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The precise characterization and mitigation of systematic effects is one of the biggest roadblocks impeding the detection of the fluctuations of cosmological 21cm signals. Missing data in radio cosmological experiments, often due to radio frequency interference (RFI), poses a particular challenge to power spectrum analysis as it could lead to the ringing of bright foreground modes in Fourier space, heavily contaminating the cosmological signals. Here we show that the problem of missing data becomes even more arduous in the presence of systematic effects. Using a realistic numerical simulation, we demonstrate that partially flagged data combined with systematic effects can introduce significant foreground ringing. We show that such an effect can be mitigated through inpainting the missing data. We present a rigorous statistical framework that incorporates the process of inpainting missing data into a quadratic estimator of the 21cm power spectrum. Under this framework, the uncertainties associated with our inpainting method and its impact on power spectrum statistics can be understood. These results are applied to the latest Phase II observations taken by the Hydrogen Epoch of Reionization Array, forming a crucial component in power spectrum analyses as we move toward detecting 21cm signals in the ever more noisy RFI environment., Comment: 25 pages, 11 figures, submitted to ApJ

Coherent quantum emitters are a central resource for advanced quantum technologies. Hexagonal boron nitride (hBN) hosts a range of quantum emitters that can be engineered using techniques such as high-temperature annealing, optical doping, and irradiation with electrons or ions. Here, we demonstrate that such processes can degrade the coherence, and hence the functionality, of quantum emitters in hBN. Specifically, we show that hBN annealing and doping methods that are used routinely in hBN nanofabrication protocols give rise to decoherence of B-center quantum emitters. The decoherence is characterized in detail, and attributed to defects that act as charge traps which fluctuate electrostatically during SPE excitation and induce spectral diffusion. The decoherence is minimal when the emitters are engineered by electron beam irradiation of as-grown, pristine flakes of hBN, where B-center linewidths approach the lifetime limit needed for quantum applications involving interference and entanglement. Our work highlights the critical importance of crystal lattice quality to achieving coherent quantum emitters in hBN, despite the common perception that the hBN lattice and hBN SPEs are highly-stable and resilient against chemical and thermal degradation. It underscores the need for nanofabrication techniques that are minimally invasive and avoid crystal damage when engineering hBN SPEs and devices for quantum-coherent technologies.

We train a generative diffusion model (DM) to simulate ultra-relativistic heavy-ion collisions from end to end. The model takes initial entropy density profiles as input and produces two-dimensional final particle spectra, successfully reproducing integrated and differential observables. It also captures higher-order fluctuations and correlations. These findings suggest that the generative model has successfully learned the complex relationship between initial conditions and final particle spectra for various shear viscosities, as well as the fluctuations introduced during initial entropy production and hadronization stages, providing an efficient framework for resource-intensive physical goals. The code and trained model are available at https://huggingface.co/Jing-An/DiffHIC/tree/main.

Hexagonal boron nitride (hBN) has emerged as a compelling platform for both classical and quantum technologies. In particular, the past decade has witnessed a surge of novel ideas and developments, which may be overwhelming for newcomers to the field. This review provides an overview of the fundamental concepts and key applications of hBN, including quantum sensing, quantum key distribution, quantum computing, and quantum memory. Additionally, we highlight critical experimental and theoretical advances that have expanded the capabilities of hBN, in a cohesive and accessible manner. The objective is to equip readers with a comprehensive understanding of the diverse applications of hBN, and provide insights into ongoing research efforts., Comment: 22 pages, 6 figures, 1 table

We provide a category theoretical framework capturing two approaches to graph-based models of chemistry: formal reactions and disconnection rules. We model a translation from the latter to the former as a functor, which is faithful, and full up to isomorphism. This allows us to state, as our main result, that the disconnection rules are sound, complete and universal with respect to the reactions. Concretely, this means that every reaction can be decomposed into a sequence of disconnection rules in an essentially unique way. This provides a uniform way to store reaction data, and gives an algorithmic interface between (forward) reaction prediction and (backward) reaction search or retrosynthesis., Comment: 22 pages, 6 figures. Extended version of an article to be published in the proceedings of ICTAC 2024

