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1,649 results on '"Shanmuganathan, P."'

1. MERLiN: Single-Shot Material Estimation and Relighting for Photometric Stereo

Author

Tiwari, Ashish, Ikehata, Satoshi, and Raman, Shanmuganathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Photometric stereo typically demands intricate data acquisition setups involving multiple light sources to recover surface normals accurately. In this paper, we propose MERLiN, an attention-based hourglass network that integrates single image-based inverse rendering and relighting within a single unified framework. We evaluate the performance of photometric stereo methods using these relit images and demonstrate how they can circumvent the underlying challenge of complex data acquisition. Our physically-based model is trained on a large synthetic dataset containing complex shapes with spatially varying BRDF and is designed to handle indirect illumination effects to improve material reconstruction and relighting. Through extensive qualitative and quantitative evaluation, we demonstrate that the proposed framework generalizes well to real-world images, achieving high-quality shape, material estimation, and relighting. We assess these synthetically relit images over photometric stereo benchmark methods for their physical correctness and resulting normal estimation accuracy, paving the way towards single-shot photometric stereo through physically-based relighting. This work allows us to address the single image-based inverse rendering problem holistically, applying well to both synthetic and real data and taking a step towards mitigating the challenge of data acquisition in photometric stereo., Comment: Accepted in ECCV 2024

Published
2024

2. LIPIDS: Learning-based Illumination Planning In Discretized (Light) Space for Photometric Stereo

Author

Tiwari, Ashish, Sutariya, Mihir, and Raman, Shanmuganathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Photometric stereo is a powerful method for obtaining per-pixel surface normals from differently illuminated images of an object. While several methods address photometric stereo with different image (or light) counts ranging from one to two to a hundred, very few focus on learning optimal lighting configuration. Finding an optimal configuration is challenging due to the vast number of possible lighting directions. Moreover, exhaustively sampling all possibilities is impractical due to time and resource constraints. Photometric stereo methods have demonstrated promising performance on existing datasets, which feature limited light directions sparsely sampled from the light space. Therefore, can we optimally utilize these datasets for illumination planning? In this work, we introduce LIPIDS - Learning-based Illumination Planning In Discretized light Space to achieve minimal and optimal lighting configurations for photometric stereo under arbitrary light distribution. We propose a Light Sampling Network (LSNet) that optimizes lighting direction for a fixed number of lights by minimizing the normal loss through a normal regression network. The learned light configurations can directly estimate surface normals during inference, even using an off-the-shelf photometric stereo method. Extensive qualitative and quantitative analyses on synthetic and real-world datasets show that photometric stereo under learned lighting configurations through LIPIDS either surpasses or is nearly comparable to existing illumination planning methods across different photometric stereo backbones., Comment: Accepted in WACV 2025

Published
2024

3. SS-SfP:Neural Inverse Rendering for Self Supervised Shape from (Mixed) Polarization

Author

Tiwari, Ashish and Raman, Shanmuganathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

We present a novel inverse rendering-based framework to estimate the 3D shape (per-pixel surface normals and depth) of objects and scenes from single-view polarization images, the problem popularly known as Shape from Polarization (SfP). The existing physics-based and learning-based methods for SfP perform under certain restrictions, i.e., (a) purely diffuse or purely specular reflections, which are seldom in the real surfaces, (b) availability of the ground truth surface normals for direct supervision that are hard to acquire and are limited by the scanner's resolution, and (c) known refractive index. To overcome these restrictions, we start by learning to separate the partially-polarized diffuse and specular reflection components, which we call reflectance cues, based on a modified polarization reflection model and then estimate shape under mixed polarization through an inverse-rendering based self-supervised deep learning framework called SS-SfP, guided by the polarization data and estimated reflectance cues. Furthermore, we also obtain the refractive index as a non-linear least squares solution. Through extensive quantitative and qualitative evaluation, we establish the efficacy of the proposed framework over simple single-object scenes from DeepSfP dataset and complex in-the-wild scenes from SPW dataset in an entirely self-supervised setting. To the best of our knowledge, this is the first learning-based approach to address SfP under mixed polarization in a completely self-supervised framework., Comment: Published in Pacific Graphics 2023

Published
2024

4. Real-world treatment patterns and outcomes in patients with primary hemophagocytic lymphohistiocytosis treated with emapalumab.

Author

Chandrakasan, Shanmuganathan, Jordan, Michael, Baker, Ashley, Behrens, Edward, Bhatla, Deepika, Chien, May, Eckstein, Olive, Henry, Michael, Hermiston, Michelle, Hinson, Ashley, Leiding, Jennifer, Oladapo, Abiola, Patel, Sachit, Pednekar, Priti, Ray, Anish, Dávila Saldaña, Blachy, Sarangi, Susmita, Walkovich, Kelly, Yee, John, Zoref-Lorenz, Adi, and Allen, Carl

Subjects
Humans, Lymphohistiocytosis, Hemophagocytic, Female, Male, Treatment Outcome, Adolescent, Child, Retrospective Studies, Child, Preschool, Infant, Young Adult, Antibodies, Monoclonal, Adult
Abstract

Hemophagocytic lymphohistiocytosis (HLH) is a rare, life-threatening, hyperinflammatory syndrome. Emapalumab, a fully human monoclonal antibody that neutralizes the proinflammatory cytokine interferon gamma, is approved in the United States to treat primary HLH (pHLH) in patients with refractory, recurrent, or progressive disease, or intolerance with conventional HLH treatments. REAL-HLH, a retrospective study, conducted across 33 US hospitals, evaluated real-world treatment patterns and outcomes in patients treated with ≥1 dose of emapalumab between 20 November 2018 and 31 October 2021. In total, 46 patients met the pHLH classification criteria. Median age at diagnosis was 1.0 year (range, 0.3-21.0). Emapalumab was initiated for treating refractory (19/46), recurrent (14/46), or progressive (7/46) pHLH. At initiation, 15 of 46 patients were in the intensive care unit, and 35 of 46 had received prior HLH-related therapies. Emapalumab treatment resulted in normalization of key laboratory parameters, including chemokine ligand 9 (24/33, 72.7%), ferritin (20/45, 44.4%), fibrinogen (37/38, 97.4%), platelets (39/46, 84.8%), and absolute neutrophil count (40/45, 88.9%). Forty-two (91.3%) patients were considered eligible for transplant. Pretransplant survival was 38 of 42 (90.5%). Thirty-one (73.8%) transplant-eligible patients proceeded to transplant, and 23 of 31 (74.2%) of those who received transplant were alive at the end of the follow-up period. Twelve-month survival probability from emapalumab initiation for the entire cohort (N = 46) was 73.1%. There were no discontinuations because of adverse events. In conclusion, results from the REAL-HLH study, which describes treatment patterns, effectiveness, and outcomes in patients with pHLH treated with emapalumab in real-world settings, are consistent with the emapalumab pivotal phase 2/3 pHLH trial.

Published
2024

5. Antiviral cellular therapy for enhancing T-cell reconstitution before or after hematopoietic stem cell transplantation (ACES): a two-arm, open label phase II interventional trial of pediatric patients with risk factor assessment.

Author

Keller, Michael, Hanley, Patrick, Chi, Yueh-Yun, Aguayo-Hiraldo, Paibel, Verneris, Michael, Kohn, Donald, Pai, Sung-Yun, Dávila Saldaña, Blachy, Hanisch, Benjamin, Quigg, Troy, Adams, Roberta, Dahlberg, Ann, Chandrakasan, Shanmuganathan, Hasan, Hasibul, Malvar, Jemily, Jensen-Wachspress, Mariah, Lazarski, Christopher, Sani, Gelina, Idso, John, Lang, Haili, Chansky, Pamela, McCann, Chase, Tanna, Jay, Abraham, Allistair, Webb, Jennifer, Shibli, Abeer, Keating, Amy, Satwani, Prakash, Muranski, Pawel, Hall, Erin, Eckrich, Michael, Shereck, Evan, Miller, Holly, Mamcarz, Ewelina, Agarwal, Rajni, Vander Lugt, Mark, Ebens, Christen, Aquino, Victor, Bednarski, Jeffrey, Chu, Julia, Parikh, Suhag, Whangbo, Jennifer, Lionakis, Michail, Zambidis, Elias, Gourdine, Elizabeth, Bollard, Catherine, Pulsipher, Michael, Dvorak, Christopher, and De Oliveira, Satiro

Subjects
Humans, Child, Epstein-Barr Virus Infections, Herpesvirus 4, Human, Virus Diseases, Risk Factors, Hematopoietic Stem Cell Transplantation
Abstract

Viral infections remain a major risk in immunocompromised pediatric patients, and virus-specific T cell (VST) therapy has been successful for treatment of refractory viral infections in prior studies. We performed a phase II multicenter study (NCT03475212) for the treatment of pediatric patients with inborn errors of immunity and/or post allogeneic hematopoietic stem cell transplant with refractory viral infections using partially-HLA matched VSTs targeting cytomegalovirus, Epstein-Barr virus, or adenovirus. Primary endpoints were feasibility, safety, and clinical responses (>1 log reduction in viremia at 28 days). Secondary endpoints were reconstitution of antiviral immunity and persistence of the infused VSTs. Suitable VST products were identified for 75 of 77 clinical queries. Clinical responses were achieved in 29 of 47 (62%) of patients post-HSCT including 73% of patients evaluable at 1-month post-infusion, meeting the primary efficacy endpoint (>52%). Secondary graft rejection occurred in one child following VST infusion as described in a companion article. Corticosteroids, graft-versus-host disease, transplant-associated thrombotic microangiopathy, and eculizumab treatment correlated with poor response, while uptrending absolute lymphocyte and CD8 T cell counts correlated with good response. This study highlights key clinical factors that impact response to VSTs and demonstrates the feasibility and efficacy of this therapy in pediatric HSCT.

