A
bstract
Attention-based transformer models have become increasingly prevalent in collider analysis, offering enhanced performance for tasks such as jet tagging. However, they are computationally ...intensive and require substantial data for training. In this paper, we introduce a new jet classification network using an MLP mixer, where two subsequent MLP operations serve to transform particle and feature tokens over the jet constituents. The transformed particles are combined with subjet information using multi-head cross-attention so that the network is invariant under the permutation of the jet constituents. We utilize two clustering algorithms to identify subjets: the standard sequential recombination algorithms with fixed radius parameters and a new IRC-safe, density-based algorithm of dynamic radii based on HDBSCAN. The proposed network demonstrates comparable classification performance to state-of-the-art models while boosting computational efficiency drastically. Finally, we evaluate the network performance using various interpretable methods, including centred kernel alignment and attention maps, to highlight network efficacy in collider analysis tasks.
A
bstract
Jets from boosted heavy particles have a typical angular scale which can be used to distinguish them from QCD jets. We introduce a machine learning strategy for jet substructure analysis ...using a spectral function on the angular scale. The angular spectrum allows us to scan energy deposits over the angle between a pair of particles in a highly visual way. We set up an artificial neural network (ANN) to find out characteristic shapes of the spectra of the jets from heavy particle decays. By taking the Higgs jets and QCD jets as examples, we show that the ANN of the angular spectrum input has similar performance to existing taggers. In addition, some improvement is seen when additional extra radiations occur. Notably, the new algorithm automatically combines the information of the multipoint correlations in the jet.
A
bstract
Classification of jets with deep learning has gained significant attention in recent times. However, the performance of deep neural networks is often achieved at the cost of ...interpretability. Here we propose an interpretable network trained on the jet spectrum
S
2
(
R
) which is a two-point correlation function of the jet constituents. The spectrum can be derived from a functional Taylor series of an arbitrary jet classifier function of energy flows. An interpretable network can be obtained by truncating the series. The intermediate feature of the network is an infrared and collinear safe C-correlator which allows us to estimate the importance of an
S
2
(
R
) deposit at an angular scale
R
in the classification. The performance of the architecture is comparable to that of a convolutional neural network (CNN) trained on jet images, although the number of inputs and complexity of the architecture is significantly simpler than the CNN classifier. We consider two examples: one is the classification of two-prong jets which differ in color charge of the mother particle, and the other is a comparison between Pythia 8 and Herwig 7 generated jets.
A
bstract
We deploy an advanced Machine Learning (ML) environment, leveraging a multi-scale cross-attention encoder for event classification, towards the identification of the
gg
→
H
→
hh
→
b
b
¯
b
b
...¯
process at the High Luminosity Large Hadron Collider (HL-LHC), where
h
is the discovered Standard Model (SM)-like Higgs boson and
H
a heavier version of it (with
m
H
>
2
m
h
). In the ensuing boosted Higgs regime, the final state consists of two fat jets. Our multi-modal network can extract information from the jet substructure and the kinematics of the final state particles through self-attention transformer layers. The diverse learned information is subsequently integrated to improve classification performance using an additional transformer encoder with cross-attention heads. We showcase that our approach surpasses current alternative methods used to establish sensitivity to this process in performance, whether solely based on kinematic analysis or combining this with mainstream ML approaches. Then, we employ various interpretive methods to evaluate the network results, including attention map analysis and visual representation of Gradient-weighted Class Activation Mapping (Grad-CAM). Finally, we note that the proposed network is generic and can be applied to analyse any process carrying information at different scales. Our code is publicly available for generic use (
https://github.com/AHamamd150/Multi-Scale-Transformer-Encoder
).
