Triggering Probability Research Articles

Breakdown voltage and triggering probability of SiPM from IV curves at different temperatures

This work presents a physical model describing the IV curves of SiPM detectors allowing to easily determine important device parameters like breakdown voltage VBD and the shape of Geiger triggering probability PGeiger. We measured IV curves and tested our IV model in a temperature range −35°C<T<+35°C on various SiPMs from two vendors (Hamamatsu devices of 3×3mm2 total area and 50×50μm2 μcell size, 2011 and 2015 year production runs and KETEK devices of 0.5×0.5mm2 total area and 50×50μm2 μcell size, 2015 production run). The shape of IV curve can be described in terms of Geiger probability and afterpulsing in a very large current range of 10−12A<Ipost−BD<10−5A over the full working range of all tested devices. It has also been found that VBD from IV curves is slightly higher (few hundred mV) than the “breakdown voltage” determined from the usual method of linear fit of gain as a function of bias voltage, this discrepancy reflecting the fundamental difference in the physical significance of the “breakdown voltage” determined by these two methods. The recent generation of SiPMs have very wide working range and there is an evidence phenomena beyond afterpulsing like heating or non-quenched pulses contributing to the fast increase of the current at high bias voltages.

A new modeling and simulation method for important statistical performance prediction of single photon avalanche diode detectors

This paper presents a new modeling and simulation method to predict the important statistical performance of single photon avalanche diode (SPAD) detectors, including photon detection efficiency (PDE), dark count rate (DCR) and afterpulsing probability (AP). Three local electric field models are derived for the PDE, DCR and AP calculations, which show analytical dependence of key parameters such as avalanche triggering probability, impact ionization rate and electric field distributions that can be directly obtained from Geiger mode Technology Computer Aided Design (TCAD) simulation. The model calculation results are proven to be in good agreement with the reported experimental data in the open literature, suggesting that the proposed modeling and simulation method is very suitable for the prediction of SPAD statistical performance.

A massively parallel pipelined reconfigurable design for M-PLN based neural networks for efficient image classification

Weightless Neural Networks (WNNs) are a powerful mechanism for pattern recognition. Aiming at enhancing their learning capabilities, Multi-valued Probabilistic Logic Nodes (M-PLN) are used, instead of crisp neurons with a 0/1 based RAM-nodes. An M-PLN stores a mapping of, or possibly, the triggering probability, for each input pattern that needs to be recognized. The M-PLN model attempts to strengthen the discrepancies between distinct patterns used during the training process and those that have not yet been processed. In this paper, an efficient yet customizable hardware architecture for M-PLN based neural network is proposed. It implements the learning and operation processes of a pyramidal network structure, augmented by a probabilistic rewarding/punishing search algorithm. The training algorithm can adapt itself to the overall hit ratio so far achieved by the network. Using class-dedicated layers, the hardware is able to handle image classification in parallel and thus, very efficiently. Furthermore, the classification process is performed in a pipelined manner so its stages never stop working until all input images are classified. Nonetheless, during network training, only one of these layers is activated. Last but not least, the architecture is customizable as its structure can be tailored in accordance to the application characteristics in terms of class number, pattern tuple size and image resolution. In order to evaluate the time and cost requirements of the proposed design, its underlying architecture was specified in VHDL and functionally tested. The presented results are two-fold: first, based on many functional simulations, estimated time and cost requirements are analyzed; second, to further assess the performance of the proposed the design, the VHDL model was synthesized to produce a semi-custom implementation on FPGAs. We also give an assessment of the quality of the entailed classification process. The architecture exhibits performance and reconfiguration capabilities that are very promising and encouraging towards the fabrication of a prototype on ASIC.

Did the Solar system form in a sequential triggered star formation event?

The presence and abundance of the short-lived radioisotopes (SLRs) $^{26}$Al and $^{60}$Fe during the formation of the Solar System is difficult to explain unless the Sun formed in the vicinity of one or more massive star(s) that exploded as supernovae. Two different scenarios have been proposed to explain the delivery of SLRs to the protosolar nebula: (i) direct pollution of the protosolar disc by supernova ejecta and (ii) the formation of the Sun in a sequential star formation event in which supernovae shockwaves trigger further star formation which is enriched in SLRs. The sequentially triggered model has been suggested as being more astrophysically likely than the direct pollution scenario. In this paper we investigate this claim by analysing a combination of $N$-body and SPH simulations of star formation. We find that sequential star formation would result in large age spreads (or even bi-modal age distributions for spatially coincident events) due to the dynamical relaxation of the first star-formation event(s). Secondly, we discuss the probability of triggering spatially and temporally discrete populations of stars and find this to be only possible in very contrived situations. Taken together, these results suggest that the formation of the Solar System in a triggered star formation event is as improbable, if not more so, than the direct pollution of the protosolar disc by a supernova.

