DEAP-3600 Dark Matter Experiment Data Acquisition and trigger system

Development of a pipelined data acquisition system for experiments at J-PARC

A test pattern compression scheme for test data volume and application time reduction is proposed. While compression reduces test data volume, the increased number of internal scan chains due to an on-chip, fixed-rate decompressor reduces test application time proportionately. Through on-chip decompression, both the number of virtual scan chains visible to the ATE and the functionality of the ATE are retained intact. Complete fault coverage is guaranteed by constructing the decompression hardware deterministically through analysis of the test pattern set.

The rapid growth of the global population has intensified the global demand for a highly skilled workforce, making effective skill development systems essential for improving employability, productivity and economic growth. Skill development plays a crucial role in reducing poverty and inequality by enhancing the capabilities of individuals to meet labour market demands. The present study was conducted in District Kupwara of the Kashmir Valley to assess knowledge gain among beneficiaries trained in three enterprises beekeeping, mushroom production and vermi-composting. A total of 108 respondents were selected, comprising 65 unit holders (30 beekeepers, 20 mushroom growers and 15 vermi-compost producers) and 43 non-unit holders (15 beekeeping trainees, 14 mushroom trainees and 15 vermi-composting trainees). In beekeeping, a majority (56.67%) of unit holders exhibited medium knowledge gain, followed by 36.66% with high gain and 6.67% with low gain, with a mean score of 37.42 (SD = 13.64). In contrast, non-unit holders showed predominantly low knowledge gain (60.00%), followed by medium (33.34%) and high (6.66%) levels. Among mushroom producers, 50.00% of unit holders reported medium knowledge gain, 35.00% high gain and 15.00% low gain, with a mean of 23.45 (SD = 6.62). Non-unit holders again showed lower improvement, with 64.28% registering low gain, 28.58% medium gain and 7.14% high gain. For vermi-composting, 46.67% of unit holders achieved high knowledge gain, 40.00% medium gain and 13.33% low gain, with a mean of 29.57 (SD = 5.15). Conversely, 64.28% of non-unit holders recorded low knowledge gain, followed by medium (28.58%) and high (7.14%) levels, with a mean score of 18.13 (SD = 4.01).The results clearly indicate that unit holders benefited more from training compared to non-unit holders across all enterprises.

The Pacific Ocean Neutrino Experiment (P-ONE) is set to deploy its first detection string in the Cascadia Basin off the coast of British Columbia, Canada. As a next-generation Cherenkov neutrino telescope, P-ONE will be sensitive to ultra-high-energy neutrinos (10³–10⁸ GeV) from astrophysical sources. To effectively capture these rare physics signatures, the experiment's trigger system must operate in a high-background environment dominated by K40 decay and bioluminescence. This poster presents the design and integration of the P-ONE trigger system, which spans multiple levels of data acquisition (DAQ). The trigger must seamlessly interface with both the slow detector controlling Maximum Integrated Data Acquisition System (MIDAS) and the back-end fastDAQ system to select and preliminarily cluster events. The trigger system operates in a hierarchical fashion: first, an initial firmware (L0) trigger identifies candidate events, which are then refined by a physics trigger that requests additional waveform data from neighboring modules. Onshore, these waveform packets and timing information are assembled into full events for storage and analysis. This talk will detail the trigger chain, with a focus on bioluminescence mitigation and physics-driven event selection, as well as its integration with the DAQ and Run Control systems.

Today’s scientific simulations generate exceptionally large volumes of data, challenging the capacities of available I/O bandwidth and storage space. This necessitates a substantial reduction in data volume, for which error-bounded lossy compression has emerged as a highly effective strategy. A crucial metric for assessing the efficacy of lossy compression is visualization. Despite extensive research on the impact of compression on visualization, there is a notable gap in the literature concerning the effects of compression on the visualization of Adaptive Mesh Refinement (AMR) data. AMR has proven to be a potent solution for addressing the rising computational intensity and the explosive growth in data volume that requires storage and transmission. However, the hierarchical and multi-resolution characteristics of AMR data introduce unique challenges to its visualization, and these challenges are further compounded when data compression comes into play. This article delves into the intricacies of how data compression influences and introduces novel challenges to the visualization of AMR data.

