Description
Id Title Presenters
2 Anomaly aware machine learning for dark matter direct detection at DARWIN Andre Scaffidi
10 Einstein Telescope: binary black holes gravitational wave signals detection from three detectors combined data using deep learning Wathela Alhassan
16 Costless Performance Gains in Nested Sampling for Applications to AI and Gravitational Waves Metha Prathaban
17 Real-Time Detection of Low-Energy Events with 2DCNN on FPGA's for the DUNE Data Selection System Akshay Malige
22 Deep Learning for Cosmic-Ray Observatories Jonas Glombitza
26 Quantum Probabilistic Diffusion Models Andrea Cacioppo
27 Long-Lived Particles Anomaly Detection with Parametrized Quantum Circuits Simone Bordoni
28 Model compression and simplification pipelines for fast and explainable deep neural network inference in FPGAs in HEP Graziella Russo
29 Leveraging Physics-Informed Graph Neural Networks for Enhanced Combinatorial Optimization Lorenzo Colantonio
33 Simulation of Z2 model using Variational Autoregressive Network (VAN). Vaibhav Chahar
36 Machine learning for radiometer calibration in global 21cm cosmology Samuel Alan Kossoff Leeney
38 Accelerating the search for mass bumps using the Data-Directed Paradigm Fannie Bilodeau
39 Efficient Parameter Space Exploration in BSM Theories with Batched Multi-Objective Constraint Active Search Mauricio A. Diaz
42 Feature selection techniques for CR isotope identification with the AMS-02 experiment in space Marta Borchiellini
46 Artificial Intelligence techniques in KM3NeT Evangelia Drakopoulou
50 Quantum Computing for Track Reconstruction at LHCb Miriam Lucio Martinez
59 The Calorimeter Pyramid: Rethinking the design of generative calorimeter shower models Simon Schnake
63 Full-event reconstruction using CNN-based models on calibrated waveforms for the Large-Sized Telescope prototype of the Cherenkov Telescope Array Iaroslava Bezshyiko
64 Embedded Neural Networks on FPGAs for Real-Time Computation of the Energy Deposited in the ATLAS Liquid Argon Calorimeter Raphael Bertrand
65 Kicking it Off(-shell) with Direct Diffusion Sofia Palacios Schweitzer
66 CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds Humberto Reyes-Gonzalez
68 Optimizing bayesian inference in cosmology with Marginal Neural Ratio Estimation Guillermo Franco Abellan
71 Extracting Dark Matter Halo Parameters with Overheated Exoplanets María Benito
72 Estimating classical mutual information for spin systems and field theories using generative neural networks Piotr Korcyl
76 Choose Your Diffusion: Efficient and flexible way to accelerate the diffusion model dynamics in fast physics simulation Cheng Jiang
87 Searching for Dark Matter Subhalos in Astronomical Data using Deep Learning Sven Põder
91 Diffusion meets Nested Sampling David Yallup
96 Flow-based generative models for particle calorimeter simulation Claudius Krause
100 Simulation-Based Supernova Ia Cosmology Konstantin Karchev
101 Gaussian processes for managing model uncertainty in gravitational wave analyses Daniel Williams
102 Emulation by committee: faster AGN fitting Benjamin Ricketts
103 Adaptive Machine Learning on FPGAs: Bridging Simulated and Real-World Data in High-Energy Physics Marius Köppel
104 Generative models and lattice field theory Mathis Gerdes
108 Normalizing flows for jointly predicting photometry and photometric redshifts Laura Cabayol Garcia
113 Exploring the Universe with Radio Astronomy and AI Lara Alegre
114 interTwin - an interdisciplinary Digital Twin Engine for Science Kalliopi Tsolaki
115 Quantum and classical methods for ground state optimisation in quantum many-body problems Thomas Spriggs
116 ML-based Unfolding Techniques for High Energy Physics Nathan Huetsch
120 Clustering Considerations for Nested Sampling Adam Ormondroyd
124 Enhancing Robustness: BSM Parameter Inference with n1D-CNN and Novel Data Augmentation Yong Sheng Koay
128 Boosted object reconstruction with Monte-Carlo truth supervised Graph Neural Networks Jacan Chaplais
129 Importance nested sampling with normalizing flows for gravitational-wave inference Michael Williams
130 Searching gravitational wave from stellar-mass binary black hole early inspiral Xue-Ting Zhang
131 Stochastic Gravitational Wave Background Analysis with SBI James Alvey
136 Hybrid quantum graph neural networks for particle tracking in high energy physics Matteo Argenton
147 Characterizing the Fermi-LAT high-latitude sky with simulation-based inference Christopher Eckner
148 lsbi: linear simulation based inference Will Handley
149 Magnet Design Optimisation with Supervised Deep Neural Networks Florian Stummer
150 PolySwyft: a sequential simulation-based nested sampler Kilian Scheutwinkel
158 Studies on track finding algorithms based on machine learning with GPU and FPGA Maria Carnesale
169 Learning new physics with a (kernel) machine Marco Letizia
173 A Hybrid Approach to Anomaly Detection in Particle Physics Dennis Noll
180 Realtime Anomaly Detection with the CMS Level-1 Global Trigger Test Crate Sioni Summers
185 Building sparse kernel methods via dictionary learning. Expressive, regularized and interpretable models for statistical anomaly detection Gaia Grosso
188 Parameter estimation from quantum-jump data using neural networks Enrico Rinaldi
190 Physics-Informed Neural Networks for Gravitational Waves Matteo Scialpi
203 Advances in developing deep neural networks for finding primary vertices in proton-proton collisions at the LHC Simon Akar
209 Differentiable Vertex Fitting for Jet Flavour Tagging Rachel Smith
210 Fast Inference of Deep Learning Models with SOFIE Lorenzo Moneta
212 Machine Learning-based Data Compression Per Alexander Ekman
219 Machine Learning applications at the ATLAS experiment Judita Mamuzic
