arXiv subjects

Joshua Villarreal

Publications and source records attributed to Joshua Villarreal.

8 recordsLinked to original sources

arXiv2026

A Simulation-Based Inference Evaluation of Tension Between MicroBooNE and MiniBooNE Results in a 3+1 Sterile Neutrino Global Fit

Compatibility between different datasets in a global fit is essential for determining whether a chosen model adequately describes the data. In a 3+1 sterile neutrino global fit, long-standing tensions between datasets sensitive to $\nu_e$ appearance and $\nu_e/\nu_\mu$ disappearance indicate a failure of the model to explain the observed data, despite an overall $> 5\sigma$ improvement over the $3\nu$ Standard Model (SM) based on a $\chi^2$ fit. Overall, a global preference for the 3+1 sterile-neutrino hypothesis with significant tension between experiments motivates consideration of more complex models, but these are currently computationally prohibitive to evaluate. This paper is the third in a series aimed at reducing computational cost by developing a Simulation-Based Inference (SBI) framework for global fits. Previous papers focused on rapidly fitting the data sets using frequentist (Feldman-Cousins) and Bayesian approaches, while in this work, we formalize a definition of tension within the SBI framework. As an example, we perform a full 3+1 fit to the charged-current quasi-elastic neutrino data from the MiniBooNE experiment and the inclusive neutrino data from the MicroBooNE experiment, located on the same beamline. Using experiment-supplied systematics as is, we find these data sets favor 3+1 at $3.6\sigma$ and $1.8\sigma$ respectively, while the tension between the two is $3.3\sigma$, when fit with the SBI procedure. After correcting for normalization differences between data and Monte Carlo in the MicroBooNE $\nu_\mu$ samples, the tension relaxes to $2.2\sigma$, indicating reduced but non-negligible disagreement. The observed tension may reflect both limitations of the 3+1 model in describing the datasets and the presence of systematic effects that impact the experiments differently.

hep-ex↗

arXiv2025

Machine Learning-Informed 3+1 Sterile Neutrino Global Fits using Posterior Density Estimation of Electron Disappearance Data

Global analyses of particle physics data are integral for validating and scrutinizing published results of experiments. Global fits of anomalous oscillation data which search for one or more eV-scale sterile neutrinos are particularly challenging both to evaluate and to reconcile in the global picture. Fits (especially joint ones) to oscillation data suffer from significant computational burdens, such as likelihood intractability, making traditional Markov Chain-Monte Carlo all but impossible. Given evidence both supporting and challenging beyond Standard Model physics across neutrino experiments of various baselines, energies, and detection techniques, the global search for sterile neutrinos requires additional tools in order to determine whether sterile neutrinos remain a viable solution to unexplained anomalies. Furthermore, both a Bayesian and frequentist interpretation of sterile neutrino data is needed for a complete assessment of longstanding tensions in the field. Techniques from the machine learning subfield of simulation-based inference have a natural application to such a problem. In this contribution, we illustrate some of the outstanding questions of the global picture of light sterile neutrinos by focusing on experiments searching with the disappearance of electron (anti)neutrinos, and look to posterior density estimation strategies to craft answers, including comparisons to a machine-learning-based frequentist approach.

hep-ph↗

arXiv2025

A Frequentist Simulation-Based Inference Treatment of Sterile Neutrino Global Fits

A critical challenge in particle physics is combining results from diverse experimental setups that measure the same physical quantity to enhance precision and statistical power, a process known as a global fit. Global fits of sterile neutrino searches, hunts for additional neutrino oscillation frequencies and amplitudes, present an intriguing case study. In such a scenario, the key assumptions underlying Wilks' theorem, a cornerstone of most classic frequentist analyses, do not hold. The method of Feldman and Cousins, a trials-based approach which does not assume Wilks' theorem, becomes computationally prohibitive for complex or intractable likelihoods. To bypass this limitation, we borrow a technique from simulation-based inference (SBI) to estimate likelihood ratios for use in building trials-based confidence intervals, speeding up test statistic evaluations by a factor $>10^4$ per grid point, resulting in a faster, but approximate, frequentist fitting framework. Applied to a subset of sterile neutrino search data involving the disappearance of muon-flavor (anti)neutrinos, our method leverages machine learning to compute frequentist confidence intervals while significantly reducing computational expense. In addition, the SBI-based approach holds additional value by recognizing underlying systematic uncertainties that the Wilks approach does not. Thus, our method allows for more robust machine learning-based analyses critical to performing accurate but computationally feasible global fits. This allows, for the first time, a global fit to sterile neutrino data without assuming Wilks' theorem. While we demonstrate the utility of such a technique studying sterile neutrino searches, it is applicable to both single-experiment and global fits of all kinds.

