Tootfinder

ReVISit 2: A Full Experiment Life Cycle User Study Framework
Zach Cutler, Jack Wilburn, Hilson Shrestha, Yiren Ding, Brian Bollen, Khandaker Abrar Nadib, Tingying He, Andrew McNutt, Lane Harrison, Alexander Lex
https://arxiv.org/abs/2508.03876

ReVISit 2: A Full Experiment Life Cycle User Study Framework
Online user studies of visualizations, visual encodings, and interaction techniques are ubiquitous in visualization research. Yet, designing, conducting, and analyzing studies effectively is still a major burden. Although various packages support such user studies, most solutions address only facets of the experiment life cycle, make reproducibility difficult, or do not cater to nuanced study designs or interactions. We introduce reVISit 2, a software framework that supports visualization resea…

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Chen Xu, Giao Vu, Ba Trung Cao, Zhen Liu, Fabian Diewald, Yong Yuan, G\"unther Meschke
https://arxiv.org/abs/2508.04538

Bridging Simulation and Experiment: A Self-Supervised Domain Adaptation Framework for Concrete Damage Classification
Reliable assessment of concrete degradation is critical for ensuring structural safety and longevity of engineering structures. This study proposes a self-supervised domain adaptation framework for robust concrete damage classification using coda wave signals. To support this framework, an advanced virtual testing platform is developed, combining multiscale modeling of concrete degradation with ultrasonic wave propagation simulations. This setup enables the generation of large-scale labeled syn…

Unified framework for precise background modeling to enhance rare event detection at the Kuo-Sheng nuclear reactor laboratory
Subhasis Parhi, Lakhwinder Singh, Venktesh Singh, Henry Tsz-King Wong
https://arxiv.org/abs/2510.05868

Unified framework for precise background modeling to enhance rare event detection at the Kuo-Sheng nuclear reactor laboratory
A background model has been designed for TEXONO experiment using Geant4 sim- ulation software. The contamination of different radioactive isotopes in the detector background has been considered from both the detector set-up and the environment. 238 U and 232 Th have been confined in the front end electronics of the HPGe detector. These two isotopes are confined by breaking the complete chains into constituent decay chains on the basis of half-life of the isotopes. 235 U isotope is also confined…

Individual utilities of life satisfaction reveal inequality aversion unrelated to political alignment
Crispin Cooper, Ana Friedrich, Tommaso Reggiani, Wouter Poortinga
https://arxiv.org/abs/2509.07793 …

Individual utilities of life satisfaction reveal inequality aversion unrelated to political alignment
How should well-being be prioritised in society, and what trade-offs are people willing to make between fairness and personal well-being? We investigate these questions using a stated preference experiment with a nationally representative UK sample (n = 300), in which participants evaluated life satisfaction outcomes for both themselves and others under conditions of uncertainty. Individual-level utility functions were estimated using an Expected Utility Maximisation (EUM) framework and tested …

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Sarvesh Chaudhari, Cristobal Mendez, Rushil Choudhary, Tathagata Banerjee, Maciej Olszewski, Jadrien Paustian, Jaehong Choi, Zhaslan Baraissov, Raul Hernandez, David Muller, Britton Plourde, Gregory Fuchs, Valla Fatemi, Tomas Arias

Active-Learning Inspired Ab Initio Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits
Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide forma…

Bayesian Design of Experiments in the Presence of Nuisance Parameters
Shirin Golchi, Luke Hagar
https://arxiv.org/abs/2508.03948 https://arxiv.org/pdf/2508…

Bayesian Design of Experiments in the Presence of Nuisance Parameters
Design of experiments has traditionally relied on the frequentist hypothesis testing framework where the optimal size of the experiment is specified as the minimum sample size that guarantees a required level of power. Sample size determination may be performed analytically when the test statistic has a known asymptotic sampling distribution and, therefore, the power function is available in analytic form. Bayesian methods have gained popularity in all stages of discovery, namely, design, analy…

Theory uncertainties in predictions of strong-field QED
T. G. Blackburn
https://arxiv.org/abs/2508.03975 https://arxiv.org/pdf/2508.03975

