Physics Event Classification Using Large Language Models

• URL: http://arxiv.org/abs/2404.05752v1
• Date: Fri, 5 Apr 2024 03:52:27 GMT
• Title: Physics Event Classification Using Large Language Models
• Abstract summary: The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This article highlights the hackathon challenge, the resources and methodology used, and the results and insights gained from analyzing physics data using the most cutting-edge tools in AI/ML.
• Score: 0.48418705470172246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The 2023 AI4EIC hackathon was the culmination of the third annual AI4EIC workshop at The Catholic University of America. This workshop brought together researchers from physics, data science and computer science to discuss the latest developments in Artificial Intelligence (AI) and Machine Learning (ML) for the Electron Ion Collider (EIC), including applications for detectors, accelerators, and experimental control. The hackathon, held on the final day of the workshop, involved using a chatbot powered by a Large Language Model, ChatGPT-3.5, to train a binary classifier neutrons and photons in simulated data from the \textsc{GlueX} Barrel Calorimeter. In total, six teams of up to four participants from all over the world took part in this intense educational and research event. This article highlights the hackathon challenge, the resources and methodology used, and the results and insights gained from analyzing physics data using the most cutting-edge tools in AI/ML.

Related papers

• Interdisciplinary Workshop on Mechanical Intelligence: Summary Report [17.27414355183946]
  Mechanical Intelligence (MI) represents the phenomenon that novel structural features of material/biological/robotic systems can encode intelligence through responsiveness, adaptivity, memory, and learning in the mechanical structure itself.<n>The two-day workshop was held at NSF headquarters on May 30-31 and included 38 invited academic researcher participants, and 8 program officers from the NSF.
  arXiv  Detail & Related papers  (2026-03-25T16:43:11Z)
• Report for NSF Workshop on AI for Electronic Design Automation [56.48556959103223]
  This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA)<n>Bringing together experts across machine learning and EDA, the workshop examined how AI-spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.<n>The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems.
  arXiv  Detail & Related papers  (2026-01-20T23:45:40Z)
• AI4X Roadmap: Artificial Intelligence for the advancement of scientific pursuit and its future directions [65.44445343399126]
  We look at AI-enabled science across biology, chemistry, climate science, mathematics, materials science, physics, self-driving laboratories and unconventional computing.<n>Several shared themes emerge: the need for diverse and trustworthy data, transferable electronic-structure and interatomic models, AI systems integrated into end-to-end scientific synthesis.<n>Across domains, we highlight how large foundation models, active learning and self-driving laboratories can close loops between prediction and validation.
  arXiv  Detail & Related papers  (2025-11-26T02:10:28Z)
• ResearchGPT: Benchmarking and Training LLMs for End-to-End Computer Science Research Workflows [109.34792911044394]
  CS-54k is a high-quality corpus of scientific Q&A pairs in computer science.<n> CS-4k is a benchmark for evaluating AI's ability to assist scientific research.<n> CS-50k is a large-scale training dataset.
  arXiv  Detail & Related papers  (2025-10-23T07:07:35Z)
• Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop [120.2806035123366]
  RoboTwin Dual-Arm Collaboration Challenge was held at the 2nd MEIS Workshop, CVPR 2025.<n>Competitors totally tackled 17 dual-arm manipulation tasks, covering rigid, deformable, and tactile-based scenarios.<n>Report outlines the competition setup, task design, evaluation methodology, key findings and future direction.
  arXiv  Detail & Related papers  (2025-06-29T17:56:41Z)
• Exploring utilization of generative AI for research and education in data-driven materials science [0.0]
  In July 2024, we organized a hackathon -- AIMHack2024 -- to explore how generative AI can facilitate research and education. Researchers from materials science, information science, bioinformatics, and condensed matter physics worked together to explore how generative AI can facilitate research and education. This paper presents topics related to AI-assisted software trials, building AI tutors for software, and developing GUI applications for software.
  arXiv  Detail & Related papers  (2025-04-09T11:15:21Z)
• MLGym: A New Framework and Benchmark for Advancing AI Research Agents [51.9387884953294]
  We introduce Meta MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing large language models on AI research tasks. This is the first Gym environment for machine learning (ML) tasks, enabling research on reinforcement learning (RL) algorithms for training such agents. We evaluate a number of frontier large language models (LLMs) on our benchmarks such as Claude-3.5-Sonnet, Llama-3.1 405B, GPT-4o, o1-preview, and Gemini-1.5 Pro.
  arXiv  Detail & Related papers  (2025-02-20T12:28:23Z)
• Intelligent experiments through real-time AI: Fast Data Processing and Autonomous Detector Control for sPHENIX and future EIC detectors [1.981222453919974]
  We develop a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors.<n>The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions.<n>For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification.
