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Stoppa, F., Vreeswijk, P., Bloemen, S., Bhattacharyya, S., Caron, S., Johannesson, G., et al. (2022). AutoSourceID-Light Fast optical source localization via U-Net and Laplacian of Gaussian. Astron. Astrophys., 662, A109–8pp.
Abstract: Aims. With the ever-increasing survey speed of optical wide-field telescopes and the importance of discovering transients when they are still young, rapid and reliable source localization is paramount. We present AutoSourceID-Light (ASID-L), an innovative framework that uses computer vision techniques that can naturally deal with large amounts of data and rapidly localize sources in optical images. Methods. We show that the ASID-L algorithm based on U-shaped networks and enhanced with a Laplacian of Gaussian filter provides outstanding performance in the localization of sources. A U-Net network discerns the sources in the images from many different artifacts and passes the result to a Laplacian of Gaussian filter that then estimates the exact location. Results. Using ASID-L on the optical images of the MeerLICHT telescope demonstrates the great speed and localization power of the method. We compare the results with SExtractor and show that our method outperforms this more widely used method. ASID-L rapidly detects more sources not only in low- and mid-density fields, but particularly in areas with more than 150 sources per square arcminute. The training set and code used in this paper are publicly available.
Keywords: astronomical databases; miscellaneous; methods; data analysis; stars; imaging; techniques; image processing
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Stoppa, F., Ruiz de Austri, R., Vreeswijk, P., Bhattacharyya, S., Caron, S., Bloemen, S., et al. (2023). AutoSourceID-FeatureExtractor Optical image analysis using a two-step mean variance estimation network for feature estimation and uncertainty characterisation. Astron. Astrophys., 680, A108–14pp.
Abstract: Aims. In astronomy, machine learning has been successful in various tasks such as source localisation, classification, anomaly detection, and segmentation. However, feature regression remains an area with room for improvement. We aim to design a network that can accurately estimate sources' features and their uncertainties from single-band image cutouts, given the approximated locations of the sources provided by the previously developed code AutoSourceID-Light (ASID-L) or other external catalogues. This work serves as a proof of concept, showing the potential of machine learning in estimating astronomical features when trained on meticulously crafted synthetic images and subsequently applied to real astronomical data.Methods. The algorithm presented here, AutoSourceID-FeatureExtractor (ASID-FE), uses single-band cutouts of 32x32 pixels around the localised sources to estimate flux, sub-pixel centre coordinates, and their uncertainties. ASID-FE employs a two-step mean variance estimation (TS-MVE) approach to first estimate the features and then their uncertainties without the need for additional information, for example the point spread function (PSF). For this proof of concept, we generated a synthetic dataset comprising only point sources directly derived from real images, ensuring a controlled yet authentic testing environment.Results. We show that ASID-FE, trained on synthetic images derived from the MeerLICHT telescope, can predict more accurate features with respect to similar codes such as SourceExtractor and that the two-step method can estimate well-calibrated uncertainties that are better behaved compared to similar methods that use deep ensembles of simple MVE networks. Finally, we evaluate the model on real images from the MeerLICHT telescope and the Zwicky Transient Facility (ZTF) to test its transfer learning abilities.
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Stoppa, F., Bhattacharyya, S., Ruiz de Austri, R., Vreeswijk, P., Caron, S., Zaharijas, G., et al. (2023). AutoSourceID-Classifier Star-galaxy classification using a convolutional neural network with spatial information. Astron. Astrophys., 680, A109–16pp.
Abstract: Aims. Traditional star-galaxy classification techniques often rely on feature estimation from catalogs, a process susceptible to introducing inaccuracies, thereby potentially jeopardizing the classification's reliability. Certain galaxies, especially those not manifesting as extended sources, can be misclassified when their shape parameters and flux solely drive the inference. We aim to create a robust and accurate classification network for identifying stars and galaxies directly from astronomical images.Methods. The AutoSourceID-Classifier (ASID-C) algorithm developed for this work uses 32x32 pixel single filter band source cutouts generated by the previously developed AutoSourceID-Light (ASID-L) code. By leveraging convolutional neural networks (CNN) and additional information about the source position within the full-field image, ASID-C aims to accurately classify all stars and galaxies within a survey. Subsequently, we employed a modified Platt scaling calibration for the output of the CNN, ensuring that the derived probabilities were effectively calibrated, delivering precise and reliable results.Results. We show that ASID-C, trained on MeerLICHT telescope images and using the Dark Energy Camera Legacy Survey (DECaLS) morphological classification, is a robust classifier and outperforms similar codes such as SourceExtractor. To facilitate a rigorous comparison, we also trained an eXtreme Gradient Boosting (XGBoost) model on tabular features extracted by SourceExtractor. While this XGBoost model approaches ASID-C in performance metrics, it does not offer the computational efficiency and reduced error propagation inherent in ASID-C's direct image-based classification approach. ASID-C excels in low signal-to-noise ratio and crowded scenarios, potentially aiding in transient host identification and advancing deep-sky astronomy.