Alborz-I array: A simulation on performance and properties of the array around the knee of the cosmic ray spectrum

The first phase of the Alborz Observatory Array (Alborz-I) consists of 20 plastic scintillation detectors each one with surface area of 0.25 m2spread over an area of 40 × 40 m2 realized to the study of Extensive Air Showers around the knee at the Sharif University of Technology campus. The first stage of the project including construction and operation of a prototype system has now been completed and the electronics that will be used in the array instrument has been tested under field conditions. In order to achieve a realistic estimate of the array performance, a large number of simulated CORSIKA showers have been used. In the present work, theoretical results obtained in the study of different array layouts and trigger conditions are described. Using Monte Carlo simulations of showers the rate of detected events per day and the trigger probability functions, i.e., the probability for an extensive air shower to trigger a ground based array as a function of the shower core distance to the center of array are presented for energies above 1 TeV and zenith angles up to 60°. Moreover, the angular resolution of the Alborz-I array is obtained.

Sources of uncertainty in liquefaction triggering procedures

ABSTRACTUncertainties in observed data and in processing field and laboratory tests are major concerns. Assigning reasonable coefficients of variation to the parameters in the conventional analyses indicates that a site with deterministic factors of safety of 1.5 can actually have liquefaction triggering probability above 20%. About a third of the variance comes from uncertainty in the load, which is independent of the resistance. Researchers have traditionally presented the results of case studies in the form of charts showing instances in which liquefaction did and did not occur and have developed relations to separate the two. Although the original researchers developed the separations informally, recent work has applied statistical methods. These give the sampling distributions of the observed data rather than the probability of triggering given the data. Researchers have addressed this issue using Bayesian methods, adopting non-informative priors. Published curves of liquefaction probabilities can be interpreted as likelihood ratios. Other independent work demonstrates that geological, meteorological, and historical data can be used to develop prior probabilities, so it may not be necessary to assume a non-informative prior. The actual prior can then be combined with the likelihood ratios to provide rational probabilities of liquefaction. We recommend that researchers publish their likelihood ratios and allow engineers faced with particular sites to use those to update their own priors.

Towards an enhanced understanding of airborne LiDAR measurements of forest vegetation

This thesis presents basic research on how airborne LiDAR measurements of forest vegetation are influenced by the interplay of the geometric-optical properties of vegetation, sensor function and acquisition settings. Within the work, examining the potential of waveform (WF) recording sensors was of particular interest. Study I focused upon discrete return LiDAR measurements of understory trees. It showed that transmission losses influenced the intensity of observations and echo triggering probabilities, and also skewed the distribution of echoes towards those triggered by highly reflective or dense targets. The intensity data were of low value for species identification, but the abundance of understory trees could be predicted based on echo height distributions. In study II, a method of close-range terrestrial photogrammetry was developed. Images were shown as being useful for visualizations and even the geometric quality control of LiDAR data. The strength of backscattering was shown to correlate with the projected area extracted from the images. In study III, a LiDAR simulation model was developed and validated against real measurements. The model was able to be used for sensitivity analyses to illustrate how plant structure or different pulse properties influence the WF data. Both simulated and real data showed that WF data were able to capture small-scale variations in the structural and optical properties of juvenile forest vegetation. Study IV illustrated the potential of WF data in the species classification of larger trees. The WF features that separated tree species were also dependent on other variables such as tree size and phenology. Inherent between-tree differences in structure were quantified and the effects of pulse density on the features were examined. Overall, the thesis provides basic findings on how LiDAR pulses interact with forest vegetation, and serves to link theory with real observations. The results contribute to an improved understanding of LiDAR measurements and their limitations, and thus provide support for further improvements in both data interpretation methods and specific sensor design.

