Recent lbnl_cs items

Quantifying Epistemic Uncertainty in Diffusion Models

Wed, 17 Jun 2026 00:00:00 +0000

To ensure high quality outputs, it is important to quantify the epistemic uncertainty of diffusion models. Existing methods are often unreliable because they mix epistemic and aleatoric uncertainty. We introduce a method based on Fisher information that explicitly isolates epistemic variance, producing more reliable plausibility scores for generated data. To make this approach scalable, we propose FLARE (Fisher-Laplace Randomized Estimator), which approximates the Fisher information using a uniformly random subset of model parameters. Empirically, FLARE improves uncertainty estimation in synthetic time-series generation tasks, achieving more accurate and reliable filtering than other methods. Theoretically, we bound the convergence rate of our randomized approximation and provide analytic and empirical evidence that last-layer Laplace approximations are insufficient for this task.

ERRATUM: Two-neutrino double electron capture of 124Xe in the first LUX-ZEPLIN exposure (2024 J. Phys. G: Nucl. Part. Phys. 52 015103)

Wed, 17 Jun 2026 00:00:00 +0000

Due to an error in production, the error margins of many values were incorrectly published. Table 1 details the locations of the errors and their corrections.

Track reconstruction as a service for collider physics

Wed, 17 Jun 2026 00:00:00 +0000

Optimizing charged-particle track reconstruction algorithms is crucial for efficient event reconstruction in Large Hadron Collider (LHC) experiments due to their significant computational demands. Existing track reconstruction algorithms have been adapted to run on massively parallel coprocessors, such as graphics processing units (GPUs), to reduce processing time. Nevertheless, challenges remain in fully harnessing the computational capacity of coprocessors in a scalable and non-disruptive manner. This paper proposes an inference-as-a-service approach for particle tracking in high energy physics experiments. To evaluate the efficacy of this approach, two distinct tracking algorithms are tested: Patatrack, a rule-based algorithm, and Exa.TrkX, a machine learning-based algorithm. The as-a-service implementations show enhanced GPU utilization and can process requests from multiple CPU cores concurrently without increasing per-request latency. The impact of data transfer is minimal...

Towards a distributed heterogeneous task scheduler for the ATLAS offline software framework*

Tue, 16 Jun 2026 00:00:00 +0000

With the increased data volumes expected to be delivered by the HLLHC, it becomes critical for the ATLAS experiment to maximize the utilization of available computing resources ranging from conventional GRID clusters to supercomputers and cloud computing platforms. To run its data processing applications on these resources, the ATLAS software framework must be capable of efficiently executing data processing tasks in heterogeneous distributed computing environments. Today, using the Gaudi Avalanche Scheduler, whose implementation is based on Intel TBB, we can efficiently schedule Athena algorithms to multiple threads within a single compute node. We aim to develop a new framework scheduler capable of supporting distributed heterogeneous environments, based on technologies like HPX or Ray. After the initial evaluation phase of these technologies, we began the development of a prototype distributed task scheduler for the Athena framework. This contribution describes this prototype...

Parton labeling without matching: unveiling emergent labelling capabilities in regression models

Tue, 16 Jun 2026 00:00:00 +0000

Parton labeling methods are widely used when reconstructing collider events with top quarks or other massive particles. State-of-the-art techniques are based on machine learning and require training data with events that have been matched using simulations with truth information. In nature, there is no unique matching between partons and final state objects due to the properties of the strong force and due to acceptance effects. We propose a new approach to parton labeling that circumvents these challenges by recycling regression models. The final state objects that are most relevant for a regression model to predict the properties of a particular top quark are assigned to said parent particle without having any parton-matched training data. This approach is demonstrated using simulated events with top quarks and outperforms the widely-used χ2$$\chi ^2$$ method.

AthenaTriton: A tool for running machine learning inference as a service in Athena

Tue, 16 Jun 2026 00:00:00 +0000

Machine learning (ML)-based algorithms play increasingly important roles in almost all aspects of the data analyses in ATLAS, including detector simulations, event reconstructions, and data analyses. These diverse ML models are being deployed in the ATLAS software framework, Athena. To harmonize the ML inference in both the Athena environment and the ROOT environment, a dual-use ML interface is defined and implemented with two distinct backends: the Open Neural Network Exchange (ONNX) Runtime and Inference as a Service. While ONNX Runtime serves as the primary inference backend in Athena, scalable inference strategies are required to address the growing demands of processing simulation and collision data, including maximizing event throughput and utilizing coprocessors like graphics processing units (GPUs). To meet this challenge, we introduce AthenaTriton, a solution that integrates the NVIDIA Triton Inference Server with Athena. In AthenaTriton, Athena operates as a Triton client...

