The PHENIX experiment at the Relativistic Heavy Ion Collider (RHIC) has measured electrons from heavy flavor (charm and bottom) decays for 0.3 < p_T < 9 GeV/c at midrapidity (|y| < 0.35) in Au+Au collisions at sqrt(s_NN) = 200 GeV. The nuclear modification factor R_AA relative to p+p collisions shows a strong suppression in central Au+Au collisions, indicating substantial energy loss of heavy quarks in the medium produced at RHIC. A large azimuthal anisotropy, v_2, with respect to the reaction plane is observed for 0.5 < p_T < 5 GeV/c indicating non-zero heavy flavor elliptic flow. Both R_AA and v_2 show a p_T dependence different from those of neutral pions. A comparison to transport models which simultaneously describe R_AA(p_T) and v_2(p_T) suggests that the viscosity to entropy density ratio is close to the conjectured quantum lower bound, i.e., near a perfect fluid.

The computing education community has a rich history of pedagogical innovation designed to support students in introductory courses, and to support teachers in facilitating student learning. Very recent advances in artificial intelligence have resulted in code generation models that can produce source code from natural language problem descriptions -- with impressive accuracy in many cases. The wide availability of these models and their ease of use has raised concerns about potential impacts on many aspects of society, including the future of computing education. In this paper, we discuss the challenges and opportunities such models present to computing educators, with a focus on introductory programming classrooms. We summarize the results of two recent articles, the first evaluating the performance of code generation models on typical introductory-level programming problems, and the second exploring the quality and novelty of learning resources generated by these models. We consider likely impacts of such models upon pedagogical practice in the context of the most recent advances at the time of writing.

We have measured direct photons for p_T&lt;5~GeV/$c$ in minimum bias and 0\%--40\% most central events at midrapidity for Cu$+$Cu collisions at $\sqrt{s_{_{NN}}}=200$ GeV. The $e^{+}e^{-}$ contribution from quasi-real direct virtual photons has been determined as an excess over the known hadronic contributions in the $e^{+}e^{-}$ mass distribution. A clear enhancement of photons over the binary scaled $p$$+$$p$ fit is observed for p_T&lt;4 GeV/$c$ in Cu$+$Cu data. The $p_T$ spectra are consistent with the Au$+$Au data covering a similar number of participants. The inverse slopes of the exponential fits to the excess after subtraction of the $p$$+$$p$ baseline are 285$\pm$53(stat)$\pm$57(syst)~MeV/$c$ and 333$\pm$72(stat)$\pm$45(syst)~MeV/$c$ for minimum bias and 0\%--40\% most central events, respectively. The rapidity density, $dN/dy$, of photons demonstrates the same power law as a function of $dN_{\rm ch}/d\eta$ observed in Au$+$Au at the same collision energy.

The fundamental building blocks of the proton, quarks and gluons, have been known for decades. However, we still have an incomplete theoretical and experimental understanding of how these particles and their dynamics give rise to the quantum bound state of the proton and its physical properties, such as for example its spin. The two up and the single down quarks that comprise the proton in the simplest picture account only for a few percent of the proton mass, the bulk of which is in the form of quark kinetic and potential energy and gluon energy from the strong force. An essential feature of this force, as described by quantum chromodynamics, is its ability to create matter-antimatter quark pairs inside the proton that exist only for a very short time. Their fleeting existence makes the antimatter quarks within protons difficult to study, but their existence is discernible in reactions where a matter-antimatter quark pair annihilates. In this picture of quark-antiquark creation by the strong force, the probability distributions as a function of momentum for the presence of up and down antimatter quarks should be nearly identical, since their masses are quite similar and small compared to the mass of the proton. In the present manuscript, we show evidence from muon pair production measurements that these distributions are significantly different, with more abundant down antimatter quarks than up antimatter quarks over a wide range of momentum. These results revive interest in several proposed mechanisms as the origin of this antimatter asymmetry in the proton that had been disfavored by the previous results and point to the future measurements that can distinguish between these mechanisms.

Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction are often in English, or more subtle, such as being afraid to ask for help in a classroom full of native English speakers. However, these barriers are frustrating because many NNES students know more about programming than they can articulate in English. Advances in generative AI (GenAI) have the potential to break down these barriers because state of the art models can support interactions in multiple languages. Moreover, recent work has shown that GenAI can be highly accurate at code generation and explanation. In this paper, we provide the first exploration of NNES students prompting in their native languages (Arabic, Chinese, and Portuguese) to generate code to solve programming problems. Our results show that students are able to successfully use their native language to solve programming problems, but not without some difficulty specifying programming terminology and concepts. We discuss the challenges they faced, the implications for practice in the short term, and how this might transform computing education globally in the long term.

