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In high-energy and astroparticle physics, event generators play an essential role, even in the simplest data analyses. As analysis techniques become more sophisticated, e.g. based on deep neural networks, their correct description of the observed event characteristics becomes even more important. Physical processes occurring in hadronic collisions are simulated within a Monte Carlo framework. A major challenge is the modeling of hadron dynamics at low momentum transfer, which includes the initial and final phases of every hadronic collision. QCD-inspired phenomenological models used for these phases cannot guarantee completeness or correctness over the full phase space. These models usually include parameters which must be tuned to suitable experimental data. Until now, event generators have been developed and tuned mainly on the basis of data from high-energy physics experiments at accelerators. The wealth of data available from the latest generation of astroparticle experiments has not yet been fully exploited, and in many cases is not satisfactorily described. Both kinds of data sets are complementary as astroparticle experiments provide sensitivity especially to hadrons produced nearly parallel to the collision axis and cover center-of-mass energies up to several hundred TeV, well beyond those reached at colliders so far. In this report, we provide an overview of state-of-the-art event generators and their tuning, including the most relevant inputs from high-energy accelerator and astroparticle experiments. We present a road map that shows, for the first time, how the unified tuning of event generators with accelerator-based and astroparticle data can be performed.
Island tune spectra are introduced, beginning with curves of resonance trajectories plotted in the (Q x , Q I ) tune domain plane (where Q x is horizontal tune and Q I is island tune) for many resonance orders N. A slice in the (Q x , Q I ) plane delivers a Q I -spectrum at a fixed horizontal tune, with one spectral line for every appropriate value of N.
GCAM assumptions typically generate scenarios that are designed to be internally consistent and globally coherent. The gcamdata tool which facilitates the compilation of data sets and user assumptions is not well suited to tailoring to specific country or regional realities, sponsor requirements, or perform harmonization for model intercomparison needs. As described in this report, the GCAM Regional Tuning project develops a computational framework that enables users to adjust GCAM parameters, so model outputs match targeted outcomes at user-defined spatial, temporal, and sectoral resolutions. The framework integrates GCAM, gcamdata, and gcamwrapper with a set of flexible “tuning directives” and an iterative numerical solver. Users can define targets (e.g., technology shares in power generation, BEV uptake, sectoral service demands), select tuners that manipulate relevant GCAM parameters (e.g., share weights, cost adders, elasticities), and export tuned parameters as reusable GCAM XML inputs for future runs. We demonstrate the approach and document usage, diagnostics, and known limitations, and we outline potential future directions.
Ferredoxins (Fds) are small proteins which shuttle electrons to pathways like biological nitrogen fixation. Physical properties tune the reactivity of Fds with different pathways, but knowledge on how these properties can be manipulated to engineer new electron transfer pathways is lacking. Recently, we showed that an evolved strain of Rhodopseudomonas palustris uses a new electron transfer pathway for nitrogen fixation. This pathway involves a variant of the primary Fd of nitrogen fixation in R. palustris, Fer1, in which threonine at position 11 is substituted for isoleucine (Fer1 T11I ). To understand why this substitution in Fer1 enables more efficient electron transfer, we used in vivo and in vitro methods to characterize Fer1 and Fer1 T11I . Electrochemical characterization revealed both Fer1 and Fer1 T11I have similar redox transitions (–480 mV and – 550 mV), indicating the reduction potential was unaffected despite the proximity of T11 to an iron-sulfur (Fe—S) cluster of Fer1. Additionally, disruption of hydrogen bonding around an Fe—S cluster in Fer1 by substituting threonine with alanine (T11A) or valine (T11V) did not increase nitrogenase activity, indicating that disruption of hydrogen bonding does not explain the difference in activity observed for Fer1 T11I . Electron paramagnetic resonance spectroscopy studies revealed key differences in the electronic structure of Fer1 and Fer1 T11I , which indicate changes to the high spin states and/or spin-spin coupling between the Fe—S clusters of Fer1. Finally, our data implicates these electronic structure differences in facilitating electron flow and sets a foundation for further investigations to understand the connection between these properties and intermolecular electron transfer.
