Updated Constraints on General Squark Flavor Mixing

We explore the phenomenological implications on non-minimal flavor violating (NMFV) processes from squark flavor mixing within the Minimal Supersymmetric Standard Model. We work under the model-independent hypothesis of general flavor mixing in the squark sector, being parametrized by a complete set of dimensionless delta^AB_ij (A,B = L, R; i,j = u, c, t or d, s, b) parameters. The present upper bounds on the most relevant NMFV processes, together with the requirement of compatibility in the choice of the MSSM parameters with the recent LHC and g-2 data, lead to updated constraints on all squark flavor mixing parameters.Comment: 30 pages, 7 figures. arXiv admin note: text overlap with arXiv:1304.2783, arXiv:1109.623

New Constraints on General Slepton Flavor Mixing

We explore the phenomenological implications on charged lepton flavor violating (LFV) processes from slepton flavor mixing within the Minimal Supersymmetric Standard Model. We work under the model-independent hypothesis of general flavor mixing in the slepton sector, being parametrized by a complete set of dimensionless delta^AB_ij (A,B = L,R; i,j = 1, 2, 3) parameters. The present upper bounds on the most relevant LFV processes, together with the requirement of compatibility in the choice of the MSSM parameters with the recent LHC and (g-2) data, lead to updated constraints on all slepton flavor mixing parameters. A comparative discussion of the most effective LFV processes to constrain the various generation mixings is included.Comment: 42 pages, 19 figures. Minor changes, version to appear in PR

Autonomous Robotic Arm Manipulation for Planetary Missions using Causal Machine Learning

Autonomous robotic arm manipulators have the potential to make planetary exploration and in-situ resource utilization missions more time efficient and productive, as the manipulator can handle the objects itself and perform goal-specific actions. We train a manipulator to autonomously study objects of which it has no prior knowledge, such as planetary rocks. This is achieved using causal machine learning in a simulated planetary environment. Here, the manipulator interacts with objects, and classifies them based on differing causal factors. These are parameters, such as mass or friction coefficient, that causally determine the outcomes of its interactions. Through reinforcement learning, the manipulator learns to interact in ways that reveal the underlying causal factors. We show that this method works even without any prior knowledge of the objects, or any previously-collected training data. We carry out the training in planetary exploration conditions, with realistic manipulator models.Comment: 8 pages, ASTRA 2023: 17th Symposium on Advanced Space Technologies in Robotics and Automation, 18-20 October 2023, Leiden, The Netherland

Erratum to: Non-decoupling SUSY in LFV Higgs decays: a window to new physics at the LHC

Journal of High Energy Physics 2015.10 (2015): 192 reproduced by permission of Scuola Internazionale Superiore di Studi Avanzati (SISSA)We wish to thank Andreas Crivellin for his several remarks and discussions on our first (wrong) results for these LR and RL cases which were not showing the decoupling behavior that he was expecting. Our corrected results included here manifest clearly this expected decoupling behavior with the SUSY mass scale mSUS

Higgs boson masses and B-physics constraints in Non-Minimal Flavor Violating SUSY scenarios

Journal of High Energy Physics 2012.5 (2012): 015 reproduced by permission of Scuola Internazionale Superiore di Studi Avanzati (SISSA)We present one-loop corrections to the Higgs boson masses in the MSSM with Non-Minimal Flavor Violation. The flavor violation is generated from the hypothesis of general flavor mixing in the squark emass matrices, and these are parametrized by a complete set of δ xY ij (X, Y = L,R; i, j = t,c,u or b, s, d). We calculate the corrections to the Higgs masses in terms of these δ xY ij taking into account all relevant restrictions from B-physics data. This includes constraints from BR(B → X sγ), BR(B s → μ +μ -) and ΔM Bs. After taking into account these constraints we find sizable corrections to the Higgs boson masses, in the case of the lightest MSSM Higgs boson mass exceeding tens of GeV. These corrections are found mainly for the low tan β case. In the case of a Higgs boson mass measurement these corrections might be used to set further constraints on δf YThe work of S.H. was supported in part by CICYT (grant FPA 2007-66387), in part by CICYT (grant FPA 2010-22163-C02-01) and by the Spanish MICINN’s Consolider-Ingenio 2010 Program under grant MultiDark CSD2009-00064. The work of M.H. and M.A.-C. was partially supported by CICYT (grant FPA2009-09017) and the Comunidad de Madrid project HEPHACOS, S2009/ESP-1473. The work of S.P. was supported by a Ramón y Cajal contract from MEC (Spain) (PDRYC-2006-000930) and partially by CICYT (grant FPA2009-09638), the Comunidad de Aragón project DCYT-DGA E24/2 and the Generalitat de Catalunya project 2009SGR502. The work is also supported in part by the European Community’s Marie-Curie Research Training Network under contract MRTNCT-2006-035505 ‘Tools and Precision Calculations for Physics Discoveries at Colliders’ and also by the Spanish Consolider-Ingenio 2010 Programme CPAN (CSD2007-00042

Erratum to: Non-decoupling SUSY in LFV Higgs decays: a window to new physics at the LHC

Corrección del artículo citado (ver "Documentos relacionados").Instituto de Física La Plat

Natural language inference with self-attention for veracity assessment of pandemic claims

We present a comprehensive work on automated veracity assessment from dataset creation to developing novel methods based on Natural Language Inference (NLI), focusing on misinformation related to the COVID-19 pandemic. We first describe the construction of the novel PANACEA dataset consisting of heterogeneous claims on COVID-19 and their respective information sources. The dataset construction includes work on retrieval techniques and similarity measurements to ensure a unique set of claims. We then propose novel techniques for automated veracity assessment based on Natural Language Inference including graph convolutional networks and attention based approaches. We have carried out experiments on evidence retrieval and veracity assessment on the dataset using the proposed techniques and found them competitive with SOTA methods, and provided a detailed discussion