Scientific Data

Deep Generative Models for Fast Photon Shower Simulation in ATLAS

(2024)

Abstract: The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Software Performance of the ATLAS Track Reconstruction for LHC Run 3

(2024)

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

Modeling Framework for Cost Optimization of Process-Scale Desalination Systems with Mineral Scaling and Precipitation.

(2024)

Cost-optimization models are powerful tools for evaluating emerging water treatment processes. However, to date, optimization models do not incorporate detailed chemical reaction phenomena, limiting the assessment of pretreatment and mineral scaling. Moreover, novel approaches for high-salinity and high-recovery desalination are typically proposed without direct quantification of pretreatment needs or mineral scaling. This work addresses a critical gap in the literature by presenting a modeling framework that includes complex water chemistry predictions with process-scale optimization. We use this approach to conduct a technoeconomic assessment on a conceptual high-recovery treatment train that includes chemical pretreatment (i.e., soda ash softening and recarbonation) and membrane-based desalination (i.e., standard and high-pressure reverse osmosis). We demonstrate how to develop and integrate accurate multidimensional surrogate models for predicting precipitation, pH, and mineral scaling tendencies. Our findings show that cost-optimal results balance the costs of pretreatment with reverse osmosis system design. Optimizing across a range of water recoveries (i.e., 50-90%) reveals multiple cost-optimal schemas that vary the chemical dosing in pretreatment and the design and operation of reverse osmosis. Our results reveal that pretreatment costs can be more than double the cost of the primary desalination process at high recoveries due to the extensive pretreatment required to control scaling. This work emphasizes the importance of and provides a framework for including chemistry and mineral scaling predictions in the evaluation of emerging technologies in high-recovery desalination.

Network-level encoding of local neurotransmitters in cortical astrocytes

(2024)

Astrocytes, the most abundant non-neuronal cell type in the mammalian brain, are crucial circuit components that respond to and modulate neuronal activity through calcium (Ca²⁺) signalling^1-7. Astrocyte Ca²⁺ activity is highly heterogeneous and occurs across multiple spatiotemporal scales-from fast, subcellular activity^3,4 to slow, synchronized activity across connected astrocyte networks^8-10-to influence many processes^5,7,11. However, the inputs that drive astrocyte network dynamics remain unclear. Here we used ex vivo and in vivo two-photon astrocyte imaging while mimicking neuronal neurotransmitter inputs at multiple spatiotemporal scales. We find that brief, subcellular inputs of GABA and glutamate lead to widespread, long-lasting astrocyte Ca²⁺ responses beyond an individual stimulated cell. Further, we find that a key subset of Ca²⁺ activity-propagative activity-differentiates astrocyte network responses to these two main neurotransmitters, and may influence responses to future inputs. Together, our results demonstrate that local, transient neurotransmitter inputs are encoded by broad cortical astrocyte networks over a minutes-long time course, contributing to accumulating evidence that substantial astrocyte-neuron communication occurs across slow, network-level spatiotemporal scales^12-14. These findings will enable future studies to investigate the link between specific astrocyte Ca²⁺ activity and specific functional outputs, which could build a consistent framework for astrocytic modulation of neuronal activity.

AutoCT: Automated CT registration, segmentation, and quantification

(2024)

The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT segmentation and quantification leveraging diffeomorphic transformations through efficient forward and inverse mappings. The extracted localized features from the deformation field allow for downstream statistical learning that may facilitate medical diagnostics. On a lightweight and portable software platform, AutoCT provides a new toolkit for the CT imaging community to underpin the deployment of artificial intelligence-driven applications.

Cover page of Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.

Bayesian inference of structured latent spaces from neural population activity with the orthogonal stochastic linear mixing model.