The convergence of topology and correlations represents a highly coveted realm in the pursuit of new quantum states of matter. Introducing electron correlations to a quantum spin Hall (QSH) insulator can lead to the emergence of a fractional topological insulator and other exotic time-reversal-symmetric topological order, not possible in quantum Hall and Chern insulator systems. Here, in this study, we report a new dual QSH insulator within the intrinsic monolayer crystal of TaIrTe 4 , arising from the interplay of its single-particle topology and density-tuned electron correlations. At charge neutrality, monolayer TaIrTe 4 demonstrates the QSH insulator, manifesting enhanced nonlocal transport and quantized helical edge conductance. After introducing electrons from charge neutrality, TaIrTe 4 shows metallic behaviour in only a small range of charge densities but quickly goes into a new insulating state, entirely unexpected on the basis of the single-particle band structure of TaIrTe 4 . This insulating state could arise from a strong electronic instability near the van Hove singularities, probably leading to a charge density wave (CDW). Remarkably, within this correlated insulating gap, we observe a resurgence of the QSH state. The observation of helical edge conduction in a CDW gap could bridge spin physics and charge orders. The discovery of a dual QSH insulator introduces a new method for creating topological flat minibands through CDW superlattices, which offer a promising platform for exploring time-reversal-symmetric fractional phases and electromagnetism.
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Temperature modulation was demonstrated as a novel way to control water partitioning during the reaction of silicate minerals with water-saturated supercritical carbon dioxide.
Time-dependent density functional theory (TDDFT) combined with range-separated hybrid (RSH) functionals and a tuned range-separation parameter γ offers a computationally economical approach for high-throughput excited- state property predictions. The γ-tuning procedure in the gas phase is well established. However, no agreement on the best γ- tuning procedure has been made when considering the solvent effect with implicit solvent models like the polarizable continuum model (PCM). To answer that question, this study created a diverse dataset with 937 molecules with experimental solutionphase UV/vis absorption spectra. Three γ-tuning methods, the gasphase γ-tuning (GPγT), the partial vertical γ-tuning (PVγT), and the strict vertical γ-tuning (SVγT), were evaluated for the ωPBEh functional over the entire dataset. Additional benchmarks are done for the optimally tuned screened range-separated hybrid combined with the PCM approach (SRSH-PCM) and the solvation-mediated tuning procedure (sol-med-OT). Our findings revealed that the optimal γ-values obtained by the PVγT and the SVγT are significantly smaller than the GPγT. This trend holds consistently across all molecules in our dataset, and we explained the origin of this phenomenon. TDDFT calculations with PVγTand SVγT-tuned γ-values and default global Fock exchange fraction achieve superior performance compared to those using GPγTtuned or default γ and slightly outperform SRSH-PCM and sol-med-OT with similar or lesser computational cost. Furthermore, we found that the smaller γ-values from SVγT captured the expected 1/(εR) asymptotic behavior in the solution phase, resulting in accurate prediction of solution-phase CT excitations, consistent with the screened asymptote behavior encoded in SRSH-PCM. These results show that SVγT is the best scheme for high-throughput UV/vis absorption spectrum calculations using the ωPBEh functional from a data-driven perspective.
We present parameter sets corresponding to new underlying event tunes for the H erwig7.3 Monte Carlo event generator. The existing H erwig tunes are in good agreement with LHC data, however, they are not typically designed for center-of-mass energies below $\sqrt{𝑠}$ = 300 GeV. The tunes presented in this study can describe midrapidity data collected at the nominal RHIC energy of $\sqrt{𝑠}$ = 200 GeV as well as higher center-of-mass energies utilized by experiments at the LHC and Tevatron. The base “New Haven” tune is developed by fitting minimum-bias simulations of proton-proton ($𝑝𝑝$) collisions to midrapidity identified hadron and jet data from the STAR experiment. The “Nashville” tune includes a separate set of parameters developed by tuning to Tevatron proton-antiproton ($𝑝\bar{𝑝}$) data at $\sqrt{𝑠}$ = 300, 900 and 1960 GeV from CDF, and LHC $𝑝𝑝$ measurements from CMS at $\sqrt{𝑠}$ =7 TeV, in addition to the STAR measurements. Both new tunes demonstrate significant improvements over the recommended default tune currently included in the latest version of H erwig for minimum bias production. As such, we advocate using these tunes for future simulation studies at midrapidity by experimental collaborations at RHIC (STAR and sPHENIX) and the LHC (ALICE, ATLAS, and CMS).