Erratum to: Search for exclusive Higgs and Z boson decays to ϕγ and ργ with the ATLAS detector

Tue, 16 Jun 2026 00:00:00 +0000

One correction is noted for the paper. The calculation of an angle used to account for meson polarisation in the signal decays was not correct.

Search for massive, long-lived particles in events with displaced vertices and displaced muons in pp collisions at s=13.6 TeV with the ATLAS experiment

Tue, 16 Jun 2026 00:00:00 +0000

A search is presented for massive long-lived particles in events featuring at least one displaced vertex and at least one displaced muon, using proton–proton collision data collected by the ATLAS detector at the Large Hadron Collider from 2022 to 2024 at a centre-of-mass energy of 13.6 TeV. The data sample corresponds to an integrated luminosity of 164 fb⁻¹. The analysis targets scenarios in which long-lived particles decay inside the ATLAS inner detector, resulting in a topology of at least one massive, displaced vertex (DV) with multiple associated tracks, and at least one muon with a large transverse impact parameter relative to the primary interaction point. The muon is not required to be associated with the DV. Two signal regions are defined by the transverse distance of the reconstructed DV from the interaction point. Background contributions are estimated by using fully data-driven techniques. No significant excess above the expected background is observed. Upper...

Correction: Application of geometric deep learning for tracking of hyperons in a straw tube detector

Tue, 16 Jun 2026 00:00:00 +0000

Correction: Application of geometric deep learning for tracking of hyperons in a straw tube detector

The Future of Scheduling in Athena on HPCs

Tue, 16 Jun 2026 00:00:00 +0000

The large increase in luminosity expected from Run 4 of the LHC presents the ATLAS experiment with a new scale of computing challenge, and we can no longer restrict our computing to CPUs in a High Throughput Computing paradigm. We must make full use of the High Performance Computing resources available to us, exploiting accelerators and making efficient use of large jobs over many nodes. Here we describe current developments in introducing these capabilities to Athena, ATLAS’s general software framework.

thornado+FLASH-X: A Hybrid Discontinuous Galerkin–Implicit-explicit and Finite-volume Framework for Neutrino-radiation Hydrodynamics in Core-collapse Supernovae

Wed, 10 Jun 2026 00:00:00 +0000

We present neutrino-transport algorithms implemented in the toolkit for high-order neutrino-radiation hydrodynamics (thornado) and their coupling to self-gravitating hydrodynamics within the adaptive mesh refinement–based multiphysics simulation framework FLASH-X. thornado, developed primarily for simulations of core-collapse supernovae (CCSNe), employs a spectral, six-species two-moment formulation with algebraic closure and special-relativistic observer corrections accurate to O(v/c) , and uses discontinuous Galerkin (DG) methods for phase-space discretization combined with implicit-explicit time stepping. A key development is a nonlinear neutrino–matter coupling algorithm based on nested fixed-point iteration with Anderson acceleration, enabling fully implicit treatment of collisional processes, including energy-coupling interactions such as neutrino–electron scattering and pair production. Coupling to finite-volume (FV) hydrodynamics is achieved through a hybrid DG-FV representation...

Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra

Wed, 10 Jun 2026 00:00:00 +0000

Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum systems as well as for understanding phenomena such as topological order. Extracting such information with small additive error is #BQP-complete in the worst case. In this work, we introduce QFAMES (quantum filtering and analysis of multiplicities in eigenvalue spectra), a quantum algorithm that efficiently identifies clusters of closely spaced dominant eigenvalues and determines their multiplicities under physically motivated assumptions, which allows us to bypass worst-case complexity barriers. QFAMES also enables the estimation of observable expectation values within targeted energy clusters, providing a powerful tool for studying quantum phase transitions and other physical properties. We validate the effectiveness of QFAMES through numerical demonstrations, including its applications to characterizing...

EPOC Deep Dive Retrospective: A Brief Overview of 7 years of Science Engagement Discussions