A first principles machine learning model has been developed aimed at studying the formation of calcium carbonate from aqueous solution using molecular dynamics simulations. The model, dubbed SCAN-ML, reproduces accurately the potential energy surface derived from ab initio density-functional theory within the SCAN approximation for the exchange and correlation functional. A broad range of properties have been calculated relevant to ions in solution, solid phases, and the calcite/water interface. Careful comparison with results from experiments and semi-empirical force fields shows that SCAN-ML provides an excellent description of this system, surpassing state-of-the-art force fields for many properties, while providing a benchmark for many quantities that are currently beyond the reach of direct ab initio molecular dynamics. A key feature of SCAN-ML is its ability to capture chemical reactions, including the carbonate to bicarbonate transformation. Our model thus paves the way for the study of reactive crystallization pathways in calcium carbonate, which are currently poorly understood., Comment: 13 pages, 6 figures

We present phenomenological findings on charm quark transport while including its energy loss in both pre-equilibrium and hydrodynamic stages of the evolution. We employed the MARTINI event generator for the production and evolution of heavy quarks in the relativistic heavy-ion collisions. The sensitivity of the heavy meson nuclear modification factor and flow coefficient to the early stage of heavy-ion collisions and bulk medium evolution is analyzed for Pb+Pb collisions at 5.02 TeV. Our study provides insights into the interaction strength of charm quarks during the early phase and within the quark-gluon plasma., Comment: 4 pages, 3 figures, Conference proceedings for 21st International Conference on Strangeness in Quark Matter (SQM2024), Strasbourg (France), June 3-7, 2024

Real-life applications of deferred-acceptance (DA) matching algorithms sometimes exhibit errors or changes to the matching inputs that are discovered only after the algorithm has been run and the results are announced to participants. Mitigating the effects of these errors is a different problem than the original match since the decision makers are often constrained by the offers they already sent out. We propose models for this new problem, along with mitigation strategies to go with these models. We explore three different error scenarios: resource reduction, additive errors, and subtractive errors. For each error type, we compute the expected number of students directly harmed, or helped, by the error, the number indirectly harmed or helped, and the number of students with justified envy due to the errors. Error mitigation strategies need to be selected based on the goals of the administrator, which include restoring stability, avoiding direct harm to any participant, and focusing the extra burden on the schools that made the error. We provide empirical simulations of the errors and the mitigation strategies.

Because standardized tests in science are not given to PreK-3 students in Ohio, this report examined the longitudinal effects of learning from a teacher who had participated in the NURTURES professional development program. Specifically, it looked at the effects on students' mathematics and reading learning in grades 2-5 and science learning in 5th grade in 2017. Students who were in 5th grade at that time could have had a NURTURES-trained teacher at any time between kindergarten and 3rd grade. Thus, the study followed students up to 5 years after having a NURTURES teacher. The sample included the population of students enrolled in the 41 elementary schools in the Toledo Public School District. Students who never learned from a teacher who participated in NURTURES served as the control group. The data came from the 2017 administration of the Ohio Measure of Academic Progress (MAP) (NWEA, 2019) for mathematics and reading and the Ohio Achievement Test in Science for science (Ohio Achievement Assessment, 2015). The total number of students from these schools who took the May 2017 reading MAP was 6759 and the total number who took mathematics was 6703. The number of those students who had at least one NURTURES teacher was 2801 (41.4%) for reading and 2707 (41.6%) for mathematics. Analysis of the reading scores showed 2.14 advantage points for NURTURES students as compared to the average non-intervention student to an annual growth rate of 7.02 units (p < .001). The treatment effect size (Hedges' g) was 0.12. For mathematics there were 1.55 advantage points to an annual growth rate estimated to be 8.17 units (p < .001) as compared to the average non-intervention student. The treatment effect size (Hedges' g) was 0.09. Analysis of the 5th grade Ohio Achievement Science Subtest showed that students associated with at least one NURTURES-trained teacher was modeled to have a 5.86 advantage points as compared to the average non-intervention student. The treatment effect size (Hedges' g) was 0.08, which is to be interpreted as a treatment group having, on average, 0.08 higher scores in standard deviation units as compared to the scores of the control cohort. When compared with our earlier evaluation report (2016; revised in 2018), we see that students who had a NURTURES-trained teacher, on average, continued to show greater gains compared to students who did not. In addition, the achievement gaps between non-minority and minority students in reading and mathematics were reduced when the minority students had a NURTURES-trained teacher and the non-minority students did not. In science, the impact of the intervention roughly compensated for the attainment gap between boys and girls and partially ameliorated the gap between minority and non-minority children's scores associated with these demographic factors. [Published May 6, 2020 with minor revisions based in WWC inquiries published January 5, 2024.]

