Fetching the paper…

Reading the bibliography…

2021

Classifying Anomalies THrough Outer Density Estimation (CATHODE)

Hallin, Anna, Isaacson, Joshua, Kasieczka, Gregor et al.

Original
PDF
Post on X

Understand

We propose a new model-agnostic search strategy for physics beyond the standard model (BSM) at the LHC, based on a novel application of neural density estimation to anomaly detection.

Our approach, which we call Classifying Anomalies THrough Outer Density Estimation (CATHODE), assumes the BSM signal is localized in a signal region (defined e.g.
using invariant mass).
By training a conditional density estimator on a collection of additional features outside the signal region, interpolating it into the signal region, and sampling from it, we produce a collection of events that follow the background model.

Classifying Anomalies THrough Outer Density Estimation (CATHODE) · Around

Built on

J. H. Collins, K. Howe, and B. Nachman, Extending the search for new resonances with machine learning, Phys. Rev. D99
Original
1902
Earlier work this paper cites.
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoders, (2019), arXiv:1903.02032 [hep-ph]
Original
1903
Earlier work this paper cites.
J. Lin, W. Bhimji, and B. Nachman, Machine Learning Templates for QCD Factorization in the Search for Physics Beyond the Standard Model, JHEP 05
Original
1903
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, and J. F. Kamenik, Uncovering latent jet substructure, Phys. Rev. D100
Original
1904
Earlier work this paper cites.
A. Blance, M. Spannowsky, and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10
Original
1905
Earlier work this paper cites.
J. H. Kim, K. Kong, B. Nachman, and D. Whiteson, The motivation and status of two-body resonance decays after the LHC Run 2 and beyond, JHEP 04
Original
1907
Earlier work this paper cites.
I. Kobyzev, S. J. D. Prince, and M. A. Brubaker, Normalizing Flows: An Introduction and Review of Current Methods, arXiv e-prints , arXiv:1908.09257 (2019), arXiv:1908.09257 [stat.ML]
Original
1908
Earlier work this paper cites.
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer, and M. Zanetti, Learning Multivariate New Physics, Eur. Phys. J. C 81
Original
1912
Earlier work this paper cites.
A. Mullin, S. Nicholls, H. Pacey, M. Parker, M. White, and S. Williams, Does SUSY have friends? A new approach for LHC event analysis, JHEP 02
Original
1912
Earlier work this paper cites.
M. Crispim Romão, N. Castro, R. Pedro, and T. Vale, Transferability of Deep Learning Models in Searches for New Physics at Colliders, Phys. Rev. D 101
Original
1912
Earlier work this paper cites.
G. Papamakarios, E. Nalisnick, D. Jimenez Rezende, S. Mohamed, and B. Lakshminarayanan, Normalizing Flows for Probabilistic Modeling and Inference, arXiv e-prints , arXiv:1912.02762 (2019), arXiv:1912.02762 [stat.ML]
Original
1912
Earlier work this paper cites.
J. Neyman and E. S. Pearson, On the problem of the most efficient tests of statistical hypotheses, Phil. Trans. R. Soc. Lond. A 231
1933
Earlier work this paper cites.
B. Knuteson, A Quasi-Model-Independent Search for New High p T p_{T} Physics at D0 , ph.D. thesis, University of California at Berkeley (2000)
2000
Earlier work this paper cites.
B. Abbott et al. (D0), Search for new physics in e μ X e\mu X data at DØ using SLEUTH: A quasi model independent search strategy for new physics, Phys. Rev. D62
2000
Earlier work this paper cites.
V. M. Abazov et al. (D0), Quasi model independent search for new physics at large transverse momentum, Phys. Rev. D64
2001
Earlier work this paper cites.
B. Abbott et al. (D0), Quasi-model-independent search for new high p T p_{T} physics at DØ, Phys. Rev. Lett. 86
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.
B. Nachman and D. Shih, Anomaly Detection with Density Estimation, Phys. Rev. D 101
Original
2001
Earlier work this paper cites.
O. Amram and C. M. Suarez, Tag N’ Train: a technique to train improved classifiers on unlabeled data, JHEP 01
Original
2002
Earlier work this paper cites.
A. Aktas et al. (H1), A General search for new phenomena in ep scattering at HERA, Phys. Lett. B602
2004
Earlier work this paper cites.
M. Crispim Romão, N. F. Castro, J. G. Milhano, R. Pedro, and T. Vale, Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders, Eur. Phys. J. C 81
Original
2004
Earlier work this paper cites.
K. S. Cranmer, Searching for new physics: Contributions to LEP and the LHC , Ph.D. thesis , Wisconsin U., Madison (2005)
2005
Earlier work this paper cites.
O. Knapp, O. Cerri, G. Dissertori, T. Q. Nguyen, M. Pierini, and J. R. Vlimant, Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quark, Eur. Phys. J. Plus 136
Original
2005
Earlier work this paper cites.
B. M. Dillon, D. A. Faroughy, J. F. Kamenik, and M. Szewc, Learning the latent structure of collider events, JHEP 10
Original
2005
Earlier work this paper cites.
G. Aad et al. (ATLAS), Dijet resonance search with weak supervision using s = 13 \sqrt{s}=13 TeV p p pp collisions in the ATLAS detector, Phys. Rev. Lett. 125
Original
2005
Earlier work this paper cites.
M. Crispim Romão, N. F. Castro, and R. Pedro, Finding New Physics without learning about it: Anomaly Detection as a tool for Searches at Colliders, Eur. Phys. J. C 81
Original
2006
Earlier work this paper cites.
M. Cacciari and G. P. Salam, Dispelling the N 3 N^{3} myth for the k t k_{t} jet-finder, Phys. Lett. B641
2006
Earlier work this paper cites.