Published
2024

12. Flow Symmetrization for Parameterized Constrained Diffeomorphisms

Author

Gangopadhyay, Aalok, Dalal, Dwip, Das, Progyan, and Raman, Shanmuganathan

Subjects
Computer Science - Graphics
Abstract

Diffeomorphisms play a crucial role while searching for shapes with fixed topological properties, allowing for smooth deformation of template shapes. Several approaches use diffeomorphism for shape search. However, these approaches employ only unconstrained diffeomorphisms. In this work, we develop Flow Symmetrization - a method to represent a parametric family of constrained diffeomorphisms that contain additional symmetry constraints such as periodicity, rotation equivariance, and transflection equivariance. Our representation is differentiable in nature, making it suitable for gradient-based optimization approaches for shape search. As these symmetry constraints naturally arise in tiling classes, our method is ideal for representing tile shapes belonging to any tiling class. To demonstrate the efficacy of our method, we design two frameworks for addressing the challenging problems of Escherization and Density Estimation. The first framework is dedicated to the Escherization problem, where we parameterize tile shapes belonging to different isohedral classes. Given a target shape, the template tile is deformed using gradient-based optimization to resemble the target shape. The second framework focuses on density estimation in identification spaces. By leveraging the inherent link between tiling theory and identification topology, we design constrained diffeomorphisms for the plane that result in unconstrained diffeomorphisms on the identification spaces. Specifically, we perform density estimation on identification spaces such as torus, sphere, Klein bottle, and projective plane. Through results and experiments, we demonstrate that our method obtains impressive results for Escherization on the Euclidean plane and density estimation on non-Euclidean identification spaces.

Published
2023

13. Measurements of charged-particle multiplicity dependence of higher-order net-proton cumulants in $p$+$p$ collisions at $\sqrt{s} =$ 200 GeV from STAR at RHIC

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kołaś, J., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matonoha, O., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nie, M., Nigmatkulov, G., Niida, T., Nishitani, R., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pal, S., Pandav, A., Panday, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Nuclear Theory
Abstract

We report on the charged-particle multiplicity dependence of net-proton cumulant ratios up to sixth order from $\sqrt{s}=200$ GeV $p$+$p$ collisions at the Relativistic Heavy Ion Collider (RHIC). The measured ratios $C_{4}/C_{2}$, $C_{5}/C_{1}$, and $C_{6}/C_{2}$ decrease with increased charged-particle multiplicity and rapidity acceptance. Neither the Skellam baselines nor PYTHIA8 calculations account for the observed multiplicity dependence. In addition, the ratios $C_{5}/C_{1}$ and $C_{6}/C_{2}$ approach negative values in the highest-multiplicity events, which implies that thermalized QCD matter may be formed in $p$+$p$ collisions., Comment: 12 pages, 6 figures, accepted version by PLB

Published
2023
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14. Learning Robust Deep Visual Representations from EEG Brain Recordings

Author

Singh, Prajwal, Dalal, Dwip, Vashishtha, Gautam, Miyapuram, Krishna, and Raman, Shanmuganathan

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Decoding the human brain has been a hallmark of neuroscientists and Artificial Intelligence researchers alike. Reconstruction of visual images from brain Electroencephalography (EEG) signals has garnered a lot of interest due to its applications in brain-computer interfacing. This study proposes a two-stage method where the first step is to obtain EEG-derived features for robust learning of deep representations and subsequently utilize the learned representation for image generation and classification. We demonstrate the generalizability of our feature extraction pipeline across three different datasets using deep-learning architectures with supervised and contrastive learning methods. We have performed the zero-shot EEG classification task to support the generalizability claim further. We observed that a subject invariant linearly separable visual representation was learned using EEG data alone in an unimodal setting that gives better k-means accuracy as compared to a joint representation learning between EEG and images. Finally, we propose a novel framework to transform unseen images into the EEG space and reconstruct them with approximation, showcasing the potential for image reconstruction from EEG signals. Our proposed image synthesis method from EEG shows 62.9% and 36.13% inception score improvement on the EEGCVPR40 and the Thoughtviz datasets, which is better than state-of-the-art performance in GAN., Comment: Accepted in WACV 2024

Published
2023

15. Observation of the Antimatter Hypernucleus $^4_{\bar{\Lambda}}\overline{\hbox{H}}$

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Mrazkova, J., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Su, Y., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Svoboda, M., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

At the origin of the Universe, asymmetry between the amount of created matter and antimatter led to the matter-dominated Universe as we know today. The origins of this asymmetry remain not completely understood yet. High-energy nuclear collisions create conditions similar to the Universe microseconds after the Big Bang, with comparable amounts of matter and antimatter. Much of the created antimatter escapes the rapidly expanding fireball without annihilating, making such collisions an effective experimental tool to create heavy antimatter nuclear objects and study their properties, hoping to shed some light on existing questions on the asymmetry between matter and antimatter. Here we report the first observation of the antimatter hypernucleus \hbox{$^4_{\bar{\Lambda}}\overline{\hbox{H}}$}, composed of a $\bar{\Lambda}$ , an antiproton and two antineutrons. The discovery was made through its two-body decay after production in ultrarelativistic heavy-ion collisions by the STAR experiment at the Relativistic Heavy Ion Collider. In total, 15.6 candidate \hbox{$^4_{\bar{\Lambda}}\overline{\hbox{H}}$} antimatter hypernuclei are obtained with an estimated background count of 6.4. The lifetimes of the antihypernuclei \hbox{$^3_{\bar{\Lambda}}\overline{\hbox{H}}$} and \hbox{$^4_{\bar{\Lambda}}\overline{\hbox{H}}$} are measured and compared with the lifetimes of their corresponding hypernuclei, testing the symmetry between matter and antimatter. Various production yield ratios among (anti)hypernuclei and (anti)nuclei are also measured and compared with theoretical model predictions, shedding light on their production mechanisms., Comment: 28 pages, 5 figures in the main paper; 16 pages, 5 figures in the methods part

Published
2023

16. Estimate of Background Baseline and Upper Limit on the Chiral Magnetic Effect in Isobar Collisions at $\sqrt{s_{\text{NN}}}=200$ GeV at the Relativistic Heavy-Ion Collider

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adams, J. R., Agakishiev, G., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aitbaev, A., Alekseev, I., Alpatov, E., Aparin, A., Aslam, S., Atchison, J., Averichev, G. S., Bairathi, V., Cap, J. G. Ball, Barish, K., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bordyuzhin, I. G., Brandenburg, J. D., Brandin, A. V., Broodo, C., Cai, X. Z., Caines, H., Calderón~de~la~Barca~Sánchez, M., Cebra, D., Ceska, J., Chakaberia, I., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Dale-Gau, G., Das, A., Dedovich, T. G., Deppner, I. M., Derevschikov, A. A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Hamed, A., Han, Y., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kechechyan, A., Khanal, A., Kiselev, A., Knospe, A. G., Ko, H. S., Kochenda, L., Korobitsin, A. A., Kraeva, A. Yu., Kravtsov, P., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lebedev, A., Lednicky, R., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Lin, T., Lin, Y., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Luong, V. B., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Manikandhan, R., Margetis, S., Matis, H. S., McNamara, G., Mezhanska, O., Mi, K., Minaev, N. G., Mohanty, B., Mondal, M. M., Mooney, I., Morozov, D. A., Mudrokh, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Nedorezov, E., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nogach, L. V., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okorokov, V. A., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pandey, A. K., Panebratsev, Y., Pani, T., Parfenov, P., Paul, A., Perkins, C., Pokhrel, B. R., Posik, M., Povarov, A., Protzman, T., Pruthi, N. K., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Ritter, H. G., Robertson, C. W., Rogachevsky, O. V., Aguilar, M. A. Rosales, Roy, D., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Samigullin, E., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seger, J., Seto, R., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stewart, D. J., Strikhanov, M., Stringfellow, B., Su, Y., Sun, C., Sun, X., Sun, Y., Surrow, B., Svirida, D. N., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Taranenko, A., Tarnowsky, T., Thomas, J. H., Tlusty, D., Todoroki, T., Tokarev, M. V., Trentalange, S., Tribedy, P., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vasiliev, A. N., Verkest, V., Videbæk, F., Vokal, S., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wieman, H., Wilks, G., Wissink, S. W., Wu, J., Wu, X., Wu, Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment, Nuclear Theory
Abstract

