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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.11942">arXiv:2411.11942</a> <span> [<a href="https://arxiv.org/pdf/2411.11942">pdf</a>, <a href="https://arxiv.org/format/2411.11942">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Instrumentation and Detectors">physics.ins-det</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> </div> </div> <p class="title is-5 mathjax"> Variable Rate Neural Compression for Sparse Detector Data </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Huang%2C+Y">Yi Huang</a>, <a href="/search/physics?searchtype=author&query=Go%2C+Y">Yeonju Go</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+J">Jin Huang</a>, <a href="/search/physics?searchtype=author&query=Li%2C+S">Shuhang Li</a>, <a href="/search/physics?searchtype=author&query=Luo%2C+X">Xihaier Luo</a>, <a href="/search/physics?searchtype=author&query=Marshall%2C+T">Thomas Marshall</a>, <a href="/search/physics?searchtype=author&query=Osborn%2C+J">Joseph Osborn</a>, <a href="/search/physics?searchtype=author&query=Pinkenburg%2C+C">Christopher Pinkenburg</a>, <a href="/search/physics?searchtype=author&query=Ren%2C+Y">Yihui Ren</a>, <a href="/search/physics?searchtype=author&query=Shulga%2C+E">Evgeny Shulga</a>, <a href="/search/physics?searchtype=author&query=Yoo%2C+S">Shinjae Yoo</a>, <a href="/search/physics?searchtype=author&query=Yoon%2C+B">Byung-Jun Yoon</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.11942v1-abstract-short" style="display: inline;"> High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has become increasingly critical. Deep learning is a promising technology that can address this challenging topic. At the newly constructed sPHENIX experiment at the Relativistic Heavy… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11942v1-abstract-full').style.display = 'inline'; document.getElementById('2411.11942v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.11942v1-abstract-full" style="display: none;"> High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has become increasingly critical. Deep learning is a promising technology that can address this challenging topic. At the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider, a Time Projection Chamber (TPC) serves as the main tracking detector, which records three-dimensional particle trajectories in a volume of a gas-filled cylinder. In terms of occupancy, the resulting data flow can be very sparse reaching $10^{-3}$ for proton-proton collisions. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. In contrast, emerging deep learning-based models, particularly those utilizing convolutional neural networks for compression, have outperformed these conventional methods in terms of compression ratios and reconstruction accuracy. However, research on the efficacy of these deep learning models in handling sparse datasets, like those produced in particle colliders, remains limited. Furthermore, most deep learning models do not adapt their processing speeds to data sparsity, which affects efficiency. To address this issue, we propose a novel approach for TPC data compression via key-point identification facilitated by sparse convolution. Our proposed algorithm, BCAE-VS, achieves a $75\%$ improvement in reconstruction accuracy with a $10\%$ increase in compression ratio over the previous state-of-the-art model. Additionally, BCAE-VS manages to achieve these results with a model size over two orders of magnitude smaller. Lastly, we have experimentally verified that as sparsity increases, so does the model's throughput. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.11942v1-abstract-full').style.display = 'none'; document.getElementById('2411.11942v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">37 pages, 12 figures, submitted to Journal of Computational Physics</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.01602">arXiv:2406.01602</a> <span> [<a href="https://arxiv.org/pdf/2406.01602">pdf</a>, <a href="https://arxiv.org/format/2406.01602">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Data Analysis, Statistics and Probability">physics.data-an</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="High Energy Physics - Experiment">hep-ex</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Nuclear Experiment">nucl-ex</span> </div> </div> <p class="title is-5 mathjax"> Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/physics?searchtype=author&query=Go%2C+Y">Yeonju Go</a>, <a href="/search/physics?searchtype=author&query=Torbunov%2C+D">Dmitrii Torbunov</a>, <a href="/search/physics?searchtype=author&query=Rinn%2C+T">Timothy Rinn</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+Y">Yi Huang</a>, <a href="/search/physics?searchtype=author&query=Yu%2C+H">Haiwang Yu</a>, <a href="/search/physics?searchtype=author&query=Viren%2C+B">Brett Viren</a>, <a href="/search/physics?searchtype=author&query=Lin%2C+M">Meifeng Lin</a>, <a href="/search/physics?searchtype=author&query=Ren%2C+Y">Yihui Ren</a>, <a href="/search/physics?searchtype=author&query=Huang%2C+J">Jin Huang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.01602v1-abstract-short" style="display: inline;"> Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where dat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01602v1-abstract-full').style.display = 'inline'; document.getElementById('2406.01602v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.01602v1-abstract-full" style="display: none;"> Artificial intelligence (AI) generative models, such as generative adversarial networks (GANs), variational auto-encoders, and normalizing flows, have been widely used and studied as efficient alternatives for traditional scientific simulations. However, they have several drawbacks, including training instability and inability to cover the entire data distribution, especially for regions where data are rare. This is particularly challenging for whole-event, full-detector simulations in high-energy heavy-ion experiments, such as sPHENIX at the Relativistic Heavy Ion Collider and Large Hadron Collider experiments, where thousands of particles are produced per event and interact with the detector. This work investigates the effectiveness of Denoising Diffusion Probabilistic Models (DDPMs) as an AI-based generative surrogate model for the sPHENIX experiment that includes the heavy-ion event generation and response of the entire calorimeter stack. DDPM performance in sPHENIX simulation data is compared with a popular rival, GANs. Results show that both DDPMs and GANs can reproduce the data distribution where the examples are abundant (low-to-medium calorimeter energies). Nonetheless, DDPMs significantly outperform GANs, especially in high-energy regions where data are rare. Additionally, DDPMs exhibit superior stability compared to GANs. The results are consistent between both central and peripheral centrality heavy-ion collision events. Moreover, DDPMs offer a substantial speedup of approximately a factor of 100 compared to the traditional Geant4 simulation method. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.01602v1-abstract-full').style.display = 'none'; document.getElementById('2406.01602v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 7 figures</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div 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