hep-ph↗

arXiv2025

The MIST-1 and MIST-2 multicusp ion sources for high-current H$_2^+$ beams

We present two iterations of the Multicusp Ion Source Technology at MIT (MIST) sources, designed to fulfill the requirements of the HCHC-XX cyclotron design. The HCHC-XX is a novel compact cyclotron accelerating H$_2^+$. Beam is injected through a radio-frequency quadrupole buncher-accelerator (embedded in the cyclotron yoke) and utilizes so-called vortex motion during acceleration. If successful, it will deliver 10 mA of protons at 60 MeV in CW mode. This scheme requires a low-emittance, high-current initial beam with high H$_2^+$ purity. We briefly summarize the design and previous results of the MIST-1 ion source and, for the first time, the detailed design of the new and improved MIST-2, including the mechanical, electrical, and control system design. We further show experimental results of using the MIST-2 backplate on the MIST-1 body, present a study using different types of permanent magnets for confinement (including no magnets), and finally, we present first results of the MIST-2 in full operation. In this first commissioning run, we were able to increase the total extracted current from the MIST-2 to 7 mA - a factor of 2 over the MIST-1.

physics.acc-ph↗

arXiv2025

Feldman-Cousins' ML Cousin: Sterile Neutrino Global Fits using Simulation-Based Inference

For many small-signal particle physics analyses, Wilks' theorem, a simplifying assumption that presumes log-likelihood asymptotic normality, does not hold. The most common alternative approach applied in particle physics is a highly computationally expensive procedure put forward by Feldman and Cousins. When many experiments are combined for a global fit to data, deviations from Wilks' theorem are exacerbated, and Feldman-Cousins becomes computationally intractable. We present a novel, machine learning-based procedure that can approximate a full-fledged Bayesian analysis 200 times faster than the Feldman-Cousins method. We demonstrate the utility of this novel method by performing a joint analysis of electron neutrino/antineutrino disappearance data within a single sterile neutrino oscillation framework. Although we present a prototypical simulation-based inference method for a sterile neutrino global fit, we anticipate that similar procedures will be useful for global fits of all kinds, especially those in which Feldman-Cousins is too computationally expensive to use.

hep-ex↗

arXiv2024

Neural Networks as Effective Surrogate Models of Radio-Frequency Quadrupole Particle Accelerator Simulations

Radio-Frequency Quadrupoles (RFQs) are multi-purpose linear particle accelerators that simultaneously bunch and accelerate charged particle beams. They are ubiquitous in accelerator physics, especially as injectors to higher-energy machines, owing to their impressive efficiency. The design and optimization of these devices can be lengthy due to the need to repeatedly perform high-fidelity simulations. Several recent papers have demonstrated that machine learning can be used to build surrogate models (fast-executing replacements of computationally costly beam simulations) for order-of-magnitude computing time speedups. However, while these pilot studies are encouraging, there is room to improve their predictive accuracy. Particularly, beam summary statistics such as emittances (an important figure of merit in particle accelerator physics) have historically been challenging to predict. For the first time, we present a surrogate model trained on 200,000 samples that yields <6% mean average percent error for the predictions of all relevant beam output parameters, solving the problem of poor emittance predictions by identifying and including hidden variables which were not accounted for previously. These surrogate models were made possible by using the Julia language and GPU computing; we briefly discuss both. We demonstrate the utility of surrogate modeling by performing a multi-objective optimization using our best model as a callback in the objective function to select an optimal RFQ design. We consider trade-offs in RFQ performance for various choices of Pareto-optimal design variables: common issues for any multi-objective optimization scheme. Lastly, we make recommendations for input data preparation, selection, and neural network architectures that pave the way for future development of production-capable surrogate models for RFQs and other particle accelerators.

physics.comp-ph↗

arXiv2023

The EPICS control system for IsoDAR

Many large accelerator facilities have adopted the open-source EPICS software as the quasi-industry standard for control systems. They typically have access to their own electronics laboratory and dedicated personnel for control system development. On the other hand, small laboratories, many based at universities, use commercial software like LabView, or entirely homebrewed systems. These often become cumbersome when the number of controlled devices increases over time. Here we present a control system setup, based on a combination of EPICS, React Automation Studio, and our own drivers for electronics available to smaller laboratories -- such as Arduinos -- that is flexible, modular, and robust. It allows small laboratories, working with off-the-shelf modular electronics, power supplies, and other devices to quickly set up a control system without a large facility overhead, while retaining maximum compatibility and upgradeability. We demonstrate our setup for the MIST-1 ion source experiment at MIT. This control system will later be used to serve the entire IsoDAR accelerator complex and, as such, must be easily expandable.

physics.ins-det↗

arXiv2021

Recent Progress in Solar Atmospheric Neutrino Searches with IceCube

Cosmic-rays interacting with nucleons in the solar atmosphere produce a cascade of particles that give rise to a flux of high-energy neutrinos and gamma-rays. Fermi has observed this gamma-ray flux; however, the associated neutrino flux has escaped observation. In this contribution, we put forward two strategies to detect these neutrinos, which, if seen, would push forward our understanding of the solar atmosphere and provide a new testing ground of neutrino properties. First, we will extend the previous analysis, which used high-energy through-going muon events collected in the years of maximum solar activity and yielded only flux upper limits, to include data taken during the solar minimum from 2018 to 2020. Extending the analysis to the solar minimum is important as the gamma-ray data collected during past solar cycles indicates a possible enhancement in the high-energy neutrino flux. Second, we will incorporate sub-TeV events and include contributions from all neutrino flavors. These will improve our analysis sensitivity since the solar atmospheric spectrum is soft and, due to oscillation, contains significant contributions of all neutrino flavors. As we will present in this contribution, these complementary strategies yield a significant improvement in sensitivity, making substantial progress towards observing this flux.

astro-ph.HE↗