For the search of the chiral magnetic effect (CME), STAR previously presented the results from isobar collisions (${^{96}_{44}\text{Ru}}+{^{96}_{44}\text{Ru}}$, ${^{96}_{40}\text{Zr}}+{^{96}_{40}\text{Zr}}$) obtained through a blind analysis. The ratio of results in Ru+Ru to Zr+Zr collisions for the CME-sensitive charge-dependent azimuthal correlator ($\Delta\gamma$), normalized by elliptic anisotropy ($v_{2}$), was observed to be close to but systematically larger than the inverse multiplicity ratio. The background baseline for the isobar ratio, $Y = \frac{(\Delta\gamma/v_{2})^{\text{Ru}}}{(\Delta\gamma/v_{2})^{\text{Zr}}}$, is naively expected to be $\frac{(1/N)^{\text{Ru}}}{(1/N)^{\text{Zr}}}$; however, genuine two- and three-particle correlations are expected to alter it. We estimate the contributions to $Y$ from those correlations, utilizing both the isobar data and HIJING simulations. After including those contributions, we arrive at a final background baseline for $Y$, which is consistent with the isobar data. We extract an upper limit for the CME fraction in the $\Delta\gamma$ measurement of approximately $10\%$ at a $95\%$ confidence level on in isobar collisions at $\sqrt{s_{\text{NN}}} = 200$ GeV, with an expected $15\%$ difference in their squared magnetic fields., Comment: 19 pages, 14 figures

Published
2023
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20. Results on Elastic Cross Sections in Proton-Proton Collisions at $\sqrt{s} = 510$ GeV with the STAR Detector at RHIC

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matis, H. S., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
High Energy Physics - Experiment, Nuclear Experiment
Abstract

We report results on an elastic cross section measurement in proton-proton collisions at a center-of-mass energy $\sqrt{s}=510$ GeV, obtained with the Roman Pot setup of the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The elastic differential cross section is measured in the four-momentum transfer squared range $0.23 \leq -t \leq 0.67$ GeV$^2$. We find that a constant slope $B$ does not fit the data in the aforementioned $t$ range, and we obtain a much better fit using a second-order polynomial for $B(t)$. The $t$ dependence of $B$ is determined using six subintervals of $t$ in the STAR measured $t$ range, and is in good agreement with the phenomenological models. The measured elastic differential cross section $\mathrm{d}\sigma/\mathrm{dt}$ agrees well with the results obtained at $\sqrt{s} = 546$ GeV for proton--antiproton collisions by the UA4 experiment. We also determine that the integrated elastic cross section within the STAR $t$-range is $\sigma^\mathrm{fid}_\mathrm{el} = 462.1 \pm 0.9 (\mathrm{stat.}) \pm 1.1 (\mathrm {syst.}) \pm 11.6 (\mathrm {scale})$~$\mu\mathrm{b}$., Comment: 9 pages, 9 figures Version as published in Physics Letters B. HEPDATA: https://www.hepdata.net/record/144920

Published
2023
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21. Longitudinal and transverse spin transfer to $\Lambda$ and $\overline{\Lambda}$ hyperons in polarized $p$+$p$ collisions at $\sqrt{s} = 200$ GeV

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Anderson, D. M., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Baker, W., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Daugherity, M., Deppner, I. M., Dhamija, A., Di Carlo, L., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Ewigleben, A., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kabir, M. L., Kagamaster, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kelsey, M., Khyzhniak, Y. V., Kikoła, D. P., Kimelman, B., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kramarik, L., Kumar, L., Kumar, S., Elayavalli, R. Kunnawalkam, Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lisa, M. A., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Llope, W. J., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Markert, C., Matis, H. S., Mazer, J. A., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Mukherjee, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nishitani, R., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pan, J., Pandav, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Raha, N., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Szymanski, P. R., Tamis, A., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Tomkiel, C. A., Trentalange, S., Tribble, R. E., Tribedy, P., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Wu, Y., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
High Energy Physics - Experiment
Abstract

The longitudinal and transverse spin transfers to $\Lambda$ ($\overline{\Lambda}$) hyperons in polarized proton-proton collisions are expected to be sensitive to the helicity and transversity distributions, respectively, of (anti-)strange quarks in the proton, and to the corresponding polarized fragmentation functions. We report improved measurements of the longitudinal spin transfer coefficient, $D_{LL}$, and the transverse spin transfer coefficient, $D_{TT}$, to $\Lambda$ and $\overline{\Lambda}$ in polarized proton-proton collisions at $\sqrt{s}$ = 200 GeV by the STAR experiment at RHIC. The data set includes longitudinally polarized proton-proton collisions with an integrated luminosity of 52 pb$^{-1}$, and transversely polarized proton-proton collisions with a similar integrated luminosity. Both data sets have about twice the statistics of previous results and cover a kinematic range of $|\eta_{\Lambda(\overline{\Lambda})}|$ $<$ 1.2 and transverse momentum $p_{T,{\Lambda(\overline{\Lambda})}}$ up to 8 GeV/$c$. We also report the first measurements of the hyperon spin transfer coefficients $D_{LL}$ and $D_{TT}$ as a function of the fractional jet momentum $z$ carried by the hyperon, which can provide more direct constraints on the polarized fragmentation functions.

Published
2023
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22. Reaction plane correlated triangular flow in Au+Au collisions at $\sqrt{s_{NN}}=3$ GeV

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matis, H. S., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pandav, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment
Abstract

We measure triangular flow relative to the reaction plane at 3 GeV center-of-mass energy in Au+Au collisions at the BNL Relativistic Heavy Ion Collider. A significant $v_3$ signal for protons is observed, which increases for higher rapidity, higher transverse momentum, and more peripheral collisions. The triangular flow is essentially rapidity-odd with a slope at mid-rapidity, $dv_3/dy|_{(y=0)}$, opposite in sign compared to the slope for directed flow. No significant $v_3$ signal is observed for charged pions and kaons. Comparisons with models suggest that a mean field potential is required to describe these results, and that the triangular shape of the participant nucleons is the result of stopping and nuclear geometry., Comment: 12 pages, 14 figures

Published
2023
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23. Upper Limit on the Chiral Magnetic Effect in Isobar Collisions at the Relativistic Heavy-Ion Collider

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adams, J. R., Agakishiev, G., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aitbaev, A., Alekseev, I., Alpatov, E., Aparin, A., Aslam, S., Atchison, J., Averichev, G. S., Bairathi, V., Cap, J. G. Ball, Barish, K., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bordyuzhin, I. G., Brandenburg, J. D., Brandin, A. V., Broodo, C., Cai, X. Z., Caines, H., Calderón~de~la~Barca~Sánchez, M., Cebra, D., Ceska, J., Chakaberia, I., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Dale-Gau, G., Das, A., Dedovich, T. G., Deppner, I. M., Derevschikov, A. A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Hamed, A., Han, Y., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kechechyan, A., Khanal, A., Kiselev, A., Knospe, A. G., Ko, H. S., Kochenda, L., Korobitsin, A. A., Kraeva, A. Yu., Kravtsov, P., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lebedev, A., Lednicky, R., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Lin, T., Lin, Y., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Luong, V. B., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Manikandhan, R., Margetis, S., Matis, H. S., McNamara, G., Mezhanska, O., Mi, K., Minaev, N. G., Mohanty, B., Mondal, M. M., Mooney, I., Morozov, D. A., Mudrokh, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Nedorezov, E., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nogach, L. V., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okorokov, V. A., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pandey, A. K., Panebratsev, Y., Pani, T., Parfenov, P., Paul, A., Perkins, C., Pokhrel, B. R., Posik, M., Povarov, A., Protzman, T., Pruthi, N. K., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Ritter, H. G., Robertson, C. W., Rogachevsky, O. V., Aguilar, M. A. Rosales, Roy, D., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Samigullin, E., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seger, J., Seto, R., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stewart, D. J., Strikhanov, M., Stringfellow, B., Su, Y., Sun, C., Sun, X., Sun, Y., Surrow, B., Svirida, D. N., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Taranenko, A., Tarnowsky, T., Thomas, J. H., Tlusty, D., Todoroki, T., Tokarev, M. V., Trentalange, S., Tribedy, P., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vasiliev, A. N., Verkest, V., Videbæk, F., Vokal, S., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wieman, H., Wilks, G., Wissink, S. W., Wu, J., Wu, X., Wu, Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment, Nuclear Theory
Abstract

The chiral magnetic effect (CME) is a phenomenon that arises from the QCD anomaly in the presence of an external magnetic field. The experimental search for its evidence has been one of the key goals of the physics program of the Relativistic Heavy-Ion Collider. The STAR collaboration has previously presented the results of a blind analysis of isobar collisions (${^{96}_{44}\text{Ru}}+{^{96}_{44}\text{Ru}}$, ${^{96}_{40}\text{Zr}}+{^{96}_{40}\text{Zr}}$) in the search for the CME. The isobar ratio ($Y$) of CME-sensitive observable, charge separation scaled by elliptic anisotropy, is close to but systematically larger than the inverse multiplicity ratio, the naive background baseline. This indicates the potential existence of a CME signal and the presence of remaining nonflow background due to two- and three-particle correlations, which are different between the isobars. In this post-blind analysis, we estimate the contributions from those nonflow correlations as a background baseline to $Y$, utilizing the isobar data as well as Heavy Ion Jet Interaction Generator simulations. This baseline is found consistent with the isobar ratio measurement, and an upper limit of 10% at 95% confidence level is extracted for the CME fraction in the charge separation measurement in isobar collisions at $\sqrt{s_{\rm NN}}=200$ GeV., Comment: 8 pages, 4 figures