The SuperKEKB/Belle II experiment aims to collect high-statistics data of B meson pairs to explore new physics beyond the Standard Model (SM). SuperKEKB, an upgraded version of the KEKB accelerator, has achieved a world-record luminosity of $4.71 \times 10^{34} \, \mathrm{cm^{-2}s^{-1}}$ in 2022 but continues to strive for higher luminosities. One of the major obstacles is Sudden Beam Loss (SBL) events, which cause substantial beam losses and damage to the Belle~II detector. To find a hint for addressing SBL challenges, advanced beam diagnostic systems and enhanced beam abort systems have been developed. The diagnostic system aims to accurately pinpoint the start of beam losses, while the upgraded abort system quickly disposes of anomalous beams to minimize damage. This paper details the development and implementation of these systems, including high-speed loss monitors, time synchronization with the White Rabbit system, and data acquisition systems. Efforts to understand the mechanisms of SBL events, using acoustic sensors to detect discharges, are also discussed. These measures aim to improve the operational stability and luminosity of SuperKEKB, contributing to the experiment's success., Comment: 17 pages, 25 figures

The JETSCAPE Collaboration reports a new determination of the jet transport parameter $\hat{q}$ in the Quark-Gluon Plasma (QGP) using Bayesian Inference, incorporating all available inclusive hadron and jet yield suppression data measured in heavy-ion collisions at RHIC and the LHC. This multi-observable analysis extends the previously published JETSCAPE Bayesian Inference determination of $\hat{q}$, which was based solely on a selection of inclusive hadron suppression data. JETSCAPE is a modular framework incorporating detailed dynamical models of QGP formation and evolution, and jet propagation and interaction in the QGP. Virtuality-dependent partonic energy loss in the QGP is modeled as a thermalized weakly-coupled plasma, with parameters determined from Bayesian calibration using soft-sector observables. This Bayesian calibration of $\hat{q}$ utilizes Active Learning, a machine--learning approach, for efficient exploitation of computing resources. The experimental data included in this analysis span a broad range in collision energy and centrality, and in transverse momentum. In order to explore the systematic dependence of the extracted parameter posterior distributions, several different calibrations are reported, based on combined jet and hadron data; on jet or hadron data separately; and on restricted kinematic or centrality ranges of the jet and hadron data. Tension is observed in comparison of these variations, providing new insights into the physics of jet transport in the QGP and its theoretical formulation., Comment: 20 pages, 10 figures, 2 tables, submitted to PRC; updated acknowledgements

