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2023

ELSA -- Enhanced latent spaces for improved collider simulations

Nachman, Benjamin, Winterhalder, Ramon

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Simulations play a key role for inference in collider physics.

We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinement).
To clearly illustrate our approaches, we use W+jets matrix element surrogate simulations based on normalizing flows as a prototypical example.
First, weights in the data space are derived using machine learning classifiers.

ELSA -- Enhanced latent spaces for improved collider simulations · Around

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R. M. Neal, MCMC using Hamiltonian dynamics , 1206.1901
Original
1901
Earlier work this paper cites.
E. Dupont, A. Doucet and Y. W. Teh, Augmented neural odes , 1904.01681
Original
1904
Earlier work this paper cites.
C. Durkan, A. Bekasov, I. Murray and G. Papamakarios, Neural spline flows , Advances in Neural Information Processing Systems 32
Original
1906
Earlier work this paper cites.
Y. Song and S. Ermon, Generative Modeling by Estimating Gradients of the Data Distribution , 1907.05600
Original
1907
Earlier work this paper cites.
A. Butter, T. Plehn and R. Winterhalder, How to GAN LHC Events , SciPost Phys. 7
Original
1907
Earlier work this paper cites.
A. Andreassen and B. Nachman, Neural Networks for Full Phase-space Reweighting and Parameter Tuning , Phys. Rev. D 101
Original
1907
Earlier work this paper cites.
A. Andreassen, P. T. Komiske, E. M. Metodiev, B. Nachman and J. Thaler, OmniFold: A Method to Simultaneously Unfold All Observables , Phys. Rev. Lett. 124
Original
1911
Earlier work this paper cites.
R. Kleiss, W. Stirling and S. Ellis, A new monte carlo treatment of multiparticle phase space at high energies , Computer Physics Communications 40
1986
Earlier work this paper cites.
S. Duane, A. Kennedy, B. J. Pendleton and D. Roweth, Hybrid monte carlo , Physics Letters B 195
1987
Earlier work this paper cites.
Y. Rubner, C. Tomasi and L. J. Guibas, The earth mover’s distance as a metric for image retrieval , Int. J. Comput. Vision 40
2000
Earlier work this paper cites.
Y. Alanazi et al., Simulation of electron-proton scattering events by a Feature-Augmented and Transformed Generative Adversarial Network (FAT-GAN) , 2001.11103
Original
2001
Earlier work this paper cites.
Springer Series in Statistics. Springer New York Inc., New York, NY, USA, 2001
T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning · 2001
Earlier work this paper cites.
A. Andreassen, B. Nachman and D. Shih, Simulation Assisted Likelihood-free Anomaly Detection , Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
H. Wu, J. Köhler and F. Noé, Stochastic normalizing flows , 2002.06707
Original
2002
Earlier work this paper cites.
C.-W. Huang, L. Dinh and A. Courville, Augmented normalizing flows: Bridging the gap between generative flows and latent variable models , 2002.07101
Original
2002
Earlier work this paper cites.
J. Chen, C. Lu, B. Chenli, J. Zhu and T. Tian, Vflow: More expressive generative flows with variational data augmentation , 2002.09741
Original
2002
Earlier work this paper cites.
R. Brent, Algorithms for minimization without derivatives , Englewood Cliffs, Prentice Hall 19
2002
Earlier work this paper cites.
J. Brehmer and K. Cranmer, Flows for simultaneous manifold learning and density estimation , 2003.13913
Original
2003
Earlier work this paper cites.
T. Che, R. Zhang, J. Sohl-Dickstein, H. Larochelle, L. Paull, Y. Cao et al., Your gan is secretly an energy-based model and you should use discriminator driven latent sampling , 2003.06060
Original
2003
Earlier work this paper cites.

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