Published
2023
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24. Jet-hadron correlations with respect to the event plane in $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV Au+Au collisions in STAR

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Daugherity, M., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, H-S., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Markert, C., Matis, H. S., Mazer, J., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pandav, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

Angular distributions of charged particles relative to jet axes are studied in $\sqrt{s_{\mathrm{NN}}}$ = 200 GeV Au+Au collisions as a function of the jet orientation with respect to the event plane. This differential study tests the expected path-length dependence of energy loss experienced by a hard-scattered parton as it traverses the hot and dense medium formed in heavy-ion collisions. A second-order event plane is used in the analysis as an experimental estimate of the reaction plane formed by the collision impact parameter and the beam direction. Charged-particle jets with $15 < p_{\rm T, jet} <$ 20 and $20 < p_{\rm T, jet} <$ 40 GeV/$c$ were reconstructed with the anti-$k_{\rm T}$ algorithm with radius parameter setting of (R=0.4) in the 20-50\% centrality bin to maximize the initial-state eccentricity of the interaction region. The reaction plane fit method is implemented to remove the flow-modulated background with better precision than prior methods. Yields and widths of jet-associated charged-hadron distributions are extracted in three angular bins between the jet axis and the event plane. The event-plane (EP) dependence is further quantified by ratios of the associated yields in different EP bins. No dependence on orientation of the jet axis with respect to the event plane is seen within the uncertainties in the kinematic regime studied. This finding is consistent with a similar experimental observation by ALICE in $\sqrt{s_{\mathrm{NN}}}$ = 2.76 TeV Pb+Pb collision data.

Published
2023

25. Search Me Knot, Render Me Knot: Embedding Search and Differentiable Rendering of Knots in 3D

Author

Gangopadhyay, Aalok, Gupta, Paras, Sharma, Tarun, Singh, Prajwal, and Raman, Shanmuganathan

Subjects
Computer Science - Graphics
Abstract

We introduce the problem of knot-based inverse perceptual art. Given multiple target images and their corresponding viewing configurations, the objective is to find a 3D knot-based tubular structure whose appearance resembles the target images when viewed from the specified viewing configurations. To solve this problem, we first design a differentiable rendering algorithm for rendering tubular knots embedded in 3D for arbitrary perspective camera configurations. Utilizing this differentiable rendering algorithm, we search over the space of knot configurations to find the ideal knot embedding. We represent the knot embeddings via homeomorphisms of the desired template knot, where the homeomorphisms are parametrized by the weights of an invertible neural network. Our approach is fully differentiable, making it possible to find the ideal 3D tubular structure for the desired perceptual art using gradient-based optimization. We propose several loss functions that impose additional physical constraints, enforcing that the tube is free of self-intersection, lies within a predefined region in space, satisfies the physical bending limits of the tube material and the material cost is within a specified budget. We demonstrate through results that our knot representation is highly expressive and gives impressive results even for challenging target images in both single view as well as multiple view constraints. Through extensive ablation study we show that each of the proposed loss function is effective in ensuring physical realizability. We construct a real world 3D-printed object to demonstrate the practical utility of our approach. To the best of our knowledge, we are the first to propose a fully differentiable optimization framework for knot-based inverse perceptual art.

Published
2023

26. Single Image LDR to HDR Conversion using Conditional Diffusion

Author

Dalal, Dwip, Vashishtha, Gautam, Singh, Prajwal, and Raman, Shanmuganathan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes, resulting in under-/overexposed images. This paper presents a deep learning-based approach for recovering intricate details from shadows and highlights while reconstructing High Dynamic Range (HDR) images. We formulate the problem as an image-to-image (I2I) translation task and propose a conditional Denoising Diffusion Probabilistic Model (DDPM) based framework using classifier-free guidance. We incorporate a deep CNN-based autoencoder in our proposed framework to enhance the quality of the latent representation of the input LDR image used for conditioning. Moreover, we introduce a new loss function for LDR-HDR translation tasks, termed Exposure Loss. This loss helps direct gradients in the opposite direction of the saturation, further improving the results' quality. By conducting comprehensive quantitative and qualitative experiments, we have effectively demonstrated the proficiency of our proposed method. The results indicate that a simple conditional diffusion-based method can replace the complex camera pipeline-based architectures.

Published
2023

27. Evolution, identification, evaluation, and characterization of a stable salinity tolerant sugarcane variety CoG 7

Author

Rajaprakasam Sudhagar, Naaganoor Ananthan Saravanan, Rajmohan Kanchanarani, Murugappan Shanmuganathan, Subbarayan Ganapathy, Chakrapani Babu, Anbalagan Thirumurugan, Venugopal Ravichandran, Chinnaswamy Appunu, and Ayyadurai Anna Durai

Subjects
Medicine, Science
Abstract

Abstract From the fluff generated during 2005, after the preliminary experiments (2005–2007), a promising clone G2005047 has been identified. It showed moderate resistance to red rot (3.6 on a 9-scale scoring system), less susceptibility to shoot borer (13.25%) and internode borers (25.35%), and resistance to woolly aphid (0%). In the Advanced Yield Trials (2008–2011), it showed advantages over check for cane yield (CY) (11.79%), commercial cane sugar percent (CCSP) (0.35%), and sugar yield (SY) (20.33%). To ascertain its large-scale cultivation suitability, it has experimented under adaptive research trials (2012–2014) at farmers’ fields. It exhibited 18.04%, 1.27%, and 19.55% supremacy over the check Co 86032 for CY, CCSP, and SY respectively. The stability of G2005047 under salinity was ascertained through a multi-environment-based experiment (2015–2017). AMMI (Additive Main-effects and Multiplicative Interactions) and GGE (Genotype × Genotype-Environment interaction) biplots were utilized. ANOVA revealed that the genotypic variation exerted the most significant effect followed by genotype × environment interaction and environment. G2005047 had the highest mean values for yield and quality traits with minimal ASV (AMMI stability value) (2.38:CY; 0.57: CCSP; & 0.58:SY) indicating its good-yielding ability and stability. AMMI I, AMMI II, and GGE biplots confirmed the stability of G2005047. In the jaggery quality assessment trials (2018 and 2019), it yielded 37.1% increased jaggery over the check. Also, the clone G2005047, exhibited moderate resistance to red rot disease, less susceptibility to shoot borer (13.25%) and internode borer (25.35%), and resistance against sugarcane woolly aphid (SWA). Due to supremacy for yield, quality, better performance under salinized situations, and tolerance to disease and pests, the clone G2005047 was released as a variety CoG 7 in 2022.

Published
2024
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28. A Graph Neural Network Approach for Temporal Mesh Blending and Correspondence

Author

Gangopadhyay, Aalok, Harish, Abhinav Narayan, Singh, Prajwal, and Raman, Shanmuganathan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract

We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence. To solve this problem, we have developed Red-Blue MPNN, a novel graph neural network that processes an augmented graph to estimate the correspondence. We have designed a novel conditional refinement scheme to find the exact correspondence when certain conditions are satisfied. We further develop a graph neural network that takes the aligned meshes and the time value as input and fuses this information to process further and generate the desired result. Using motion capture datasets and human mesh designing software, we create a large-scale synthetic dataset consisting of temporal sequences of human meshes in motion. Our results demonstrate that our approach generates realistic deformation of body parts given complex inputs.

Published
2023

29. Predicting Pulmonary Hypertension by Electrocardiograms Using Machine Learning

Author

Kosaraju, Eashan and Shanmuganathan, Praveen Kumar Pandian

Subjects
Electrical Engineering and Systems Science - Signal Processing, Computer Science - Machine Learning
Abstract

Pulmonary hypertension (PH) is a condition of high blood pressure that affects the arteries in the lungs and the right side of the heart (Mayo Clinic, 2017). A mean pulmonary artery pressure greater than 25 mmHg is defined as Pulmonary hypertension. The estimated 5-year survival rate from the time of diagnosis of pulmonary hypertension is only 57% without therapy and patients with right heart failure only survive for approximately 1 year without treatment (Benza et al., 2012). Given the indolent nature of the disease, early detection of PH remains a challenge leading to delays in therapy. Echocardiography is currently used as a screening tool for diagnosing PH. However, electrocardiography (ECG), a more accessible, simple to use, and cost-effective tool compared to echocardiography, is less studied and explored for screening at-risk patients for PH. The goal of this project is to create a neural network model which can process an ECG signal and detect the presence of PH with a confidence probability. I created a dense neural network (DNN) model that has an accuracy of 98% over the available training sample. For future steps, the current model will be updated with a model suited for time-series data. To balance the dataset with proper training samples, I will generate additional data using data augmentation techniques. Through early and accurate detection of conditions such as PH, we widen the spectrum of innovation in detecting chronic life-threatening health conditions and reduce associated mortality and morbidity., Comment: 19 pages, 5 figures

Published
2023

30. Collision-energy Dependence of Deuteron Cumulants and Proton-deuteron Correlations in Au+Au collisions at RHIC

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Broodo, C., Cai, X. Z., Caines, H., Sánchez, M. Calderónde la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Deppner, I. M., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Khanal, A., Khyzhniak, Y. V., Kikoła, D. P., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, D., Li, H-S., Li, H., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Luo, J., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Manikandhan, R., Margetis, S., Markert, C., Matis, H. S., McNamara, G., Mezhanska, O., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pal, S., Pandav, A., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Trentalange, S., Tribedy, P., Tripathy, S. K., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, K., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment
Abstract