The 21 cm transition from neutral Hydrogen promises to be the best observational probe of the Epoch of Reionisation (EoR). This has led to the construction of low-frequency radio interferometric arrays, such as the Hydrogen Epoch of Reionization Array (HERA), aimed at systematically mapping this emission for the first time. Precision calibration, however, is a requirement in 21 cm radio observations. Due to the spatial compactness of HERA, the array is prone to the effects of mutual coupling, which inevitably lead to non-smooth calibration errors that contaminate the data. When unsmooth gains are used in calibration, intrinsically spectrally-smooth foreground emission begins to contaminate the data in a way that can prohibit a clean detection of the cosmological EoR signal. In this paper, we show that the effects of mutual coupling on calibration quality can be reduced by applying custom time-domain filters to the data prior to calibration. We find that more robust calibration solutions are derived when filtering in this way, which reduces the observed foreground power leakage. Specifically, we find a reduction of foreground power leakage by 2 orders of magnitude at k=0.5.

With the use of MARTINI, a model which considers evolving QCD jets against a fluid dynamical background, it is shown that the introduction of formation time to the parton shower after the initial hard scattering is essential for a simultaneous description of charged hadron and jet $R_{AA}$. This inclusion also improves jet shape ratio at small angle and jet fragmentation function ratios of leading charged hadrons. The MARTINI framework is then aimed at a study of the leading order, next-to-leading-order, and non perturbative collision kernels. Sizable differences in the modification of jet substructure observables, i.e., jet shape and fragmentation functions are observed. Such differences are caused by the difference in the radiation rates of relatively soft gluons that survive in the evolution in medium.

A new framework, called x-scape, for the combined study of both hard and soft transverse momentum sectors in high energy proton-proton ($p$-$p$) and proton-nucleus ($p$-$A$) collisions is set up. A dynamical initial state is set up using the 3d-Glauber model with transverse locations of hotspots within each incoming nucleon. A hard scattering that emanates from two colliding hotspots is carried out using the Pythia generator. Initial state radiation from the incoming hard partons is carried out in a new module called I-matter, which includes the longitudinal location of initial splits. The energy-momentum of both the initial hard partons and their associated beam remnants is removed from the hot spots, depleting the energy-momentum available for the formation of the bulk medium. Outgoing showers are simulated using the matter generator, and results are presented for both cases, allowing for and not allowing for energy loss. First comparisons between this hard-soft model and single inclusive hadron and jet data from $p$-$p$ and minimum bias $p$-$Pb$ collisions are presented. Single hadron spectra in $p$-$p$ are used to carry out a limited (in number of parameters) Bayesian calibration of the model. Fair comparisons with data are indicative of the utility of this new framework. Theoretical studies of the correlation between jet $p_T$ and event activity at mid and forward rapidity are carried out., Comment: 18 pages, 15 figures

With the alarming rise in active shooter incidents (ASIs) in the United States, enhancing public safety through building design has become a pressing need. This study proposes a reinforcement learning-based simulation approach addressing gaps in existing research that has neglected the dynamic behaviours of shooters. We developed an autonomous agent to simulate an active shooter within a realistic office environment, aiming to offer insights into the interactions between building design parameters and ASI outcomes. A case study is conducted to quantitatively investigate the impact of building exit numbers (total count of accessible exits) and configuration (arrangement of which exits are available or not) on evacuation and harm rates. Findings demonstrate that greater exit availability significantly improves evacuation outcomes and reduces harm. Exits nearer to the shooter's initial position hold greater importance for accessibility than those farther away. By encompassing dynamic shooter behaviours, this study offers preliminary insights into effective building safety design against evolving threats.

