Search

Operating on a surface with high noise rates and requiring susceptible, yet small-scale detectors, the Neutrinos-Angra detector is an excellent platform for technological development and expertise in new detection methods. This report details the primary features of the detector, the electronics involved, and preliminary physics results from the operational phase, particularly the ON-OFF analysis comparing the signals with the reactor in operation (ON) and during the maintenance shut-down (OFF), demonstrating the detector's capability to monitor reactor activity. Additionally, we will briefly discuss the prospects of using a cryogenic calorimeter to detect neutrinos via Coherent Elastic Neutrino-Nucleus Scattering (CEvNS), highlighting potential advancements in neutrino detection technology. Looking ahead, the project promises to play a crucial role in the integration of Latin American scientists and engineers into global scientific collaborations, significantly contributing to the LASF4RI and the broader HECAP strategic framework., Comment: White paper submited to III LASF4RI for HECAP Symposium: Update of the Strategic Plan (26-29/August/2024, ICTP-SAIFR, S\~ao Paulo, Brazil) \url{https://www.ictp-saifr.org/hecap2024/}

Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical objects' rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analyzed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific Strong Lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named Strong Gravitational Lensing Challenge (SGLC). This work presents the Deep Learning strategies and methodology used to design the highest-scoring algorithm in the II SGLC. We discuss the approach used for this dataset, the choice for a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a dataset representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable and accurate analyses of strong lenses with deep learning frameworks., Comment: 14 pages, 12 figures

Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements derived from the Mediatrix Filamentation Method as entries to an Artificial Neural Network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input to the ANN after a preselection. However, this completeness drops to $\sim$ 70% on the HST images. The false detections are of the order of 3% of the objects detected in these images. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method., Comment: Updated to match published version