|
|
Vicente, A. (2019). Higgs Lepton Flavor Violating Decays in Two Higgs Doublet Models. Front. Physics, 7, 174–13pp.
Abstract: The discovery of a non-zero rate for a lepton flavor violating decay mode of the Higgs boson would definitely be an indication of New Physics. We review the prospects for such signal in Two Higgs Doublet Models, in particular for Higgs boson decays into tau μfinal states. We will show that this scenario contains all the necessary ingredients to provide large flavor violating rates and still be compatible with the stringent limits from direct searches and low-energy flavor experiments.
|
|
|
|
Sanchis-Lozano, M. A., Sarkisyan-Grinbaum, E. K., & Moreno-Picot, S. (2016). Searching for hidden sector in multiparticle production at LHC. Phys. Lett. B, 754, 353–359.
Abstract: We study the impact of a hidden sector beyond the Standard Model, e.g. a Hidden Valley model, on factorial moments and cumulants of multiplicity distributions in multiparticle production with a special emphasis on the prospects for LHC results.
|
|
|
|
Sanchis-Lozano, M. A., & Sarkisyan-Grinbaum, E. K. (2018). Searching for new physics with three-particle correlations in pp collisions at the LHC. Phys. Lett. B, 781, 505–509.
Abstract: New phenomena involving pseudorapidity and azimuthal correlations among final-state particles in pp collisions at the LHC can hint at the existence of hidden sectors beyond the Standard Model. In this paper we rely on a correlated-cluster picture of multiparticle production, which was shown to account for the ridge effect, to assess the effect of a hidden sector on three-particle correlations concluding that there is a potential signature of new physics that can be directly tested by experiments using well-known techniques.
|
|
|
|
MoEDAL Collaboration(Acharya, B. et al), Bernabeu, J., Garcia, C., King, M., Mitsou, V. A., Vento, V., et al. (2014). The physics programme of the MoEDAL experiment at the LHC. Int. J. Mod. Phys. A, 29(23), 1430050–91pp.
Abstract: The MoEDAL experiment at Point 8 of the LHC ring is the seventh and newest LHC experiment. It is dedicated to the search for highly-ionizing particle avatars of physics beyond the Standard Model, extending significantly the discovery horizon of the LHC. A MoEDAL discovery would have revolutionary implications for our fundamental understanding of the Microcosm. MoEDAL is an unconventional and largely passive LHC detector comprised of the largest array of Nuclear Track Detector stacks ever deployed at an accelerator, surrounding the intersection region at Point 8 on the LHC ring. Another novel feature is the use of paramagnetic trapping volumes to capture both electrically and magnetically charged highly-ionizing particles predicted in new physics scenarios. It includes an array of TimePix pixel devices for monitoring highly-ionizing particle backgrounds. The main passive elements of the MoEDAL detector do not require a trigger system, electronic readout, or online computerized data acquisition. The aim of this paper is to give an overview of the MoEDAL physics reach, which is largely complementary to the programs of the large multipurpose LHC detectors ATLAS and CMS.
|
|
|
|
Kasieczka, G. et al, & Sanz, V. (2021). The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics. Rep. Prog. Phys., 84(12), 124201–64pp.
Abstract: A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.
|
|
