT002, arXiv:hep-ex/0305090. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
ATLAS Commander: an ATLAS production tool, V. Berten, L. Goossens, C. L. Tan, eConf C0303241 (2003) MONT002, arXiv:hep-ex/0305089. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
NICOS System of Nightly Builds for Distributed Development, A. Undrus, eConf C0303241 (2003) TUJT006, arXiv:hep-ex/0305087. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
A Web Based Document Database, E. W. Vaandering, eConf C0303241 (2003) MONT007, arXiv:hep-ex/0305086. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
AMANDA - first running experiment to use GRID in production, T. Harenberg, K.-H. Becker, W. Rhode, C. Schmitt, eConf C0303241 (2003) MOAT010, arXiv:physics/0305081. 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003.
Quaero: Motivation, Summary, Status, Bruce Knuteson, eConf C0303241 (2003) TULT001, arXiv:hep-ex/0305065. CHEP 2003, UC San Diego.
Predictive Mining of Time Series Data in Astronomy, E. Perlman, A. Java, ASP Conf.Ser. 295 (2003) 431, arXiv:astro-ph/0212413. Astronomy Data Analysis and Software Systems meeting, Baltimore 2002.
Automated Calculation and Simulation Systems, Thorsten Ohl, Nucl. Instrum. Meth. A502 (2003) 818, arXiv:hep-ph/0211058. ACAT 2002.
Status report on TAUOLA, its environment, and its applications, Z. Was, eConf C0209101 (2002) FR01, arXiv:hep-ph/0210386. Seventh International Workshop on Tau Lepton Physics (TAU02), Santa Cruz, Ca, USA, Sept 2002.
Foam: A General purpose Monte Carlo Cellular Algorithm, S. Jadach (NuTeV), J. Phys. G29 (2003) 1919-1924, arXiv:physics/0210061. ICHEP 2002.
XML-Based Formulation of Field Theoretical Models. A Proposal for a Future Standard and Data Base for Model Storage, Exchange and Cross-checking of Results, A.Rodionov A.Demichev, A.Kryukov, arXiv:hep-ph/0203102, 2002. International Workshop 'Automatic Calculation for Future Colliders' (CPP2001), November 28-30, 2001, Tokyo, Japan.
Big Science with a Small Budget: Non-Embarrassingly Parallel Applications in a Non-Dedicated Network of Workstations, Angel de Vicente, Nayra Rodriguez, arXiv:cs.DC/0510094, 20cs. ADASS XV.

6 - Sampling

Unbiased Elimination of Negative Weights in Monte Carlo Samples, Jeppe R. Andersen, Andreas Maier, Eur.Phys.J.C 82 (2022) 433, arXiv:2109.07851.
Nested sampling with any prior you like, Justin Alsing, Will Handley, Mon.Not.Roy.Astron.Soc. 505 (2021) L95-L99, arXiv:2102.12478.
Nested Sampling Methods, Johannes Buchner, arXiv:2101.09675, 2021.
Exhaustive Neural Importance Sampling applied to Monte Carlo event generation, Sebastian Pina-Otey, Federico Sanchez, Thorsten Lux, Vicens Gaitan, Phys.Rev. D102 (2020) 013003, arXiv:2005.12719.
Information gains from Monte Carlo Markov Chains, Ahmad Mehrabi, A. Ahmadi, Eur.Phys.J.Plus 135 (2020) 393, arXiv:1904.11920.
Exploring Theory Space with Monte Carlo Reweighting, James S. Gainer, Joseph Lykken, Konstantin T. Matchev, Stephen Mrenna, Myeonghun Park, JHEP 1410 (2014) 78, arXiv:1404.7129.
MC-TESTER v. 1.23: a universal tool for comparisons of Monte Carlo predictions, N. Davidson, P. Golonka, T. Przedzinski, Z. Was, Comput. Phys. Commun. 182 (2011) 779-789, arXiv:0812.3215.
MINT: a Computer Program for Adaptive Monte Carlo Integration and Generation of Unweighted Distributions, P. Nason, arXiv:0709.2085, 2007.
Multimodal nested sampling: an efficient and robust alternative to MCMC methods for astronomical data analysis, Farhan Feroz, M.P. Hobson, Mon.Not.Roy.Astron.Soc. 384 (2008) 449, arXiv:0704.3704.
Error in Monte Carlo, quasi-error in Quasi-Monte Carlo, R.H. Kleiss, A. Lazopoulos, Comput. Phys. Commun. 175 (2006) 93-115, arXiv:hep-ph/0504085.