Modern precision neutrino experiments like Double Chooz require a highly efficient trigger system in order to reduce systematic uncertainties. The trigger and timing system of the Double Chooz experiment was designed according to this goal. The Double Chooz trigger system is driven by the basic idea of triggering on multiple thresholds according to the total visible energy and additionally triggering on the number of active photomultiplier tubes (PMTs) in the detector. To do so, the trigger system continuously monitors the analogue signals from all PMTs in the detector. The amplitudes of these PMT-signals are summed for groups of certain PMTs (group signals) and for all PMTs (sum signal), respectively. The group signals are discriminated by two thresholds for each input channel and four thresholds for the sum signal. The resulting signals are processed by the trigger logic unit which is implemented in a FPGA. In addition to the proper trigger, the trigger system provides a common clock signal for all subsequent data acquisition systems to guarantee a synchronous readout of the Double Chooz detectors. The present design of the system provides a high flexibility for the applied logic and settings, making it useful for experiments other than Double Chooz. The Double Chooz trigger and timing system was installed and commissioned in 2011. This article describes the hardware of the trigger and timing system. Furthermore the setup, implemented trigger logic and performance of the trigger and timing system for the Double Chooz experiment is presented.

For the several decades, the non-baryonic dark matter has been searched in many ways and by many research teams. Non-baryonic dark matter is known as a weakly interaction and neutral particle. In the direct detection ways, it is not easy to identify a non-baryonic dark matter from other particles, such as gamma ray and neutron, due to its electric neutrality. So, the identification of neutral particle is an essential part for the direct detection method of dark matter in a cosmic ray free laboratory with a detector made of radioactive free materials. A pulse shape discrimination (PSD) method was introduced more than fifty years ago. And it has been used by various elementary particle experiments. We need a more precise PSD method to detect directly dark matter interacting rarely. We can see the primary and the secondary signals in the scintillation produced by an elastic scattering between an incident particle and a target atom. The primary signal is composed of the scintillations from an excitation process of target nuclear and an ionization process of target atom while the secondary signal is composed of scintillation from ionization process, only. The ratio of scintillations from excitation and ionization processes gives the clue to identify the neutral particles. Here, we introduce an improved way to separate the primary signal into the scintillations induced by excitation and ionization processes through the elastic scattering between a target atom and a non-baryonic dark matter. This separation of primary signal can provide a big hint to identify a non-baryonic dark matter.

Reducing data volume in big data: Parallel processing based data filtering techniques Professor Shikharesh Majumdar from Carleton University examines reducing data volume in big data, focusing on parallel processing based data filtering techniques. Volume, velocity, and variety are three well-known characteristics of big data. The large data volumes often introduce formidable challenges to processing such data in a timely and economical manner. Research on data filtering is underway under the leadership of Shikharesh Majumdar at the Real Time and Distributed Systems Research Centre at Carleton University. Users are often interested only in a subset of the raw data.

Advanced algorithmic solutions are necessary to process the ever-increasing amounts of mass spectrometry data that are being generated. In this study, we describe the falcon spectrum clustering tool for efficient clustering of millions of MS/MS spectra. falcon succeeds in efficiently clustering large amounts of mass spectral data using advanced techniques for fast spectrum similarity searching. First, high-resolution spectra are binned and converted to low-dimensional vectors using feature hashing. Next, the spectrum vectors are used to construct nearest neighbor indexes for fast similarity searching. The nearest neighbor indexes are used to efficiently compute a sparse pairwise distance matrix without having to exhaustively perform all pairwise spectrum comparisons within the relevant precursor mass tolerance. Finally, density-based clustering is performed to group similar spectra into clusters. Several state-of-the-art spectrum clustering tools were evaluated using a large draft human proteome data set consisting of 25 million spectra, indicating that alternative tools produce clustering results with different characteristics. Notably, falcon generates larger highly pure clusters than alternative tools, leading to a larger reduction in data volume without the loss of relevant information for more efficient downstream processing. falcon is a highly efficient spectrum clustering tool, which is publicly available as an open source under the permissive BSD license at https://github.com/bittremieux/falcon.

3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.

Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data interpretation and compression substrate over RFID streams to address these challenges in enterprise supply-chain environments. Our results show that our inference techniques provide good accuracy while retaining efficiency, and our compression algorithm yields significant reduction in data volume.

