Drift Detector Research Articles

Analysis of the ion mobility spectra of chloroacetophenone, tris(2-chloroethyl)amine, and methanethiol

Objectives. To determine the ion mobilities of chloroacetophenone, tris(2-chloroethyl)amine, and methanethiol; the structure of ions corresponding to characteristic signals; the detection limits of chloroacetophenone, tris(2-chloroethyl)amine, and methanethiol with the Kerber-T ion drift detector and the Segment automatic stationary gas detector.Methods. Ion mobility spectrometry was used in order to determine the ion mobilities and detect analytes. The enthalpies of reactions of ion formation were calculated using the ORCA 4.1.1 software by means of the B3LYP density functional method with the 6-31G(d,p) basis set.Results. The ion mobilities of chloroacetophenone, tris(2-chloroethyl)amine, and methanethiol were determined. A method for recording ion mobility spectra and their mathematical processing was developed. The dependencies of the change in ion mobility spectra on the analyte concentration were also studied. Possible mechanisms were proposed for the formation of the ion mobility spectra observed, in accordance with the ionization features of chloroacetophenone, tris(2-chloroethyl)amine, and methanethiol. The enthalpies of ion formation were calculated. The ionization schemes of the compounds were shown. The generalized results of experimental studies were presented, as were the features of compound identification taking into account the structure of the spectra, the concentrations of substances, and the detection conditions.Conclusions. Characteristic signals of chloroacetophenone, tris(2-chloroethyl)amine, and methanethiol were identified. All studied hazardous substances can be detected with an ion mobility spectrometer at concentrations at the ppm level. The following detection limits of the substances were determined with the Segment gas detector: chloroacetophenone, 245 mg/m3; tris(2-chloroethyl)amine, 0.01 mg/m3; and methanethiol, 0.8 mg/m3.

SIDDHARTA-2 apparatus for kaonic atoms research on the DAΦNE collider

SIDDHARTA-2 is a state-of-the-art experiment designed to perform measurements of kaonic atoms, which are exotic atoms composed of a negatively charged kaon orbiting nucleus. These atoms constitute an exceptional probe for investigating the strong interaction in the non-perturbative regime involving strangeness. The experiment is installed at the DAΦNE electron-positron collider of the INFN National Laboratory of Frascati (INFN-LNF) in Italy, aiming to perform the first-ever measurement of the 2p → 1s X-ray transitions in kaonic Deuterium, a crucial step towards determining the isospin-dependent antikaon-nucleon scattering lengths. Based on the experience gained with the previous SIDDHARTA experiment, which performed the most precise measurement of the kaonic Hydrogen 2p → 1s X-ray transitions, the present apparatus has been upgraded with innovative X-Ray Silicon Drift Detectors (SDDs), distributed around a cryogenic gaseous target placed in a vacuum chamber at a short distance above the interaction region of the collider, two new veto systems and a charged kaon detector. The current work presents a comprehensive description of the SIDDHARTA-2 setup, including the optimization of its various components during the commissioning phase of the collider.

Charge Sharing Assessment and Active Collimation in Monolithic Arrays of Silicon Drift Detectors

Charge Sharing Assessment and Active Collimation in Monolithic Arrays of Silicon Drift Detectors

Hoeffding adaptive trees for multi-label classification on data streams

Data stream learning is a very relevant paradigm because of the increasing real-world scenarios generating data at high velocities and in unbounded sequences. Stream learning aims at developing models that can process instances as they arrive, so models constantly adapt to new concepts and the temporal evolution in the stream. In multi-label data stream environments where instances have the peculiarity of belonging simultaneously to more than one class, the problem becomes even more complex and poses unique challenges such as different concept drifts impacting different labels at simultaneous or distinct times, higher class imbalance, or new labels emerging in the stream. This paper proposes a novel approach to multi-label data stream classification called Multi-Label Hoeffding Adaptive Tree (MLHAT). MLHAT leverages the Hoeffding adaptive tree to address these challenges by considering possible relations and label co-occurrences in the partitioning process of the decision tree, dynamically adapting the learner in each leaf node of the tree, and implementing a concept drift detector that can quickly detect and replace tree branches that are no longer performing well. The proposed approach is compared with other 18 online multi-label classifiers on 41 datasets. The results, validated with statistical analysis, show that MLHAT outperforms other state-of-the-art approaches in 12 well-known multi-label metrics.