A systematic study of intermediate invariant mass dilepton production in Pb+Pb collisions at $\sqrt{s_{NN}} = 5.02$ TeV is performed, using next-to-leading-order (NLO) thermal QCD dilepton emission rates with a multistage dynamical approach which includes event-by-event IP-Glasma initial conditions, relativistic viscous fluid dynamics, and a hadronic afterburner. Considering dilepton yield and anisotropic flow, special attention is paid to the out-of-equilibrium aspects, both thermal and chemical, and to the contribution of the Drell-Yan process. The relative contribution of each of those different channels to dilepton observables is calculated and discussed., Comment: 13 pages, 10 figures

Interferometric experiments designed to detect the highly redshifted 21-cm signal from neutral hydrogen are producing increasingly stringent constraints on the 21-cm power spectrum, but some k-modes remain systematics-dominated. Mutual coupling is a major systematic that must be overcome in order to detect the 21-cm signal, and simulations that reproduce effects seen in the data can guide strategies for mitigating mutual coupling. In this paper, we analyse 12 nights of data from the Hydrogen Epoch of Reionization Array and compare the data against simulations that include a computationally efficient and physically motivated semi-analytic treatment of mutual coupling. We find that simulated coupling features qualitatively agree with coupling features in the data; however, coupling features in the data are brighter than the simulated features, indicating the presence of additional coupling mechanisms not captured by our model. We explore the use of fringe-rate filters as mutual coupling mitigation tools and use our simulations to investigate the effects of mutual coupling on a simulated cosmological 21-cm power spectrum in a "worst case" scenario where the foregrounds are particularly bright. We find that mutual coupling contaminates a large portion of the "EoR Window", and the contamination is several orders-of-magnitude larger than our simulated cosmic signal across a wide range of cosmological Fourier modes. While our fiducial fringe-rate filtering strategy reduces mutual coupling by roughly a factor of 100 in power, a non-negligible amount of coupling cannot be excised with fringe-rate filters, so more sophisticated mitigation strategies are required., Comment: 19 pages, 12 figures, submitted to MNRAS

Design inspiration is crucial for establishing the direction of a design as well as evoking feelings and conveying meanings during the conceptual design process. Many practice designers use text-based searches on platforms like Pinterest to gather image ideas, followed by sketching on paper or using digital tools to develop concepts. Emerging generative AI techniques, such as diffusion models, offer a promising avenue to streamline these processes by swiftly generating design concepts based on text and image inspiration inputs, subsequently using the AI generated design concepts as fresh sources of inspiration for further concept development. However, applying these generative AI techniques directly within a design context has challenges. Firstly, generative AI tools may exhibit a bias towards particular styles, resulting in a lack of diversity of design outputs. Secondly, these tools may struggle to grasp the nuanced meanings of texts or images in a design context. Lastly, the lack of integration with established design processes within design teams can result in fragmented use scenarios. Focusing on these challenges, we conducted workshops, surveys, and data augmentation involving teams of experienced automotive designers to investigate their current practices in generating concepts inspired by texts and images, as well as their preferred interaction modes for generative AI systems to support the concept generation workflow. Finally, we developed a novel generative AI system based on diffusion models to assist conceptual automotive design.

The rapid growth in the size of deep learning models strains the capabilities of traditional dense computation paradigms. Leveraging sparse computation has become increasingly popular for training and deploying large-scale models, but existing deep learning frameworks lack extensive support for sparse operations. To bridge this gap, we introduce Scorch, a library that seamlessly integrates efficient sparse tensor computation into the PyTorch ecosystem, with an initial focus on inference workloads on CPUs. Scorch provides a flexible and intuitive interface for sparse tensors, supporting diverse sparse data structures. Scorch introduces a compiler stack that automates key optimizations, including automatic loop ordering, tiling, and format inference. Combined with a runtime that adapts its execution to both dense and sparse data, Scorch delivers substantial speedups over hand-written PyTorch Sparse (torch.sparse) operations without sacrificing usability. More importantly, Scorch enables efficient computation of complex sparse operations that lack hand-optimized PyTorch implementations. This flexibility is crucial for exploring novel sparse architectures. We demonstrate Scorch's ease of use and performance gains on diverse deep learning models across multiple domains. With only minimal code changes, Scorch achieves 1.05-5.78x speedups over PyTorch Sparse on end-to-end tasks. Scorch's seamless integration and performance gains make it a valuable addition to the PyTorch ecosystem. We believe Scorch will enable wider exploration of sparsity as a tool for scaling deep learning and inform the development of other sparse libraries., Comment: 25 pages, 8 figures