Distinguishing cause from correlation in tokamak experiments to trigger edge-localised plasma instabilities

The generic question is considered: How can we determine the probability of an otherwise quasi-random event, having been triggered by an external influence? A specific problem is the quantification of the success of techniques to trigger, and hence control, edge-localised plasma instabilities (ELMs) in magnetically confined fusion (MCF) experiments. The development of such techniques is essential to ensure tolerable heat loads on components in large MCF fusion devices, and is necessary for their development into economically successful power plants. Bayesian probability theory is used to rigorously formulate the problem and to provide a formal solution. Accurate but pragmatic methods are developed to estimate triggering probabilities, and are illustrated with experimental data. These allow results from experiments to be quantitatively assessed, and rigorously quantified conclusions to be formed. Example applications include assessing whether triggering of ELMs is a statistical or deterministic process, and the establishment of thresholds to ensure that ELMs are reliably triggered.

Probabilities of triggering and validation in a digital silicon photomultiplier

A digital silicon photomultiplier (dSiPM) consists of an array of single photon avalanche diodes (SPADs) operating in Geiger-mode. Each SPAD is connected to a dedicated CMOS quenching and digitization circuit. One type of dSiPM is the Digital Photon Counter (DPC) manufactured by Philips Digital Photon Counting (PDPC). Due to the digital architecture of the device, a DPC can be adapted and optimized for different applications through a set of adjustable acquisition parameters. The influence of these parameters must be well understood for the correct use of the sensor. In particular, for applications involving low light intensity, such as in scintillation detectors, it is essential to carefully set all the parameters related to triggering and validation in order to discriminate between noise and true events. The triggering and validation processes in a DPC intrinsically are of a probabilistic nature since their fully digital implementations are based on configurable logic interconnections between groups of SPADs. In this study we develop an analytical model that relates the probability of triggering and validation to the number of fired SPADs in a given event, for some of the available trigger and validation schemes. The model is shown to accurately predict experimental data acquired on a PDPC DPC3200-22-44.

The Impact of Skew on the Pricing of Coco Bonds

This paper presents a Heston-based pricing model for contingent convertible bonds (CoCos). The main finding is that skew in the implied volatility surface has a significant impact on the CoCo price. Hence stochastic volatility models, like the Heston model, which incorporate smile and skew are appropriate in the context of pricing CoCos.The financial crisis of 2007-2008 triggered an avalanche of financial worries for financial institutions around the globe. After the collapse of Lehman Brothers, governments intervened and bailed out banks using tax-payer’s money. Preventing such bail-outs in the future and designing a more stable banking sector in general, requires both higher capital levels and regulatory capital of a higher quality. Bank debt needed therefore to be made absorbing. This is where CoCos come in. The Lloyds Banking Group introduced the first CoCo bonds as early as December 2009. Since then a lot of other banks followed Lloyds and the market of CoCos, currently around $70bn, is expanding very rapidly.CoCos are hybrid financial instruments that convert into equity or suffer a write-down of the face value upon the appearance of a trigger event, often in terms of the bank’s CET1 level in combination with a regulatory trigger. The valuation of CoCos boils down to the quantification of the trigger probability and the expected loss suffered by the investors if such a trigger event eventually takes place. There are at least two schools of thought regarding valuation of CoCos. Structural models can be put at work or investors can rely on market implied models. The latter category uses market data (share prices, CDS levels and implied volatility, ...) in order to calculate the theoretical price of a CoCo bond. In De Spiegeleer & Schoutens (2012a), the pricing of CoCo notes has been worked out in a market implied Black-Scholes context.In this paper we move away from the assumption of a constant volatility which is the back-bone of Black-Scholes based valuation and put the Heston model at work and study CoCos in a stochastic volatility context. The existence of a semi closed-form formula for European options pricing under the Heston model allows for a fast calibration of the model. In our approach we combined market quotes of listed option prices with CDS data. As a case study, the procedure was applied on the Tier 2 10NC CoCo issued by Barclays in 2012.

