Towards Foundation Models for Experimental Readout Systems Combining Discrete and Continuous Data
We present a (proto) Foundation Model for Nuclear Physics, capable of operating on low-level detector inputs from Imaging Cherenkov Detectors at the future Electron Ion Collider. To address limitations in existing next-token prediction approaches-namely resolution loss from VQ-VAE tokenization and lack of conditional generation-we propose three key innovations: (i) separate vocabularies for discrete spatial features and continuous variates, combined via Causal Multi-Head Cross-Attention (CMHCA), (ii) continuous kinematic conditioning through prepended context embeddings, and (iii) scalable and simple, high-resolution continuous variate tokenization without joint vocabulary inflation. Our model enables fast, high-fidelity generation of pixel and time sequences for Cherenkov photons, validated through closure tests in the High Performance DIRC. We also show our model generalizes to reconstruction tasks such as pion and kaon identification, in which we show its ability to leverage fine-tuning.
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
A new class of non-homogeneous state-affine systems is introduced for use in reservoir computing. Sufficient conditions are identified that guarantee first, that the associated reservoir computers with linear readouts ar…
LUNA: LUT-Based Neural Architecture for Fast and Low-Cost Qubit Readout
Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. Deep neural networks (DNNs) have recently emerged as a promising solution to im…
Dimensionality ReductionReservoir Computing Universality With Stochastic Inputs
The universal approximation properties with respect to $L ^p $-type criteria of three important families of reservoir computers with stochastic discrete-time semi-infinite inputs is shown. First, it is proved that linear…
Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification
We investigate variational quantum classifiers (VQCs) for land-cover classification from multispectral satellite imagery, adopting a feature-map perspective in which the quantum circuit defines a nonlinear data embedding…
Discrete-time signatures and randomness in reservoir computing
A new explanation of geometric nature of the reservoir computing phenomenon is presented. Reservoir computing is understood in the literature as the possibility of approximating input/output systems with randomly chosen …