Fast simulation of detector effects in Rivet

We describe the design and implementation of detector-bias emulation in the Rivet MC event analysis system. Implemented using C++ efficiency and kinematic smearing functors, it allows detector effects to be specified within an analysis routine, customised to the exact phase-space and reconstruction working points of the analysis. A set of standard detector functions for the physics objects of Runs 1 and 2 of the ATLAS and CMS experiments is also provided. Finally, as jet substructure is an important class of physics observable usually considered to require an explicit detector simulation, we demonstrate that a smearing approach, tuned to available substructure data and implemented in Rivet, can accurately reproduce jet-structure biases observed by ATLAS.

Rancang Bangun Detektor Standart Preform Botol Minuman Menggunakan Metode Jaringan Saraf Tiruan (Design Of Standart Detector Standart Drink Bottle Using Artificial Neural Network Method)

Preform is a semi-finished material from a bottle before cooking in the blowing process. Standards form most, same shapes and colors in one production. However, it does not have to close in one production which requires several preforms that have different colors and weights than other preforms so that they are not included in the standard and must be rejected. In this case a standard detector and color of the preform drink bottle were made using backpropagation neural network method where hardware that loaded arduino uno, photodiode sensor, load cell and HX 711 module and LCD i2c 16 x 2. Photodiode sensors can be used in blue preform together with load cell which is translated directly preform which is directly converted by the HX711 module. Two input data is then processed in the Arduino UNO module. Data output from Arduino UNO is approved on the LCD and processed in the Artificial Neural Network in Matlab on the laptop. The final output of the research results will be displayed in the command window matlab column containing rich "YES" or "NO". In this study backpropagation artificial neural networks as a method to provide accurate assessment by displaying the test results with 19 grams, color density 8 with a voltage of 0.038 Volts and output data is 1 with error data -4.75E13.

Study on temperature coefficient of CdTe detector used for x-rays detection

The temperature of the working environment is one of the key factors in determining the properties of semiconductor detectors, and it affects the absolute accuracy and stability of the standard detector. In order to determine the temperature coefficient of CdTe detector used for X-rays detection, a precise temperature control system was designed. In this experiment, detectors and radiographic source were set inside a thermostat with temperature of 0~40?C, so that the temperature can be regulated for the test of the temperature coefficient of CdTe detector. Studies had shown that, with the increase of the temperature, the energy resolution and detection efficiency of the CdTe detector would deteriorate, and under 10?C the detectors have better performance with the 8 keV X-rays.

The spectral calibration method for a crop nitrogen sensor

Purpose – The purpose of this paper is to improve the accuracy and signal-to-noise ratio (SN) of a crop nitrogen sensor. Design/methodology/approach – The accuracy and wide adaptability of two spectral calibration methods for a crop nitrogen sensor based on standard reflectivity gray plates and standard detector, respectively, were compared. Findings – The calibration method based on standard detector could significantly improve the measurement accuracy and the SN of this crop nitrogen sensor. When compared with the method based on standard gray plates, the measurement accuracy and the SN of the crop nitrogen sensor calibrated based on the standard detector method improved by 50 and 10 per cent, respectively. Originality/value – This research analysed the calibration problems faced by the crop nitrogen sensor (type CGMD302) based on standard gray plates, and proposed a sensor calibration method based on a standard detector. Finally, the results of the two calibration methods were compared in terms of measurement accuracy and the SN of the crop nitrogen sensor.

Can Smartphones Measure Radiation Exposures?

Ionizing radiation, such as X-rays, is potentially harmful to humans. Ionizing radiation can be detected by radiation detectors, which are not easily available to the public. Thus, the feasibility of using smartphones to detect and measure X-ray exposures was investigated in this work. Two sets of experiments were conducted using an Apple iPhone 4 smartphone. For one experiment, the smartphone was used as an X-ray source, while the second experiment tested the use of the iPhone as an exposure meter. Using the iPhone 4, it was found that when videos were taken during X-ray exposures, white tracks appeared in the videos, which indicated a radiation absorption event. By counting the total number of tracks in the videos (using image processing software), X-ray exposures could be determined using a calibration factor obtained from the first set of experiments. It was found that the calibration factor was strongly dependent on the video settings, but weakly dependent on the incident angle of X-rays on the phone as long as the incident angle was within ±45 degrees from the normal incidence. It was observed that, as an exposure meter, the iPhone 4 was ±20% accurate compared to a standard detector used by hospitals. The results of this work suggest that it is feasible to use an iPhone 4 to measure radiation exposures.Les rayonnements ionisants comme les rayons X, peuvent être nuisible sans être sensiblement distingués par des humains. La faisabilité de l’utilisation des smartphones qui peuvent détecter des rayons X, et ce, en mesurant l’exposition à de tels rayons faisait l’objet de cet étude. Deux séries d’expériences ont été fait avec un iPhone4. Une série portait sur le calibrage de l’iPhone avec une source de rayon X. L’autre série portait sur l’utilisation de l’iPhone comme dispositif de photométrie. L’expérience a révélé que lors de la prise de vidéo pendant une exposition aux rayons X, des brillantes traces blanches se sont apparues dans les vidéos dont chacune a indiqué un événement d’absorption de radiation. En comptant le nombre total de traces dans les vidéos (utilisant un logiciel de traitement d’image), des expositions radiographiques pourraient être déterminées en utilisant un facteur de calibrage obtenu de la première série d’expériences. Les paramètres de vidéo ont eu une importante influence sur le facteur de calibrage, tandis que l’influence de l’angle d’incident de radiographies au téléphone leur signifiait moins tant que l’angle d’incident était d’environ ±45 degrés de l’incidence normale. L’iPhone comme dispositif de photométrie révélait être d’environ ±20 % précis par rapport à un détecteur standard utilisé dans des hôpitaux.