7 - Machine Learning

Partition Pooling for Convolutional Graph Network Applications in Particle Physics, M. Bachlechner, T. Birkenfeld, P. Soldin, A. Stahl, C. Wiebusch, arXiv:2208.05952, 2022.
Deficit hawks: robust new physics searches with unknown backgrounds, Jelle Aalbers, arXiv:2204.03264, 2022.
Learning new physics efficiently with nonparametric methods, Marco Letizia, Gianvito Losapio, Marco Rando, Gaia Grosso, Andrea Wulzer, Maurizio Pierini, Marco Zanetti, Lorenzo Rosasco, arXiv:2204.02317, 2022.
Deep learning applications for quality control in particle detector construction, N. Akchurin, J. Damgov, S. Dugad, P. G C, S. Gronroos, K. Lamichhane, J. Martinez, T. Quast, S. Undleeb, A. Whitbeck, arXiv:2203.08969, 2022.
A simple guide from Machine Learning outputs to statistical criteria, Charanjit K. Khosa, Veronica Sanz, Michael Soughton, arXiv:2203.03669, 2022.
Model selection and signal extraction using Gaussian Process regression, Abhijith Gandrakota, Amitabh Lath, Alexandre V. Morozov, Sindhu Murthy, arXiv:2202.05856, 2022.
Maximum likelihood reconstruction of water Cherenkov events with deep generative neural networks, Mo Jia, Karan Kumar, Liam S. Mackey, Alexander Putra, Cristovao Vilela, Michael J. Wilking, Junjie Xia, Chiaki Yanagisawa, Karan Yang, Front.Big Data 5 (2022) 868333, arXiv:2202.01276.
Improving Parametric Neural Networks for High-Energy Physics (and Beyond), Luca Anzalone, Tommaso Diotalevi, Daniele Bonacorsi, arXiv:2202.00424, 2022.
, 2022.
Feed-forward neural network unfolding, Ming-Liang Wong, Andrew Edmonds, Chen Wu, arXiv:2112.08180, 2021.
Non-Parametric Data-Driven Background Modelling using Conditional Probabilities, A. Chisholm, T. Neep, K. Nikolopoulos, R. Owen, E. Reynolds, J. Silva, arXiv:2112.00650, 2021.
Studying Hadronization by Machine Learning Techniques, Gabor Biro, Bence Tanko-Bartalis, Gergely Gabor Barnafoldi, arXiv:2111.15655, 2021.
Learning New Physics from an Imperfect Machine, Raffaele Tito d'Agnolo, Gaia Grosso, Maurizio Pierini, Andrea Wulzer, Marco Zanetti, Eur.Phys.J.C 82 (2022) 275, arXiv:2111.13633.
How to use Machine Learning to improve the discrimination between signal and background at particle colliders, Xabier Cid Vidal, Lorena Dieste Maronas, Alvaro Dosil Suarez, Appl.Sciences 11 (2021) 11076, arXiv:2110.15099.
Presenting Unbinned Differential Cross Section Results, Miguel Arratia et al., JINST 17 (2022) P01024, arXiv:2109.13243.
Uncertainty Aware Learning for High Energy Physics, Aishik Ghosh, Benjamin Nachman, Daniel Whiteson, Phys.Rev.D 104 (2021) 056026, arXiv:2105.08742.
Deep Neural Network as an alternative to Boosted Decision Trees for PID, Denis Stanev, Riccardo Riva, Michele Umassi, arXiv:2104.14045, 2021.
Comparison of Machine Learning Approach to other Unfolding Methods, Petr Baron, Acta Phys.Polon.B 52 (2021) 863, arXiv:2104.03036.
Segmentation of EM showers for neutrino experiments with deep graph neural networks, Vladislav Belavin, Ekaterina Trofimova, Andrey Ustyuzhanin, JINST 16 (2021) P12035, arXiv:2104.02040.
Quantum Convolutional Neural Networks for High Energy Physics Data Analysis, Samuel Yen-Chi Chen, Tzu-Chieh Wei, Chao Zhang, Haiwang Yu, Shinjae Yoo, Phys.Rev.Res. 4 (2022) 013231, arXiv:2012.12177.
Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge, Sang Eon Park, Dylan Rankin, Silviu-Marian Udrescu, Mikaeel Yunus, Philip Harris, JHEP 2106 (2021) 030, arXiv:2011.03550.
GPU-accelerated machine learning inference as a service for computing in neutrino experiments, Michael Wang, Tingjun Yang, Maria Acosta Flechas, Philip Harris, Benjamin Hawks, Burt Holzman, Kyle Knoepfel, Jeffrey Krupa, Kevin Pedro, Nhan Tran, Front.Big Data 3 (2021) 604083, arXiv:2009.04509.