We report the first measurements of cumulants, up to $4^{th}$ order, of deuteron number distributions and proton-deuteron correlations in Au+Au collisions recorded by the STAR experiment in phase-I of Beam Energy Scan (BES) program at the Relativistic Heavy Ion Collider. Deuteron cumulants, their ratios, and proton-deuteron mixed cumulants are presented for different collision centralities covering a range of center-of-mass energy per nucleon pair $\sqrt{s_{NN}}$~=~7.7 to 200~GeV. It is found that the cumulant ratios at lower collision energies favor a canonical ensemble over a grand canonical ensemble in thermal models. An anti-correlation between proton and deuteron multiplicity is observed across all collision energies and centralities, consistent with the expectation from global baryon number conservation. The UrQMD model coupled with a phase-space coalescence mechanism qualitatively reproduces the collision-energy dependence of cumulant ratios and proton-deuteron correlations., Comment: 10 pages, 4 figures; published in Physics Letters B

Published
2023
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31. Event-by-event correlations between $\Lambda$ ($\bar{\Lambda}$) hyperon global polarization and handedness with charged hadron azimuthal separation in Au+Au collisions at $\sqrt{s_{\text{NN}}} = 27 \text{ GeV}$ from STAR

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adams, J. R., Agakishiev, G., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aitbaev, A., Alekseev, I., Anderson, D. M., Aparin, A., Aslam, S., Atchison, J., Averichev, G. S., Bairathi, V., Baker, W., Cap, J. G. Ball, Barish, K., Bhagat, P., Bhasin, A., Bhatta, S., Bordyuzhin, I. G., Brandenburg, J. D., Brandin, A. V., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Dale-Gau, G., Das, A., Daugherity, M., Dedovich, T. G., Deppner, I. M., Derevschikov, A. A., Dhamija, A., Di Carlo, L., Didenko, L., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Ewigleben, A., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Hamed, A., Han, Y., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kabir, M. L., Kalinkin, D., Kang, K., Kapukchyan, D., Keane, D., Kechechyan, A., Kelsey, M., Kimelman, B., Kiselev, A., Knospe, A. G., Ko, H. S., Kochenda, L., Korobitsin, A. A., Kravtsov, P., Kumar, L., Kumar, S., Elayavalli, R. Kunnawalkam, Lacey, R., Landgraf, J. M., Lebedev, A., Lednicky, R., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Lin, T., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Llope, W. J., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Luong, V. B., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Matis, H. S., Mazer, J. A., McNamara, G., Mi, K., Minaev, N. G., Mohanty, B., Mondal, M. M., Mooney, I., Morozov, D. A., Mudrokh, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nishitani, R., Nogach, L. V., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okorokov, V. A., Okubo, K., Page, B. S., Pak, R., Pan, J., Pandav, A., Pandey, A. K., Panebratsev, Y., Pani, T., Parfenov, P., Paul, A., Perkins, C., Pokhrel, B. R., Posik, M., Protzman, T., Pruthi, N. K., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Raha, N., Ray, R. L., Ritter, H. G., Robertson, C. W., Rogachevsky, O. V., Aguilar, M. A. Rosales, Roy, D., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Samigullin, E., Sato, S., Schmidke, W. B., Schmitz, N., Seger, J., Seto, R., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stewart, D. J., Strikhanov, M., Stringfellow, B., Su, Y., Sun, C., Sun, X., Sun, Y., Surrow, B., Svirida, D. N., Sweger, Z. W., Tamis, A., Tang, A. H., Tang, Z., Taranenko, A., Tarnowsky, T., Thomas, J. H., Tlusty, D., Todoroki, T., Tokarev, M. V., Tomkiel, C. A., Trentalange, S., Tribble, R. E., Tribedy, P., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vasiliev, A. N., Verkest, V., Videbæk, F., Vokal, S., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wieman, H., Wilks, G., Wissink, S. W., Wu, J., Wu, X., Wu, Y., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment
Abstract

Global polarizations ($P$) of $\Lambda$ ($\bar{\Lambda}$) hyperons have been observed in non-central heavy-ion collisions. The strong magnetic field primarily created by the spectator protons in such collisions would split the $\Lambda$ and $\bar{\Lambda}$ global polarizations ($\Delta P = P_{\Lambda} - P_{\bar{\Lambda}} < 0$). Additionally, quantum chromodynamics (QCD) predicts topological charge fluctuations in vacuum, resulting in a chirality imbalance or parity violation in a local domain. This would give rise to an imbalance ($\Delta n = \frac{N_{\text{L}} - N_{\text{R}}}{\langle N_{\text{L}} + N_{\text{R}} \rangle} \neq 0$) between left- and right-handed $\Lambda$ ($\bar{\Lambda}$) as well as a charge separation along the magnetic field, referred to as the chiral magnetic effect (CME). This charge separation can be characterized by the parity-even azimuthal correlator ($\Delta\gamma$) and parity-odd azimuthal harmonic observable ($\Delta a_{1}$). Measurements of $\Delta P$, $\Delta\gamma$, and $\Delta a_{1}$ have not led to definitive conclusions concerning the CME or the magnetic field, and $\Delta n$ has not been measured previously. Correlations among these observables may reveal new insights. This paper reports measurements of correlation between $\Delta n$ and $\Delta a_{1}$, which is sensitive to chirality fluctuations, and correlation between $\Delta P$ and $\Delta\gamma$ sensitive to magnetic field in Au+Au collisions at 27 GeV. For both measurements, no correlations have been observed beyond statistical fluctuations., Comment: 10 pages, 10 figures; paper from the STAR Collaboration

Published
2023
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32. Observation of the electromagnetic field effect via charge-dependent directed flow in heavy-ion collisions at the Relativistic Heavy Ion Collider

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adams, J. R., Agakishiev, G., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aitbaev, A., Alekseev, I., Alpatov, E., Aparin, A., Aslam, S., Atchison, J., Averichev, G. S., Bairathi, V., Cap, J. G. Ball, Barish, K., Bhagat, P., Bhasin, A., Bhatta, S., Bhosale, S. R., Bordyuzhin, I. G., Brandenburg, J. D., Brandin, A. V., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Dale-Gau, G., Das, A., Dash, A. P., Daugherity, M., Dedovich, T. G., Deppner, I. M., Derevschikov, A. A., Dhamija, A., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Hamed, A., Han, Y., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kechechyan, A., Kiselev, A., Knospe, A. G., Ko, H. S., Kochenda, L., Korobitsin, A. A., Kraeva, A. Yu., Kravtsov, P., Kumar, L., Labonte, M. C., Lacey, R., Landgraf, J. M., Lebedev, A., Lednicky, R., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, H-S., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Lin, T., Lin, Y., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Luong, V. B., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Matis, H. S., McNamara, G., Mi, K., Minaev, N. G., Mohanty, B., Mondal, M. M., Mooney, I., Morozov, D. A., Mudrokh, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Nedorezov, E., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nogach, L. V., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okorokov, V. A., Okubo, K., Page, B. S., Pak, R., Pandav, A., Panebratsev, Y., Pani, T., Parfenov, P., Paul, A., Perkins, C., Pokhrel, B. R., Posik, M., Povarov, A., Protzman, T., Pruthi, N. K., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Rana, A., Ray, R. L., Ritter, H. G., Robertson, C. W., Rogachevsky, O. V., Aguilar, M. A. Rosales, Roy, D., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Samigullin, E., Sato, S., Schaefer, B. C., Schmidke, W. B., Schmitz, N., Seger, J., Seto, R., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stewart, D. J., Strikhanov, M., Stringfellow, B., Su, Y., Sun, C., Sun, X., Sun, Y., Surrow, B., Svirida, D. N., Sweger, Z. W., Tamis, A. C., Tang, A. H., Tang, Z., Taranenko, A., Tarnowsky, T., Thomas, J. H., Tlusty, D., Todoroki, T., Tokarev, M. V., Trentalange, S., Tribedy, P., Tripathy, S. K., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vasiliev, A. N., Verkest, V., Videbæk, F., Vokal, S., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wieman, H., Wilks, G., Wissink, S. W., Wu, J., Wu, X., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

The deconfined quark-gluon plasma (QGP) created in relativistic heavy-ion collisions enables the exploration of the fundamental properties of matter under extreme conditions. Non-central collisions can produce strong magnetic fields on the order of $10^{18}$ Gauss, which offers a probe into the electrical conductivity of the QGP. In particular, quarks and anti-quarks carry opposite charges and receive contrary electromagnetic forces that alter their momenta. This phenomenon can be manifested in the collective motion of final-state particles, specifically in the rapidity-odd directed flow, denoted as $v_1(\mathsf{y})$. Here we present the charge-dependent measurements of $dv_1/d\mathsf{y}$ near midrapidities for $\pi^{\pm}$, $K^{\pm}$, and $p(\bar{p})$ in Au+Au and isobar ($_{44}^{96}$Ru+$_{44}^{96}$Ru and $_{40}^{96}$Zr+$_{40}^{96}$Zr) collisions at $\sqrt{s_{\rm NN}}=$ 200 GeV, and in Au+Au collisions at 27 GeV, recorded by the STAR detector at the Relativistic Heavy Ion Collider. The combined dependence of the $v_1$ signal on collision system, particle species, and collision centrality can be qualitatively and semi-quantitatively understood as several effects on constituent quarks. While the results in central events can be explained by the $u$ and $d$ quarks transported from initial-state nuclei, those in peripheral events reveal the impacts of the electromagnetic field on the QGP. Our data put valuable constraints on the electrical conductivity of the QGP in theoretical calculations.