High-energy physics experiments studying neutrinos rely heavily on simulations of their interactions with atomic nuclei. Limitations in the theoretical understanding of these interactions typically necessitate ad hoc tuning of simulation model parameters to data. Traditional tuning methods for neutrino experiments have largely relied on simple algorithms for numerical optimization. While adequate for the modest goals of initial efforts, the complexity of future neutrino tuning campaigns is expected to increase substantially, and new approaches will be needed to make progress. In this paper, we examine the application of simulation-based inference (SBI) to the neutrino interaction model tuning for the first time. Using a previous tuning study performed by the MicroBooNE experiment as a test case, we find that our SBI algorithm can correctly infer the tuned parameter values when confronted with a mock data set generated according to the MicroBooNE procedure. This initial proof-of-principle illustrates a promising new technique for next-generation simulation tuning campaigns for the neutrino experimental community.
High-energy physics experiments studying neutrinos rely heavily on simulations of their interactions with atomic nuclei. Limitations in the theoretical understanding of these interactions typically necessitate ad hoc tuning of simulation model parameters to data. Traditional tuning methods for neutrino experiments have largely relied on simple algorithms for numerical optimization. While adequate for the modest goals of initial efforts, the complexity of future neutrino tuning campaigns is expected to increase substantially, and new approaches will be needed to make progress. In this paper, we examine the application of simulation-based inference (SBI) to the neutrino interaction model tuning for the first time. Using a previous tuning study performed by the MicroBooNE experiment as a test case, we find that our SBI algorithm can correctly infer the tuned parameter values when confronted with a mock data set generated according to the MicroBooNE procedure. This initial proof-of-principle illustrates a promising new technique for next-generation simulation tuning campaigns for the neutrino experimental community.
Fine-tuning existing LLMs for specialized tasks has become a very attractive alternative due to its low cost and quick development cycle. With many pre-trained LLMs available, it is an increasingly complex task to choose the correct model as the starting point or base model. In this work we discuss ChatPORT - a specialized fine-tuned LLM geared towards providing correctly translated codes from one programming model to another. We evaluate a number of base models and compare and contrast their features and characteristics that make them a viable starting point. In this paper, we focus on the OpenMP offload porting capabilities of ChatPORT. We build our training data using kernels from the Heterogeneous Computing Benchmarks (HeCBench) [12] and the OpenMP Validation and Verification suite [5] to fine-tune the base models. We then test the model using unseen kernels extracted from the HeCBench benchmark suite. Our results show that: (1) not all open LLMs geared towards HPC are aware of programming models like OpenMP, (2) although all base models benefit from fine-tuning they learn differently and produce different correctness rates, (3) depending on the memory size and compute resource available, different base models can be used for fine-tuning without significantly affecting the quality of transpiled code they generate, (4) fine-tuning improved the correctness rate of the LLM by an average of 43.2%, and (5) feedback-based training data further increased the correctness rate by an average of 6% over the LLMs tested.
Polymeric thermal switches that can reversibly tune and significantly enhance their thermal conductivities are desirable for diverse applications in electronics, aerospace, automotives, and medicine; however, they are rarely achieved. Here, we report a polymer-based thermal switch consisting of an end-linked star-shaped thermoset with two independent thermal conductivity tuning mechanisms—strain and temperature modulation—that rapidly, reversibly, and cyclically modulate thermal conductivity. The end-linked star-shaped thermoset exhibits a strain-modulated thermal conductivity enhancement up to 11.5 at a fixed temperature of 60 °C (increasing from 0.15 to 2.1 W m –1 K –1 ). Additionally, it demonstrates a temperature-modulated thermal conductivity tuning ratio up to 2.3 at a fixed stretch of 2.5 (increasing from 0.17 to 0.39 W m –1 K –1 ). When combined, these two effects collectively enable the end-linked star-shaped thermoset to achieve a thermal conductivity tuning ratio up to 14.2. Moreover, the end-linked star-shaped thermoset demonstrates reversible tuning for over 1000 cycles. The reversible two-way tuning of thermal conductivity is attributed to the synergy of aligned amorphous chains, oriented crystalline domains, and increased crystallinity by elastically deforming the end-linked star-shaped thermoset.