MLaaS4HEP: Machine Learning as a Service for HEP, Valentin Kuznetsov, Luca Giommi, Daniele Bonacorsi, Comput.Softw.Big Sci. 5 (2021) 17, arXiv:2007.14781.
Graph Neural Networks in Particle Physics, Jonathan Shlomi, Peter Battaglia, Jean-Roch Vlimant, arXiv:2007.13681, 2020.
ECoPANN: A Framework for Estimating Cosmological Parameters using Artificial Neural Networks, Guo-Jian Wang, Si-Yao Li, Jun-Qing Xia, Astrophys. J. Suppl. 249 (2020) 25, arXiv:2005.07089.
Efficiency Parameterization with Neural Networks, C. Badiali, F.A. Di Bello, G. Frattari, E. Gross, V. Ippolito, M. Kado, J. Shlomi, Comput.Softw.Big Sci. 5 (2021) 14, arXiv:2004.02665.
Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows, Sebastian Pina-Otey, Federico Sanchez, Vicens Gaitan, Phys.Rev. D101 (2020) 113001, arXiv:2002.09436.
Learning Multivariate New Physics, Raffaele Tito D'Agnolo, Gaia Grosso, Maurizio Pierini, Andrea Wulzer, Marco Zanetti, Eur.Phys.J. C81 (2021) 89, arXiv:1912.12155.
Machine Learning as a Service for HEP, Valentin Kuznetsov, arXiv:1811.04492, 2018.
Learning New Physics from a Machine, Raffaele Tito D'Agnolo, Andrea Wulzer, Phys.Rev. D99 (2019) 015014, arXiv:1806.02350.
What is the Machine Learning?, Spencer Chang, Timothy Cohen, Bryan Ostdiek, Phys.Rev.D 97 (2018) 056009, arXiv:1709.10106.
(Machine) Learning to Do More with Less, Timothy Cohen, Marat Freytsis, Bryan Ostdiek, JHEP 1802 (2018) 034, arXiv:1706.09451.
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks, Evan Racah et al., arXiv:1601.07621, 2016.
BAMBI: blind accelerated multimodal Bayesian inference, Philip Graff, Farhan Feroz, Michael P. Hobson, Anthony Lasenby, Mon. Not. Roy. Astron. Soc. 421 (2012) 169-180, arXiv:1110.2997.
Two-Photon Exchange Effect Studied with Neural Networks, Krzysztof M. Graczyk, Phys. Rev. C84 (2011) 034314, arXiv:1106.1204.
Neural Network Parameterizations of Electromagnetic Nucleon Form Factors, Krzysztof M. Graczyk, Piotr Plonski, Robert Sulej, JHEP 09 (2010) 053, arXiv:1006.0342.
The Bjorken sum rule with Monte Carlo and Neural Network techniques, Luigi Del Debbio, Alberto Guffanti, Andrea Piccione, JHEP 11 (2009) 060, arXiv:0907.2506.
A determination of parton distributions with faithful uncertainty estimation, Richard D. Ball et al. (NNPDF), Nucl. Phys. B809 (2009) 1-63, arXiv:0808.1231.
Recent progress on NNPDF for LHC, M. Ubiali et al. (NNPDF), arXiv:0805.3100, 2008. 43rd Rencontres de Moriond on QCD and Hadronic Interactions, La Thuile, Italy, 8-15 Mar 2008.
Progress on neural parton distributions, J. Rojo et al. (NNPDF), arXiv:0706.2130, 2007. 15th International Workshop on Deep-Inelastic Scattering and Related Subjects (DIS2007), Munich, Germany, 16-20 Apr 2007.
Neural network determination of parton distributions: the nonsinglet case, Luigi Del Debbio, Stefano Forte, Jose I. Latorre, Andrea Piccione, Joan Rojo (NNPDF), JHEP 03 (2007) 039, arXiv:hep-ph/0701127.
Polarized DIS structure functions from neural networks, L. Del Debbio, Alberto Guffanti, A. Piccione, AIP Conf. Proc. 915 (2007) 424-427. 17th International Spin Physics Symposium (SPIN06), Kyoto, Japan, 2-7 Oct 2006.
Unbiased determination of the proton structure function F2(p) with faithful uncertainty estimation, Luigi Del Debbio, Stefano Forte, Jose I. Latorre, Andrea Piccione, Joan Rojo (NNPDF), JHEP 03 (2005) 080, arXiv:hep-ph/0501067.
Neural network parametrization of deep-inelastic structure functions, Stefano Forte, Lluis Garrido, Jose I. Latorre, Andrea Piccione, JHEP 05 (2002) 062, arXiv:hep-ph/0204232.

