SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
Abramowiczet al.(ILD Concept Group), Interna- tional Large Detector: Interim Design Report, (2020), arXiv:2003.01116 [physics.ins-det]
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A kinematic reconstruction technique for tau pairs at Higgs factories, using momenta and impact parameters with weighted solutions, enables spin polarimeter extraction and shows photon angular resolution is the dominant detector need.
Presents CaloTrilogy, a unified one-step generative model for high-granularity calorimeter showers that combines velocity field integration, learned priors, and physics losses to match SOTA quality.
Gradient boosted decision trees suppress diphoton backgrounds while adaptive symbolic memetic regression corrects beam deflection biases, reaching luminosity uncertainties below 10^{-4} and 5x10^{-6}.
ML-based reconstruction yields ~20% better energy resolution at low energies and corrects leakage at high energies for SiW-ECAL, enabling subsequent design reoptimization.
Precision Higgs factory measurements can probe composite Higgs models with top quark partners above 3 TeV while also predicting deviations in top-Z couplings.
The paper summarizes adaptations needed to apply ILC detector concepts to other Higgs Factory projects like FCC-ee.
citing papers explorer
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SPADE: Split-and-Delay Embeddings for Autoregressive High-Granularity Calorimeter Simulation
SPADE is a split-and-delay embedding technique for multi-feature autoregressive transformers that achieves competitive performance on high-granularity calorimeter shower simulation.
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Experimental aspects of the Quantum Tomography of tau lepton pairs at a Higgs factory collider
A kinematic reconstruction technique for tau pairs at Higgs factories, using momenta and impact parameters with weighted solutions, enables spin polarimeter extraction and shows photon angular resolution is the dominant detector need.
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CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeters
Presents CaloTrilogy, a unified one-step generative model for high-granularity calorimeter showers that combines velocity field integration, learned priors, and physics losses to match SOTA quality.
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Novel Machine Learning Methods to Improve Z Pole Integrated Luminosity at Future Colliders
Gradient boosted decision trees suppress diphoton backgrounds while adaptive symbolic memetic regression corrects beam deflection biases, reaching luminosity uncertainties below 10^{-4} and 5x10^{-6}.
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Optimisation of a silicon-tungsten electromagnetic calorimeter energy response to photons
ML-based reconstruction yields ~20% better energy resolution at low energies and corrects leakage at high energies for SiW-ECAL, enabling subsequent design reoptimization.
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The Sensitivity of Higgs Factories to Composite Higgs Models via Precision Measurements
Precision Higgs factory measurements can probe composite Higgs models with top quark partners above 3 TeV while also predicting deviations in top-Z couplings.
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Adapting ILC detector concepts to other facilities
The paper summarizes adaptations needed to apply ILC detector concepts to other Higgs Factory projects like FCC-ee.