In this work, we perform a model-to-data comparison for U+U and Au+Au collisions performed at RHIC at $\sqrt{s_{\rm NN}}$ = $193$ GeV and $200$ GeV, using a multistage framework. Model calculations for various configurations of $^{238}$U and $^{197}$Au are used to compare with measurements of $\rho(v_n\{2\}^2,\left\langle p_T \right\rangle)$, the elliptic-flow-momentum correlator, for the first time. It is found that momentum-flow correlations measured in high-energy nuclear collisions, combined with the flow harmonics themselves, are excellent probes of nuclear deformation and of nuclear structure in general., Comment: 7 Pages, 5 Figures, 1 Table

Hexagonal boron nitride (hBN) holds promise as a solid state, van der Waals host of single photon emitters for on-chip quantum photonics. The B-centre defect emitting at 436 nm is particularly compelling as it can be generated by electron beam irradiation. However, the emitter generation mechanism is unknown, the robustness of the method is variable, and it has only been applied successfully to thick flakes of hBN (>> 10 nm). Here, we use in-situ time-resolved cathodoluminescence (CL) spectroscopy to investigate the kinetics of B-centre generation. We show that the generation of B-centres is accompanied by quenching of a carbon-related emission at ~305 nm and that both processes are rate-limited by electromigration of defects in the hBN lattice. We identify problems that limit the efficacy and reproducibility of the emitter generation method, and solve them using a combination of optimized electron beam parameters and hBN pre- and post-processing treatments. We achieve B-centre quantum emitters in hBN flakes as thin as 8 nm, elucidate the mechanisms responsible for electron beam restructuring of quantum emitters in hBN, and gain insights towards identification of the atomic structure of the B-centre quantum emitter.

In this work, we first derive the evolution equation for the general energy-momentum moment of $\delta f$, where $\delta f$ is the deviation from the local equilibrium phase space density. We then introduce a relativistic extension of regularized hydrodynamics developed in the non-relativistic case by Struchtrup and Torrilhon that combines the method of moments and Chapman-Enskog expansion. Hydrodynamic equations up to the third-order in gradients are then systematially derived within the context of a single species system and the relaxation time approximation. This is followed by a series of linear stability and causality analysis. For the system of massless particles without any charge conservation, the third-order hydrodynamics is shown to be linearly stable and causal., Comment: 37 pages with extensive appendices. 8 figures. Version accepted by Physical Review C

We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve excellent performance on language. It has a fixed-sized state, which reduces memory use and enables efficient inference on long sequences. We provide two sizes of models, containing 2B and 9B parameters, and provide pre-trained and instruction tuned variants for both. Our models achieve comparable performance to similarly-sized Gemma baselines despite being trained on fewer tokens.

A successful negotiation requires a range of capabilities, including comprehension of the conversation context, Theory-of-Mind (ToM) skills to infer the partner's motives, strategic reasoning, and effective communication, making it challenging for automated systems. Despite the remarkable performance of LLMs in various NLP tasks, there is no systematic evaluation of their capabilities in negotiation. Such an evaluation is critical for advancing AI negotiation agents and negotiation research, ranging from designing dialogue systems to providing pedagogical feedback and scaling up data collection practices. This work aims to systematically analyze the multifaceted capabilities of LLMs across diverse dialogue scenarios throughout the stages of a typical negotiation interaction. Our analysis highlights GPT-4's superior performance in many tasks while identifying specific challenges, such as making subjective assessments and generating contextually appropriate, strategically advantageous responses., Comment: Accepted to Findings of EMNLP 2024

