Search

The NEON experiment, situated at the Hanbit Nuclear Power Plant, is designed to observe coherent neutrinonucleus scattering (CE{\nu}NS) and search for dark sector particle such as axion-like particles (ALPs). Using six NaI(Tl) detector modules, data were collected during both reactor-on and reactor-off periods between April 2022 and June 2023, providing a total exposure of 1596 kg{\cdot}days and 1467 kg{\cdot}days, respectively. The search for ALPs leverages the difference between reactor-on and reactor-off datasets. A thorough understanding of time-dependent backgrounds, including cosmogenic activation and seasonal variations of radon contamination, is essential to the analysis. This paper presents detailed modeling of these backgrounds, identifying their contributions across different energy ranges and detector modules. Systematic uncertainties arising from energy resolution, background shape, and rate variations are considered in the final analysis. The results provide insights into the future potential of ALP searches in short-baseline reactor experiments and demonstrate the efficacy of background reduction techniques in the NEON experiment., Comment: We have included information on the time-dependent background model for the NEON experiment in this manuscript. The manuscript will be updated later with the corrected results from the ALP search

We study the expected sensitivity to the mixing between sterile and tau neutrinos directly from the tau neutrino disappearance in the high-energy fixed target experiment. Here, the beam energy is large enough to produce tau neutrinos at the target with large luminosity. During their propagation to the detector, tau neutrinos may oscillate into sterile neutrinos. By examining the energy spectrum of the observed tau neutrino events, we can probe the mixing between sterile and tau neutrinos directly. In this paper, we consider Scattering and Neutrino Detector (SND) at SHiP experiment as a showcase, which uses 400 GeV protons from SPS at CERN, and expect to observe 7,300 tau and anti-tau neutrinos from the $2\times 10^{20}$ POT for 5 years operation. Assuming the uncertainty of 10\%, we find the sensitivity $|U_{\tau 4}|^2 \sim 0.08$\, (90\% CL) for $\Delta m_{41}^2 \sim 500\ \mathrm{eV}^2$ with 10\% background to the signal. We also consider a far SND at the end of the SHiP Hidden Sector Decay Spectrometer (HSDS), in which case the sensitivity would be enhanced to $|U_{\tau 4}|^2 \sim 0.02$. Away from this mass, the sensitivity becomes lower than $|U_{\tau 4}|^2 \sim 0.15$ for $\Delta m_{41}^2 \lesssim 100\ \mathrm{eV}^2$ or $\Delta m_{41}^2\gtrsim 10^4 \mathrm{eV}^2$., Comment: 14 pages, 8 figures

Accurate lung nodule segmentation is crucial for early-stage lung cancer diagnosis, as it can substantially enhance patient survival rates. Computed tomography (CT) images are widely employed for early diagnosis in lung nodule analysis. However, the heterogeneity of lung nodules, size diversity, and the complexity of the surrounding environment pose challenges for developing robust nodule segmentation methods. In this study, we propose an efficient end-to-end framework, the multi-encoder-based self-adaptive hard attention network (MESAHA-Net), for precise lung nodule segmentation in CT scans. MESAHA-Net comprises three encoding paths, an attention block, and a decoder block, facilitating the integration of three types of inputs: CT slice patches, forward and backward maximum intensity projection (MIP) images, and region of interest (ROI) masks encompassing the nodule. By employing a novel adaptive hard attention mechanism, MESAHA-Net iteratively performs slice-by-slice 2D segmentation of lung nodules, focusing on the nodule region in each slice to generate 3D volumetric segmentation of lung nodules. The proposed framework has been comprehensively evaluated on the LIDC-IDRI dataset, the largest publicly available dataset for lung nodule segmentation. The results demonstrate that our approach is highly robust for various lung nodule types, outperforming previous state-of-the-art techniques in terms of segmentation accuracy and computational complexity, rendering it suitable for real-time clinical implementation.

The COSINE-100 experiment has been operating with 106 kg of low-background NaI(Tl) detectors to test the results from the DAMA/LIBRA experiment, which claims to have observed dark matter. However, since the background of the NaI(Tl) crystals used in the COSINE-100 experiment is 2-3 times higher than that in the DAMA detectors, no conclusion regarding the claimed observation from the DAMA/LIBRA experiment could be reached. Therefore, we plan to upgrade the current COSINE-100 experiment to the next phase, COSINE-200, by using ultra-low background NaI(Tl) detectors. The basic principle was already proved with the commercially available Astro-grade NaI powder from Sigma-Aldrich company. However, we have developed a mass production process of ultra-pure NaI powder at the Center for Underground Physics (CUP) of the Institute for Basic Science (IBS), Korea, using the direct purification of the raw NaI powder. We plan to produce more than 1,000 kg of ultra-pure powder for the COSINE200 experiment. With our crystal grower installed at CUP, we have successfully grown a low-background crystal using our purification technique for the NaI powder. We have assembled a low-background NaI(Tl) detector. In this article, we report the performance of this ultra-pure NaI(Tl) crystal detector produced at IBS, Korea.