Theory uncertainties in predictions of strong-field QED
Experiments using high-power lasers and relativistic electron beams will soon be capable of precision testing of the theory of strong-field quantum electrodynamics. The comparison between experiment and theory always occurs via numerical simulations, but the lack of quantitative uncertainty bounds for the latter means that the origin of any discrepancies cannot be identified conclusively. Here I present a simulation framework that can place meaningful bounds on the theory uncertainties inherent…

Mathematical research with GPT-5: a Malliavin-Stein experiment
Charles-Philippe Diez, Luis da Maia, Ivan Nourdin
https://arxiv.org/abs/2509.03065 https://a…

Mathematical research with GPT-5: a Malliavin-Stein experiment
On August 20, 2025, GPT-5 was reported to have solved an open problem in convex optimization. Motivated by this episode, we conducted a controlled experiment in the Malliavin--Stein framework for central limit theorems. Our objective was to assess whether GPT-5 could go beyond known results by extending a \emph{qualitative} fourth-moment theorem to a \emph{quantitative} formulation with explicit convergence rates, both in the Gaussian and in the Poisson settings. To the best of our knowledge, t…

The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics
Nathan Grieser, Eduardo Rodrigues, Niladri Sahoo, Shuqi Sheng, Nicole Skidmore, Mark Smith
https://arxiv.org/abs/2509.05294

The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics
The LHCb Stripping project is a pivotal component of the experiment's data processing framework, designed to refine vast volumes of collision data into manageable samples for offline analysis. It ensures the re-analysis of Runs 1 and 2 legacy data, maintains the software stack, and executes (re-)Stripping campaigns. As the focus shifts toward newer data sets, the project continues to optimize infrastructure for both legacy and live data processing. This paper provides a comprehensive overview o…

An Online Data Analysis Framework for Accelerator-Based Physics Experiments
Hayden Ramm, Pascal Simon, Paraskevi Alexaki, Christopher Arran, Robert Bingham, Alice Goillot, Jon Tomas Gudmundsson, Jonathan Halliday, Bryn Lloyd, Eva Los, Vasiliki Stergiou, Sifei Zhang, Gianluca Gregori, Nikolaos Charitonidis
https://arxiv.org/abs/2508.00705…

An Online Data Analysis Framework for Accelerator-Based Physics Experiments
A robust and flexible architecture capable of providing real-time analysis on diagnostic data in experimental physics is of crucial importance to physics experiments. In this paper, we present such an online framework, used in June 2025 as part of the HRMT-68 experiment, performed at the HiRadMat facility at CERN, using the Super Proton Synchrotron (SPS) beam line. HRMT-68 was a fixed-target laboratory astrophysics experiment aiming to identify plasma instabilities generated by a relativistic e…

afspm: A Framework for Manufacturer-Agnostic Automation in Scanning Probe Microscopy
Nicholas J. Sullivan, Julio J. Vald\'es, Kirk H. Bevan, Peter Grutter
https://arxiv.org/abs/2509.00113

afspm: A Framework for Manufacturer-Agnostic Automation in Scanning Probe Microscopy
Scanning probe microscopy (SPM) is a valuable technique by which one can investigate the physical characteristics of the surfaces of materials. However, its widespread use is hampered by the time-consuming nature of running an experiment and the significant domain knowledge required. Recent studies have shown the value of multiple forms of automation in improving this, but their use is limited due to the difficulty of integrating them with SPMs other than the one it was developed for. With this…

A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments
Adam A. Corrao, Phillip M. Maffettone, Bruce Ravel, Thomas A. Caswell, Howie Joress, Stuart I. Wilkins, Daniel Olds
https://arxiv.org/abs/2509.22959

A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments
High-throughput materials discovery and studies of complex functional materials increasingly rely on multi-modal characterization performed at synchrotron light sources. However, measurements are typically done with no use of data until after an experiment, neglecting opportunities for data-driven insights to guide measurements. We developed a modular, open-source framework that incorporates artificial intelligence within the Bluesky control and data streaming infrastructure at NSLS-II, enablin…

