Feng, J. L. et al, Garcia Soto, A., & Hirsch, M. (2023). The Forward Physics Facility at the High-Luminosity LHC. J. Phys. G, 50(3), 030501–410pp.
Abstract: High energy collisions at the High-Luminosity Large Hadron Collider (LHC) produce a large number of particles along the beam collision axis, outside of the acceptance of existing LHC experiments. The proposed Forward Physics Facility (FPF), to be located several hundred meters from the ATLAS interaction point and shielded by concrete and rock, will host a suite of experiments to probe standard model (SM) processes and search for physics beyond the standard model (BSM). In this report, we review the status of the civil engineering plans and the experiments to explore the diverse physics signals that can be uniquely probed in the forward region. FPF experiments will be sensitive to a broad range of BSM physics through searches for new particle scattering or decay signatures and deviations from SM expectations in high statistics analyses with TeV neutrinos in this low-background environment. High statistics neutrino detection will also provide valuable data for fundamental topics in perturbative and non-perturbative QCD and in weak interactions. Experiments at the FPF will enable synergies between forward particle production at the LHC and astroparticle physics to be exploited. We report here on these physics topics, on infrastructure, detector, and simulation studies, and on future directions to realize the FPF's physics potential.
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Gonzalez, P. (2017). A quark model study of strong decays of X(3915). J. Phys. G, 44(7), 075004–13pp.
Abstract: Strong decays of X(3915) are analyzed from two quark model descriptions of X(3915), a conventional one in terms of the Cornell potential and an unconventional one from a generalized screened potential. We conclude that the experimental suppression of the OZI allowed decay X(3915) -> D (D) over bar might be explained in both cases due to the momentum dependence of the decay amplitude. However, the experimental significance of the OZI forbidden decay X(3915) -> omega J/psi could favor an unconventional description.
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Gonzalez, P. (2014). Generalized screened potential model. J. Phys. G, 41(9), 095001–12pp.
Abstract: A new non relativistic quark model to calculate the spectrum of heavy quark mesons is developed. The model is based on an interquark potential interaction that implicitly incorporates screening effects from meson-meson configurations. An analysis of the bottomonium spectrum shows the appearance of extra states as compared to conventional non screened potential models.
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IDS Collaboration(Lica, R. et al), & Morales, A. I. (2017). beta decay studies of n-rich Cs isotopes with the ISOLDE Decay Station. J. Phys. G, 44(5), 054002–14pp.
Abstract: Neutron-rich Ba isotopes are expected to exhibit octupolar correlations, reaching their maximum in isotopes around mass A = 146. The odd-A neutron-rich members of this isotopic chain show typical patterns related to non-axially symmetric shapes, which are however less marked compared to even-A ones, pointing to a major contribution from vibrations. In the present paper we present results from a recent study focused on Cs148-150 beta-decay performed at the ISOLDE Decay Station equipped with fast-timing detectors. A detailed analysis of the measured decay half-lives and decay scheme of Ba-149 is presented, giving a first insight in the structure of this neutron-rich nucleus.
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Khosa, C. K., Mars, L., Richards, J., & Sanz, V. (2020). Convolutional neural networks for direct detection of dark matter. J. Phys. G, 47(9), 095201–20pp.
Abstract: The XENON1T experiment uses a time projection chamber (TPC) with liquid xenon to search for weakly interacting massive particles (WIMPs), a proposed dark matter particle, via direct detection. As this experiment relies on capturing rare events, the focus is on achieving a high recall of WIMP events. Hence the ability to distinguish between WIMP and the background is extremely important. To accomplish this, we suggest using convolutional neural networks (CNNs); a machine learning procedure mainly used in image recognition tasks. To explore this technique we use XENON collaboration open-source software to simulate the TPC graphical output of dark matter signals and main backgrounds. A CNN turns out to be a suitable tool for this purpose, as it can identify features in the images that differentiate the two types of events without the need to manipulate or remove data in order to focus on a particular region of the detector. We find that the CNN can distinguish between the dominant background events (ER) and 500 GeV WIMP events with a recall of 93.4%, precision of 81.2% and an accuracy of 87.2%.
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