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Valle, J. W. F. (2015). Status and implications of neutrino masses: a brief panorama. Int. J. Mod. Phys. A, 30(13), 1530034–13pp.
Abstract: With the historic discovery of the Higgs boson our picutre of particle physics would have been complete were it nor for the neutrino sector and cosmology. I briefly discuss the role of neutrino masses and mixing upon gauge coupling unification, electroweak breaking and the flavor sector. Time is ripe for new discoveries such as leptonic CP violation, charged lepton flavor violation and neutrinoless double beta decay. Neutrinos could also play a role is elucidating the nature of dark matter and cosmic inflation.
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Valdes-Cortez, C., Niatsetski, Y., Perez-Calatayud, J., Ballester, F., & Vijande, J. (2022). A Monte Carlo study of the relative biological effectiveness in surface brachytherapy. Med. Phys., 49, 5576–5588.
Abstract: Purpose This work aims to simulate clustered DNA damage from ionizing radiation and estimate the relative biological effectiveness (RBE) for radionuclide (rBT)- and electronic (eBT)-based surface brachytherapy through a hybrid Monte Carlo (MC) approach, using realistic models of the sources and applicators. Methods Damage from ionizing radiation has been studied using the Monte Carlo Damage Simulation algorithm using as input the primary electron fluence simulated using a state-of-the-art MC code, PENELOPE-2018. Two Ir-192 rBT applicators, Valencia and Leipzig, one Co-60 source with a Freiburg Flap applicator (reference source), and two eBT systems, Esteya and INTRABEAM, have been included in this study implementing full realizations of their geometries as disclosed by the manufacturer. The role played by filtration and tube kilovoltage has also been addressed. Results For rBT, an RBE value of about 1.01 has been found for the applicators and phantoms considered. In the case of eBT, RBE values for the Esteya system show an almost constant RBE value of about 1.06 for all depths and materials. For INTRABEAM, variations in the range of 1.12-1.06 are reported depending on phantom composition and depth. Modifications in the Esteya system, filtration, and tube kilovoltage give rise to variations in the same range. Conclusions Current clinical practice does not incorporate biological effects in surface brachytherapy. Therefore, the same absorbed dose is administered to the patients independently on the particularities of the rBT or eBT system considered. The almost constant RBE values reported for rBT support that assumption regardless of the details of the patient geometry, the presence of a flattening filter in the applicator design, or even significant modifications in the photon energy spectra above 300 keV. That is not the case for eBT, where a clear dependence on the eBT system and the characteristics of the patient geometry are reported. A complete study specific for each eBT system, including detailed applicator characteristics (size, shape, filtering, among others) and common anatomical locations, should be performed before adopting an existing RBE value.
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T2K Collaboration(Abe, K. et al), Antonova, M., Cervera-Villanueva, A., Molina Bueno, L., & Novella, P. (2022). Scintillator ageing of the T2K near detectors fro 2010 to 2021. J. Instrum., 17(10), P10028–36pp.
Abstract: The T2K experiment widely uses plastic scintillator as a target for neutrino interactions and an active medium for the measurement of charged particles produced in neutrino interactions at its near detector complex. Over 10 years of operation the measured light yield recorded by the scintillator based subsystems has been observed to degrade by 0.9-2.2% per year. Extrapolation of the degradation rate through to 2040 indicates the recorded light yield should remain above the lower threshold used by the current reconstruction algorithms for all subsystems. This will allow the near detectors to continue contributing to important physics measurements during the T2K-II and Hyper-Kamiokande eras. Additionally, work to disentangle the degradation of the plastic scintillator and wavelength shifting fibres shows that the reduction in light yield can be attributed to the ageing of the plastic scintillator. The long component of the attenuation length of the wavelength shifting fibres was observed to degrade by 1.3-5.4% per year, while the short component of the attenuation length did not show any conclusive degradation.
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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.
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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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SCiMMA and SNEWS Collaborations(Baxter, A. L. et al), & Colomer, M. (2022). Collaborative experience between scientific software projects using Agile Scrum development. Softw.-Pract. Exp., 52, 2077–2096.
Abstract: Developing sustainable software for the scientific community requires expertise in software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for software engineering practices in the scientific community, and the complexity of developing for evolving scientific contexts. While open-source software can partially address these concerns, it can introduce complicating dependencies and delay development. These issues can be reduced if scientists and software developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of open-source software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting a user-group while maintaining core development. These issues were mitigated by creating a second Agile Scrum framework in parallel with the developers' ongoing Agile Scrum process. This Agile collaboration promoted communication, ensured that the scientists had an active role in development, and allowed the developers to evaluate and implement the scientists' software requirements. The collaboration provided benefits for each group: the scientists actuated their development by using an existing platform, and the developers utilized the scientists' use-case to improve their systems. This case study suggests that scientists and software developers can avoid scientific computing issues by collaborating and that Agile Scrum methods can address emergent concerns.
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Salesa Greus, F., & Sanchez Losa, A. (2021). Multimessenger Astronomy with Neutrinos. Universe, 7(11), 397–11pp.
Abstract: Multimessenger astronomy is arguably the branch of the astroparticle physics field that has seen the most significant developments in recent years. In this manuscript, we will review the state-of-the-art, the recent observations, and the prospects and challenges for the near future. We will give special emphasis to the observation carried out with neutrino telescopes.
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Sahin, E. et al, Gadea, A., & Algora, A. (2012). Structure of the N=50 As, Ge, Ga nuclei. Nucl. Phys. A, 893, 1–12.
Abstract: The level structures of the N = 50 As-83, Ge-82, and Ga-81 isotones have been investigated by means of multi-nucleon transfer reactions. A first experiment was performed with the CLARA PRISMA setup to identify these nuclei. A second experiment was carried out with the GASP array in order to deduce the gamma-ray coincidence information. The results obtained on the high-spin states of such nuclei are used to test the stability of the N = 50 shell closure in the region of Ni-78 (Z = 28). The comparison of the experimental level schemes with the shell-model calculations yields an N = 50 energy gap value of 4.7(3) MeV at Z = 28. This value, in a good agreement with the prediction of the finite-range liquid-drop model as well as with the recent large-scale shell model calculations, does not support a weakening of the N = 50 shell gap down to Z = 28.
Keywords: NUCLEAR REACTIONS U-238(Se-82, Ga-81), (Se-82, Ge-82), (Se-82, As-83), E=515 MeV; measured E-gamma, I-gamma (theta), gamma gamma-coin, reaction fragments, (fragment)gamma-coin using PRISMA magnetic spectrometer, gamma after deexcitation using Ge Compton-suppressed detectors of CLARA array, thin and thick target; deduced sigma(theta), levels, J, pi; calculated levels, J, pi using shell model
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Panes, B., Eckner, C., Hendriks, L., Caron, S., Dijkstra, K., Johannesson, G., et al. (2021). Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge. Astron. Astrophys., 656, A62–18pp.
Abstract: Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of similar to 70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.
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