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Lerendegui-Marco, J., Babiano-Suarez, V., Balibrea-Correa, J., Caballero, L., Calvo, D., Ladarescu, I., et al. (2024). Simultaneous Gamma-Neutron Vision device: a portable and versatile tool for nuclear inspections. EPJ Tech. Instrum., 11(1), 2–17pp.
Abstract: This work presents GN-Vision, a novel dual gamma-ray and neutron imaging system, which aims at simultaneously obtaining information about the spatial origin of gamma-ray and neutron sources. The proposed device is based on two position sensitive detection planes and exploits the Compton imaging technique for the imaging of gamma-rays. In addition, spatial distributions of slow- and thermal-neutron sources (<100 eV) are reconstructed by using a passive neutron pin-hole collimator attached to the first detection plane. The proposed gamma-neutron imaging device could be of prime interest for nuclear safety and security applications. The two main advantages of this imaging system are its high efficiency and portability, making it well suited for nuclear applications were compactness and real-time imaging is important. This work presents the working principle and conceptual design of the GN-Vision system and explores, on the basis of Monte Carlo simulations, its simultaneous gamma-ray and neutron detection and imaging capabilities for a realistic scenario where a Cf-252 source is hidden in a neutron moderating container.
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Albiol, F., Corbi, A., & Albiol, A. (2019). Densitometric Radiographic Imaging With Contour Sensors. IEEE Access, 7, 18902–18914.
Abstract: We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient's volume to derive densitometric images. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient's volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (X). In order to derive this map of lengths (L), the camera equations of the X-ray system and the contour sensor are determined. The patient's surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived L is applied to X as a physical operation (subtraction), obtaining soft tissue-(D-S) or bone-enhanced (D'(B)) figures. In the D-S type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding X). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed X instances. In the D-B' type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination.
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Albiol, F., Corbi, A., & Albiol, A. (2016). Geometrical Calibration of X-Ray Imaging With RGB Cameras for 3D Reconstruction. IEEE Trans. Med. Imaging, 35(8), 1952–1961.
Abstract: We present a methodology to recover the geometrical calibration of conventional X-ray settings with the help of an ordinary video camera and visible fiducials that are present in the scene. After calibration, equivalent points of interest can be easily identifiable with the help of the epipolar geometry. The same procedure also allows the measurement of real anatomic lengths and angles and obtains accurate 3D locations from image points. Our approach completely eliminates the need for X-ray-opaque reference marks (and necessary supporting frames) which can sometimes be invasive for the patient, occlude the radiographic picture, and end up projected outside the imaging sensor area in oblique protocols. Two possible frameworks are envisioned: a spatially shifting X-ray anode around the patient/object and a moving patient that moves/rotates while the imaging system remains fixed. As a proof of concept, experiences with a device under test (DUT), an anthropomorphic phantom and a real brachytherapy session have been carried out. The results show that it is possible to identify common points with a proper level of accuracy and retrieve three-dimensional locations, lengths and shapes with a millimetric level of precision. The presented approach is simple and compatible with both current and legacy widespread diagnostic X-ray imaging deployments and it can represent a good and inexpensive alternative to other radiological modalities like CT.
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Linhart, V., Burdette, D., Chessi, E., Cindro, V., Clinthorne, N. H., Cochran, E., et al. (2011). Spectroscopy study of imaging devices based on silicon Pixel Array Detector coupled to VATAGP7 read-out chips. J. Instrum., 6, C01092–8pp.
Abstract: Spectroscopic and timing response studies have been conducted on a detector module consisting of a silicon Pixel Array Detector bonded on two VATAGP7 read-out chips manufactured by Gamma-Medica Ideas using laboratory gamma sources and the internal calibration facilities (the calibration system of the read-out chips). The performed tests have proven that the chips have (i) non-linear calibration curves which can be approximated by power functions, (ii) capability to measure the energy of photons with energy resolution better than 2 keV (exact range and resolution depend on experimental setup), (iii) the internal calibration facility which provides 6 out of 16 available internal calibration charges within our region of interest (spanning the Compton edge of 511 keV photons). The peaks induced by the internal calibration facility are suitable for a fit of the calibration curves. However, they are not suitable for measurements of equivalent noise charge because their full width at half maximum varies with their amplitude. These facts indicate that the VATAGP7 chips are useful and precise tools for a wide variety of spectroscopic devices. We have also explored time walk of the module and peaking time of the spectroscopy signals provided by the chips. We have observed that (iv) the time walk is caused partly by the peaking time of the signals provided by the fast shaper of the chips and partly by the timing uncertainty related to the varying position of the photon interaction, (v) the peaking time of the spectroscopy signals provided by the chips increases with increasing pulse height.
