F. A. Di Bello
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View article: TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction Open
Event reconstruction at the LHC, the task of assigning observed physics objects to their true origins, is a central challenge for precision measurements and searches. Many existing machine learning approaches address this problem but rely …
View article: HGPflow: extending hypergraph particle flow to collider event reconstruction
HGPflow: extending hypergraph particle flow to collider event reconstruction Open
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been expl…
View article: Techno-Functional and Sensorial Properties of Biscuits Produced with Toasted African Bush Pear (Dacryodes edulis) Seed, Acha (Digitaria exilis) and Grasshopper (Zonocerus variegatus)
Techno-Functional and Sensorial Properties of Biscuits Produced with Toasted African Bush Pear (Dacryodes edulis) Seed, Acha (Digitaria exilis) and Grasshopper (Zonocerus variegatus) Open
International audience
View article: Accelerating graph-based tracking tasks with symbolic regression
Accelerating graph-based tracking tasks with symbolic regression Open
The reconstruction of particle tracks from hits in tracking detectors is a computationally intensive task due to the large combinatorics of detector signals. Recent efforts have proven that ML techniques can be successfully applied to the …
View article: HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction
HGPflow: Extending Hypergraph Particle Flow to Collider Event Reconstruction Open
In high energy physics, the ability to reconstruct particles based on their detector signatures is essential for downstream data analyses. A particle reconstruction algorithm based on learning hypergraphs (HGPflow) has previously been expl…
View article: Single-track ATLAS-like Inner Detector hit data for Tracking Studies
Single-track ATLAS-like Inner Detector hit data for Tracking Studies Open
A dataset to study the performance of tracking tools. The events contain single tracks with pile-up and detector noise according to Run 3 ATLAS conditions. More details can be found here. The root files contain two trees: "tree" and "track…
View article: Fast neural network inference on FPGAs for triggering on long-lived particles at colliders
Fast neural network inference on FPGAs for triggering on long-lived particles at colliders Open
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge a…
View article: Set-conditional set generation for particle physics
Set-conditional set generation for particle physics Open
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the large Hadron collider, where observational set-valued data is generated conditional on a set of incoming particle…
View article: Configurable calorimeter simulation for AI applications
Configurable calorimeter simulation for AI applications Open
A configurable calorimeter simulation for AI (CoCoA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning al…
View article: LLPinMS
LLPinMS Open
Dataset of neutral long-lived particles (LLP) decaying to charged particles in a monitored-drift-tube like detector. The dataset is split according to the number of charged particles, from two to ten, the neutral LLP is decaying into and i…
View article: LLPinMS
LLPinMS Open
Dataset of neutral long-lived particles (LLP) decaying to charged particles in a monitored-drift-tube like detector. The dataset is split according to the number of charged particles, from two to ten, the neutral LLP is decaying into and i…
View article: Reconstructing particles in jets using set transformer and hypergraph prediction networks
Reconstructing particles in jets using set transformer and hypergraph prediction networks Open
The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlation…
View article: Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliders
Fast Neural Network Inference on FPGAs for Triggering on Long-Lived Particles at Colliders Open
Experimental particle physics demands a sophisticated trigger and acquisition system capable to efficiently retain the collisions of interest for further investigation. Heterogeneous computing with the employment of FPGA cards may emerge a…
View article: Machine learning and LHC event generation
Machine learning and LHC event generation Open
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide rang…
View article: Single-jet datasets for particle reconstruction with deep learning
Single-jet datasets for particle reconstruction with deep learning Open
Training and test datasets used for [1]* . singleQuarkJet_train.root : N=60649 singleQuarkJet_test.root: N=38922 singleGluonJet_test.root: N=38295 The events are formed by a single initial state quark or gluon followed by parton shower gen…
View article: Single-jet datasets for particle reconstruction with deep learning
Single-jet datasets for particle reconstruction with deep learning Open
Training and test datasets used for [1]* . singleQuarkJet_train.root : N=60649 singleQuarkJet_test.root: N=38922 singleGluonJet_test.root: N=38295 The events are formed by a single initial state quark or gluon followed by parton shower gen…
View article: Configurable calorimeter simulation for AI applications
Configurable calorimeter simulation for AI applications Open
A configurable calorimeter simulation for AI (COCOA) applications is presented, based on the Geant4 toolkit and interfaced with the Pythia event generator. This open-source project is aimed to support the development of machine learning al…
View article: Reconstructing particles in jets using set transformer and hypergraph prediction networks
Reconstructing particles in jets using set transformer and hypergraph prediction networks Open
The task of reconstructing particles from low-level detector response data to predict the set of final state particles in collision events represents a set-to-set prediction task requiring the use of multiple features and their correlation…
View article: Set-Conditional Set Generation for Particle Physics
Set-Conditional Set Generation for Particle Physics Open
The simulation of particle physics data is a fundamental but computationally intensive ingredient for physics analysis at the Large Hadron Collider, where observational set-valued data is generated conditional on a set of incoming particle…
View article: Machine Learning and LHC Event Generation
Machine Learning and LHC Event Generation Open
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide rang…
View article: An Assessment of the Impact Of N-Power Programme on Youth Employability and Income in Yobe State, Nigeria
An Assessment of the Impact Of N-Power Programme on Youth Employability and Income in Yobe State, Nigeria Open
This study assessed the impact of N-Power programme on the employability and income generation among its beneficiaries in Yobe State. Specifically, the study gives emphasis to the employability, income generating capacity as well as entrep…
View article: Efficiency Parameterization with Neural Networks
Efficiency Parameterization with Neural Networks Open
Multidimensional efficiency maps are commonly used in high-energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned efficiency maps are however strongly limited by statistics. We…
View article: Efficiency Parameterization with Neural Networks
Efficiency Parameterization with Neural Networks Open
Multidimensional efficiency maps are commonly used in high energy physics experiments to mitigate the limitations in the generation of large samples of simulated events. Binned multidimensional efficiency maps are however strongly limited …
View article: A 50 ps resolution monolithic active pixel sensor without internal gain in SiGe BiCMOS technology
A 50 ps resolution monolithic active pixel sensor without internal gain in SiGe BiCMOS technology Open
A monolithic pixelated silicon detector designed for high time resolution has\nbeen produced in the SG13G2 130 nm SiGe BiCMOS technology of IHP\nMikroelektronik. This proof-of-concept chip contains hexagonal pixels of 65\n{\\mu}m and 130 {…
View article: Test beam measurement of ams H35 HV-CMOS capacitively coupled pixel sensor prototypes with high-resistivity substrate
Test beam measurement of ams H35 HV-CMOS capacitively coupled pixel sensor prototypes with high-resistivity substrate Open
In the context of the studies of the ATLAS High Luminosity LHC programme, radiation tolerant pixel detectors in CMOS technologies are investigated. To evaluate the effects of substrate resistivity on CMOS sensor performance, the H35DEMO de…
View article: Fermilab Test Beam Facility Annual Report FY17
Fermilab Test Beam Facility Annual Report FY17 Open
This Technical Memorandum (TM) summarizes the Fermilab Test Beam operations for FY2017. It is one of a series of annual publications intended to gather information in one place. In this case, the information concerns the individual experim…
View article: Optimisation of the ATLAS $b$-tagging algorithms for the 2017-2018 LHC data-taking
Optimisation of the ATLAS $b$-tagging algorithms for the 2017-2018 LHC data-taking Open
This contribution describes the performance of the ATLAS $b$-tagging algorithms for the 2017-18 datataking at the LHC. Novel taggers based on soft muons from semi-leptonic decays of the $b$/$c$-hadrons and a RecurrentNeural Network based o…