Fetching the paper…
Reading the bibliography…
We investigate how to improve new physics detection strategies exploiting variational autoencoders and normalizing flows for anomaly detection at the Large Hadron Collider.
Parametric correspondence and Chamfer matching: Two new techniques for image matching
Barrow, H. G., Tenenbaum, J. M., Bolles, R. C., and Wolf, H. C. (1977) · 1977
Earlier work this paper cites.
Khosa, C. K. and Sanz, V. (2020) · 2007
Earlier work this paper cites.
Global Search for New Physics with 2.0 fb -1
Aaltonen, T. et al. (2009) · 2009
Earlier work this paper cites.
A General Search for New Phenomena at HERA
Aaron, F. D. et al. (2009) · 2009
Earlier work this paper cites.
Trial factors for the look elsewhere effect in high energy physics
Gross, E. and Vitells, O. (2010) · 2010
Earlier work this paper cites.
Anomaly Detection for Physics Analysis and Less than Supervised Learning
Nachman, B. (2020) · 2010
Earlier work this paper cites.
Density estimation by dual ascent of the log-likelihood
Tabak, E. G. and Vanden-Eijnden, E. (2010) · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., et al. (2011) · 2011
Earlier work this paper cites.
Model independent search for new phenomena in p p ¯ p\bar{p} collisions at s = 1.96 \sqrt{s}=1.96 TeV
D0 Collaboration (2012) · 2012
Earlier work this paper cites.
A family of nonparametric density estimation algorithms
Tabak, E. G. and Turner, C. V. (2013) · 2013
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M. (2014) · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D. (2014) · 2014
Earlier work this paper cites.
The CMS High Level Trigger
Trocino, D. (2014) · 2014
Earlier work this paper cites.
Variational autoencoder based anomaly detection using reconstruction probability
An, J. and Cho, S. (2015) · 2015
Earlier work this paper cites.
MADE: Masked autoencoder for distribution estimation
Germain, M., Gregor, K., Murray, I., and Larochelle, H. (2015) · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2015) · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Rezende, D. and Mohamed, S. (2015) · 2015
Earlier work this paper cites.
Improving variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M. (2016) · 2016
Earlier work this paper cites.
Machine learning and multivariate goodness of fit
Weisser, C. and Williams, M. (2016) · 2016
Earlier work this paper cites.
Geometric deep learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P. (2017) · 2017
Earlier work this paper cites.
MUSiC, a Model Unspecific Search for New Physics, in pp Collisions at s = 8 TeV \sqrt{s}=8\,\mathrm{TeV}
[Dataset] CMS-PAS-EXO-14-016 (2017) · 2017
Earlier work this paper cites.
A point set generation network for 3D object reconstruction from a single image
Fan, H., Su, H., and Guibas, L. J. (2017) · 2017
Earlier work this paper cites.
beta-VAE: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C. P., Glorot, X., Botvinick, M., et al. (2017) · 2017
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M. (2017) · 2017
Cited alongside, same era.
Improving variational auto-encoders using convex combination linear inverse autoregressive flow
Tomczak, J. M. and Welling, M. (2017) · 2017
Cited alongside, same era.
Sylvester normalizing flows for variational inference
Berg, R. v. d., Hasenclever, L., Tomczak, J. M., and Welling, M. (2018) · 2018
Cited alongside, same era.
Anomaly Detection for Resonant New Physics with Machine Learning
Collins, J. H., Howe, K., and Nachman, B. (2018) · 2018
Cited alongside, same era.
Convolutional normalizing flows
Zheng, G., Yang, Y., and Carbonell, J. (2018) · 2018
Searching for New Physics with Deep Autoencoders
Farina, M., Nakai, Y., and Shih, D. (2020) · 2020
Later among the works it cites.
Novelty Detection Meets Collider Physics
Hajer, J., Li, Y.-Y., Liu, T., and Wang, H. (2020) · 2020
Later among the works it cites.
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Kasieczka, G. et al. (2021) · 2020
Later among the works it cites.
Normalizing flows: An introduction and review of current methods
Kobyzev, I., Prince, S., and Brubaker, M. (2020) · 2020
Later among the works it cites.
Anomaly Detection with Density Estimation
Nachman, B. and Shih, D. (2020) · 2020
Later among the works it cites.
Graph neural networks in particle physics
Shlomi, J., Battaglia, P., and Vlimant, J.-R. (2020) · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
Aaboud, M. et al. (2019) · 2019
Cited alongside, same era.
Adversarially-trained autoencoders for robust unsupervised new physics searches
Blance, A., Spannowsky, M., and Waite, P. (2019) · 2019
Cited alongside, same era.
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
Cerri, O., Nguyen, T. Q., Pierini, M., Spiropulu, M., and Vlimant, J.-R. (2019) · 2019
Cited alongside, same era.
Extending the search for new resonances with machine learning
Collins, J. H., Howe, K., and Nachman, B. (2019) · 2019
Cited alongside, same era.
Learning New Physics from a Machine
D’Agnolo, R. T. and Wulzer, A. (2019) · 2019
Cited alongside, same era.
Guiding New Physics Searches with Unsupervised Learning
De Simone, A. and Jacques, T. (2019) · 2019
Cited alongside, same era.
Performance of the CMS Level-1 trigger in proton-proton collisions at s = \sqrt{s}= 13 TeV
Sirunyan, A. M. et al. (2020) · 2020
Later among the works it cites.
FSPool: Learning set representations with featurewise sort pooling
Zhang, Y., Hare, J., and Prügel-Bennett, A. (2020) · 2020
Later among the works it cites.
Aarrestad, T. et al. (2021) · 2021
Closest in time.
Tag N’ Train: a technique to train improved classifiers on unlabeled data
Amram, O. and Suarez, C. M. (2021) · 2021
Closest in time.
Bump Hunting in Latent Space
Bortolato, B., Dillon, B. M., Kamenik, J. F., and Smolkovič, A. (2021) · 2021
Closest in time.
Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC
Caron, S., Hendriks, L., and Verheyen, R. (2021) · 2021
Closest in time.
Comparing weak- and unsupervised methods for resonant anomaly detection
Collins, J. H., Martín-Ramiro, P., Nachman, B., and Shih, D. (2021) · 2021
Closest in time.
Learning multivariate new physics
D’Agnolo, R. T., Grosso, G., Pierini, M., Wulzer, A., and Zanetti, M. (2021) · 2021
Closest in time.
Autoencoders for unsupervised anomaly detection in high energy physics
Finke, T., Krämer, M., Morandini, A., Mück, A., and Oleksiyuk, I. (2021) · 2021
Closest in time.
High-dimensional Anomaly Detection with Radiative Return in e + e − e^{+}e^{-} Collisions
Gonski, J., Lai, J., Nachman, B., and Ochoa, I. (2021) · 2021
Closest in time.
Classifying Anomalies THrough Outer Density Estimation (CATHODE)
Hallin, A., Isaacson, J., Kasieczka, G., Krause, C., Nachman, B., Quadfasel, T., et al. (2021) · 2021
Closest in time.
mpp-hep/DarkFlow
Jawahar, P. and Pierini, M. (2021) · 2021
Closest in time.
Deep Set Auto Encoders for Anomaly Detection in Particle Physics
Ostdiek, B. (2021) · 2021
Closest in time.
Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2021) · 2021
Closest in time.
Quasi Anomalous Knowledge: Searching for new physics with embedded knowledge
Park, S. E., Rankin, D., Udrescu, S.-M., Yunus, M., and Harris, P. (2020) · 2021
Closest in time.
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., et al. (2020) · 2021
Closest in time.