Crustal earthquake triggering by pre-historic great earthquakes on subduction zone thrusts

Triggering of earthquakes on upper plate faults during and shortly after recent great (M > 8.0) subduction thrust earthquakes raises concerns about earthquake triggering following Cascadia subduction zone earthquakes. Of particular regard to Cascadia was the previously noted, but only qualitatively identified, clustering of M > ~6.5 crustal earthquakes in the Puget Sound region between about 1200–900 cal years B.P. and the possibility that this was triggered by a great Cascadia thrust subduction thrust earthquake, and therefore portends future such clusters. We confirm quantitatively the extraordinary nature of the Puget Sound region crustal earthquake clustering between 1200–900 cal years B.P., at least over the last 16,000. We conclude that this cluster was not triggered by the penultimate, and possibly full-margin, great Cascadia subduction thrust earthquake. However, we also show that the paleoseismic record for Cascadia is consistent with conclusions of our companion study of the global modern record outside Cascadia, that M > 8.6 subduction thrust events have a high probability of triggering at least one or more M > ~6.5 crustal earthquakes.

Triggered tremor sweet spots in Alaska

To better understand what controls fault slip along plate boundaries, we have exploited the abundance of seismic and geodetic data available from the richly varied tectonic environments composing Alaska. A search for tremor triggered by 11 large earthquakes throughout all of seismically monitored Alaska reveals two tremor “sweet spots”—regions where large‐amplitude seismic waves repeatedly triggered tremor between 2006 and 2012. The two sweet spots locate in very different tectonic environments—one just trenchward and between the Aleutian islands of Unalaska and Akutan and the other in central mainland Alaska. The Unalaska/Akutan spot corroborates previous evidence that the region is ripe for tremor, perhaps because it is located where plate‐interface frictional properties transition between stick‐slip and stably sliding in both the dip direction and laterally. The mainland sweet spot coincides with a region of complex and uncertain plate interactions, and where no slow slip events or major crustal faults have been noted previously. Analyses showed that larger triggering wave amplitudes, and perhaps lower frequencies (&lt;~0.03 Hz), may enhance the probability of triggering tremor. However, neither the maximum amplitude in the time domain or in a particular frequency band, nor the geometric relationship of the wavefield to the tremor source faults alone ensures a high probability of triggering. Triggered tremor at the two sweet spots also does not occur during slow slip events visually detectable in GPS data, although slow slip below the detection threshold may have facilitated tremor triggering.

Factors Determining the Recruitment of Inositol Trisphosphate Receptor Channels During Calcium Puffs

Puffs are localized, transient elevations in cytosolic Ca2+ that serve both as the building blocks of global cellular Ca2+ signals and as local signals in their own right. They arise from clustered inositol 1,4,5-trisphosphate receptor/channels (IP3Rs), whose openings are coordinated by Ca2+-induced Ca2+ release (CICR). We utilized total internal reflection fluorescence imaging of Ca2+ signals in neuroblastoma cells with single-channel resolution to elucidate the mechanisms determining the triggering, amplitudes, kinetics, and spatial spread of puffs. We find that any given channel in a cluster has a mean probability of ∼66% of opening following opening of an initial “trigger” channel, and the probability of puff triggering thus increases steeply with increasing number of channels in a cluster (cluster size). Mean puff amplitudes scale with cluster size, but individual amplitudes vary widely, even at sites of similar cluster size, displaying similar proportions of events involving any given number of the channels in the cluster. Stochastic variation in numbers of Ca2+-inhibited IP3Rs likely contributes to the variability of amplitudes of repeated puffs at a site but the amplitudes of successive puffs were uncorrelated, even though we observed statistical correlations between interpuff intervals and puff amplitudes. Initial puffs evoked following photorelease of IP3—which would not be subject to earlier Ca2+-inhibition—also showed wide variability, indicating that mechanisms such as stochastic variation in IP3 binding and channel recruitment by CICR further determine puff amplitudes. The mean termination time of puffs lengthened with increasing puff amplitude size, consistent with independent closings of channels after a given mean open time, but we found no correlation of termination time with cluster size independent of puff amplitude. The spatial extent of puffs increased with their amplitude, and puffs of similar size were of similar width, independent of cluster size.