Learning causal representations from observational and interventional data in the absence of known ground-truth graph structures necessitates implicit latent causal representation learning. Implicit learning of causal mechanisms typically involves two categories of interventional data: hard and soft interventions. In real-world scenarios, soft interventions are often more realistic than hard interventions, as the latter require fully controlled environments. Unlike hard interventions, which directly force changes in a causal variable, soft interventions exert influence indirectly by affecting the causal mechanism. However, the subtlety of soft interventions impose several challenges for learning causal models. One challenge is that soft intervention's effects are ambiguous, since parental relations remain intact. In this paper, we tackle the challenges of learning causal models using soft interventions while retaining implicit modelling. We propose ICLR-SM, which models the effects of soft interventions by employing a causal mechanism switch variable designed to toggle between different causal mechanisms. In our experiments, we consistently observe improved learning of identifiable, causal representations, compared to baseline approaches.

Radio interferometers targeting the 21cm brightness temperature fluctuations at high redshift are subject to systematic effects that operate over a range of different timescales. These can be isolated by designing appropriate Fourier filters that operate in fringe-rate (FR) space, the Fourier pair of local sidereal time (LST). Applications of FR filtering include separating effects that are correlated with the rotating sky vs. those relative to the ground, down-weighting emission in the primary beam sidelobes, and suppressing noise. FR filtering causes the noise contributions to the visibility data to become correlated in time however, making interpretation of subsequent averaging and error estimation steps more subtle. In this paper, we describe fringe rate filters that are implemented using discrete prolate spheroidal sequences, and designed for two different purposes -- beam sidelobe/horizon suppression (the `mainlobe' filter), and ground-locked systematics removal (the `notch' filter). We apply these to simulated data, and study how their properties affect visibilities and power spectra generated from the simulations. Included is an introduction to fringe-rate filtering and a demonstration of fringe-rate filters applied to simple situations to aid understanding., Comment: 21 pages, 18 figures, submitted to Monthly Notices of the Royal Astronomical Society

Prompt photons are created in the early stages of heavy ion collisions and traverse the QGP medium without any interaction. Therefore, photon-triggered jets can be used to study the jet quenching in the QGP medium. In this work, photon-triggered jets are studied through different jet and jet substructure observables for different collision systems and energies using the JETSCAPE framework. Since the multistage evolution used in the JETSCAPE framework is adequate to describe a wide range of experimental observables simultaneously using the same parameter tune, we use the same parameters tuned for jet and leading hadron studies. The same isolation criteria used in the experimental analysis are used to identify prompt photons for better comparison. For the first time, high-accuracy JETSCAPE results are compared with multi-energy LHC and RHIC measurements to better understand the deviations observed in prior studies. This study highlights the importance of multistage evolution for the simultaneous description of experimental observables through different collision systems and energies using a single parameter tune., Comment: 4 pages, 5 figures. Proceedings of Quark Matter 2023 - XXXth International Conference on Ultra-relativistic Nucleus-Nucleus Collisions, Houston, TX, 3-9 September 2023

In this paper we deal with a scale of reproducing kernel Hilbert spaces $H^{(n)}_2$, $n\ge 0$, which are linear subspaces of the classical Hilbertian Hardy space on the right-hand half-plane $\mathbb{C}^+$. They are obtained as ranges of the Laplace transform in extended versions of the Paley-Wiener theorem which involve absolutely continuous functions of higher degree. An explicit integral formula is given for the reproducing kernel $K_{z,n}$ of $H^{(n)}_2$, from which we can find the estimate $\Vert K_{z,n}\Vert\sim\vert z\vert^{-1/2}$ for $z\in\mathbb{C}^+$. Then composition operators $C_\varphi :H_2^{(n)} \to H_2^{(n)}$, $C_\varphi f=f\circ \varphi $, on these spaces are discussed, giving some necessary and some sufficient conditions for analytic maps $\varphi: \mathbb{C}^+\to \mathbb{C}^+$ to induce bounded composition operators., Comment: 27 pp