Accurate performance calibration of multijunction (MJ) solar cells is critical for advancing this technology toward large-scale terrestrial application, yet existing testing methods developed by photovoltaics calibration laboratories remain prohibitively complex and resource intensive for most research laboratories. Current approaches rely on expensive multisource simulators and/or intricate spectral tuning algorithms, limiting accessibility and hindering standardized comparisons of emerging MJ technologies such as perovskite-based tandems. This paper introduces a streamlined spectral irradiance adjustment method for light-emitting diode (LED)-based solar simulators, which are increasingly adopted in the photovoltaics community due to their versatility and cost-effectiveness. The method we present bins LED channels into groups based on the number of junctions of the MJ photovoltaic device under test (DUT) and their corresponding band gaps and incorporates an automated tuning algorithm that eliminates the need to adjust each channel’s irradiance individually. This tuning algorithm requires the relative spectral responsivities of both the DUT and a broadband reference cell, as well as a calibrated spectroradiometer. Measurement validation across various MJ devices, including III-V and perovskite/silicon tandems, demonstrates excellent agreement within 1% with well-established xenon-tungsten multisource simulators and associated tuning algorithms. By enabling precise spectral tuning with readily available equipment and a simple tuning algorithm, our approach bridges the measurement accuracy gap between research laboratories and accredited testing facilities, fostering more reliable device comparisons and accelerating the translation of MJ technologies into real-world energy systems.
The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens doors to a wider chemical audience, as field-specific featurization techniques can be omitted. In this work, we explore the potential and limitations of this approach. We studied the performance of fine-tuning three open-source LLMs (GPT-J-6B, Llama-3.1-8B, and Mistral-7B) for a range of different chemical questions. We benchmark their performances against “traditional” machine learning models and find that, in most cases, the fine-tuning approach is superior for a simple classification problem. Depending on the size of the dataset and the type of questions, we also successfully address more sophisticated problems. The most important conclusions of this work are that, for all datasets considered, their conversion into an LLM fine-tuning training set is straightforward and that fine-tuning with even relatively small datasets leads to predictive models. These results suggest that the systematic use of LLMs to guide experiments and simulations will be a powerful technique in any research study, significantly reducing unnecessary experiments or computations.
Accuracy-optimized convolutional neural networks (CNNs) have emerged as highly effective models at predicting neural responses in brain areas along the primate ventral stream, but it is largely unknown whether they effectively model neurons in the complementary primate dorsal stream. We explored how well CNNs model the optic flow tuning properties of neurons in dorsal area MSTd and we compared our results with the Non-Negative Matrix Factorization (NNMF) model, which successfully models many tuning properties of MSTd neurons. To better understand the role of computational properties in the NNMF model that give rise to optic flow tuning that resembles that of MSTd neurons, we created additional CNN model variants that implement key NNMF constraints – non-negative weights and sparse coding of optic flow. While the CNNs and NNMF models both accurately estimate the observer's self-motion from purely translational or rotational optic flow, NNMF and the CNNs with nonnegative weights yield substantially less accurate estimates than the other CNNs when tested on more complex optic flow that combines observer translation and rotation. Despite its poor accuracy, NNMF gives rise to tuning properties that align more closely with those observed in primate MSTd than any of the accuracy-optimized CNNs. This work offers a step toward a deeper understanding of the computational properties and constraints that describe the optic flow tuning of primate area MSTd.
We demonstrate a novel broad-frequency tuning mechanism for superconducting microwave cavities designed for dark matter searches. Using a Nb$_3$Sn-coated cigar-shaped cavity operating at approximately 9 GHz, we achieve continuous frequency tuning exceeding 1 GHz by mechanically separating the two cavity halves: a "tuning-by-opening" technique. Finite-element method simulations predict that radiative losses do not degrade the quality factor even for large openings, as a closed cavity with an intrinsic quality factor of $10^7$ maintains this value for apertures up to 9 mm, corresponding to a tuning range from 9.0 to 7.5 GHz. Experimental validation using both copper ring spacers and a continuous sliding mechanism confirms $Q_0$ values exceeding the dark matter quality factor across the entire explored frequency range, despite mechanical imperfections and film non-uniformities. This tuning approach avoids inserting elements into the resonant volume, making it particularly suitable for high-Q superconducting cavities in axion haloscope experiments and readily applicable to REBCO-based implementations capable of operating in multi-tesla magnetic fields.