Published
2023

33. Measuring the effect of newborn screening on survival after haematopoietic cell transplantation for severe combined immunodeficiency: a 36-year longitudinal study from the Primary Immune Deficiency Treatment Consortium.

Author

Thakar, Monica, Logan, Brent, Puck, Jennifer, Dunn, Elizabeth, Buckley, Rebecca, Cowan, Morton, OReilly, Richard, Kapoor, Neena, Satter, Lisa, Pai, Sung-Yun, Heimall, Jennifer, Chandra, Sharat, Ebens, Christen, Chellapandian, Deepak, Williams, Olatundun, Burroughs, Lauri, Saldana, Blachy, Rayes, Ahmad, Madden, Lisa, Chandrakasan, Shanmuganathan, Bednarski, Jeffrey, DeSantes, Kenneth, Cuvelier, Geoffrey, Teira, Pierre, Gillio, Alfred, Eissa, Hesham, Knutsen, Alan, Goldman, Frederick, Aquino, Victor, Shereck, Evan, Moore, Theodore, Caywood, Emi, Lugt, Mark, Rozmus, Jacob, Broglie, Larisa, Yu, Lolie, Shah, Ami, Andolina, Jeffrey, Liu, Xuerong, Parrott, Roberta, Dara, Jasmeen, Prockop, Susan, Martinez, Caridad, Kapadia, Malika, Jyonouchi, Soma, Sullivan, Kathleen, Bleesing, Jack, Chaudhury, Sonali, Petrovic, Aleksandra, Keller, Michael, Quigg, Troy, Parikh, Suhag, Shenoy, Shalini, Seroogy, Christine, Rubin, Tamar, Decaluwe, Hélène, Routes, John, Torgerson, Troy, Leiding, Jennifer, Pulsipher, Michael, Kohn, Donald, Griffith, Linda, Haddad, Elie, Dvorak, Christopher, and Notarangelo, Luigi

Subjects
Humans, Infant, Newborn, Graft vs Host Disease, Hematopoietic Stem Cell Transplantation, Longitudinal Studies, Neonatal Screening, Proportional Hazards Models, Severe Combined Immunodeficiency
Abstract

BACKGROUND: Severe combined immunodeficiency (SCID) is fatal unless durable adaptive immunity is established, most commonly through allogeneic haematopoietic cell transplantation (HCT). The Primary Immune Deficiency Treatment Consortium (PIDTC) explored factors affecting the survival of individuals with SCID over almost four decades, focusing on the effects of population-based newborn screening for SCID that was initiated in 2008 and expanded during 2010-18. METHODS: We analysed transplantation-related data from children with SCID treated at 34 PIDTC sites in the USA and Canada, using the calendar time intervals 1982-89, 1990-99, 2000-09, and 2010-18. Categorical variables were compared by χ2 test and continuous outcomes by the Kruskal-Wallis test. Overall survival was estimated by the Kaplan-Meier method. A multivariable analysis using Cox proportional hazards regression models examined risk factors for HCT outcomes, including the variables of time interval of HCT, infection status and age at HCT, trigger for diagnosis, SCID type and genotype, race and ethnicity of the patient, non-HLA-matched sibling donor type, graft type, GVHD prophylaxis, and conditioning intensity. FINDINGS: For 902 children with confirmed SCID, 5-year overall survival remained unchanged at 72%-73% for 28 years until 2010-18, when it increased to 87% (95% CI 82·1-90·6; n=268; p=0·0005). For children identified as having SCID by newborn screening since 2010, 5-year overall survival was 92·5% (95% CI 85·8-96·1), better than that of children identified by clinical illness or family history in the same interval (79·9% [69·5-87·0] and 85·4% [71·8-92·8], respectively [p=0·043]). Multivariable analysis demonstrated that the factors of active infection (hazard ratio [HR] 2·41, 95% CI 1·56-3·72; p

Published
2023

36. Comparative characterization of the infant gut microbiome and their maternal lineage by a multi-omics approach

Author

Barker-Tejeda, Tomás Clive, Zubeldia-Varela, Elisa, Macías-Camero, Andrea, Alonso, Lola, Martín-Antoniano, Isabel Adoración, Rey-Stolle, María Fernanda, Mera-Berriatua, Leticia, Bazire, Raphaëlle, Cabrera-Freitag, Paula, Shanmuganathan, Meera, Britz-McKibbin, Philip, Ubeda, Carles, Francino, M. Pilar, Barber, Domingo, Ibáñez-Sandín, María Dolores, Barbas, Coral, Pérez-Gordo, Marina, and Villaseñor, Alma

Published
2024
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38. Secondary bone marrow graft loss after third-party virus-specific T cell infusion: Case report of a rare complication

Author

Keller, Michael D., Schattgen, Stefan A., Chandrakasan, Shanmuganathan, Allen, E. Kaitlynn, Jensen-Wachspress, Mariah A., Lazarski, Christopher A., Qayed, Muna, Lang, Haili, Hanley, Patrick J., Tanna, Jay, Pai, Sung-Yun, Parikh, Suhag, Berger, Seth I., Gottschalk, Stephen, Pulsipher, Michael A., Thomas, Paul G., and Bollard, Catherine M.

Published
2024
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44. Hyperon polarization along the beam direction relative to the second and third harmonic event planes in isobar collisions at $\sqrt{s_{NN}}$ = 200 GeV

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adams, J. R., Agakishiev, G., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Aitbaev, A., Alekseev, I., Anderson, D. M., Aparin, A., Aslam, S., Atchison, J., Averichev, G. S., Bairathi, V., Baker, W., Cap, J. G. Ball, Barish, K., Bhagat, P., Bhasin, A., Bhatta, S., Bordyuzhin, I. G., Brandenburg, J. D., Brandin, A. V., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Dale-Gau, G., Das, A., Daugherity, M., Dedovich, T. G., Deppner, I. M., Derevschikov, A. A., Dhamija, A., Di Carlo, L., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Ewigleben, A., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Hamed, A., Han, Y., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kabir, M. L., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kechechyan, A., Kelsey, M., Kimelman, B., Kiselev, A., Knospe, A. G., Ko, H. S., Kochenda, L., Korobitsin, A. A., Kravtsov, P., Kumar, L., Kumar, S., Elayavalli, R. Kunnawalkam, Lacey, R., Landgraf, J. M., Lebedev, A., Lednicky, R., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Lin, T., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Llope, W. J., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Luong, V. B., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Matis, H. S., Mazer, J. A., McNamara, G., Mi, K., Minaev, N. G., Mohanty, B., Mondal, M. M., Mooney, I., Morozov, D. A., Mudrokh, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nishitani, R., Nogach, L. V., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okorokov, V. A., Okubo, K., Page, B. S., Pak, R., Pan, J., Pandav, A., Pandey, A. K., Panebratsev, Y., Pani, T., Parfenov, P., Paul, A., Perkins, C., Pokhrel, B. R., Posik, M., Protzman, T., Pruthi, N. K., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Raha, N., Ray, R. L., Ritter, H. G., Robertson, C. W., Rogachevsky, O. V., Aguilar, M. A. Rosales, Roy, D., Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Samigullin, E., Sato, S., Schmidke, W. B., Schmitz, N., Seger, J., Seto, R., Seyboth, P., Shah, N., Shahaliev, E., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stewart, D. J., Strikhanov, M., Stringfellow, B., Su, Y., Sun, C., Sun, X., Sun, Y., Surrow, B., Svirida, D. N., Sweger, Z. W., Tamis, A., Tang, A. H., Tang, Z., Taranenko, A., Tarnowsky, T., Thomas, J. H., Tlusty, D., Todoroki, T., Tokarev, M. V., Tomkiel, C. A., Trentalange, S., Tribble, R. E., Tribedy, P., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vasiliev, A. N., Verkest, V., Videbæk, F., Vokal, S., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wieman, H., Wilks, G., Wissink, S. W., Wu, J., Wu, X., Wu, Y., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment
Abstract

The polarization of $\Lambda$ and $\bar{\Lambda}$ hyperons along the beam direction has been measured relative to the second and third harmonic event planes in isobar Ru+Ru and Zr+Zr collisions at $\sqrt{s_{NN}}$ = 200 GeV. This is the first experimental evidence of the hyperon polarization by the triangular flow originating from the initial density fluctuations. The amplitudes of the sine modulation for the second and third harmonic results are comparable in magnitude, increase from central to peripheral collisions, and show a mild $p_T$ dependence. The azimuthal angle dependence of the polarization follows the vorticity pattern expected due to elliptic and triangular anisotropic flow, and qualitatively disagree with most hydrodynamic model calculations based on thermal vorticity and shear induced contributions. The model results based on one of existing implementations of the shear contribution lead to a correct azimuthal angle dependence, but predict centrality and $p_T$ dependence that still disagree with experimental measurements. Thus, our results provide stringent constraints on the thermal vorticity and shear-induced contributions to hyperon polarization. Comparison to previous measurements at RHIC and the LHC for the second-order harmonic results shows little dependence on the collision system size and collision energy., Comment: 6 pages, 5 figures, Published in Physical Review Letters