We study reproducing kernel Hilbert spaces introduced as ranges of generalized Ces\`aro-Hardy operators, in one real variable and in one complex variable. Such spaces can be seen as formed by absolutely continuous functions on the positive half-line (or paths of infinite length) of fractional order, in the real case. A theorem of Paley-Wiener type is given which connects the real setting with the complex one. These spaces are related with fractional operations in the context of integrated Brownian processes. We give estimates of the norms of the corresponding reproducing kernels., Comment: 32 pp

This paper presents the design and deployment of the Hydrogen Epoch of Reionization Array (HERA) phase II system. HERA is designed as a staged experiment targeting 21 cm emission measurements of the Epoch of Reionization. First results from the phase I array are published as of early 2022, and deployment of the phase II system is nearing completion. We describe the design of the phase II system and discuss progress on commissioning and future upgrades. As HERA is a designated Square Kilometer Array (SKA) pathfinder instrument, we also show a number of "case studies" that investigate systematics seen while commissioning the phase II system, which may be of use in the design and operation of future arrays. Common pathologies are likely to manifest in similar ways across instruments, and many of these sources of contamination can be mitigated once the source is identified.

The JETSCAPE Collaboration reports the first multi-messenger study of the QGP jet transport parameter $\hat{q}$ using Bayesian inference, incorporating all available hadron and jet inclusive yield and jet substructure data from RHIC and the LHC. The theoretical model utilizes virtuality-dependent in-medium partonic energy loss coupled to a detailed dynamical model of QGP evolution. Tension is observed when constraining $\hat{q}$ for different kinematic cuts of the inclusive hadron data. The addition of substructure data is shown to improve the constraint on $\hat{q}$, without inducing tension with the constraint due to inclusive observables. These studies provide new insight into the mechanisms of jet interactions in matter, and point to next steps in the field for comprehensive understanding of jet quenching as a probe of the QGP., Comment: 4 pages, 2 figures. Proceedings of Quark Matter 2023 - XXXth International Conference on Ultra-relativistic Nucleus-Nucleus Collisions, Houston, TX, 3-9 September 2023

Considerable information about the early-stage dynamics of heavy-ion collisions is encoded in the rapidity dependence of measurements. To leverage the large amount of experimental data, we perform a systematic analysis using three-dimensional hydrodynamic simulations of multiple collision systems -- large and small, symmetric and asymmetric. Specifically, we perform fully 3D multi-stage hydrodynamic simulations initialized by a parameterized model for rapidity-dependent energy deposition, which we calibrate on the hadron multiplicity and anisotropic flow coefficients. We utilize Bayesian inference to constrain properties of the early- and late- time dynamics of the system, and highlight the impact of enforcing global energy conservation in our 3D model.

We study the transport dynamics of charm quarks within an expanding quark-gluon plasma for Pb+Pb collisions at 2.76 TeV. The analysis incorporates the hydrodynamical approach-MUSIC with fluctuating IP-Glasma initial state and Bayesian-quantified viscous coefficients. We study the interaction strength of charm quarks in the medium, including elastic collisional processes with medium constituents, gluon emission processes, and the impact of non-perturbative interactions on heavy quark transport. Further, we analyze the dynamics of heavy flavors using a hybrid framework that incorporates the MARTINI event generator, with PYTHIA8.1 for the initial production of heavy quarks, and Langevin dynamics to describe the evolution of heavy quarks., Comment: 4 pages, 2 figures, Contribution to the proceedings of the "Hot Quarks 2022 (HQ2022)"

Minijets traversing through the QGP formed in heavy-ion collisions deposit significant amount of energy in the bulk medium. They also create gradients in temperatures which alter the flow profile and enhance entropy production. We study the effects of minijets in a simultaneous hydro + jet framework and find that inclusion of minijets requires recalibration of transport properties extracted from the model-to-data comparisons., Comment: 4 pages, 2 figures, Proceedings for Quark Matter 2023