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NEXT Collaboration(Alvarez, V. et al), Ball, M., Carcel, S., Cervera-Villanueva, A., Diaz, J., Ferrario, P., et al. (2013). Design and characterization of the SiPM tracking system of NEXT-DEMO, a demonstrator prototype of the NEXT-100 experiment. J. Instrum., 8, T05002–18pp.
Abstract: NEXT-100 experiment aims at searching the neutrinoless double-beta decay of the Xe-136 isotope using a TPC filled with a 100 kg of high-pressure gaseous xenon, with 90% isotopic enrichment. The experiment will take place at the Laboratorio Subterraneo de Canfranc (LSC), Spain. NEXT-100 uses electroluminescence (EL) technology for energy measurement with a resolution better than 1% FWHM. The gaseous xenon in the TPC additionally allows the tracks of the two beta particles to be recorded, which are expected to have a length of up to 30 cm at 10 bar pressure. The ability to record the topological signature of the beta beta 0 nu events provides a powerful background rejection factor for the beta beta experiment. In this paper, we present a novel 3D imaging concept using SiPMs coated with tetraphenyl butadiene (TPB) for the EL read out and its first implementation in NEXT-DEMO, a large-scale prototype of the NEXT-100 experiment. The design and the first characterization measurements of the NEXT-DEMO SiPM tracking system are presented. The SiPM response uniformity over the tracking plane drawn from its gain map is shown to be better than 4%. An automated active control system for the stabilization of the SiPMs gain was developed, based on the voltage supply compensation of the gain drifts. The gain is shown to be stabilized within 0.2% relative variation around its nominal value, provided by Hamamatsu, in a temperature range of 10 degrees C. The noise level from the electronics and the SiPM dark noise is shown to lay typically below the level of 10 photoelectrons (pe) in the ADC. Hence, a detection threshold at 10 pe is set for the acquisition of the tracking signals. The ADC full dynamic range (4096 channels) is shown to be adequate for signal levels of up to 200 pe/mu s, which enables recording most of the tracking signals.
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NEXT Collaboration(Simon, A. et al), Gomez-Cadenas, J. J., Alvarez, V., Benlloch-Rodriguez, J. M., Botas, A., Carcel, S., et al. (2017). Application and performance of an ML-EM algorithm in NEXT. J. Instrum., 12, P08009–22pp.
Abstract: The goal of the NEXT experiment is the observation of neutrinoless double beta decay in Xe-136 using a gaseous xenon TPC with electroluminescent amplification and specialized photodetector arrays for calorimetry and tracking. The NEXT Collaboration is exploring a number of reconstruction algorithms to exploit the full potential of the detector. This paper describes one of them: the Maximum Likelihood Expectation Maximization (ML-EM) method, a generic iterative algorithm to find maximum-likelihood estimates of parameters that has been applied to solve many different types of complex inverse problems. In particular, we discuss a bi-dimensional version of the method in which the photosensor signals integrated over time are used to reconstruct a transverse projection of the event. First results show that, when applied to detector simulation data, the algorithm achieves nearly optimal energy resolution (better than 0.5% FWHM at the Q value of 136Xe) for events distributed over the full active volume of the TPC.
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Olleros, P., Caballero, L., Domingo-Pardo, C., Babiano, V., Ladarescu, I., Calvo, D., et al. (2018). On the performance of large monolithic LaCl3(Ce) crystals coupled to pixelated silicon photosensors. J. Instrum., 13, P03014–17pp.
Abstract: We investigate the performance of large area radiation detectors, with high energy-and spatial-resolution, intended for the development of a Total Energy Detector with gamma-ray imaging capability, so-called i-TED. This new development aims for an enhancement in detection sensitivity in time-of-flight neutron capture measurements, versus the commonly used C6D6 liquid scintillation total-energy detectors. In this work, we study in detail the impact of the readout photosensor on the energy response of large area (50 x 50 mm(2)) monolithic LaCl3(Ce) crystals, in particular when replacing a conventional mono-cathode photomultiplier tube by an 8 x 8 pixelated silicon photomultiplier. Using the largest commercially available monolithic SiPM array (25 cm(2)), with a pixel size of 6 x 6 mm(2), we have measured an average energy resolution of 3.92% FWHM at 662 keV for crystal thick-nesses of 10, 20 and 30 mm. The results are confronted with detailed Monte Carlo (MC) calculations, where optical processes and properties have been included for the reliable tracking of the scintillation photons. After the experimental validation of the MC model, we use our MC code to explore the impact of a smaller photosensor segmentation on the energy resolution. Our optical MC simulations predict only a marginal deterioration of the spectroscopic performance for pixels of 3 x 3 mm(2).
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