Calibration-sample free distortion correction of electron diffraction patterns using deep learning
Matthew R. C. Fitzpatrick, Arthur M. Blackburn, Cristina Cordoba
https://arxiv.org/abs/2509.01075

Calibration-sample free distortion correction of electron diffraction patterns using deep learning
The accuracy of the information that can be extracted from electron diffraction patterns is often limited by the presence of optical distortions. Existing distortion characterization techniques typically require knowledge of the reciprocal lattice of either the sample of interest or a separate calibration sample, the latter of which would need to be swapped in, thus adding time and inconvenience to an experiment. To overcome this limitation, we develop a deep learning (DL) framework for measuri…

Quantum Measurement Trees, I: Two Preliminary Examples of Induced Contextual Boolean Algebras
Peter J Hammond
https://arxiv.org/abs/2509.22594 https://arxi…

Quantum Measurement Trees, I: Two Preliminary Examples of Induced Contextual Boolean Algebras
Quantum randomness evidently transcends the classical framework of random variables defined on a single comprehensive Kolmogorov probability space. One prominent example is the quantum double-slit experiment due to Feynman (1951, 1966). A related non-quantum example, inspired by Boole (1862) and Vorob$'$ev (1962), has three two-valued random variables $X$, $Y$ and $Z$, where the pairs $X, Y$ and $X, Z$ are perfectly correlated, yet $Y, Z$ are perfectly anti-correlated. Such examples can be acco…

ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
Tom Burgert, Oliver Stoll, Paolo Rota, Beg\"um Demir
https://arxiv.org/abs/2509.20234

ImageNet-trained CNNs are not biased towards texture: Revisiting feature reliance through controlled suppression
The hypothesis that Convolutional Neural Networks (CNNs) are inherently texture-biased has shaped much of the discourse on feature use in deep learning. We revisit this hypothesis by examining limitations in the cue-conflict experiment by Geirhos et al. To address these limitations, we propose a domain-agnostic framework that quantifies feature reliance through systematic suppression of shape, texture, and color cues, avoiding the confounds of forced-choice conflicts. By evaluating humans and n…

A General Framework for Estimating Preferences Using Response Time Data
Federico Echenique, Alireza Fallah, Michael I. Jordan
https://arxiv.org/abs/2507.20403 https://

A General Framework for Estimating Preferences Using Response Time Data
We propose a general methodology for recovering preference parameters from data on choices and response times. Our methods yield estimates with fast ($1/n$ for $n$ data points) convergence rates when specialized to the popular Drift Diffusion Model (DDM), but are broadly applicable to generalizations of the DDM as well as to alternative models of decision making that make use of response time data. The paper develops an empirical application to an experiment on intertemporal choice, showing tha…

Replaced article(s) found for hep-ex. https://arxiv.org/list/hep-ex/new
[2/2]:
- An Online Data Analysis Framework for Accelerator-Based Physics Experiments
Hayden Ramm, et al.

Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?
Bin Han, Deuksin Kwon, Spencer Lin, Kaleen Shrestha, Jonathan Gratch
https://arxiv.org/abs/2508.21087

Can LLMs Generate Behaviors for Embodied Virtual Agents Based on Personality Traits?
This study proposes a framework that employs personality prompting with Large Language Models to generate verbal and nonverbal behaviors for virtual agents based on personality traits. Focusing on extraversion, we evaluated the system in two scenarios: negotiation and ice breaking, using both introverted and extroverted agents. In Experiment 1, we conducted agent to agent simulations and performed linguistic analysis and personality classification to assess whether the LLM generated language re…

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
A. Gavrikov, A. Serafini, D. Dolzhikov, A. Garfagnini, M. Gonchar, M. Grassi, R. Brugnera, V. Cerrone, L. V. D'Auria, R. M. Guizzetti, L. Lastrucci, G. Andronico, V. Antonelli, A. Barresi, D. Basilico, M. Beretta, A. Bergnoli, M. Borghesi, A. Brigatti, R. Bruno, A. Budano, B. Caccianiga, A. Cammi, R. Caruso, D. Chiesa, C. Clementi, C. Coletta, S. Dusini, A. Fabbri, G. Felici, G. Ferrante, M.…