Similar

T. Aaltonen et al. (CDF), Model-Independent Global Search for New High-pT Physics at CDF 10.2172/922303 (2007), arXiv:0712.2534 [hep-ex]
Original
2007
Cited alongside, same era.
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette, and T. Golling, Variational Autoencoders for Anomalous Jet Tagging, (2020), arXiv:2007.01850 [hep-ph]
Original
2007
Cited alongside, same era.
C. K. Khosa and V. Sanz, Anomaly Awareness, (2020), arXiv:2007.14462 [cs.LG]
Original
2007
Cited alongside, same era.
P. Thaprasop, K. Zhou, J. Steinheimer, and C. Herold, Unsupervised Outlier Detection in Heavy-Ion Collisions, Phys. Scripta 96
Original
2007
Cited alongside, same era.
T. Aaltonen et al. (CDF), Model-Independent and Quasi-Model-Independent Search for New Physics at CDF, Phys. Rev. D78

Then

J. H. Collins, K. Howe, and B. Nachman, Anomaly Detection for Resonant New Physics with Machine Learning, Phys. Rev. Lett. 121
Original
2018
Later among the works it cites.
LHCb Collaboration, Publications of the QCD, Electroweak and Exotica Working Group (2019), http://lhcbproject.web.cern.ch/lhcbproject/Publications/LHCbProjectPublic/Summary_QEE.html
2019
Later among the works it cites.
N. Craig, P. Draper, K. Kong, Y. Ng, and D. Whiteson, The unexplored landscape of two-body resonances, Acta Phys. Polon. B 50
Original
2019
Later among the works it cites.
R. T. D’Agnolo and A. Wulzer, Learning New Physics from a Machine, Phys. Rev. D99
Original
2019
Later among the works it cites.
T. Heimel, G. Kasieczka, T. Plehn, and J. M. Thompson, QCD or What?, SciPost Phys. 6

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2008

Cited alongside, same era.

S. Alexander, S. Gleyzer, H. Parul, P. Reddy, M. W. Toomey, E. Usai, and R. Von Klar, Decoding Dark Matter Substructure without Supervision, (2020), arXiv:2008.12731 [astro-ph.CO]

2008

Cited alongside, same era.

J. A. Aguilar-Saavedra, F. R. Joaquim, and J. F. Seabra, Mass Unspecific Supervised Tagging (MUST) for boosted jets, JHEP 03

2008

Cited alongside, same era.

T. Sjöstrand, S. Mrenna, and P. Z. Skands, A Brief Introduction to PYTHIA 8.1, Comput. Phys. Commun. 178

2008

Cited alongside, same era.

A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, GANplifying event samples, SciPost Phys. 10

2008

Cited alongside, same era.

F. D. Aaron et al. (H1), A General Search for New Phenomena at HERA, Phys. Lett. B674

2009

Cited alongside, same era.

T. Aaltonen et al. (CDF), Global Search for New Physics with 2.0 fb -1

2009

Cited alongside, same era.

