The UV research groups (GIUV) are regulated in the 1st chapter of the Regulation ACGUV48/2013, which explains the procedure for creating new research structures. They are basic research and organizational structures that result from the voluntary association of researchers that share objectives, facilities, resources and common lines of research. These researchers are also committed to the consolidation and stability of their activity, work in groups and the capability to achieve a sustainable funding.
The research groups included in the previously mentioned Regulation are registered in the Register of Research Structures of the Universitat de València (REIUV), managed by the Office of the Vice-principal for Research and Scientific Policies. The basic information of these organisms can be found in this website.
Participants
Data related to research groups featured in various information dissemination channels shall not, under any circumstances, imply a statement or commitment regarding the employment or academic affiliation of individuals associated with the Universitat de València. Their inclusion is solely the responsibility of the group directors. Updates will be made upon request from interested parties.
- Registered groups in the Register of Research Structures of the Universitat de València - (REIUV)
Reference of the Group:
Description of research activity: Our group carries out its research activity in the field of experimental Particle Physics within the ATLAS experiment at the CERN LHC, covering in an integrated manner detector operation, its upgrade for the High-Luminosity phase (HL-LHC), and the scientific exploitation of the data. Our work includes key contributions to tracking, inner detector alignment and detector performance studies, as well as to data acquisition systems, trigger, and associated electronics.
The group leads and actively participates in precision analyses of the Standard Model and in searches for new physics, with a particular emphasis on Higgs boson physics, top quark physics, the study of their couplings, and searches for long-lived particles. In addition, it maintains a strategic contribution to ATLAS distributed computing, including the development and operation of Tier-2 and Tier-3 infrastructures, and the use of advanced data analysis and machine learning techniques.
The expertise acquired extends to the transfer of technologies and methodologies to applications aimed at addressing challenges of social relevance.
Web:
Scientific-technical goals: - Exploit the scientific potential of LHC and future HL-LHC data through precision analyses of the Standard Model and searches for new physics with the ATLAS detector.
- Lead and contribute to the development, operation, and optimization of the ATLAS inner detector, including the development and improvement of tracking and alignment algorithms, and performance studies under high-luminosity conditions.
- Participate in the ATLAS detector upgrade for the HL-LHC phase, with a particular focus on the ITk silicon strip tracking detector and readout electronics.
- Develop and lead searches for new physics with unconventional signatures, in particular long-lived particles, including the design of new reconstruction and trigger strategies.
- Contribute strategically to ATLAS distributed computing, ensuring efficient exploitation of large data volumes through Tier-2 and Tier-3 infrastructures.
- Apply advanced data analysis and machine learning techniques to both fundamental physics and technological and societal problems, promoting knowledge transfer.
Research lines: - Higgs boson and top quark physics.The group carries out precision analyses in the Higgs and top quark sectors, which are fundamental for characterizing the mechanism of mass generation and for exploring possible deviations from the Standard Model. These include measurements of cross sections and differential distributions, studies of couplings (including the top¿Higgs Yukawa coupling), and interpretations within effective field theories (EFT). These activities make use of advanced event selection techniques and machine learning to maximize sensitivity to new physics effects.
- Searches for new physics and leadership in long-lived particles.One of the group¿s strategic research lines is leadership in searches for phenomena beyond the Standard Model with unconventional experimental signatures. In particular, the group leads searches for long-lived particles, which require dedicated developments in trigger, reconstruction, and analysis, including displaced vertices, displaced jets, and out-of-time signals. These searches probe hidden sector scenarios, supersymmetry, exotic particles, and dark matter models, and are especially relevant in the context of the HL-LHC.
- Tracking, alignment and detector performance.The group has strong expertise in the tracking system and alignment of the ATLAS inner detector, ranging from the development and improvement of detector alignment algorithms and the control of systematic effects to studies of the impact of tracking on the reconstruction of physics objects. This line of work is key to maintaining detector performance under high-occupancy and high pile-up conditions, and to ensuring the quality of the data used in physics analyses.
- Distributed computing, software and machine learning.The group maintains a strategic contribution to ATLAS distributed computing, participating in the operation and evolution of Tier-2 and Tier-3 infrastructures and in the development of advanced data management and access models. In parallel, it develops and applies machine learning and deep learning techniques for physics analyses, simulation, reconstruction, and the reduction of computational costs, including the use of HPC resources and hardware accelerators.
- Technology transfer and applications to societal challenges.The group¿s expertise in advanced instrumentation, data acquisition, and large-scale analysis is transferred to applications beyond particle physics. Notable examples include the development of instrumentation for medical physics, as well as the application of machine learning techniques to socially relevant problems, such as monitoring critical infrastructures or studying natural phenomena with technological impact.
- ATLAS detector upgrade for the HL-LHC.The group is actively involved in the upgrade of the ATLAS detector for the HL-LHC phase, with significant contributions to the new inner tracking detector (ITk) and to the readout electronics. The activities include the design, production, integration, quality assurance and validation of modules, as well as firmware developments and reconstruction algorithms adapted to the new experimental conditions.
Group members:
Associated structure: - Atomic, Molecular and Nuclear Physics