Development of silicon drift detectors for synchrotron radiation sources

Synchrotron radiation facilities are essential interdisciplinary research platforms, with performance and efficiency closely tied to detector technology. To meet future requirements, especially the High Energy Photon Source (HEPS), we have conducted key technological research and development on single-element and array Silicon Drift Detectors (SDDs). The research and testing were conducted at the Platform of Advanced Photon Source Technology (PAPS). The test results indicate that the single-element SDD has achieved an energy resolution of 143 eV@5.9 keV (−35 °C), and the 20-unit array-SDD has reached an energy resolution of better than 300 eV@13.96 keV (−40 °C).

On metafeatures’ ability of implicit concept identification

Concept drift in data stream processing remains an intriguing challenge and states a popular research topic. Methods that actively process data streams usually employ drift detectors, whose performance is often based on monitoring the variability of different stream properties. This publication provides an overview and analysis of metafeatures variability describing data streams with concept drifts. Five experiments conducted on synthetic, semi-synthetic, and real-world data streams examine the ability of over 160 metafeatures from 9 categories to recognize concepts in non-stationary data streams. The work reveals the distinctions in the considered sources of streams and specifies 17 metafeatures with a high ability of concept identification.

Insight into the Impact of Electron Drift Trajectory on Charge Collection in Silicon Drift Detector

The internal electric field distribution is one key design consideration, which affects the charge collection efficiency in silicon drift detectors (SDDs). The internal electrostatic potential distributions along SDD front and back surfaces, which are determined by the applied voltages at cathode electrodes, define the final internal field distribution. Front-back bias coupling leads to the complexity of electrode structure design and voltage tuning. Device simulation is performed to investigate the performance of SDDs with varied bias voltages. When the cathode bias is −40 V with the first ring bias of −15 V and the outermost ring bias of −80 V, the detector is biased with a uniform electric field distribution, favorable electron drift trajectories. The simulation results provide new insight into the influence of internal electric field and electron drift trajectories on the charge collection efficiency. According to the analysis of simulation results, a 2000 × 2000 μm area concentric silicon drift detector was designed and fabricated. The electrical characteristics of the designed detectors were studied to show the validity of the proposed device design methodology. The internal electric field distribution and electron drift trajectories can be tuned to improve the charge collection efficiency.

Testing the Pauli Exclusion Principle across the Periodic Table with the VIP-3 Experiment.

The Pauli exclusion principle (PEP), a cornerstone of quantum mechanics and whole science, states that in a system, two fermions can not simultaneously occupy the same quantum state. Several experimental tests have been performed to place increasingly stringent bounds on the validity of PEP. Among these, the series of VIP experiments, performed at the Gran Sasso Underground National Laboratory of INFN, is searching for PEP-violating atomic X-ray transitions in copper. In this paper, the upgraded VIP-3 setup is described, designed to extend these investigations to higher-Z elements such as zirconium, silver, palladium, and tin. We detail the enhanced design of this setup, including the implementation of cutting-edge, 1 mm thick, silicon drift detectors, which significantly improve the measurement sensitivity at higher energies. Additionally, we present calculations of expected PEP-violating energy shifts in the characteristic lines of these elements, performed using the multi-configurational Dirac-Fock method from first principles. The VIP-3 realization will contribute to ongoing research into PEP violation for different elements, offering new insights and directions for future studies.

