arXiv subjects

Tyler Johnson

Publications and source records attributed to Tyler Johnson.

8 recordsLinked to original sources

arXiv2026

Design of ALPHA Phase I: A Plasma Haloscope for 10--20 GHz Post-Inflation Axions

The axion is a well-motivated hypothetical particle capable of resolving both the strong CP problem and the dark matter mystery, with recent post-inflationary cosmological simulations favoring masses above 40 {\mu}eV. Plasma haloscopes serve as a promising experimental approach to reach theoretically preferred sensitivities in this mass range. ALPHA, hosted at Yale Wright Laboratory, is an international collaboration developing plasma haloscopes to search for QCD dark matter axions. In this letter we present the detailed design and sensitivity projection for the first phase of the ALPHA experiment, which will search the mass range from 10 GHz to 20 GHz (~40 {\mu}eV to 80 {\mu}eV). This search will make use of wire-array plasma resonators to decouple the physical size from the resonant frequency, a limitation typically faced by traditional microwave cavities, allowing broadband sensitivity approaching KSVZ coupling strengths.

hep-ex↗

arXiv2026

Memory Efficient Audio Synthesis with Decoupled Temporal Depth Diffusion Transformers

Siri Expressive Voices synthesize rich, configurable speech in real time and entirely on device, powered by AFM 3 Core Advanced, Apple's most powerful on-device foundation model. This work presents the memory-efficient audio synthesis architecture behind that capability: a detokenizer that converts the semantic audio tokens emitted by the foundation model into high-fidelity audio within the tight compute and memory budget of the Apple Matrix Coprocessor (AMX). We convert semantic audio tokens to a residual vector quantization (RVQ) representation with a three-component design, a streaming encoder, a temporal decoder, and a depth decoder, that systematically decouples temporal and depth processing. A single reusable depth decoder with Diffusion Transformer (DiT)-style stage conditioning generates all RVQ levels autoregressively, replacing the dedicated per-level decoders of prior multi-decoder architectures, while causal sliding window attention with fixed-window key-value caching yields constant memory complexity independent of sequence length. Deployed on the AMX, the detokenizer sustains roughly 10 ms per generation step, about 16x faster than real time, with a peak runtime memory of only 21 MB and 329 MB of on-device assets, enabling continuous streaming synthesis of 20-320 seconds of audio. This constant, small footprint replaces the linear and quadratic memory scaling of conventional transformer- and GAN-based approaches. Ablation studies validate the key architectural components, and audio quality assessment confirms that the architecture maintains synthesis fidelity while achieving efficiency gains over existing methods. Operating at a 1-billion-parameter activation size within AFM 3 Core Advanced, it improves Mean Opinion Score by +0.28 overall (4.15 vs. 3.87) and by +0.42 on conversational speech (4.24 vs. 3.82) over the prior on-device text-to-speech system.

cs.SD↗

arXiv2026

The chemRIXS Instrument for the LCLS-II X-Ray Free Electron Laser

The chemRIXS instrument at the Linac Coherent Light Source offers new opportunities for studying solution-phase systems with time-resolved soft X-ray spectroscopy through the recently commissioned high-repetition-rate LCLS-II X-ray free electron laser. The orders-of-magnitude X-ray flux improvement provided by the superconducting accelerator, combined with corresponding advances in the optical laser system and the liquid jet recirculation system, enables studies on dilute systems with high signal-to-noise compared to what was possible with the LCLS-I copper accelerator. These capabilities open up time-resolved X-ray absorption spectroscopy and resonant inelastic X-ray scattering to entirely new classes of samples, as well as enabling the development of new soft X-ray spectroscopies on liquid samples. An overview of the beamline components and the first LCLS-II commissioning results are presented.

physics.ins-det↗

arXiv2025

Should We Simultaneously Calibrate Multiple Computer Models?