In this study, we propose a lung nodule detection scheme which fully incorporates the clinic workflow of radiologists. Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i.e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net). The proposed architecture first condenses 3D patch input to three channels by using a dense block which consists of dense units which effectively examine the nodule presence from 2D axial slices. This condensed information, along with the forward and backward MIP images, is fed to three different encoders to learn the most meaningful representation, which is forwarded into the decoded block at various levels. At the decoder block, we employ a self-distillation mechanism by connecting the distillation block, which contains five lung nodule detectors. It helps to expedite the convergence and improves the learning ability of the proposed architecture. Finally, the proposed scheme reduces the false positives by complementing the main detector with auxiliary detectors. The proposed scheme has been rigorously evaluated on 888 scans of LUNA16 dataset and obtained a CPM score of 93.6\%. The results demonstrate that incorporating of bi-direction MIP images enables MEDS-Net to effectively distinguish nodules from surroundings which help to achieve the sensitivity of 91.5% and 92.8% with false positives rate of 0.25 and 0.5 per scan, respectively.

Accurate segmentation of mandibular canals in lower jaws is important in dental implantology. Medical experts determine the implant position and dimensions manually from 3D CT images to avoid damaging the mandibular nerve inside the canal. In this paper, we propose a novel dual-stage deep learning-based scheme for the automatic segmentation of the mandibular canal. Particularly, we first enhance the CBCT scans by employing the novel histogram-based dynamic windowing scheme, which improves the visibility of mandibular canals. After enhancement, we design 3D deeply supervised attention U-Net architecture for localizing the volumes of interest (VOIs), which contain the mandibular canals (i.e., left and right canals). Finally, we employed the multi-scale input residual U-Net architecture (MS-R-UNet) to segment the mandibular canals using VOIs accurately. The proposed method has been rigorously evaluated on 500 scans. The results demonstrate that our technique outperforms the current state-of-the-art segmentation performance and robustness methods., Comment: 7 Pages

Accurate quantification of pulmonary nodules can greatly assist the early diagnosis of lung cancer, which can enhance patient survival possibilities. A number of nodule segmentation techniques have been proposed, however, all of the existing techniques rely on radiologist 3-D volume of interest (VOI) input or use the constant region of interest (ROI) and only investigate the presence of nodule voxels within the given VOI. Such approaches restrain the solutions to investigate the nodule presence outside the given VOI and also include the redundant structures into VOI, which may lead to inaccurate nodule segmentation. In this work, a novel semi-automated approach for 3-D segmentation of nodule in volumetric computerized tomography (CT) lung scans has been proposed. The proposed technique can be segregated into two stages, at the first stage, it takes a 2-D ROI containing the nodule as input and it performs patch-wise investigation along the axial axis with a novel adaptive ROI strategy. The adaptive ROI algorithm enables the solution to dynamically select the ROI for the surrounding slices to investigate the presence of nodule using deep residual U-Net architecture. The first stage provides the initial estimation of nodule which is further utilized to extract the VOI. At the second stage, the extracted VOI is further investigated along the coronal and sagittal axis with two different networks and finally, all the estimated masks are fed into the consensus module to produce the final volumetric segmentation of nodule. The proposed approach has been rigorously evaluated on the LIDC dataset, which is the largest publicly available dataset. The result suggests that the approach is significantly robust and accurate as compared to the previous state of the art techniques., Comment: The manuscript is currently under review and copyright shall be transferred to the publisher upon acceptance

Along a single strand of micro-capillary optical waveguide, we achieved an efficient transfer of the light momentum onto the liquid contained there within, successfully atomizing it into nanoscale droplets. A hollow optical fiber (HOF), with a ring core and central air hole, was used to optically drive jetting of non-conducting transparent liquid of sub-pico liter volume, out of a surface-treated facet orifice, producing droplets ranging from nano to micrometer scale. These droplets were carried over the propagating light field forming a spherical cone, which were then deposited on a silica substrate in a Gaussian spatial distribution. The deposited patterns and sizes of individual droplets were characterized as a function of the laser power, irradiation time, and distance between the HOF and a substrate. This HOF based laser driven atomization technique obviates imperative electrode or aerial pressure requirements in prior methods, opening a new pathway to drastically scale down the form-factor of liquid jetting devices, and has a high potential to in-situ atomization and delivery of bio-medical non-conducting liquids in a microscopic environment, which was not possible in prior arts.