Argonne National Laboratory’s Post

AI and ML: Transforming Nuclear Physics and Energy Imagine AI not just in tech but revolutionizing nuclear physics, high-energy physics, and fusion research. AI and ML have surged, promising real-time data breakthroughs across fields. Fast ML in Nuclear Physics: AI enhances discovery from quantum to cosmic scales, pivotal in experiment design and facility operations globally. Key Applications: Detector Design: AI tackles complex optimization with Bayesian methods, improving particle ID at Jefferson Lab. Streaming Readout: LHCb uses AI for seamless data processing, enhancing decay product reconstruction. Particle Tracking: Deep learning speeds up tracking at high luminosity, crucial for Intensity Frontier experiments. Impact on Energy: AI advances nuclear energy and fusion by optimizing reactor designs and simulations for safer, efficient solutions. Call to Action: Are you in AI, ML, nuclear physics, or energy? Let's collaborate: AI/ML Researchers: How to adapt ML for nuclear experiments? Nuclear Physicists: What challenges can AI solve? Energy Innovators: What's the future of nuclear with AI? Join the conversation, share insights, or propose projects. DM me to connect! #AI #MachineLearning #NuclearPhysics #EnergyInnovation #ScientificDiscovery

Diamonds are a researcher's best friend. 💎 Researchers from Nuclear, Plasma & Radiological Engineering at Illinois and Case Western Reserve University have found boron-doped diamonds to be an exceptional material for high-power electronics and next-generation quantum optics. Their findings could pave the way for new types of biomedical and quantum optical devices—faster, more efficient, and capable of processing information in ways that classical technologies cannot. Read more about their research: https://lnkd.in/gQgFCDpM

Artificial Intelligence (#AI) accelerates the research with Nuclear Magnetic Resonance (#NMR). Our customers leverage advanced AI tools to significantly speed up their NMR analysis, achieving breakthroughs faster than ever before. Here https://lnkd.in/dcDc8aCF, researchers share their success stories and insights, showcasing how AI integration has transformed their work or how they envision future projects to be faster. #Bruker #StructuralBiology #ProteinResearch #ScientificResearch #Innovation

1/2:Title: Statistical Algorithms for Analyzing Gamma Rays: Correlations with Atomic Number and Atomic Weight Conclusion The application of statistical algorithms to gamma ray analysis offers valuable insights into atomic properties such as atomic number and atomic weight. By aligning gamma ray data with these fundamental characteristics, researchers can enhance their understanding of elemental interactions and improve the accuracy of elemental analysis. Future developments in statistical algorithms and gamma ray technology will further advance our capabilities in material science and nuclear research. References • Knoll, G. F. (2010). Radiation Detection and Measurement. Wiley. • Harris, T. L., & Cardella, A. (2005). Statistical Analysis of Gamma Ray Spectra. Springer. • Cember, H., & Johnson, T. E. (2009). Introduction to Health Physics. McGraw-Hill. • Brown, R. M. (1992). Principles of Nuclear Physics. Oxford University Press. • Borges, A. J. (2006). Statistical Methods for Nuclear and Particle Physics. Cambridge University Press. Courtsey to Priya Waller Media and Communications Experts UK 🇬🇧

🚀 **Revolutionizing Innovation: A Math Student’s Journey to Building a Nuclear Reactor with AI!** ⚛️ In a groundbreaking blend of education and technology, a determined math student has harnessed the power of artificial intelligence to construct a functioning nuclear reactor! This incredible feat not only showcases the potential of AI in engineering but also highlights a new frontier in innovation. As we witness AI’s rapid rise in U.S. business, this story serves as a reminder that the future of technology lies in the hands of the curious and the bold. Imagine what we can achieve when creativity meets cutting-edge technology! Let’s discuss: 🔍 How can AI empower young innovators? 🤖 What role does AI play in reshaping traditional industries? 🌍 What other fields can benefit from this collaboration? Join the conversation and let’s explore the next chapter of innovation together! #Innovation #AI #FutureOfWork #STEM #TechnologyTransformation Link to full post here: https://buff.ly/418P2P3