We report a new measurement of $D^0$-meson production at mid-rapidity ($|y|$\,&lt;\,1) in Au+Au collisions at ${\sqrt{s_{\rm NN}} = \rm{200\,GeV}}$ utilizing the Heavy Flavor Tracker, a high resolution silicon detector at the STAR experiment. Invariant yields of $D^0$-mesons with transverse momentum $p_{T}$ $\lesssim 9$\,GeV/$c$ are reported in various centrality bins (0--10\%, 10--20\%, 20--40\%, 40--60\% and 60--80\%). Blast-Wave thermal models are used to fit the $D^0$-meson $p_{T}$ spectra to study $D^0$ hadron kinetic freeze-out properties. The average radial flow velocity extracted from the fit is considerably smaller than that of light hadrons ($\pi,K$ and $p$), but comparable to that of hadrons containing multiple strange quarks ($\phi,\Xi^-$), indicating that $D^0$ mesons kinetically decouple from the system earlier than light hadrons. The calculated $D^0$ nuclear modification factors re-affirm that charm quarks suffer large amount of energy loss in the medium, similar to those of light quarks for $p_{T}$\,&gt;\,4\,GeV/$c$ in central 0--10\% Au+Au collisions. At low $p_{T}$, the nuclear modification factors show a characteristic structure qualitatively consistent with the expectation from model predictions that charm quarks gain sizable collective motion during the medium evolution. The improved measurements are expected to offer new constraints to model calculations and help gain further insights into the hot and dense medium created in these collisions.

Matched filtering is a commonly used technique in gravitational wave searches for signals from compact binary systems and from rapidly rotating neutron stars. A common issue in these searches is dealing with four extrinsic parameters which do not affect the phase evolution of the system: the overall amplitude, initial phase, and two angles determining the overall orientation of the system. The F-statistic maximizes the likelihood function analytically over these parameters, while the B-statistic marginalizes over them. The B-statistic, while potentially more powerful and capable of incorporating astrophysical priors, is not as widely used because of the computational difficulty of performing the marginalization. In this paper we address this difficulty and show how the marginalization can be done analytically by combining the four parameters into a set of complex amplitudes. The results of this paper are applicable to both transient non-precessing binary coalescence events, and to long lived signals from rapidly rotating neutron stars.

Recent developments in deep learning have resulted in code-generation models that produce source code from natural language and code-based prompts with high accuracy. This is likely to have profound effects in the classroom, where novices learning to code can now use free tools to automatically suggest solutions to programming exercises and assignments. However, little is currently known about how novices interact with these tools in practice. We present the first study that observes students at the introductory level using one such code auto-generating tool, Github Copilot, on a typical introductory programming (CS1) assignment. Through observations and interviews we explore student perceptions of the benefits and pitfalls of this technology for learning, present new observed interaction patterns, and discuss cognitive and metacognitive difficulties faced by students. We consider design implications of these findings, specifically in terms of how tools like Copilot can better support and scaffold the novice programming experience.

The PHENIX experiement has measured the electron-positron pair mass spectrum from 0 to 8 GeV/c^2 in p+p collisions at sqrt(s)=200 GeV. The contributions from light meson decays to e^+e^- pairs have been determined based on measurements of hadron production cross sections by PHENIX. They account for nearly all e^+e^- pairs in the mass region below 1 GeV/c^2. The e^+e^- pair yield remaining after subtracting these contributions is dominated by semileptonic decays of charmed hadrons correlated through flavor conservation. Using the spectral shape predicted by PYTHIA, we estimate the charm production cross section to be 544 +/- 39(stat) +/- 142(syst) +/- 200(model) \mu b, which is consistent with QCD calculations and measurements of single leptons by PHENIX.

The fundamental building blocks of the proton, quarks and gluons, have been known for decades. However, we still have an incomplete theoretical and experimental understanding of how these particles and their dynamics give rise to the quantum bound state of the proton and its physical properties, such as for example its spin. The two up and the single down quarks that comprise the proton in the simplest picture account only for a few percent of the proton mass, the bulk of which is in the form of quark kinetic and potential energy and gluon energy from the strong force. An essential feature of this force, as described by quantum chromodynamics, is its ability to create matter-antimatter quark pairs inside the proton that exist only for a very short time. Their fleeting existence makes the antimatter quarks within protons difficult to study, but their existence is discernible in reactions where a matter-antimatter quark pair annihilates. In this picture of quark-antiquark creation by the strong force, the probability distributions as a function of momentum for the presence of up and down antimatter quarks should be nearly identical, since their masses are quite similar and small compared to the mass of the proton. In the present manuscript, we show evidence from muon pair production measurements that these distributions are significantly different, with more abundant down antimatter quarks than up antimatter quarks over a wide range of momentum. These results revive interest in several proposed mechanisms as the origin of this antimatter asymmetry in the proton that had been disfavored by the previous results and point to the future measurements that can distinguish between these mechanisms.

Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounter multiple metacognitive difficulties while programming. Novices are typically unaware of how these difficulties are hindering their progress. Meanwhile, many novices are now programming with generative AI (GenAI), which can provide complete solutions to most introductory programming problems, code suggestions, hints for next steps when stuck, and explain cryptic error messages. Its impact on novice metacognition has only started to be explored. Here we replicate a previous study that examined novice programming problem solving behavior and extend it by incorporating GenAI tools. Through 21 lab sessions consisting of participant observation, interview, and eye tracking, we explore how novices are coding with GenAI tools. Although 20 of 21 students completed the assigned programming problem, our findings show an unfortunate divide in the use of GenAI tools between students who accelerated and students who struggled. Students who accelerated were able to use GenAI to create code they already intended to make and were able to ignore unhelpful or incorrect inline code suggestions. But for students who struggled, our findings indicate that previously known metacognitive difficulties persist, and that GenAI unfortunately can compound them and even introduce new metacognitive difficulties. Furthermore, struggling students often expressed cognitive dissonance about their problem solving ability, thought they performed better than they did, and finished with an illusion of competence. Based on our observations from both groups, we propose ways to scaffold the novice GenAI experience and make suggestions for future work.

The invariant yield of electrons from open-heavy-flavor decays for $1

Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community.

Introductory programming courses often emphasize mastering syntax and basic constructs before progressing to more complex and interesting programs. This bottom-up approach can be frustrating for novices, shifting the focus away from problem solving and potentially making computing less appealing to a broad range of students. The rise of generative AI for code production could partially address these issues by fostering new skills via interaction with AI models, including constructing high-level prompts and evaluating code that is automatically generated. In this experience report, we explore the inclusion of two prompt-focused activities in an introductory course, implemented across four labs in a six-week module. The first requires students to solve computational problems by writing natural language prompts, emphasizing problem-solving over syntax. The second involves students crafting prompts to generate code equivalent to provided fragments, to foster an understanding of the relationship between prompts and code. Most of the students in the course had reported finding programming difficult to learn, often citing frustrations with syntax and debugging. We found that self-reported difficulty with learning programming had a strong inverse relationship with performance on traditional programming assessments such as tests and projects, as expected. However, performance on the natural language tasks was less strongly related to self-reported difficulty, suggesting they may target different skills. Learning how to communicate with AI coding models is becoming an important skill, and natural language prompting tasks may appeal to a broad range of students.

A key part of learning to program is learning to understand programming error messages. They can be hard to interpret and identifying the cause of errors can be time-consuming. One factor in this challenge is that the messages are typically intended for an audience that already knows how to program, or even for programming environments that then use the information to highlight areas in code. Researchers have been working on making these errors more novice friendly since the 1960s, however progress has been slow. The present work contributes to this stream of research by using large language models to enhance programming error messages with explanations of the errors and suggestions on how to fix the error. Large language models can be used to create useful and novice-friendly enhancements to programming error messages that sometimes surpass the original programming error messages in interpretability and actionability. These results provide further evidence of the benefits of large language models for computing educators, highlighting their use in areas known to be challenging for students. We further discuss the benefits and downsides of large language models and highlight future streams of research for enhancing programming error messages.

Recent advancements in generative AI systems have raised concerns about academic integrity among educators. Beyond excelling at solving programming problems and text-based multiple-choice questions, recent research has also found that large multimodal models (LMMs) can solve Parsons problems based only on an image. However, such problems are still inherently text-based and rely on the capabilities of the models to convert the images of code blocks to their corresponding text. In this paper, we further investigate the capabilities of LMMs to solve graph and tree data structure problems based only on images. To achieve this, we computationally construct and evaluate a novel benchmark dataset comprising 9,072 samples of diverse graph and tree data structure tasks to assess the performance of the GPT-4o, GPT-4v, Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 1.0 Pro Vision, and Claude 3 model families. GPT-4o and Gemini 1.5 Flash performed best on trees and graphs respectively. GPT-4o achieved 87.6% accuracy on tree samples, while Gemini 1.5 Flash, achieved 56.2% accuracy on graph samples. Our findings highlight the influence of structural and visual variations on model performance. This research not only introduces an LMM benchmark to facilitate replication and further exploration but also underscores the potential of LMMs in solving complex computing problems, with important implications for pedagogy and assessment practices.

The cross section and transverse single-spin asymmetries of $\mu^{-}$ and $\mu^{+}$ from open heavy-flavor decays in polarized $p$+$p$ collisions at $\sqrt{s}=200$ GeV were measured by the PHENIX experiment during 2012 at the Relativistic Heavy Ion Collider. Because heavy-flavor production is dominated by gluon-gluon interactions at $\sqrt{s}=200$ GeV, these measurements offer a unique opportunity to obtain information on the trigluon correlation functions. The measurements are performed at forward and backward rapidity (1.4&lt;|y|&lt;2.0) over the transverse momentum range of $1.25