Simulation-based inference for Precision Neutrino Physics through Neural Monte Carlo tuning
Precise modeling of detector energy response is crucial for next-generation neutrino experiments which present computational challenges due to lack of analytical likelihoods. We propose a solution using neural likelihood estimation within the simulation-based inference framework. We develop two complementary neural density estimators that model likelihoods of calibration data: conditional normalizing flows and a transformer-based regressor. We adopt JUNO - a large neutrino experiment - as a cas…

AIs Structural Impact on Indias Knowledge Intensive Startup Ecosystem: A Natural Experiment in Firm Efficiency and Design
Venkat Ram Reddy Ganuthula, Ramesh Kuruva
https://arxiv.org/abs/2507.19775

AIs Structural Impact on Indias Knowledge Intensive Startup Ecosystem: A Natural Experiment in Firm Efficiency and Design
This study explores the structural and performance impacts of artificial intelligence (AI) adoption on Indias knowledge intensive startups, spanning information technology, financial technology, health technology, and educational technology, founded between 2016 and 2025. Using a natural experiment framework with the founding year as an exogenous treatment proxy, it examines firm size, revenue productivity, valuation efficiency, and capital utilization across pre AI and AI era cohorts. Findings…

BabyMOSS stitched sensors: results of characterisation tests for ALICE ITS3 upgrade
Alessandro Sturniolo (on behalf of the ALICE Collaboration)
https://arxiv.org/abs/2510.01834 …

BabyMOSS stitched sensors: results of characterisation tests for ALICE ITS3 upgrade
During the Long Shutdown 3 (LS3, scheduled 2026-2030), the innermost 3 layers (Inner Barrel, or IB) of the present ALICE ITS2 will be replaced with large-area, flexible, stitched CMOS 65 nm sensors, arranged in 2 half-barrels of 3 half-layers each, in the framework of the ITS3 upgrade project. For the first time in a High Energy Physics experiment, such large-scale sensors will be bent into truly half-cylindrical shaped half-layers, requiring little mechanical support. This will also help lower…

Elicitability
Yaron Azrieli, Christopher Chambers, Paul Healy, Nicolas Lambert
https://arxiv.org/abs/2510.00879 https://arxiv.org/pdf/2510.00879

Elicitability
An analyst is tasked with producing a statistical study. The analyst is not monitored and is able to manipulate the study. He can receive payments contingent on his report and trusted data collected from an independent source, modeled as a statistical experiment. We describe the information that can be elicited with appropriately shaped incentives, and apply our framework to a variety of common statistical models. We then compare experiments based on the information they enable us to elicit. Th…

Investigating the Task Load of Investigating the Task Load in Visualization Studies
Daniel Pahr, Sara Di Bartolomeo
https://arxiv.org/abs/2509.24643 https://

Investigating the Task Load of Investigating the Task Load in Visualization Studies
The NASA task load index (short: NASA-TLX) is a common metric to evaluate the workload of a user in a visualization study. Yet, it is rarely performed as initially intended, as the sources-of-workload evaluation is often omitted for various reasons. We conduct an online survey to investigate the task load of administering different versions of the NASA-TLX in a meta-study using the ReVISit framework. Our results show that it is not the slight increase in experiment time, but rather participants…

$J/\psi$ Polarization and $p_T$ distribution in $c\ \!\bar{c}$ associated hadroproduction at $\mathcal{O}(\alpha_s^5)$
Qi-Ming Feng, Cong-Feng Qiao
https://arxiv.org/abs/2507.20654

$J/ψ$ Polarization and $p_T$ distribution in $c\ \!\bar{c}$ associated hadroproduction at $\mathcal{O}(α_s^5)$
The quarkonium production in association with a heavy-quark pair of the same flavor was found plays an important role in various production schemes, let alone it provides a distinctive signature that could be studied in experiment. Within the NRQCD framework, we perform the first complete calculation of the hadroproduction process $gg\to J/ψ+c\bar{c}$ at the next-to-leading order (NLO) in the expansion of strong coupling constant $α_s$. The result tells that the NLO corrections substantially …