Delighted that our CEO Professor Tim Dodwell is speaking at the Enhancing the Nuclear Economy event this May. Machine learning and uncertainty quantification methods have a key role to play in the nuclear industry, from fundamental science to grid deployment. Already digiLab have used our platform twinLab to help the nuclear industry, and we're excited for the ML-enhanced nuclear economy of the future! #nuclearenergy #cleanenergy #ai #machinelearning #climatechange

In fact, no science can be a source of knowledge, wisdom and innovation if it has nothing to do with human concerns. We have seen this earlier with nuclear energy science which could be fruitful only when it was controlled for human purposes. The same is happening with green tech. Petty selfishness and short-sightedness for the near future are ruining the potential and possibilities of the future. The same is happening with artificial intelligence, for which the larger and present and future human concerns have been dishonestly covered up by big tech companies for immediate financial gains at the expense of future generations, as a result of which science may have to bear the brunt of threats and possible revolts in the future. Remember that science becomes unscientific when society moves out or is removed from the circle of its questions. In this situation science becomes a carrier of ignorance.

Now, build a DIY nuclear reactor in your bedroom with a little help from AI. And, that's exactly what this student did. And, the story is inspiring🚀! Read more... Meet Hudhayfa Nazoordeen, a mathematics undergraduate at the University of Waterloo who defied the odds by building a fusion reactor in his bedroom—despite having zero hardware experience. With the guidance of Anthropic's Claude 3.5 Sonnet AI, Hudhayfa navigated the complexities of constructing a fusor, a device capable of initiating nuclear fusion. His journey showcases how AI can empower individuals to embark on ambitious scientific projects from the comfort of home, proving that determination and creativity can lead to remarkable breakthroughs. Hudhayfa’s story serves as a testament to the transformative power of artificial intelligence in democratizing access to advanced scientific exploration. By harnessing AI tools like Claude 3.5 Sonnet, he not only achieved plasma generation but also set a precedent for aspiring scientists everywhere. This milestone highlights the potential for AI to level the playing field, allowing anyone with a passion for discovery to push the boundaries of what’s possible. Let’s celebrate the spirit of innovation and the future of scientific exploration! 🔬✨ #FutureOfScience #AIInResearch #ClaudeSonnet #Empowerment #ScientificBreakthroughs #Education, 🌟 #Innovation #AI #FusionReactor #STEM #DIYScience,

Last week, I presented my work on double detonation Type Ia supernovae at the American Physical Society Division of Nuclear Physics meeting in Boston, MA. as a part of the Conference Experience for Undergraduates (CEU), an award distributed by the University of Wisconsin-La Crosse. Double detonation Type Ia supernovae, triggered by a detonation in the helium accretion layer of sub-Chandrasekhar white dwarfs, offer an intriguing alternative to the traditional Chandrasekhar mass progenitor models. Our findings corroborate the belief that prohibiting nuclear burning in underresolved numerical shocks is crucial to prevent erroneous fuel consumption. This is particularly important in supernova models that rely on accurate shock wave modeling, such as the double detonation model, where carbon fusion in the white dwarf’s core is triggered by the convergence of a shock wave. Additionally, our simulations demonstrate that the width of the white dwarf-accretion transition zone is a key parameter in the success of the detonation. Narrower transition zones result in less fuel mixing, thereby reducing the impact of shock underresolution and limiting the potential for the flame to dig into the white dwarf instead of circumnavigating it. This white dwarf-accretion transition width could serve as a parameter for computational astrophysicists to focus on in future double detonation simulations.

This is an algorithm called unscented transform, which predicts mean, std and covariance from given set of variable, modified version of Bayesian statistics, I tried to reproduce results of one paper if Nuclear statistics, Nowadays Uncertainty Quantification in Nuclear Physics is hot topic, people are trying with sandwich theorem, UTKF and Monte Carlo and UT proved to be the smartest. This algorithm generates sigma points which is the smartest way to predict Mean and Cov for a given Primary variable and Initial Cov. One bro from previous group got Post Doc in Lawrence Livermore National Lab just by using this method and called it deterministic sampling. I owe my calculation to Dr. Naohiko Otsuka (IAEA, Austria, Vienna) and Vidya Devi(IAEA) and Dr. Jagjit Singh Matharu. From them I learnt the calculation for UT, MC and UTKF.