A benchmark and survey of fully unsupervised concept drift detectors on real-world data streams

A benchmark and survey of fully unsupervised concept drift detectors on real-world data streams

Results from Cryo-PoF: Power over fiber for fundamental and applied physics at cryogenic temperature

The Power over Fiber (PoF) technology delivers electrical power by sending laser light through an optical fiber to a photovoltaic power converter, in order to power sensors or electrical devices. This solution offers several advantages: removal of noise induced by power lines, robustness in a hostile environment, spark free operation when electric fields are present and no interference with electromagnetic fields.This technology is at the basis of the Cryo-PoF project: an R&D funded by the Italian Institute for Nuclear Research (INFN) in Milano-Bicocca (Italy). This project is inspired by the needs of the DUNE Vertical Drift detector, where the VUV light of liquid argon must be collected at the cathode, i.e. on a surface whose voltage exceeds 300 kV. We developed a cryogenic system, which is solely based on optoelectronic devices and a single laser input line, to power both the Photon Detection devices and its electronic amplifier. The Milano-Bicocca setup employ a commercial GaAs laser source with 2 W maximum power and a photovoltaic power converter with efficiency of ∼ 30% at liquid nitrogen temperature. The results obtained in Milano Bicocca will be presented with emphasis on performance and potential application in the field of applied physics.

Variance Feedback Drift Detection Method for Evolving Data Streams Mining

Learning from changing data streams is one of the important tasks of data mining. The phenomenon of the underlying distribution of data streams changing over time is called concept drift. In classification decision-making, the occurrence of concept drift will greatly affect the classification efficiency of the original classifier, that is, the old decision-making model is not suitable for the new data environment. Therefore, dealing with concept drift from changing data streams is crucial to guarantee classifier performance. Currently, most concept drift detection methods apply the same detection strategy to different data streams, with little attention to the uniqueness of each data stream. This limits the adaptability of drift detectors to different environments. In our research, we designed a unique solution to address this issue. First, we proposed a variance estimation strategy and a variance feedback strategy to characterize the data stream’s characteristics through variance. Based on this variance, we developed personalized drift detection schemes for different data streams, thereby enhancing the adaptability of drift detection in various environments. We conducted experiments on data streams with various types of drifts. The experimental results show that our algorithm achieves the best average ranking for accuracy on the synthetic dataset, with an overall ranking 1.12 to 1.5 higher than the next-best algorithm. In comparison with algorithms using the same tests, our method improves the ranking by 3 to 3.5 for the Hoeffding test and by 1.12 to 2.25 for the McDiarmid test. In addition, they achieve a good balance between detection delay and false positive rates. Finally, our algorithm ranks higher than existing drift detection methods across the four key metrics of accuracy, CPU time, false positives, and detection delay, meeting our expectations.

Light Element (C, N, O) Quantification by EDXS: Application to Meteorite Water Content and Organic Composition.

Quantifying light elements such as carbon, nitrogen, and oxygen in a transmission electron microscope (TEM) is a challenging however essential task in biology, materials, or earth and planetary sciences. We have developed an approach that allows precise quantification by energy-dispersive X-ray spectroscopy (EDXS), using sensitive windowless silicon drift detectors and homemade Python routines for hyperspectral data processing. K-factors were determined using wedge-shaped focused ion beam sections. To correct for X-ray absorption within the sample, the sample mass thickness is determined by the-revisited-two-lines method (Morris, 1980). No beam current measurement is required. Applying this method to the K and L lines of iron, we found that the tabulated mass absorption coefficient at the energy of the iron L lines was too low. This is due to X-ray self-absorption at the iron edge. Using reference material, we experimentally determined an absorption coefficient that gave the expected results. We then analyzed the complex phyllosilicate mixture of the Orgueil meteorite. We show that the N/C ratio of organics can be obtained with an accuracy better than 5 at.% and that oxygen can be quantified accurately enough to infer the hydroxyl content of phyllosilicates.