Various compression schemes have been suggested for storage and distribution of hyperspectral remotely sensed data. Hyperspectral forestry applications that rely on the measurement of subtle variations in the spectral signature of the forest canopy can be affected by modification of the spectra induced by compression. As part of an experiment for the Canadian Space Agency (CSA), Hyperion data cubes acquired over the Greater Victoria Watershed District (GVWD) were compressed using the Successive Approximation Multi-stage Vector Quantization (SAMVQ) and Hierarchical Self-Organizing Cluster Vector Quantization (HSOCVQ) algorithms developed by CSA. The data were compressed using compression ratios 10:1 and 20:1 and were returned uncompressed. The data cubes were classified into forest species using the same supervised classification methodology as applied to the original data. The classification accuracies were compared. For some applications, one can achieve significant reductions in data volume through compression. Of the compression algorithms and ratios tested, SAMVQ 10:1 has the least overall effect but still reduces classification accuracies on difficult to separate classes. While uncompressed data are preferred, SAMVQ 10:1 compression may be suitable for forest inventory.

Abstract. This paper discusses a current issue for several experimental science disciplines, which is the Big Data Problem (BDP). This research study focused on light intensity and ranging (LiDAR) datasets, which are collected for modelling spatial features found on the surface of the earth. Currently, LiDAR datasets are known to be extremely redundant for many applications. Using a formula that allows for calculating the variance of the target-induced error (so-called T-error) caused by the discretisation and quantisation of a 3D surface as a criterion for the quantitative assessment of the fidelity of a model, the use of a Q-tree-based split of the surface is proposed for cells of various sizes depending on the fidelity requirements. A LiDAR dataset representing a 1 km x 1 km terrain surface tile using approximately 12 x 106 points was used during the experiments. The initial LiDAR dataset was used to produce a digital terrain model (DTM) at a 0.5 m x 0.5 m resolution, which was used as a reference model. Subsequently, the initial LiDAR dataset was decimated at various rates, and the resulting DTMs were compared with the reference model. The Q-tree based data structure was utilised to illustrate that the Q-tree approach allows for the production of DTMs at a ‘controlled’ fidelity with a considerable reduction in data volume.

Using computer programs to simulate protein complex formation is an important approach for better comprehension of the interaction mechanisms of such molecules. The parametric representation and the Zernike polynomial method are both compact representations of molecular surfaces based on spherical harmonics, used for visualization and comparison of molecules and for protein-protein docking. They pose, however, limitations regarding surface topology and difficulty in representing arbitrary functions. In this study, the representation capacity of such methods were refined to attain high quality reproductions and applicability to arbitrary topologies. Throughout the analysis of several algorithms, the stages of surface mesh calculation and spherical mapping were identified as highly influential on the quality of the spherical harmonics parametric representation, while high sensibility to changes in the function values were identified as an influential factor for projections in 3D Zernike. A surface calculation method that generates a highly regular mesh was adopted and paired with a modern spherical mapping algorithm to yield reconstructions with low level of distortions and high quality surfaces. Similarity among surfaces from different structures in a conformational ensemble were detected to allow sharing of portions of the description among several surfaces, with corresponding reduction in data volume and processing demand and controllable influence of distortions. A new input data organization method improved the reconstruction quality of general functions in 3D Zernike, although introducing a map to restore the function. Results indicate promising application of the methods in highly detailed protein-protein docking.

This study presents a real-time, adaptive UAV system designed to enhance ecological surveys by overcoming the trade-off between wide-area coverage and high-resolution data collection. The Modular Detection and Targeting System (MDTS) integrates thermal imaging for broad detection and high-resolution RGB zoom imaging for precise species identification. Field trials demonstrated the system’s ability to detect and record both avian and mammalian species with significantly reduced redundant data and improved survey efficiency. Compared to traditional UAV methods, the MDTS achieved over 300-fold improvements in image resolution and up to a 1000-fold reduction in data volume. The system’s modular design enables rapid adaptation to diverse ecological applications, providing classification-ready data while minimizing post-processing demands. These results highlight the MDTS as a scalable, efficient tool for wildlife monitoring and environmental research, bridging the gap between detection and actionable ecological insights.