(2024)

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector

(2024)

This Letter presents the first study of Higgs boson production in association with a vector boson (V=W or Z) in the fully hadronic qqbb final state using data recorded by the ATLAS detector at the LHC in proton-proton collisions at sqrt[s]=13 TeV and corresponding to an integrated luminosity of 137 fb^{-1}. The vector bosons and Higgs bosons are each reconstructed as large-radius jets and tagged using jet substructure techniques. Dedicated tagging algorithms exploiting b-tagging properties are used to identify jets consistent with Higgs bosons decaying into bb[over ¯]. Dominant backgrounds from multijet production are determined directly from the data, and a likelihood fit to the jet mass distribution of Higgs boson candidates is used to extract the number of signal events. The VH production cross section is measured inclusively and differentially in several ranges of Higgs boson transverse momentum: 250-450, 450-650, and greater than 650 GeV. The inclusive signal yield relative to the standard model expectation is observed to be μ=1.4_{-0.9}^{+1.0} and the corresponding cross section is 3.1±1.3(stat)_{-1.4}^{+1.8}(syst) pb.

A precise measurement of the Z-boson double-differential transverse momentum and rapidity distributions in the full phase space of the decay leptons with the ATLAS experiment at s=8 TeV

(2024)

Abstract: This paper presents for the first time a precise measurement of the production properties of the Z boson in the full phase space of the decay leptons. This is in contrast to the many previous precise unfolded measurements performed in the fiducial phase space of the decay leptons. The measurement is obtained from proton–proton collision data collected by the ATLAS experiment in 2012 at $$\sqrt{s} = 8$$ s = 8 TeV at the LHC and corresponding to an integrated luminosity of 20.2 fb$$^{-1}$$ - 1 . The results, based on a total of 15.3 million Z-boson decays to electron and muon pairs, extend and improve a previous measurement of the full set of angular coefficients describing Z-boson decay. The double-differential cross-section distributions in Z-boson transverse momentum $$p_{\text {T}}$$ p T and rapidity $$y$$ y are measured in the pole region, defined as $$80< m^{\ell \ell }< 100$$ 80 < m ℓ ℓ < 100 GeV, over the range $$|y| < 3.6$$ | y | < 3.6 . The total uncertainty of the normalised cross-section measurements in the peak region of the $$p_{\text {T}}$$ p T distribution is dominated by statistical uncertainties over the full range and increases as a function of rapidity from 0.5–1.0% for $$|y| < 2.0$$ | y | < 2.0 to $$2-7\%$$ 2 - 7 % at higher rapidities. The results for the rapidity-dependent transverse momentum distributions are compared to state-of-the-art QCD predictions, which combine in the best cases approximate N$$^4$$ 4 LL resummation with N$$^3$$ 3 LO fixed-order perturbative calculations. The differential rapidity distributions integrated over $$p_{\text {T}}$$ p T are even more precise, with accuracies from 0.2–0.3% for $$|y| < 2.0$$ | y | < 2.0 to 0.4–0.9% at higher rapidities, and are compared to fixed-order QCD predictions using the most recent parton distribution functions. The agreement between data and predictions is quite good in most cases.

Measurement of the Centrality Dependence of the Dijet Yield in p+Pb Collisions at sNN=8.16 TeV with the ATLAS Detector

(2024)

ATLAS measured the centrality dependence of the dijet yield using 165 nb^{-1} of p+Pb data collected at sqrt[s_{NN}]=8.16 TeV in 2016. The event centrality, which reflects the p+Pb impact parameter, is characterized by the total transverse energy registered in the Pb-going side of the forward calorimeter. The central-to-peripheral ratio of the scaled dijet yields, R_{CP}, is evaluated, and the results are presented as a function of variables that reflect the kinematics of the initial hard parton scattering process. The R_{CP} shows a scaling with the Bjorken x of the parton originating from the proton, x_{p}, while no such trend is observed as a function of x_{Pb}. This analysis provides unique input to understanding the role of small proton spatial configurations in p+Pb collisions by covering parton momentum fractions from the valence region down to x_{p}∼10^{-3} and x_{Pb}∼4×10^{-4}.

Numerical characterization of support recovery in sparse regression with correlated design

(2024)

Computing Sciences

Scientific Data