K. Benkendorfer, L. L. Pottier, and B. Nachman, Simulation-assisted decorrelation for resonant anomaly detection, Phys. Rev. D 104

2009

Cited alongside, same era.

2019

Later among the works it cites.

A. De Simone and T. Jacques, Guiding New Physics Searches with Unsupervised Learning, Eur. Phys. J. C79

2019

Later among the works it cites.

A. Casa and G. Menardi, Nonparametric semisupervised classification for signal detection in high energy physics, (2019), arXiv:1809.02977 [hep-ex]

2019

Later among the works it cites.

G. Kasieczka, B. Nachman, and D. Shih, R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge, 10.5281/zenodo.4536377 (2019)

2019

Later among the works it cites.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, Pytorch: An imperative style, high-performance deep learning library, in Advances in Neural Information Processing Systems 32 , edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019) pp. 8024–8035

2019

Later among the works it cites.

CMS Collaboration, MUSiC, a model unspecific search for new physics, in p p pp collisions at s = 13 \sqrt{s}=13 TeV, https://cds.cern.ch/record/2718811 (2020)

2020

Later among the works it cites.

M. Farina, Y. Nakai, and D. Shih, Searching for New Physics with Deep Autoencoders, Phys. Rev. D 101

2020

Later among the works it cites.

J. Hajer, Y.-Y. Li, T. Liu, and H. Wang, Novelty Detection Meets Collider Physics, Phys. Rev. D 101

2020

Later among the works it cites.

G. Kasieczka et al. , The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics, Rept. Prog. Phys. 84

2021

Closest in time.

P. Chakravarti, M. Kuusela, J. Lei, and L. Wasserman, Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests, (2021), arXiv:2102.07679 [stat.AP]

2021

Closest in time.

B. Bortolato, B. M. Dillon, J. F. Kamenik, and A. Smolkovič, Bump Hunting in Latent Space, (2021), arXiv:2103.06595 [hep-ph]

2021

Closest in time.

J. H. Collins, P. Martín-Ramiro, B. Nachman, and D. Shih, Comparing weak- and unsupervised methods for resonant anomaly detection, Eur. Phys. J. C 81

2021

Closest in time.

B. M. Dillon, T. Plehn, C. Sauer, and P. Sorrenson, Better Latent Spaces for Better Autoencoders, (2021), arXiv:2104.08291 [hep-ph]

2021

Closest in time.

T. Finke, M. Krämer, A. Morandini, A. Mück, and I. Oleksiyuk, Autoencoders for unsupervised anomaly detection in high energy physics, (2021), arXiv:2104.09051 [physics.data-an]

2021

Closest in time.

D. Shih, M. R. Buckley, L. Necib, and J. Tamanas, Via Machinae: Searching for stellar streams using unsupervised machine learning, Mon. Not. Roy. Astron. Soc. 509

2021

Closest in time.

O. Atkinson, A. Bhardwaj, C. Englert, V. S. Ngairangbam, and M. Spannowsky, Anomaly detection with Convolutional Graph Neural Networks, (2021), arXiv:2105.07988 [hep-ph]

2021

Closest in time.

A. Kahn, J. Gonski, I. Ochoa, D. Williams, and G. Brooijmans, Anomalous Jet Identification via Sequence Modeling, (2021), arXiv:2105.09274 [hep-ph]

2021

Closest in time.

T. Aarrestad et al. , The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider, (2021), arXiv:2105.14027 [hep-ph]

2021

Closest in time.

T. Dorigo, M. Fumanelli, C. Maccani, M. Mojsovska, G. C. Strong, and B. Scarpa, RanBox: Anomaly Detection in the Copula Space, (2021), arXiv:2106.05747 [physics.data-an]

2021

Closest in time.

S. Caron, L. Hendriks, and R. Verheyen, Rare and Different: Anomaly Scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC, (2021), arXiv:2106.10164 [hep-ph]

Original

2021

Closest in time.

M. Feickert and B. Nachman, A Living Review of Machine Learning for Particle Physics, (2021), arXiv:2102.02770 [hep-ph]

2021

Closest in time.

C. Krause and D. Shih, CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows, (2021), arXiv:2106.05285 [physics.ins-det]

2021

Closest in time.

A. Butter, S. Diefenbacher, G. Kasieczka, B. Nachman, and T. Plehn, Amplifying Statistics with Ensembles of Generative Models, ICLR 2021 SimDL Workshop https://simdl.github.io/files/18.pdf (2021a)

2021

Closest in time.