8 - Machine Learning - Talks

Scalable, End-to-End, Deep-Learning-Based Data Reconstruction Chain for Particle Imaging Detectors, Francois Drielsma, Kazuhiro Terao, Laura Domine, Dae Heun Koh, arXiv:2102.01033, 2021. Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020), Vancouver, Canada.
The use of Convolutional Neural Networks for signal-background classification in Particle Physics experiments, Venkitesh Ayyar, Wahid Bhimji, Lisa Gerhardt, Sally Robertson, Zahra Ronaghi, EPJ Web Conf. 245 (2020) 06003, arXiv:2002.05761. CHEP 2019, Nov 4-8, Adelaide, Australia.
Approximating the solution to wave propagation using deep neural networks, Wilhelm E. Sorteberg, Stef Garasto, Alison S. Pouplin, Chris D. Cantwell, Anil A. Bharath, arXiv:1812.01609, 2018. NeurIPS 2018 Workshop 'Modeling the Physical World: Perception, Learning, and Control'.
Parametrizing Compton form factors with neural networks, Kresimir Kumericki, Dieter Mueller, Andreas Schafer, Nucl. Phys. Proc. Suppl. 222-224 (2012) 199-203, arXiv:1112.1958. Ringberg HERA Workshop.
Update on Neural Network Parton Distributions: NNPDF1.1, Juan Rojo et al. (NNPDF), arXiv:0811.2288, 2008. 38th International Symposium on Multiparticle Dynamics ISMD08, Hamburg, Germany, 15-20 Sep 2008.
Neural network determination of the non-singlet quark distribution, Andrea Piccione, Luigi Del Debbio, Stefano Forte, Jose I. Latorre, Joan Rojo (NNPDF), arXiv:hep-ph/0607199, 2006. Tsukuba 2006, Deep inelastic scattering.
Neural network approach to parton distributions fitting, Andrea Piccione, Luigi Del Debbio, Stefano Forte, Jose I. Latorre, Joan Rojo (NNPDF), Nucl. Instrum. Meth. A559 (2006) 203-206, arXiv:hep-ph/0509067. 10th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 05), Zeuthen, Germany, 22-27 May 2005.
The neural network approach to parton distributions, L. Del Debbio, S. Forte, J. I. Latorre, Andrea Piccione, Joan Rojo (NNPDF), arXiv:hep-ph/0509059, 2005. HERA and the LHC: A Workshop on the Implications of HERA and LHC Physics (Startup Meeting, CERN, 26-27 March 2004; Midterm Meeting, CERN, 11-13 October 2004), Hamburg, Germany, 21-24 Mar 2005.
The neural network approach to parton fitting, Joan Rojo, Luigi Del Debbio, Stefano Forte, Jose I. Latorre, Andrea Piccione (NNPDF), AIP Conf. Proc. 792 (2005) 376-379, arXiv:hep-ph/0505044. 13th International Workshop on Deep Inelastic Scattering (DIS 05), Madison, Wisconsin, 27 Apr - 1 May 2005.
Automatic Classification using Self-Organising Neural Networks in Astrophysical Experiments, P. Boinee, A. De Angelis, E. Milotti, arXiv:cs.NE/0307031, 20cs. Science with the New Generation of High Energy Gamma-ray Experiments, Perugia, Italy, May 2003.

9 - Database Management

A Relational Model of Data for Large Shared Data Banks, E. F. Codd, Commun. ACM 13 (1970) 377-387.

10 - Database Management - Talks

Data Preservation in High Energy Physics, David M. South (ICFA DPHEP Study Group), J. Phys. Conf. Ser. 331 (2011) 012005, arXiv:1101.3186. 18th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2010).

11 - Abduction

Compilability of Abduction, Paolo Liberatore, Marco Schaerf, arXiv:cs.AI/0210007, 20cs.
Abduction with Penalization in Logic Programming, Giovambattista Ianni, Nicola Leone, Simona Perri, Francesco Scarcello, arXiv:cs.LO/0111010, 20cs.

12 - Education

Visualisation of Cherenkov Radiation and the Fields of a Moving Charge, Robert N. C. Pfeifer, Timo A. Nieminen, arXiv:physics/0602061, 2006.

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