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Advanced detector R&D requires performing computationally intensive and detailed simulations as part of the detector-design optimization process.
doi:10.1073/pnas.38.8.716
R. Bellman, Dynamic Programming, Rand Corporation research study, Princeton University Press, (1957) · 1957
Earlier work this paper cites.
doi:10.1016/0168-9002(94)90532-0
T. Ypsilantis, J. Séguinot, Theory of ring imaging Cherenkov counters, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 343 (1) (1994) 30–51 · 1994
Earlier work this paper cites.
doi:10.1016/0168-9002(94)01006-4
R. Abjean, A. Bideau-Mehu, Y. Guern, Refractive index of hexafluoroethane (C 2 · 1995
Earlier work this paper cites.
doi:10.1023/A:1008306431147
D. R. Jones, M. Schonlau, W. J. Welch, Efficient global optimization of expensive black-box functions, J. Global Optim. 13 (4) (1998) 455–492 · 1998
Earlier work this paper cites.
J. H. Friedman, Greedy function approximation: a gradient boosting machine, Annals of statistics (2001) 1189–1232
2001
Earlier work this paper cites.
arXiv:0104033
N. Akopov, et al., The HERMES Dual-Radiator Ring Imaging Cherenkov Detector, Nucl. Instrum. Meth. A 479 (2) (2002) 511–530 · 2002
Earlier work this paper cites.
The LHCb Collaboration, R. A. Nobrega, et al., LHCb reoptimized detector design and performance: Technical Design Report , Tech. Rep. No. CERN-LHCC-2003-030; LHCb-TDR-9 (2003)
2003
Earlier work this paper cites.
doi:10.1109/ISAP.2005.1599245
P. Ngatchou, A. Zarei, A. El-Sharkawi, Pareto multi objective optimization, in: Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems, IEEE, (2005), pp. 84–91 · 2005
Earlier work this paper cites.
doi:10.1393/ncr/i2006-10004-6
E. Nappi, J. Seguinot, Ring Imaging Cherenkov Detectors: The state of the art and perspectives, Riv. Nuovo Cimento 28 (8-9) (2005) 1–130 · 2005
Earlier work this paper cites.
doi:10.1007/978-3-540-28650-9_4
C. K. Williams, C. E. Rasmussen, Gaussian processes for machine learning, Vol. 2, MIT press Cambridge, MA, (2006) · 2006
Earlier work this paper cites.
D. J. Lizotte, T. Wang, M. H. Bowling, D. Schuurmans, Automatic Gait Optimization with Gaussian Process Regression., in: IJCAI, Vol. 7, (2007), pp. 944–949
2007
Earlier work this paper cites.
doi:10.1088/1748-0221/3/08/S08005
The LHCb Collaboration, A. Alves Jr, et al., The LHCb Detector at the LHC, J. Instrum. 3 (2008) S08005 · 2008
Earlier work this paper cites.
doi:10.1088/1748-0221/3/08/S08005
The LHCb Collaboration, A. Alves Jr, et al., The LHCb detector at the LHC, J. Instrum. 3 (08) (2008) S08005 · 2008
Cited alongside, same era.
B. Eric, N. D. Freitas, A. Ghosh, Active preference learning with discrete choice data , in: J. C. Platt, D. Koller, Y. Singer, S. T. Roweis (Eds.), Adv. Neural Inf. Process Syst., Curran Associates, Inc., (2008), pp. 409–416. URL http://papers.nips.cc/paper/3219-active-preference-learning-with-discrete-choice-data.pdf
2008
Cited alongside, same era.
E. Brochu, V. M. Cora, N. De Freitas, A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning (2010) · 2010
Cited alongside, same era.
E. Brochu, T. Brochu, N. de Freitas, A Bayesian Interactive Optimization Approach to Procedural Animation Design , in: Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, SCA ’10, Eurographics Association, Goslar Germany, Germany, (2010), pp. 103–112. URL http://dl.acm.org/citation.cfm?id=1921427.1921443
S. Pereira, et al., Test of the CLAS12 RICH large-scale prototype in the direct proximity focusing configuration, Eur. Phys. J. A 52 (2) (2016) 1–15 · 2016
Later among the works it cites.