Published
2023
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45. Measurement of electrons from open heavy-flavor hadron decays in Au+Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV with the STAR detector

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Anderson, D. M., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Baker, W., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Daugherity, M., Deppner, I. M., Dhamija, A., Di Carlo, L., Didenko, L., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Ewigleben, A., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kabir, M. L., Kagamaster, S., Kalinkin, D., Kang, K., Kapukchyan, D., Keane, D., Kelsey, M., Khyzhniak, Y. V., Kikoła, D. P., Kimelman, B., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kramarik, L., Kumar, L., Kumar, S., Elayavalli, R. Kunnawalkam, Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lisa, M. A., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Llope, W. J., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Markert, C., Matis, H. S., Mazer, J. A., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Mukherjee, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Niida, T., Nishitani, R., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pan, J., Pandav, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Raha, N., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Szymanski, P., Tamis, A., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Tomkiel, C. A., Trentalange, S., Tribble, R. E., Tribedy, P., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Wu, Y., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment
Abstract

We report a new measurement of the production of electrons from open heavy-flavor hadron decays (HFEs) at mid-rapidity ($|y|<$ 0.7) in Au+Au collisions at $\sqrt{s_{\rm NN}}=200$ GeV. Invariant yields of HFEs are measured for the transverse momentum range of $3.5 < p_{\rm T} < 9$ GeV/$c$ in various configurations of the collision geometry. The HFE yields in head-on Au+Au collisions are suppressed by approximately a factor of 2 compared to that in $p$+$p$ collisions scaled by the average number of binary collisions, indicating strong interactions between heavy quarks and the hot and dense medium created in heavy-ion collisions. Comparison of these results with models provides additional tests of theoretical calculations of heavy quark energy loss in the quark-gluon plasma.

Published
2023
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46. Elliptic Flow of Heavy-Flavor Decay Electrons in Au+Au Collisions at $\sqrt{s_{_{\rm NN}}}$ = 27 and 54.4 GeV at RHIC

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Anderson, D. M., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Baker, W., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Cai, X. Z., Caines, H., Calderón~de~la~Barca~Sánchez, M., Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Daugherity, M., Deppner, I. M., Dhamija, A., Di Carlo, L., Didenko, L., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Ewigleben, A., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kabir, M. L., Kagamaster, S., Kalinkin, D., Kang, K., Kapukchyan, D., Keane, D., Kelsey, M., Khyzhniak, Y. V., Kikoła, D. P., Kimelman, B., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kramarik, L., Kumar, L., Kumar, S., Elayavalli, R. Kunnawalkam, Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lisa, M. A., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Llope, W. J., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Markert, C., Matis, H. S., Mazer, J. A., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Mukherjee, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Niida, T., Nishitani, R., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pan, J., Pandav, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Raha, N., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Szymanski, P., Tamis, A., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Tomkiel, C. A., Trentalange, S., Tribble, R. E., Tribedy, P., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Wu, Y., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment, High Energy Physics - Experiment
Abstract

We report on new measurements of elliptic flow ($v_2$) of electrons from heavy-flavor hadron decays at mid-rapidity ($|y|<0.8$) in Au+Au collisions at $\sqrt{s_{_{\rm NN}}}$ = 27 and 54.4 GeV from the STAR experiment. Heavy-flavor decay electrons ($e^{\rm HF}$) in Au+Au collisions at $\sqrt{s_{_{\rm NN}}}$ = 54.4 GeV exhibit a non-zero $v_2$ in the transverse momentum ($p_{\rm T}$) region of $p_{\rm T}<$ 2 GeV/$c$ with the magnitude comparable to that at $\sqrt{s_{_{\rm NN}}}=200$ GeV. The measured $e^{\rm HF}$ $v_2$ at 54.4 GeV is also consistent with the expectation of their parent charm hadron $v_2$ following number-of-constituent-quark scaling as other light and strange flavor hadrons at this energy. These suggest that charm quarks gain significant collectivity through the evolution of the QCD medium and may reach local thermal equilibrium in Au+Au collisions at $\sqrt{s_{_{\rm NN}}}=54.4$ GeV. The measured $e^{\rm HF}$ $v_2$ in Au+Au collisions at $\sqrt{s_{_{\rm NN}}}=$ 27 GeV is consistent with zero within large uncertainties. The energy dependence of $v_2$ for different flavor particles ($\pi,\phi,D^{0}/e^{\rm HF}$) shows an indication of quark mass hierarchy in reaching thermalization in high-energy nuclear collisions., Comment: 12 pages, 7 figures, 1 table

Published
2023
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47. EEG2IMAGE: Image Reconstruction from EEG Brain Signals

Author

Singh, Prajwal, Pandey, Pankaj, Miyapuram, Krishna, and Raman, Shanmuganathan

Subjects
Computer Science - Human-Computer Interaction, Quantitative Biology - Neurons and Cognition
Abstract

Reconstructing images using brain signals of imagined visuals may provide an augmented vision to the disabled, leading to the advancement of Brain-Computer Interface (BCI) technology. The recent progress in deep learning has boosted the study area of synthesizing images from brain signals using Generative Adversarial Networks (GAN). In this work, we have proposed a framework for synthesizing the images from the brain activity recorded by an electroencephalogram (EEG) using small-size EEG datasets. This brain activity is recorded from the subject's head scalp using EEG when they ask to visualize certain classes of Objects and English characters. We use a contrastive learning method in the proposed framework to extract features from EEG signals and synthesize the images from extracted features using conditional GAN. We modify the loss function to train the GAN, which enables it to synthesize 128x128 images using a small number of images. Further, we conduct ablation studies and experiments to show the effectiveness of our proposed framework over other state-of-the-art methods using the small EEG dataset., Comment: Accepted in ICASSP 2023