  arXiv  Detail & Related papers  (2025-01-08T21:13:50Z)
• Reflections from the 2024 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry [68.72590517877455]
  We present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry. The event engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications.
  arXiv  Detail & Related papers  (2024-11-20T23:08:01Z)
• DSBench: How Far Are Data Science Agents to Becoming Data Science Experts? [58.330879414174476]
  We introduce DSBench, a benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks, sourced from Eloquence and Kaggle competitions. Our evaluation of state-of-the-art LLMs, LVLMs, and agents shows that they struggle with most tasks, with the best agent solving only 34.12% of data analysis tasks and achieving a 34.74% Relative Performance Gap (RPG)
  arXiv  Detail & Related papers  (2024-09-12T02:08:00Z)
• MRAC Track 1: 2nd Workshop on Multimodal, Generative and Responsible Affective Computing [19.895638232394262]
  MRAC 2024 Track 1 workshop seeks to extend these principles to real-world, large-scale contexts. workshop also aims to highlight the potential implications of generative technology, along with the ethical consequences of its use.
  arXiv  Detail & Related papers  (2024-09-11T13:25:42Z)
• InternLM2 Technical Report [159.70692271378581]
  This paper introduces InternLM2, an open-source Large Language Models (LLMs) that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages.
  arXiv  Detail & Related papers  (2024-03-26T00:53:24Z)
• In-Context Language Learning: Architectures and Algorithms [73.93205821154605]
  We study ICL through the lens of a new family of model problems we term in context language learning (ICLL) We evaluate a diverse set of neural sequence models on regular ICLL tasks.
  arXiv  Detail & Related papers  (2024-01-23T18:59:21Z)
• AI and Generative AI for Research Discovery and Summarization [3.8601741392210434]
  AI and generative AI tools have burst onto the scene this year, creating incredible opportunities to increase work productivity and improve our lives. One area that these tools can make a substantial impact is in research discovery and summarization. We review the developments in AI and generative AI for research discovery and summarization, and propose directions where these types of tools are likely to head in the future.
  arXiv  Detail & Related papers  (2024-01-08T18:42:55Z)
• Surfer: Progressive Reasoning with World Models for Robotic Manipulation [51.26109827779267]
  We introduce a novel and simple robot manipulation framework, called Surfer. Surfer treats robot manipulation as a state transfer of the visual scene, and decouples it into two parts: action and scene. It is based on the world model, treats robot manipulation as a state transfer of the visual scene, and decouples it into two parts: action and scene.
  arXiv  Detail & Related papers  (2023-06-20T07:06:04Z)
• Artificial intelligence to advance Earth observation: : A review of models, recent trends, and pathways forward [60.43248801101935]
  This article gives a bird's eye view of the essential scientific tools and approaches informing and supporting the transition from raw EO data to usable EO-based information. We cover the impact of (i) Computer vision; (ii) Machine learning; (iii) Advanced processing and computing; (iv) Knowledge-based AI; (v) Explainable AI and causal inference; (vi) Physics-aware models; (vii) User-centric approaches; and (viii) the much-needed discussion of ethical and societal issues related to the massive use of ML technologies in EO.
  arXiv  Detail & Related papers  (2023-05-15T07:47:24Z)
• End-to-end AI framework for interpretable prediction of molecular and crystal properties [3.8878792624088856]
  The framework is based on state-of-the-art AI models including CGCNN, PhysNet, SchNet, MPNN, MPNN-transformer, and TorchMD-NET. We employ these AI models along with the benchmark QM9, hMOF, and MD17 datasets to showcase how the models can predict user-specified material properties.
  arXiv  Detail & Related papers  (2022-12-21T19:27:51Z)
• Physical Computing for Materials Acceleration Platforms [81.09376948478891]
  We argue that the same simulation and AI tools that will accelerate the search for new materials, as part of the MAPs research program, also make possible the design of fundamentally new computing mediums. We outline a simulation-based MAP program to design computers that use physics itself to solve optimization problems. We expect to introduce a new era of innovative collaboration between materials researchers and computer scientists.
  arXiv  Detail & Related papers  (2022-08-17T23:03:54Z)
• Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networks [0.0]
  We propose a physics-informed neural network (PINN) framework that fuses data and first physical principles. This is the first application of PINN to three dimensional AM processes modeling. The PINN can accurately predict the temperature and melt pool dynamics during metal AM processes with only a moderate amount of labeled data-sets.
  arXiv  Detail & Related papers  (2020-07-28T20:34:38Z)
• Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs [0.16777183511743468]
  We present studies of scaling a LArTPC classification problem on multiple architectures, spanning multiple nodes. To our knowledge, this is the first use of state-of-the-art Convolutional Neural Networks for particle physics. We expect benefits to accrue particularly to the Deep Underground Neutrino Experiment (DUNE) LArTPC program.
  arXiv  Detail & Related papers  (2020-04-17T20:21:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.