Adaptive bagging-based dynamic ensemble selection in nonstationary environments

The classification of nonstationary data streams presents a substantial challenge mainly due to the simultaneous presence of class imbalance and concept drift. Ensemble learning is a widely acknowledged approach to address these two problems. Nevertheless, most of the existing methods establish independent strategies for each, ignoring their relationship and interactions, which prevents the corresponding strategies from performing as expected. This paper presents a novel algorithm AB-DES (Adaptive Bagging-based Dynamic Ensemble Selection), which employs bagging technique and dynamic ensemble selection system. In AB-DES, instances are stored to balance subsequent data chunks instead of relying solely on resampling methods. Then, all base classifiers are updated using a new dual adaptive sampling bagging strategy, which can help to reduce the classification inaccuracies involving by class imbalance. In the prediction phase, DES system constructs different ensemble models for different instances. The inclusion of a sliding window-based drift detector makes the above two strategies adapt to concept drift. Experimental results show that AB-DES achieves higher classification accuracy and exhibits enhanced robustness compared to 8 state-of-the-art methods on 10 synthetic datasets and 5 real world datasets with different types of concept drift.

Development of high-efficiency X-ray detectors based on 1 mm thick monolithic SDD arrays

We present the novel 2× 4 monolithic Silicon Drift Detectors (SDD) array of 1 mm thickness developed within the context of the SIDDHARTA-2 experiment for high-energy X-ray spectroscopy measurements of light kaonic atom transitions. It represents a state-of-the-art advancement in terms of detection efficiency with respect to the previous generation of detectors, having a thickness of 450 μm. The sensor features eight square SDD units with an active area of 8 × 8 mm2 each, arranged in a 2 × 4 matrix. Therefore, the total active area of the array is 32 × 16 mm2 while the total chip area is 36 × 20 mm2, including a 2-mm dead region on each side of the array. This new version of SDD arrays, manufactured by Fondazione Bruno Kessler (FBK), includes an additional electrode on its entrance window, designed to reduce charge sharing between adjacent channels and improve energy resolution. This article describes two different detection modules based on these arrays: the first module includes a single array, whereas the second one is composed of two 1 mm thick SDD arrays in a stacked configuration, in order to reach 2 mm of effective thickness and further increase the module detection efficiency. The first spectroscopic measurements obtained with the two modules will be also reported in the paper, showing the spectroscopic improvements that can be obtained with the additional electrode on the window and the efficiency improvements that can be obtained with the stacked module.

Feature-based analyses of concept drift

Feature selection is one of the most relevant preprocessing and analysis techniques in machine learning. It can dramatically increase the performance of learning algorithms and at the same time provide relevant information on the data. In the scenario of online and stream learning, concept drift, i.e., changes in the underlying distribution over time, can cause significant problems for learning models and data analysis. While there do exist feature selection methods for online learning, none of the methods targets feature selection for drift detection, i.e., the challenge to increase the performance of drift detectors by analyzing the drift rather than increasing model accuracy. However, this challenge is particularly relevant for common unsupervised scenarios. In this work, we study feature selection for drift detection and drift monitoring. We develop a formal definition for a feature-wise notion of drift that allows semantic interpretation. Besides, we derive an efficient algorithm by reducing the problem to classical feature selection and analyze the applicability of our approach to feature selection for drift detection on a theoretical level. Finally, we empirically show the relevance of our considerations on several benchmarks.

Development of a silicon drift detector array to search for keV-scale sterile neutrinos with the KATRIN experiment

Sterile neutrinos in the keV mass range present a viable candidate for dark matter. They can be detected through single β -decay, where they cause small spectral distortions. The Karlsruhe Tritium Neutrino (KATRIN) experiment aims to search for keV-scale sterile neutrinos with high sensitivity. To achieve this, the KATRIN beamline will be equipped with a novel multi-pixel silicon drift detector focal plane array named TRISTAN. In this study, we present the performance of a TRISTAN detector module, a component of the eventual 9-module system. Our investigation encompasses spectroscopic aspects such as noise performance, energy resolution, linearity, and stability.