Published
2023

48. Energy Dependence of Intermittency for Charged Hadrons in Au+Au Collisions at RHIC

Author

STAR Collaboration, Abdulhamid, M. I., Aboona, B. E., Adam, J., Adamczyk, L., Adams, J. R., Aggarwal, I., Aggarwal, M. M., Ahammed, Z., Anderson, D. M., Aschenauer, E. C., Aslam, S., Atchison, J., Bairathi, V., Baker, W., Cap, J. G. Ball, Barish, K., Bellwied, R., Bhagat, P., Bhasin, A., Bhatta, S., Bielcik, J., Bielcikova, J., Brandenburg, J. D., Cai, X. Z., Caines, H., Sánchez, M. Calderón de la Barca, Cebra, D., Ceska, J., Chakaberia, I., Chaloupka, P., Chan, B. K., Chang, Z., Chatterjee, A., Chen, D., Chen, J., Chen, J. H., Chen, Z., Cheng, J., Cheng, Y., Choudhury, S., Christie, W., Chu, X., Crawford, H. J., Csanád, M., Dale-Gau, G., Das, A., Daugherity, M., Deppner, I. M., Dhamija, A., Di Carlo, L., Dixit, P., Dong, X., Drachenberg, J. L., Duckworth, E., Dunlop, J. C., Engelage, J., Eppley, G., Esumi, S., Evdokimov, O., Ewigleben, A., Eyser, O., Fatemi, R., Fazio, S., Feng, C. J., Feng, Y., Finch, E., Fisyak, Y., Flor, F. A., Fu, C., Gagliardi, C. A., Galatyuk, T., Gao, T., Geurts, F., Ghimire, N., Gibson, A., Gopal, K., Gou, X., Grosnick, D., Gupta, A., Guryn, W., Hamed, A., Han, Y., Harabasz, S., Harasty, M. D., Harris, J. W., Harrison-Smith, H., He, W., He, X. H., He, Y., Herrmann, N., Holub, L., Hu, C., Hu, Q., Hu, Y., Huang, H., Huang, H. Z., Huang, S. L., Huang, T., Huang, X., Huang, Y., Humanic, T. J., Isenhower, D., Isshiki, M., Jacobs, W. W., Jalotra, A., Jena, C., Jentsch, A., Ji, Y., Jia, J., Jin, C., Ju, X., Judd, E. G., Kabana, S., Kabir, M. L., Kagamaster, S., Kalinkin, D., Kang, K., Kapukchyan, D., Kauder, K., Keane, D., Kelsey, M., Khyzhniak, Y. V., Kikoła, D. P., Kimelman, B., Kincses, D., Kisel, I., Kiselev, A., Knospe, A. G., Ko, H. S., Kosarzewski, L. K., Kramarik, L., Kumar, L., Kumar, S., Elayavalli, R. Kunnawalkam, Lacey, R., Landgraf, J. M., Lauret, J., Lebedev, A., Lee, J. H., Leung, Y. H., Lewis, N., Li, C., Li, W., Li, X., Li, Y., Li, Z., Liang, X., Liang, Y., Licenik, R., Lin, T., Lin, Y., Lisa, M. A., Liu, C., Liu, F., Liu, G., Liu, H., Liu, L., Liu, T., Liu, X., Liu, Y., Liu, Z., Ljubicic, T., Llope, W. J., Lomicky, O., Longacre, R. S., Loyd, E. M., Lu, T., Lukow, N. S., Luo, X. F., Ma, L., Ma, R., Ma, Y. G., Magdy, N., Mallick, D., Margetis, S., Markert, C., Matis, H. S., Mazer, J. A., McNamara, G., Mi, K., Mioduszewski, S., Mohanty, B., Mondal, M. M., Mooney, I., Mukherjee, A., Nagy, M. I., Nain, A. S., Nam, J. D., Nasim, M., Neff, D., Nelson, J. M., Nemes, D. B., Nie, M., Nigmatkulov, G., Niida, T., Nishitani, R., Nonaka, T., Odyniec, G., Ogawa, A., Oh, S., Okubo, K., Page, B. S., Pak, R., Pan, J., Pandav, A., Pandey, A. K., Pani, T., Paul, A., Pawlik, B., Pawlowska, D., Perkins, C., Pluta, J., Pokhrel, B. R., Posik, M., Protzman, T., Prozorova, V., Pruthi, N. K., Przybycien, M., Putschke, J., Qin, Z., Qiu, H., Quintero, A., Racz, C., Radhakrishnan, S. K., Raha, N., Ray, R. L., Reed, R., Ritter, H. G., Robertson, C. W., Robotkova, M., Aguilar, M. A. Rosales, Roy, D., Chowdhury, P. Roy, Ruan, L., Sahoo, A. K., Sahoo, N. R., Sako, H., Salur, S., Sato, S., Schmidke, W. B., Schmitz, N., Seck, F-J., Seger, J., Seto, R., Seyboth, P., Shah, N., Shanmuganathan, P. V., Shao, T., Sharma, M., Sharma, N., Sharma, R., Sharma, S. R., Sheikh, A. I., Shen, D., Shen, D. Y., Shen, K., Shi, S. S., Shi, Y., Shou, Q. Y., Si, F., Singh, J., Singha, S., Sinha, P., Skoby, M. J., Smirnov, N., Söhngen, Y., Song, Y., Srivastava, B., Stanislaus, T. D. S., Stefaniak, M., Stewart, D. J., Stringfellow, B., Su, Y., Suaide, A. A. P., Sumbera, M., Sun, C., Sun, X., Sun, Y., Surrow, B., Sweger, Z. W., Szymanski, P., Tamis, A., Tang, A. H., Tang, Z., Tarnowsky, T., Thomas, J. H., Timmins, A. R., Tlusty, D., Todoroki, T., Tomkiel, C. A., Trentalange, S., Tribble, R. E., Tribedy, P., Truhlar, T., Trzeciak, B. A., Tsai, O. D., Tsang, C. Y., Tu, Z., Tyler, J., Ullrich, T., Underwood, D. G., Upsal, I., Van Buren, G., Vanek, J., Vassiliev, I., Verkest, V., Videbæk, F., Voloshin, S. A., Wang, F., Wang, G., Wang, J. S., Wang, J., Wang, X., Wang, Y., Wang, Z., Webb, J. C., Weidenkaff, P. C., Westfall, G. D., Wielanek, D., Wieman, H., Wilks, G., Wissink, S. W., Witt, R., Wu, J., Wu, X., Wu, Y., Xi, B., Xiao, Z. G., Xie, G., Xie, W., Xu, H., Xu, N., Xu, Q. H., Xu, Y., Xu, Z., Yan, G., Yan, Z., Yang, C., Yang, Q., Yang, S., Yang, Y., Ye, Z., Yi, L., Yip, K., Yu, Y., Zbroszczyk, H., Zha, W., Zhang, C., Zhang, D., Zhang, J., Zhang, S., Zhang, W., Zhang, X., Zhang, Y., Zhang, Z. J., Zhang, Z., Zhao, F., Zhao, J., Zhao, M., Zhou, C., Zhou, J., Zhou, S., Zhou, Y., Zhu, X., Zurek, M., and Zyzak, M.

Subjects
Nuclear Experiment
Abstract

Density fluctuations near the QCD critical point can be probed via an intermittency analysis in relativistic heavy-ion collisions. We report the first measurement of intermittency in Au$+$Au collisions at $\sqrt{s_\mathrm{_{NN}}}$ = 7.7-200 GeV measured by the STAR experiment at the Relativistic Heavy Ion Collider (RHIC). The scaled factorial moments of identified charged hadrons are analyzed at mid-rapidity and within the transverse momentum phase space. We observe a power-law behavior of scaled factorial moments in Au$+$Au collisions and a decrease in the extracted scaling exponent ($\nu$) from peripheral to central collisions. The $\nu$ is consistent with a constant for different collisions energies in the mid-central (10-40\%) collisions. Moreover, the $\nu$ in the 0-5\% most central Au$+$Au collisions exhibits a non-monotonic energy dependence that reaches a possible minimum around $\sqrt{s_\mathrm{_{NN}}}$ = 27 GeV. The physics implications on the QCD phase structure are discussed., Comment: 8 pages, 4 figures. Published in Physics Letters B

Published
2023
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49. Functional and Radiological Outcomes of All-Posterior Surgical Correction of Dystrophic Curves in Patients with Neurofibromatosis Type 1

Author

Ajoy Prasad Shetty, Jalaj Meena, Chandhan Murugan, Rounak Milton, Rishi Mugesh Kanna, and Shanmuganathan Rajasekaran

Subjects
neurofibromatosis 1, dystrophic curves, all-posterior approach, computer-assisted navigation, multiple anchor point technique, Medicine
Abstract

Study Design A retrospective cohort study. Purpose To determine outcomes following all-posterior surgery using computed tomography navigation, hybrid stabilization, and multiple anchor point techniques in patients with neurofibromatosis type 1 (NF-1) and dystrophic scoliosis. Overview of Literature Previous studies favored antero-posterior fusion as the most reliable method; however, approaching the spine anteriorly was fraught with significant complications. With the advent of computer assisted navigation and multiple anchor point method, posterior only approach is reporting successful outcomes. Methods This study included patients who underwent all-posterior surgical deformity correction for dystrophic NF-1 curves. Coronal and sagittal Cobbs angles, apical rotation, and the presence of dystrophic features were evaluated before surgery. Postoperatively, sagittal, coronal, and axial correction, implant position, and implant densities were evaluated. The decline in curve correction and implant-related complications were evaluated at follow-up. Clinical outcomes were evaluated using the Scoliosis Research Society-22 revised index. Results This study involved 50 patients with a mean age of 13.6 years and a mean follow-up duration of 5.52 years. With a mean coronal flexibility of 18.7%, the mean apical vertebral rotation (AVR), preoperative coronal Cobb angle, and sagittal kyphosis were 27.4°, 64.01°, and 47.70°, respectively. The postoperative mean coronal Cobb angle was 30.17° (p

Published
2024
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50. Does Magnetic Resonance Imaging Predict Neurological Deficit in Patients with Traumatic Lower Lumbar Fractures?

Author

Karthik Ramachandran, R Dinesh Iyer, Prashasth Belludi Suresh, Ajoy Prasad Shetty, Puspha Bhari Thippeswamy, Rishi Mugesh Kanna, and Shanmuganathan Rajasekaran

Subjects
lower lumbar fractures, neurological deficit, canal encroachment ratio, epidural hematoma, magnetic resonance imaging, Medicine
Abstract

Study Design A retrospective cohort study. Purpose This study aimed to understand the role of magnetic resonance imaging (MRI) in predicting neurological deficits in traumatic lower lumbar fractures (LLFs; L3–L5). Overview of Literature Despite studies on the radiological risk factors for neurological deficits in thoracolumbar fractures, very few have focused on LLFs. Moreover, the potential utility of MRI in LLFs has not been evaluated. Methods In total, 108 patients who underwent surgery for traumatic LLFs between January 2010 and January 2020 were reviewed to obtain their demographic details, injury level, and neurology status at the time of presentation (American Spinal Injury Association [ASIA] grade). Preoperative computed tomography scans were used to measure parameters such as anterior vertebral body height, posterior vertebral body height, loss of vertebral body height, local kyphosis, retropulsion of fracture fragment, interpedicular distance, canal compromise, sagittal transverse ratio, and presence of vertical lamina fracture. MRI was used to measure the canal encroachment ratio (CER), cross-sectional area of the thecal sac (CSAT), and presence of an epidural hematoma. Results Of the 108 patients, 9 (8.3%) had ASIA A, 4 (3.7%) had ASIA B, 17 (15.7%) had ASIA C, 21 (19.4%) had ASIA D, and 57 (52.9%) had ASIA E neurology upon admission. The Thoracolumbar Injury Classification and Severity score (p=0.000), CER (p=0.050), and CSAT (p=0.019) were found to be independently associated with neurological deficits on the multivariate analysis. The receiver operating characteristic curves showed that only CER (area under the curve [AUC], 0.926; 95% confidence interval [CI], 0.860–0.968) and CSAT (AUC, 0.963; 95% CI, 0.908–0.990) had good discriminatory ability, with the optimal cutoff of 50% and 65.3 mm2, respectively. Conclusions Based on the results, the optimal cutoff values of CER >50% and CSAT >65.3 mm2 can predict the incidence of neurological deficits in LLFs.

Published
2024
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