In an increasing number of applications designers have access to multiple computer models which typically have different levels of fidelity and cost. Traditionally, designers calibrate these models one at a time against some high-fidelity data (e.g., experiments). In this paper, we question this tradition and assess the potential of calibrating multiple computer models at the same time. To this end, we develop a probabilistic framework that is founded on customized neural networks (NNs) that are designed to calibrate an arbitrary number of computer models. In our approach, we (1) consider the fact that most computer models are multi-response and that the number and nature of calibration parameters may change across the models, and (2) learn a unique probability distribution for each calibration parameter of each computer model, (3) develop a loss function that enables our NN to emulate all data sources while calibrating the computer models, and (4) aim to learn a visualizable latent space where model-form errors can be identified. We test the performance of our approach on analytic and engineering problems to understand the potential advantages and pitfalls in simultaneous calibration of multiple computer models. Our method can improve predictive accuracy, however, it is prone to non-identifiability issues in higher-dimensional input spaces that are normally constrained by underlying physics.

cs.LG↗

arXiv2024

Towards Low-bit Communication for Tensor Parallel LLM Inference

Tensor parallelism provides an effective way to increase server large language model (LLM) inference efficiency despite adding an additional communication cost. However, as server LLMs continue to scale in size, they will need to be distributed across more devices, magnifying the communication cost. One way to approach this problem is with quantization, but current methods for LLMs tend to avoid quantizing the features that tensor parallelism needs to communicate. Taking advantage of consistent outliers in communicated features, we introduce a quantization method that reduces communicated values on average from 16 bits to 4.2 bits while preserving nearly all of the original performance. For instance, our method maintains around 98.0% and 99.5% of Gemma 2 27B's and Llama 2 13B's original performance, respectively, averaged across all tasks we evaluated on.

cs.AI↗

arXiv2023

Probabilistic Neural Data Fusion for Learning from an Arbitrary Number of Multi-fidelity Data Sets

In many applications in engineering and sciences analysts have simultaneous access to multiple data sources. In such cases, the overall cost of acquiring information can be reduced via data fusion or multi-fidelity (MF) modeling where one leverages inexpensive low-fidelity (LF) sources to reduce the reliance on expensive high-fidelity (HF) data. In this paper, we employ neural networks (NNs) for data fusion in scenarios where data is very scarce and obtained from an arbitrary number of sources with varying levels of fidelity and cost. We introduce a unique NN architecture that converts MF modeling into a nonlinear manifold learning problem. Our NN architecture inversely learns non-trivial (e.g., non-additive and non-hierarchical) biases of the LF sources in an interpretable and visualizable manifold where each data source is encoded via a low-dimensional distribution. This probabilistic manifold quantifies model form uncertainties such that LF sources with small bias are encoded close to the HF source. Additionally, we endow the output of our NN with a parametric distribution not only to quantify aleatoric uncertainties, but also to reformulate the network's loss function based on strictly proper scoring rules which improve robustness and accuracy on unseen HF data. Through a set of analytic and engineering examples, we demonstrate that our approach provides a high predictive power while quantifying various sources uncertainties.

cs.LG↗

arXiv2021

The Time-resolved Atomic, Molecular and Optical Science Instrument at the Linac Coherent Light Source

The newly constructed Time-resolved atomic, Molecular and Optical science instrument (TMO), is configured to take full advantage of both linear accelerators at SLAC National Accelerator Laboratory, the copper accelerator operating at a repetition rate of 120 Hz providing high per pulse energy, as well as the superconducting accelerator operating at a repetition rate of about 1 MHz providing high average intensity. Both accelerators build a soft X-ray free electron laser with the new variable gab undulator section. With this flexible light sources, TMO supports many experimental techniques not previously available at LCLS and will have two X-ray beam focus spots in line. Thereby, TMO supports Atomic, Molecular and Optical (AMO), strong-field and nonlinear science and will host a designated new dynamic reaction microscope with a sub-micron X-ray focus spot. The flexible instrument design is optimized for studying ultrafast electronic and molecular phenomena and can take full advantage of the sub-femtosecond soft X-ray pulse generation program.

physics.ins-det↗

arXiv2020

Measurement of Proton Quenching in a Plastic Scintillator Detector

The non-linear energy response of the plastic scintillator EJ-260 is measured with the MicroCHANDLER detector, using neutron beams of energy 5 to 27 MeV at the Triangle Universities Nuclear Laboratory. The first and second order Birks' constants are extracted from the data, and found to be $k_B = (8.70 \pm 0.93)\times 10^{-3}\ {\rm g/cm^2/MeV}$ and $k_C = (1.42 \pm 1.00) \times 10^{-5}\ {\rm (g/cm^2/MeV)^2}$. This result covers a unique energy range that is of direct relevance for fast neutron backgrounds in reactor inverse beta decay detectors. These measurements will improve the energy non-linearity modeling of plastic scintillator detectors. In particular, the updated energy response model will lead to an improvement of fast neutron modeling for detectors based on the CHANDLER reactor neutrino detector technology.

physics.ins-det↗