Permanently enhanced dynamic triggering probabilities as evidenced by two M ≥ 7.5 earthquakes

The 2012 M7.7 Haida Gwaii earthquake radiated waves that likely dynamically triggered the 2013 M7.5 Craig earthquake, setting two precedents. First, the triggered earthquake is the largest dynamically triggered shear failure event documented to date. Second, the events highlight a connection between geologic structure, sedimentary troughs that act as waveguides, and triggering probability. The Haida Gwaii earthquake excited extraordinarily large waves within and beyond the Queen Charlotte Trough, which propagated well into mainland Alaska and likely triggering the Craig earthquake along the way. Previously, focusing and associated dynamic triggering have been attributed to unpredictable source effects. This case suggests that elevated dynamic triggering probabilities may exist along the many structures where sedimentary troughs overlie major faults, such as subduction zones’ accretionary prisms and transform faults’ axial valleys. Although data are sparse, I find no evidence of accelerating seismic activity in the vicinity of the Craig rupture between it and the Haida Gwaii earthquake.

ELM pacing and trigger investigations at JET with the new ITER-like wall

During the installation of the new ITER-like wall (ILW) at JET, the high frequency pellet injector has been further improved. The launching system is now capable of delivering reliable fuelling size pellets from the magnetic outboard side up to 15 Hz repetition rate. Pacing size pellets can be produced at rates up to 50 Hz but pellet trains suffer some losses during the transfer to the plasma. A significant fraction of the pellet train can arrive at the plasma when launched from the outboard, while only a few pellets make it to the vessel inboard launching site. Stable and reliable edge-localized mode (ELM) control was achieved when using outboard fuelling size pellets. This tool was successfully applied for scenario development purposes in the ITER baseline H-mode scenario at 2.5 MA. Employed for ELM sustainment and impurity control, pellets prevented the ELM frequency from becoming so low as to cause a radiative collapse of the discharge. Despite technical limitations, injecting outboard pacing size pellets resulted in a transient enhancement of the initial ELM frequency up to a factor 4.5. This could be achieved in cases where a continuous train of sufficiently large and fast pellets were arriving in the plasma at a frequency of up to 31 Hz. Pacing size pellets were also used to investigate the ELM trigger threshold. Three basic parameters could be identified for outboard pellet launch. The ELM triggering probability increased with (i) the time elapsed since the previous ELM occurred, (ii) pellet mass and (iii) pellet speed. An indication for dependence of the ELM trigger threshold on the poloidal pellet launch position has been found; inboard launched pellets seem to reveal a higher trigger capability than pellets launched from the outboard. Finally, we compared the pellet penetration depth required for ELM triggering in the actual JET configuration with plasma-facing components to similar previous experiments performed with a carbon wall. This comparison indicates that pellet ELM triggering requires deeper penetration in the ILW configuration.

A time probabilistic approach to seismic landslide hazard estimates in Iran

Understanding where seismically induced landslides are most likely to occur is crucial in land use planning and civil protection actions aimed at reducing property damage and loss of life in future earthquakes. For this purpose an approach proposed by Del Gaudio et al. [1] has been applied to the whole Iranian territory to provide the basis to assess location and temporal recurrence of conditions of seismic activation of slope failures, according to the Newmark's model [2]. Following this approach, occurrence probabilities for different levels of seismic shaking in a time interval of interest (50 years) were first obtained through a standard hazard estimate procedure. Then, empirical formulae in the form proposed by Jibson et al. [3] and calibrated for the main seismogenic Iranian regions were used to evaluate the slope critical acceleration (Ac)x for which a prefixed probability exists that, under seismic shakings, Newmark's displacement DN exceeds a threshold×corresponding to landslide triggering conditions. The obtained (Ac)x values represent the minimum slope resistance required to limit the probability of landslide seismic triggering within the prefixed value. A map reporting the spatial distribution of these values gives comparative indications on regional different exposure of slopes to shaking capable of inducing failures and provides a reference for hazard estimate at local scale. The obtained results show that the exposure to landslide seismic induction is maximum in the Alborz Mountains region, where critical accelerations up to ∼0.1g are required to limit the probability of seismic triggering of coherent type landslides within 10% in 50 years.

Research on Mechanism of Variable Event in CLP System

Constraint inference engine as the core part of constraint logic programming system comprises variable set, temporary container and constraint filter and inference engine and adopts branching strategy, exploration strategy and node backtracking strategy to complete the inference task. This study introduces variable event to reduce the triggering times of constraint filter and analyzes the trigger probability of inference engine. The experiments show that variable event setting can enhance the efficiency of constraint inference engine.

