J. García Pardiñas
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View article: Scalable multi-task learning for particle collision event reconstruction with heterogeneous graph neural networks
Scalable multi-task learning for particle collision event reconstruction with heterogeneous graph neural networks Open
The growing luminosity frontier at the Large Hadron Collider is challenging the reconstruction and analysis of particle collision events. Increased particle multiplicities are straining latency and storage requirements at the data acquisit…
View article: Human-in-the-loop reinforcement learning for data quality monitoring in particle physics experiments
Human-in-the-loop reinforcement learning for data quality monitoring in particle physics experiments Open
Ensuring data integrity via data quality monitoring (DQM) is critical in large-scale particle physics experiments. This traditionally relies on labor-intensive manual inspection or static machine learning models, which are adequate for sta…
View article: DINAMO: Dynamic and INterpretable anomaly MOnitoring for large-scale particle physics experiments
DINAMO: Dynamic and INterpretable anomaly MOnitoring for large-scale particle physics experiments Open
Ensuring reliable data collection in large-scale particle physics experiments demands data quality monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity. Traditionally, this resource-intensive tas…
View article: Dataset of paper "Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks"
Dataset of paper "Scalable Multi-Task Learning for Particle Collision Event Reconstruction with Heterogeneous Graph Neural Networks" Open
Scalable Multi-Task Learning for Particle Collision Event Reconstruction with HGNNs Dataset The full description can also be found in README.md. The dataset was used in the paper “Scalable Multi-Task Learning for Particle Collision Event R…
View article: DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments
DINAMO: Dynamic and INterpretable Anomaly MOnitoring for Large-Scale Particle Physics Experiments Open
Ensuring reliable data collection in large-scale particle physics experiments demands Data Quality Monitoring (DQM) procedures to detect possible detector malfunctions and preserve data integrity. Traditionally, this resource-intensive tas…
View article: Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments Open
Data Quality Monitoring (DQM) is a crucial task in large particle physics experiments, since detector malfunctioning can compromise the data. DQM is currently performed by human shifters, which is costly and results in limited accuracy. In…
View article: GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions
GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions Open
The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple back…
View article: GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions
GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions Open
The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple back…
View article: Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"
Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions" Open
DFEI dataset The full description can also be found in README.md. The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project de…
View article: Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"
Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions" Open
DFEI dataset The full description can also be found in README.md. The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project de…
View article: RooHammerModel: interfacing the HAMMER software tool with HistFactory and RooFit
RooHammerModel: interfacing the HAMMER software tool with HistFactory and RooFit Open
Recent B -physics results have sparkled great interest in the search for beyond-the-Standard-Model (BSM) physics in b ⟶ cℓν̅ transitions. The need to analyse in a consistent manner big datasets for these searches, using high-statistics Mont…
View article: RooHammerModel: interfacing the HAMMER software tool with the HistFactory package
RooHammerModel: interfacing the HAMMER software tool with the HistFactory package Open
Recent $B$-physics results have sparkled great interest in the search for beyond-the-Standard-Model (BSM) physics in $b\to c\ell \barν$ transitions. The need to analyse in a consistent manner big datasets for these searches, using high-sta…
View article: Report on the ECFA Early-Career Researchers Debate on the 2020 European Strategy Update for Particle Physics
Report on the ECFA Early-Career Researchers Debate on the 2020 European Strategy Update for Particle Physics Open
A group of Early-Career Researchers (ECRs) has been given a mandate from the European Committee for Future Accelerators (ECFA) to debate the topics of the current European Strategy Update (ESU) for Particle Physics and to summarise the out…
View article: Velo Upgrade Module Nomenclature
Velo Upgrade Module Nomenclature Open
The nomenclature and geometry of the various constituents of velo upgrade hybrid sensors are defined, as well as their position within the modules. The modules nomenclature and their position along the z-axis are defined.
View article: Ipanema-\beta : tools and examples for HEP analysis on GPU
Ipanema-\beta : tools and examples for HEP analysis on GPU Open
We present here a set of examples, classes and tools which can be used for\nstatistical analysis in Graphics Processing Units (GPU). This includes binned\nand unbinned maximum likelihood fits, pseudo-experiment generation,\nconvolutions, M…
View article: CPV results from time-dependent analysis of $B^0_{s}\to (K^{+}\pi^{-})(K^{-}\pi^{+})$
CPV results from time-dependent analysis of $B^0_{s}\to (K^{+}\pi^{-})(K^{-}\pi^{+})$ Open
The B 0 s → K ∗ 0 K ∗ 0 decay constitutes an excellent candidate to search for New Physics in the field of CP violation, and LHCb is the only current experiment capable of fully studying this channel. The first observation of this mode and…
View article: Ipanema-β: tools and examples for HEP analysis on GPU
Ipanema-β: tools and examples for HEP analysis on GPU Open
We present here a set of examples, classes and tools which can be used for statistical analysis in Graphics Processing Units (GPU). This includes binned and unbinned maximum likelihood fits, pseudo-experiment generation, convolutions, Mark…