A thermionic electron gun to characterize silicon drift detectors with electrons

The TRISTAN detector is a new detector for electron spectroscopy at the Karlsruhe Tritium Neutrino (KATRIN) experiment. The semiconductor detector utilizes the silicon drift detector technology and will enable the precise measurement of the entire tritium β-decay electron spectrum. Thus, a significant fraction of the parameter space of potential neutrino mass eigenstates in the keV-mass regime can be probed. We developed a custom electron gun based on the effect of thermionic emission to characterize the TRISTAN detector modules with mono-energetic electrons before installation into the KATRIN beamline. The electron gun provides an electron beam with up to 25 keV kinetic energy and an electron rate in the order of 105 electrons per second. This manuscript gives an overview of the design and commissioning of the electron gun. In addition, we will shortly discuss a first measurement with the electron gun to characterize the electron response of the TRISTAN detector.

Online Concept Drift Detector: Optimally Balancing Delay Detection, Runtime, Memory, and Accuracy.

Online learning and real-time data processing are becoming increasingly vital across various domains such as sensor networks, banking, and telecommunications. A significant challenge in this context is concept drift, wherein the statistical properties of the data change over time. Traditional drift detectors often grapple with high memory usage, extended delay in detection, prolonged runtime, and accuracy inconsistencies. This paper introduces a novel Online Drift Detector that meticulously balances these four aspects. By processing data instance-by-instance, our proposed detector optimizes the trade-offs between delay detection, runtime, memory consumption, and accuracy. We incorporate a unique diversity calculation tailored for multi-label problems, ensuring swift drift detection with minimized memory usage and enhanced runtime efficiency. Comparative analyses reveal the dominance of our approach over contemporary drift detection techniques, particularly in the realms of memory efficiency, detection speed, and accuracy. This work substantially augments the field of online data stream processing by offering a refined strategy for timely and efficient concept drift detection across a myriad of applications.

Development of an x-ray polarimeter at the SOLEIL synchrotron.

Synchrotron radiation facilities provide highly polarized x-ray beams across a wide energy range. However, the exact type and degree of polarization vary according to the beamline and experimental setup. To accurately determine the angle and degree of linear polarization, a portable x-ray polarimeter has been developed. This setup consists of a silicon drift detector that rotates around a target made of high-density polyethylene. The imprint generated in the angular distribution of scattered photons from the target at a 90-degree angle between the incident x-rays and detector has been exploited to determine the beam polarization. Measurements were conducted at the GALAXIES beamline of the SOLEIL synchrotron. The expected angular distribution of the scattered photons for a given beam polarization was obtained through simulations using the Geant4 simulation toolkit. An excellent agreement between simulations and the collected data has been obtained, validating the setup and enabling a precise determination of the beam polarization.

Static friction testing of various materials in liquid nitrogen

The Deep Underground Neutrino Experiment will utilize charge readout planes (CRPs) that are submerged within liquid argon. These charge readout planes are part of a drift detector used measure the cascade of charged particles generated by the interaction of neutrinos with the argon nuclei. The CRPs require specially designed supports to ensure they remain precisely positioned even after the thermal contraction of the sensors and the cryostat that occurs during cooldown. These supports must also protect the cryostat floor and CRPs from potential damage due to thermal strain during cooldown. These objectives are satisfied by utilizing an intermediate slip plane within the support to provide a stress relief point with a lower friction coefficient than the interface between the sensor support and the cryostat floor. Therefore, the design of these supports requires knowledge of the static coefficient of friction for various materials at cryogenic temperatures. It was found that the coefficient of friction associated with materials such as metals, ceramics, and polymers have been studied very little at cryogenic temperatures. Specifically, there is not much data relative to the static coefficient of friction associated with polymers at low temperatures. Therefore, a test apparatus was developed to measure various materials’ cryogenic static coefficient of friction with the geometry required for the support design. Several tests with different materials were carried out using this test apparatus to identify a material combination with very different static coefficients of friction that can be used for the membrane to support interface and intermediate slip plane.