M. Abadi, et al., Tensorflow: Large-scale machine learning on heterogeneous distributed systems, (2016) · 2016
Later among the works it cites.
doi:10.1016/j.nima.2017.03.032
A. Del Dotto, et al., Design and R&D of RICH detectors for EIC experiments, Nucl. Instrum. Meth. A 876 (2017) 237–240 · 2017
Later among the works it cites.
doi:10.1016/j.nima.2017.07.001
C. Wong, et al., Modular focusing ring imaging Cherenkov detector for electron-ion collider experiments, Nucl. Instrum. Meth. A 871 (2017) 13–19 · 2017
Later among the works it cites.
doi:https://doi.org/10.1016/j.nima.2017.02.068
M. Contalbrigo, et al., Aerogel mass production for the CLAS12 RICH: Novel characterization methods and optical performance, Nuc. Instrum. Meth. A 876 (2017) 168–172 · 2017
Later among the works it cites.
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2010
Cited alongside, same era.
doi:10.1109/TIT.2011.2182033
N. Srinivas, A. Krause, S. M. Kakade, M. W. Seeger, Information-theoretic regret bounds for gaussian process optimization in the bandit setting, IEEE T. Inform. Theory 58 (5) (2012) 3250–3265 · 2011
Cited alongside, same era.
doi:10.1007/978-3-642-25566-3_40
F. Hutter, H. H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general algorithm configuration, in: International Conference on Learning and Intelligent Optimization, Springer, (2011), pp. 507–523 · 2011
Cited alongside, same era.
J. Snoek, H. Larochelle, R. P. Adams, Practical bayesian optimization of machine learning algorithms, in: Adv. Neural Inf. Process. Syst., (2012), pp. 2951–2959 · 2012
Cited alongside, same era.
J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization , J. Mach. Learn. Res. 13 (Feb) (2012) 281–305. URL http://jmlr.csail.mit.edu/papers/volume13/bergstra12a/bergstra12a.pdf
2012
Cited alongside, same era.
doi:10.1140/epjc/s10052-013-2431-9
The LHCb Collaboration, M. Adinolfi, et al., Performance of the LHCb RICH detector at the LHC, Eur. Phys. J. C 73 (5) (2013) 1–17 · 2013
Cited alongside, same era.
J. Bergstra, D. Yamins, D. D. Cox, Hyperopt: A python library for optimizing the hyperparameters of machine learning algorithms , in: Proceedings of the 12th Python in science conference, Citeseer, (2013), pp. 13–20. URL http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.704.3494
2013
Cited alongside, same era.
J. Snoek, et al., Scalable bayesian optimization using deep neural networks, in: International conference on machine learning, (2015), pp. 2171–2180 · 2015
Cited alongside, same era.
A. Accardi, et al., Electron-Ion Collider: The next QCD frontier, Eur. Phys. J. A 52 (9) (2016) 268 · 2016
Cited alongside, same era.
P. Ilten, M. Williams, Y. Yang, Event generator tuning using Bayesian optimization, Journ. of Instrum. 12 (04) (2017) P04028 · 2017
Later among the works it cites.
N. Knudde, J. van der Herten, T. Dhaene, I. Couckuyt, GPflowopt: A Bayesian optimization library using tensorflow, (2017) · 2017
Later among the works it cites.
doi:10.17226/25171
National Academies of Sciences, Engineering, and Medicine, An Assessment of U.S.-Based Electron-Ion Collider Science. (2018) · 2018
Later among the works it cites.
doi:10.1088/1748-0221/13/04/C04018
G. Kalicy et al., High-performance DIRC detector for the future Electron Ion Collider experiment, J. Instrum. 13 (4) (2018) C04018 · 2018
Later among the works it cites.
T. Head, et al., scikit-optimize/scikit-optimize: v0.5.2 (Version v0.5.2), Zenodo. http://doi.org/10.5281/zenodo.1207017 , march 25 (2018)
2018
Later among the works it cites.
E. Aschenauer, et al., Electron-Ion Collider Detector Requirements and R&D Handbook (Version 1.1) (2019)
2019
Closest in time.
D. Romanov, JLEIC Detector and Simulation (Jefferson Lab 2019 Users Organization), www.jlab.org/user_resources/meetings/JLUO_6_19/Romanov.pptx (2019)
2019
Closest in time.