The detection of gravitational wave modes and polarizations could constitute an extremely important signature to discriminate among different theories of gravity. According to this statement, it is ...possible to prove that higher-order non-local gravity has formally the same gravitational spectrum of higher-order local gravity. In particular, we consider the cases of f(R,□R,□2R,⋯,□nR)=R+∑i=1nαiR□iR gravity, linear with respect to both R and □iR and f(R,□R)=R+α(□R)2 gravity, quadratic with respect to □R, where it is demonstrated the graviton amplitude changes if compared with General Relativity. We also obtain the gravitational spectrum of higher-order non-local gravity f(R,□−1R,□−2R,⋯,□−nR)=R+∑i=1nαiR□−iR. In this case, we have three state of polarization and n+3 oscillation modes. More in detail, it is possible to derive two transverse tensor (+) and (×) standard polarization modes of frequency ω1, massless and with 2-helicity; n+1 further scalar modes of frequency ω2,…,ωn+2, massive and with 0-helicity, each of which has the same mixed polarization, partly longitudinal and partly transverse.
A
bstract
Deep neural networks trained on jet images have been successful in classifying different kinds of jets. In this paper, we identify the crucial physics features that could reproduce the ...classification performance of the convolutional neural network in the top jet vs. QCD jet classification. We design a neural network that considers two types of sub-structural features: two-point energy correlations, and the IRC unsafe counting variables of a morphological analysis of jet images. The new set of IRC unsafe variables can be described by Minkowski functionals from integral geometry. To integrate these features into a single framework, we reintroduce two-point energy correlations in terms of a graph neural network and provide the other features to the network afterward. The network shows a comparable classification performance to the convolutional neural network. Since both networks are using IRC unsafe features at some level, the results based on simulations are often dependent on the event generator choice. We compare the classification results of Pythia 8 and Herwig 7, and a simple reweighting on the distribution of IRC unsafe features reduces the difference between the results from the two simulations.
We discuss the production and evolution of cosmological gravitons showing how the cosmological background affects their dynamics. Besides, the detection of cosmological gravitons could constitute an ...extremely important signature to discriminate among different cosmological models. Here, we consider the cases of scalar-tensor gravity and f(R) gravity where it is demonstrated the amplification of graviton amplitude changes if compared with General Relativity. Possible observational constraints are discussed.
A
bstract
A beam dump experiment can be seamlessly added to the proposed Inter- national Linear Collider (ILC) program because the high energy electron beam should be dumped after the collision ...point. The ILC beam dump experiment will provide an excellent opportunity to search for new long-lived particles. Since many of them can be produced by a rare decay of standard model particles, we evaluate spectra of the mesons and
τ
lepton at the decay based on the PHITS and PYTHIA8 simulations. As a motivated physics case, we study the projected sensitivity of heavy neutral leptons at the ILC beam dump experiment. The heavy neutral leptons can also be produced via deep inelastic scattering and
Z
boson decay at the ILC main detector, which we include in the projection. With the multi-track signal, the reach would be greatly extended in mass and coupling, even compared with the other proposed searches.
False vacuum decay in gauge theory Endo, Motoi; Moroi, Takeo; Nojiri, Mihoko M. ...
The journal of high energy physics,
11/2017, Letnik:
2017, Številka:
11
Journal Article
Recenzirano
Odprti dostop
A
bstract
The decay rate of a false vacuum is studied in gauge theory, paying particular attention to its gauge invariance. Although the decay rate should not depend on the gauge parameter ξ ...according to the Nielsen identity, the gauge invariance of the result of a perturbative calculation has not been clearly shown. We give a prescription to perform a one-loop calculation of the decay rate, with which a manifestly gauge-invariant expression of the decay rate is obtained. We also discuss the renormalization necessary to make the result finite, and show that the decay rate is independent of the gauge parameter even after the renormalization.
In this Letter we study pair annihilation processes of dark matter (DM) in the Universe, in the case that the DM is an electroweak gauge nonsinglet. In the current Universe, in which the DM is highly ...nonrelativistic, the nonperturbative effect may enhance the DM annihilation cross sections, especially for that to two photons, by several orders of magnitude. We also discuss sensitivities in future searches for anomalous gamma rays from the galactic center, which originate from DM annihilation.
PDF
