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  1. Free, publicly-accessible full text available January 1, 2023
  2. Free, publicly-accessible full text available April 28, 2023
  3. Abstract The selection of low-radioactive construction materials is of the utmost importance for rare-event searches and thus critical to the XENONnT experiment. Results of an extensive radioassay program are reported, in which material samples have been screened with gamma-ray spectroscopy, mass spectrometry, and $$^{222}$$ 222 Rn emanation measurements. Furthermore, the cleanliness procedures applied to remove or mitigate surface contamination of detector materials are described. Screening results, used as inputs for a XENONnT Monte Carlo simulation, predict a reduction of materials background ( $$\sim $$ ∼ 17%) with respect to its predecessor XENON1T. Through radon emanation measurements, the expected $$^{222}$$ 222more »Rn activity concentration in XENONnT is determined to be 4.2 ( $$^{+0.5}_{-0.7}$$ - 0.7 + 0.5 )  $$\upmu $$ μ Bq/kg, a factor three lower with respect to XENON1T. This radon concentration will be further suppressed by means of the novel radon distillation system.« less
    Free, publicly-accessible full text available July 1, 2023
  4. Free, publicly-accessible full text available July 1, 2023
  5. We propose an innovative machine learning paradigm enabling precision medicine for AD biomarker discovery. The paradigm tailors the imaging biomarker discovery process to individual characteristics of a given patient. We implement this paradigm using a newly developed learning-to-rank method 𝙿𝙻𝚃𝚁 . The 𝙿𝙻𝚃𝚁 model seamlessly integrates two objectives for joint optimization: pushing up relevant biomarkers and ranking among relevant biomarkers. The empirical study of 𝙿𝙻𝚃𝚁 conducted on the ADNI data yields promising results to identify and prioritize individual-specific amyloid imaging biomarkers based on the individual’s structural MRI data. The resulting top ranked imaging biomarker has the potential to aid personalizedmore »diagnosis and disease subtyping.« less
  6. Brain imaging genetics is an important research topic in brain science, which combines genetic variations and brain structures or functions to uncover the genetic basis of brain disorders. Imaging data collected by different technologies, measuring the same brain distinctly, might carry complementary but different information. Unfortunately, we do not know the extent to which phenotypic variance is shared among multiple imaging modalities, which might trace back to the complex genetic mechanism. In this study, we propose a novel dirty multi-task SCCA to analyze imaging genetics problems with multiple modalities of brain imaging quantitative traits (QTs) involved. The proposed method canmore »not only identify the shared SNPs and QTs across multiple modalities, but also identify the modality-specific SNPs and QTs, showing a flexible capability of discovering the complex multi-SNP-multi-QT associations. Compared with the multi-view SCCA and multi-task SCCA, our method shows better canonical correlation coefficients and canonical weights on both synthetic and real neuroimaging genetic data. This demonstrates that the proposed dirty multi-task SCCA could be a meaningful and powerful alternative method in multi-modal brain imaging genetics.« less
  7. Abstract A novel online distillation technique was developed for the XENON1T dark matter experiment to reduce intrinsic background components more volatile than xenon, such as krypton or argon, while the detector was operating. The method is based on a continuous purification of the gaseous volume of the detector system using the XENON1T cryogenic distillation column. A krypton-in-xenon concentration of (360±60)ppq was achieved. It is the lowest concentration measured in the fiducial volume of an operating dark matter detector to date. A model was developed and fit to the data to describe the krypton evolution in the liquid and gas volumesmore »of the detector system for several operation modes over the time span of 550 days, including the commissioning and science runs of XENON1T. The online distillation was also successfully applied to remove 37Ar after its injection for a low energy calibration in XENON1T. This makes the usage of 37Ar as a regular calibration source possible in the future. The online distillation can be applied to next-generation LXe TPC experiments to remove krypton prior to, or during, any science run. The model developed here allows further optimization of the distillation strategy for future large scale detectors.« less
    Free, publicly-accessible full text available April 29, 2023