Energy flow networks: deep sets for particle jets

Multiparticle correlators are mathematical objects frequently encountered in quantum field theory and collider physics. By translating multiparticle correlators into the language of graph theory, we can gain new insights into their structure as well as identify efficient ways to manipulate them. In this paper, we highlight the power of this graph-theoretic approach by "cutting open" the vertices and edges of the graphs, allowing us to systematically classify linear relations among multiparticle correlators and develop faster methods for their computation. The naive computational complexity of an N -point correlator among M particles is O(M N ), but when the pairwise distances between particles can be cast as an inner product, we show that all such correlators can be computed in linear O(M ) runtime. With the help of new tensorial objects called Energy Flow Moments, we achieve a fast implementation of jet substructure observables like C2 and D2, which are widely used at the Large Hadron Collider to identify boosted hadronic resonances. As another application, we compute the number of leafless multigraphs with d edges up to d = 16 (15,641,159), conjecturing that this is the same as the number of independent kinematic polynomials of degree d, previously known only to d = 8 (279).

show abstract

“…This section also explains two cryptic comments we made in previous papers: footnote 8 of Ref [9]. and footnote 4 of Ref [40]…”

mentioning

confidence: 80%

Cutting multiparticle correlators down to size

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The stress-energy flow [21][22][23] is a particularly powerful probe of jets, since it in principle contains all the information about a jet that is infrared and collinear (IRC) safe [24][25][26]. A variety of observables have been built around the energy flow concept [27][28][29][30][31], including recent work on machine learning for jet substructure [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

Exploring the space of jets with CMS open data

et al. 2020

Self Cite

View full text Add to dashboard Cite

We explore the metric space of jets using public collider data from the CMS experiment. Starting from 2.3 fb −1 of proton-proton collisions at √ s = 7 TeV collected at the Large Hadron Collider in 2011, we isolate a sample of 1,690,984 central jets with transverse momentum above 375 GeV. To validate the performance of the CMS detector in reconstructing the energy flow of jets, we compare the CMS Open Data to corresponding simulated data samples for a variety of jet kinematic and substructure observables. Even without detector unfolding, we find very good agreement for trackbased observables after using charged hadron subtraction to mitigate the impact of pileup. We perform a range of novel analyses, using the "energy mover's distance" (EMD) to measure the pairwise difference between jet energy flows. The EMD allows us to quantify the impact of detector effects, visualize the metric space of jets, extract correlation dimensions, and identify the most and least typical jet configurations. To facilitate future jet studies with CMS Open Data, we make our datasets and analysis code available, amounting to around two gigabytes of distilled data and one hundred gigabytes of simulation files. CONTENTS

show abstract

“…Tagging these top jets, which contains all the decay products of hadronically decaying top quarks is quite a mature field. A plethora of tagging algorithms have been proposed which range from the substructure analyses [2][3][4][5][6][7][8][9][10][11][12][13] to methods taking full advantage of recent advances in the machine learning [14][15][16][17][18][19][20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Jets with electrons from boosted top quarks

Chatterjee

Godbole

Roy

2020

J. High Energ. Phys.

View full text Add to dashboard Cite

We propose a method to identify jets consisting of all the visible remnants of boosted top particles when these decay semileptonically to electrons. Within these jets, the electron shower overlaps with the shower initiated by the b quark, which makes the identification of the electron hard. Even if an electron inside a jet is identified, it is difficult to pinpoint whether the electron rich jet is indeed due to top quark decay or not, since the invisible neutrino carries away a nontrivial part of the energy-momentum of the original top quark. Broadly speaking, the method proposed here has three key components. It uses the distribution of energy in various parts of the detector to identify whether the observed jet is consistent with a jet containing an energetic electron. It uses the substructure of the jet to determine the momentum associated with the electron. Finally, it constructs new variables that carry tell-tale features of top quark decay kinematics using an extra ansatz that, there exists a massless invisible four-momentum roughly collimated to the electron, which reconstructs a W and a top when it is combined with the electron and the full jet respectively. We demonstrate the efficacy of this proposal using simulated data and show that our method not only reduces the backgrounds from light flavor jets, b jets from QCD, and hadronic top jets, it can also tell apart jets rich in electrons but not due to top quark decays.

show abstract

Energy flow networks: deep sets for particle jets

Cited by 257 publications

References 106 publications

Cutting multiparticle correlators down to size

Cutting multiparticle correlators down to size

Exploring the space of jets with CMS open data

Jets with electrons from boosted top quarks

Contact Info

Product

Resources

About