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Register a new userDetecting an axion-like particle with machine learning at the LHC
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Axion-Like particles (ALPs) appear in various new physics models with spontaneous global symmetry breaking. When the ALP mass is in the range of MeV to GeV, the cosmology and astrophysics bounds are so far quite weak. In this work, we investigate such light ALPs through the ALP-strahlung production process pp to Va(a to {gamma}{gamma}) at the 14TeV LHC with an integrated luminosity of 3000 fb^(-1)(HL-LHC). Building on the concept of jet image which uses calorimeter towers as the pixels of the image and measures a jet as an image, we investigate the potential of machine learning techniques based on convolutional neural network (CNN) to identify the highly boosted ALPs which decay to a pair of highly collimated photons. With the CNN tagging algorithm, we demonstrate that our approach can extend current LHC sensitivity and probe the ALP mass range from 0.3GeV to 10GeV. The obtained bounds are significantly stronger than the existing limits on the ALP-photon coupling.
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We propose a new collider probe for axion-like particles (ALPs), and more generally for pseudo-Goldstone bosons: non-resonant searches which take advantage of the derivative nature of their interactions with Standard Model particles. ALPs can participate as off-shell mediators in the $s$-channel of $2 to 2$ scattering processes at colliders like the LHC. We exemplify the power of this novel type of search by deriving new limits on ALP couplings to gauge bosons via the processes $p p to Z Z$, $p p to gamma gamma$ and $p p to j j$ using Run 2 CMS public data, probing previously unexplored areas of the ALP parameter space. In addition, we propose future non-resonant searches involving the ALP coupling to other electroweak bosons and/or the Higgs particle.
We study the tagging of Higgs exotic decay signals using different types of deep neural networks (DNNs), focusing on the $W^pm h$ associated production channel followed by Higgs decaying into $n$ $b$-quarks with $n=4$, 6 and 8. All the Higgs decay products are collected into a fat-jet, to which we apply further selection using the DNNs. Three kinds of DNNs are considered, namely convolutional neural network (CNN), recursive neural network (RecNN) and particle flow network (PFN). The PFN can achieve the best performance because its structure allows enfolding more information in addition to the four-momentums of the jet constituents, such as particle ID and tracks parameters. Using the PFN as an example, we verify that it can serve as an efficient tagger even though it is trained on a different event topology with different $b$-multiplicity from the actual signal. The projected sensitivity to the branching ratio of Higgs decaying into $n$ $b$-quarks at the HL-LHC are 10%, 3% and 1%, for $n=4$, 6 and 8, respectively.
Axion-like particles (ALPs) are predicted by many extensions of the Standard Model (SM). When ALP mass lies in the range of MeV to GeV, the cosmology and astrophysics will be largely irrelevant. In this work, we investigate such light ALPs through the ALP-strahlung process $pp to V a (to gammagamma)$ at the LHC. With the photon-jet algorithm, we demonstrate that our approach can extend the LHC sensitivity and improve the existing limits on ALP-photon coulping in the ALP mass range from 0.3 GeV to 10 GeV at the 14 TeV LHC with an integrated luminosity of 3000 fb$^{-1}$.
Axion-like particles (ALPs), relatively light (pseudo-)scalars coupled to two gauge bosons, are a common feature of many extensions of the Standard Model. Up to now there has been a gap in the sensitivity to such particles in the MeV to 10 GeV range. In this note we show that LEP data on $Ztogammagamma$ decays provides significant constraints in this range (and indeed up to the $Z$-mass). We also discuss the sensitivities of LHC and future colliders. Particularly the LHC shows promising sensitivity in searching for a pseudo-scalar with $4 lesssim m_a lesssim 60$ GeV in the channel $pp to 3 gamma$ with $m_{3gamma}approx m_{Z}$.
The milliQan Collaboration has proposed to search for millicharged particles by looking for very weakly ionizing tracks in a detector installed in a cavern near the CMS experiment at the LHC. We note that another form of exotica can also yield weakly ionizing tracks. If a heavy neutrino has an electric dipole moment (EDM), then the milliQan experiment may be sensitive to it as well. In particular, writing the general dimension-5 operator for an EDM with a scale of a TeV and a one-loop factor, one finds a potential EDM as high as a few times $10^{-17}$ e-cm, and models exist where it is an order of magnitude higher. Redoing the Bethe calculation of ionization energy loss for an EDM, it is found that the milliQan detector is sensitive to EDMs as small as $10^{-17}$ e-cm. Using the production cross-section and analyzing the acceptance of the milliQan detector, we find the expected $95%$ exclusion and $3sigma$ sensitivity over the range of neutrino masses from $5-1000$ GeV for integrated luminosities of $300$ and $3000 {rm fb}^{-1}$ at the LHC.
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