The triggering probability of radio-loud AGN

Low luminosity radio-loud active galactic nuclei (AGN) are generally found in massive red elliptical galaxies, where they are thought to be powered through gas accretion from their surrounding hot halos in a radiatively inefficient manner. These AGN are often referred to as "low-excitation" radio galaxies (LERGs). When radio-loud AGN are found in galaxies with a young stellar population and active star formation, they are usually high-power radiatively-efficient radio AGN ("high-excitation", HERG). Using a sample of low-redshift radio galaxies identified within the Sloan Digital Sky Survey (SDSS), we determine the fraction of galaxies that host a radio-loud AGN, $f_{RL}$, as a function of host galaxy stellar mass, $M_*$, star formation rate, color (defined by the 4000 $\angstrom$ break strength), radio luminosity and excitation state (HERG/LERG). We find the following: 1. LERGs are predominantly found in red galaxies. 2. The radio-loud AGN fraction of LERGs hosted by galaxies of any color follows a $f^{LE}_{RL} \propto M^{2.5}_*$ power law. 3. The fraction of red galaxies hosting a LERG decreases strongly for increasing radio luminosity. For massive blue galaxies this is not the case. 4. The fraction of green galaxies hosting a LERG is lower than that of either red or blue galaxies, at all radio luminosities. 5. The radio-loud AGN fraction of HERGs hosted by galaxies of any color follows a $f^{HE}_{RL} \propto M^{1.5}_*$ power law. 6. HERGs have a strong preference to be hosted by green or blue galaxies. 7. The fraction of galaxies hosting a HERG shows only a weak dependence on radio luminosity cut. 8. For both HERGs and LERGs, the hosting probability of blue galaxies shows a strong dependence on star formation rate. This is not observed in galaxies of a different color.[abridged]

SELECTION EFFECTS ON THE OBSERVED REDSHIFT DEPENDENCE OF GAMMA-RAY BURST JET OPENING ANGLES

Apparent redshift dependence of the jet opening angles ($\theta_{\rm j}$) of gamma-ray bursts (GRBs) is observed from current GRB sample. We investigate whether this dependence can be explained with instrumental selection effects and observational biases by a bootstrapping method. Assuming that (1) the GRB rate follows the star formation history and the cosmic metallicity history and (2) the intrinsic distributions of the jet-corrected luminosity ($L_{\rm \gamma}$) and $\theta_{\rm j}$ are a Gaussian or a power-law function, we generate a mock {\em Swift}/BAT sample by considering various instrumental selection effects, including the flux threshold and the trigger probability of BAT, the probabilities of a GRB jet pointing to the instrument solid angle and the probability of redshift measurement. Our results well reproduce the observed $\theta_{\rm j}-z$ dependence. We find that in case of $L_{\gamma}\propto \theta_{\rm j}^2$ good consistency between the mock and observed samples can be obtained, indicating that both $L_{\rm \gamma}$ and $\theta_{\rm j}$ are degenerate for a flux-limited sample. The parameter set $(L_{\rm \gamma}, \theta_{\rm j})=(4.9\times 10^{49} \rm {erg\ s}^{-1},\ 0.054 {rad})$ gives the best consistency for the current {\em Swift} GRB sample. Considering the beaming effect, the derived intrinsic local GRB rate accordingly is $2.85\times 10^2$ Gpc$^{-3}$ yr$^{-1}$, inferring that $\sim 0.59%$ of Type Ib/c SNe may be accompanied by a GRB.

Wavelet Based Dynamic Spectrum Sensing for Cognitive Radio under Noisy Environment

Optimum utilization of available spectra is a major issue of concern in the field of wireless communication technology. Ever growing demand of wireless services poses the problem of frequency scarcity. To improve the spectrum efficiency in order to accumulate more and more users researchers have proposed cognitive radio technology. Cognitive radio, due their adaptive nature adapt themselves as per the available spectrum their by enhancing data transmission, without disturbing other users. The charm of cognitive radio is the spectrum reuse functionality. For cognitive radio environment the primary task is the sensing and identification of spectrum holes in wireless environment. This paper develop a wavelet based efficient spectrum sensing technique for noisy channel with high probability of detection and reduced probability of false triggering. To the best of our knowledge, it is first time that someone has applied the wavelet as a de-nosing tool for improvisation in existing energy detection algorithm for spectrum sensing.