Quark-gluon tagging: Machine learning vs detector

Kasieczka, G.; Kiefer, Nicholas M.; Plehn, Tilman; Thompson, J.

doi:10.21468/scipostphys.6.6.069

Cited by 58 publications

(43 citation statements)

References 95 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The prevailing choice is a sequence, where particles are sorted in a specific way (e.g., with decreasing transverse momentum) and organized into a 1D list. Using particle sequences as inputs, jet tagging tasks have been tackled with recurrent neural networks (RNNs) [36][37][38][39]44], 1D CNNs [40][41][42][43] and physics-oriented neural networks [45][46][47]. Another interesting choice is a binary tree, which is well motivated from the QCD theory perspective.…”

Section: Particle-based Representationmentioning

confidence: 99%

“…Recently, machine learning (ML) has injected fresh blood in jet tagging. Jets are regarded as images [25][26][27][28][29][30][31][32][33][34][35], as sequences [36][37][38][39][40][41][42][43][44][45][46][47], trees [48,49] or graphs [50] of particles, and ML techniques, most notably, deep neural networks (DNNs), are used to build new jet tagging algorithms automatically from (labelled) simulated samples or even (unlabelled) real data [51][52][53][54], leading to new insights and improvements in jet tagging.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Jet tagging via particle clouds

2020

View full text Add to dashboard Cite

How to represent a jet is at the core of machine learning on jet physics. Inspired by the notion of point cloud, we propose a new approach that considers a jet as an unordered set of its constituent particles, effectively a "particle cloud". Such particle cloud representation of jets is efficient in incorporating raw information of jets and also explicitly respects the permutation symmetry. Based on the particle cloud representation, we propose ParticleNet, a customized neural network architecture using Dynamic Graph CNN for jet tagging problems. The ParticleNet architecture achieves state-of-the-art performance on two representative jet tagging benchmarks and improves significantly over existing methods.ArXiv ePrint: 1902.08570

show abstract

Section: Particle-based Representationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Jet tagging via particle clouds

2020

View full text Add to dashboard Cite

show abstract

“…The jet substructure manifests color, electric charge, flavor, etc. of the jet ancestral partons and hence is useful in discriminating, e.g., quark/gluon jets [25,26,[26][27][28][29][30]. The jet superstructure is usually formulated if the jet ancestral partons share the same parent particle, where they tend to be showered in a correlated way [31].…”

Section: Introductionmentioning

confidence: 99%

Learning physics at future e−e+ colliders with machine

Liu

et al. 2020

J. High Energ. Phys.

View full text Add to dashboard Cite

Information deformation and loss in jet clustering are one of the major limitations for precisely measuring hadronic events at future e−e+ colliders. Because of their dominance in data, the measurements of such events are crucial for advancing the precision frontier of Higgs and electroweak physics in the next decades. We show that this difficulty can be well-addressed by synergizing the event-level information into the data analysis, with the techniques of deep neutral network. In relation to this, we introduce a CMB-like observable scheme, where the event-level kinematics is encoded as Fox-Wolfram (FW) moments at leading order and multi-spectra of spherical harmonics at higher orders. Then we develop a series of jet-level (w/ and w/o the FW moments) and event-level classifiers, and analyze their sensitivity performance comparatively with two-jet and four-jet events. As an application, we analyze measuring Higgs decay width at e−e+ colliders with the data of 5ab−1@240GeV. The precision obtained is significantly better than the baseline ones presented in documents. We expect this strategy to be applied to many other hadronic- event measurements at future e−e+ colliders, and to open a new angle for evaluating their physics capability.

show abstract

“…For this problem, a large number of architectures based on fully connected neural networks [1,2], image-based methods [3,4], recursive clustering [5,6], physics variables [7][8][9][10], sets [11], and graphs [12,13] have been studied [14][15][16]. Related challenges of identifying vector bosons [17,18], b-quarks [19,20], and Higgs bosons [13,21] and of distinguishing light quark from gluon jets [22][23][24][25] have seen similar progress. Beyond classifying single particles in an event, there is also work on developing holistic methods that classify full events according to the likely physics process that produced them [26,27].…”

mentioning

confidence: 99%

Robust Jet Classifiers through Distance Correlation

Kasieczka

Shih

2020

Phys. Rev. Lett.

Self Cite

View full text Add to dashboard Cite

While deep learning has proven to be extremely successful at supervised classification tasks at the LHC and beyond, for practical applications, raw classification accuracy is often not the only consideration. One crucial issue is the stability of network predictions, either versus changes of individual features of the input data or against systematic perturbations. We present a new method based on a novel application of "distance correlation," a measure quantifying nonlinear correlations, that achieves equal performance to state-of-the-art adversarial decorrelation networks but is much simpler and more stable to train. To demonstrate the effectiveness of our method, we carefully recast a recent ATLAS study of decorrelation methods as applied to boosted, hadronic W tagging. We also show the feasibility of regularization with distance correlation for more powerful convolutional neural networks, as well as for the problem of hadronic top tagging.

show abstract

Quark-gluon tagging: Machine learning vs detector

Cited by 58 publications

References 95 publications

Jet tagging via particle clouds

Jet tagging via particle clouds

Learning physics at future e−e+ colliders with machine

Robust Jet Classifiers through Distance Correlation

Contact Info

Product

Resources

About