Environment Reconstruction in Terahertz Monostatic Sensing: Joint Millimeter-level Geometry Mapping and Material Identification
Zitong Fang, Yejian Lyu, Ziming Yu, Chong Han
https://arxiv.org/abs/2508.10372

Environment Reconstruction in Terahertz Monostatic Sensing: Joint Millimeter-level Geometry Mapping and Material Identification
Terahertz (THz) integrated sensing and communication (ISAC) offers high-speed communication alongside precise environmental sensing. This paper presents a computationally efficient framework for THz-based environment reconstruction by integrating connected component analysis (CCA)-assisted multipath component (MPC) estimation with a sliding-window refinement strategy. To start with, a monostatic sensing experiment is conducted in an indoor scenario using a vector network analyzer (VNA)-based so…

Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings
Valerii Andreichev, Silvan K\"aser, Erica L. Bocanegra, Madeeha Salik, Mark A. Johnson, Markus Meuwly
https://arxiv.org/abs/2508.06419

Dynamics of Protonated Oxalate from Machine-Learned Simulations and Experiment: Infrared Signatures, Proton Transfer Dynamics and Tunneling Splittings
The infrared spectroscopy and proton transfer dynamics together with the associated tunneling splittings for H/D-transfer in oxalate are investigated using a machine learning-based potential energy surface (PES) of CCSD(T) quality, calibrated against the results of new spectroscopic measurements. Second order vibrational perturbation calculations (VPT2) very successfully describe both the framework and H-transfer modes compared with the experiments. In particular, a new low-intensity signature …

$\Xi_c \to \Xi$ Semileptonic Decays: An LCSR View on the Experiment-Lattice Tension
T. M. Aliev, S. Bilmis, M. Savci
https://arxiv.org/abs/2509.10181 https://

$Ξ_c \to Ξ$ Semileptonic Decays: An LCSR View on the Experiment-Lattice Tension
We present a light-cone QCD sum rule analysis of the semileptonic decays of $Ξ_c$ baryons, focusing on the channels $Ξ_c^0 \to Ξ^- \ell^+ ν_\ell$, and $Ξ_c^+ \to Ξ^0 \ell^+ ν_\ell$. The transition form factors are calculated within the light-cone QCD sum rules framework, using the distribution amplitudes of the heavy $Ξ_c$ baryons. The obtained form factors are then used to compute the differential and total decay widths, as well as the branching fractions. Our numerical results for the…

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers
Seokju Chung, Jack Cleeve, Akshay Malige, Georgia Karagiorgi, Lino Gerlach, Adrian A. Pol, Isobel Ojalvo
https://arxiv.org/abs/2509.21817

Real-time Anomaly Detection for Liquid Argon Time Projection Chambers
We present a real-time anomaly detection framework for liquid argon time projection chambers (LArTPCs), targeting applications in particle physics experiments such as the Short Baseline Near Detector (SBND) or the future Deep Underground Neutrino Experiment (DUNE). These experiments employ detectors that generate and stream high-resolution but sparse images of neutrino and other particle interactions. Our approach utilizes anomaly detection with autoencoders, compressed through knowledge distil…

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments
S. Ganguly (Fermi National Accelerator Laboratory)
https://arxiv.org/abs/2508.06645

AI-driven neutrino diagnostics and radiation-hard beam instrumentation for next-generation neutrino experiments
The Long Baseline Neutrino Facility (LBNF) at Fermilab will deliver a high-intensity, multi-megawatt neutrino beam to the Deep Underground Neutrino Experiment (DUNE), enabling precision tests of the three-neutrino paradigm, CP violation searches, neutrino mass ordering determination, and supernova neutrino studies. In order to accelerate DUNE's physics reach and ensure robust beam operations, we propose an integrated AI-driven framework with real-time diagnostics and radiation-hardened instrume…