Browsing by Author "Reyes, Ignacio"

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    Deep Learning Identification of Galaxy Hosts in Transients (DELIGHT)
    (2022) Forster, Francisco; Muñoz Arancibia, Alejandra M.; Reyes, Ignacio; Gagliano, Alexander; Britt, Dylan J.; Cuellar-Carrillo, Sara; Figueroa-Tapia, Felipe; Polzin, Ava; Yousef, Yara; Arredondo, Javier; Rodríguez-Mancini, Diego; Correa-Orellana, Javier; Bayo, Amelia; Bauer, Franz E.; Catelan, Márcio; Cabrera-Vives, Guillermo; Dastidar, Raya; Estévez, Pablo A.; Pignata, Giuliano; Hernández-Garcia, Lorena; Huijse, Pablo; Reyes, Esteban; Sánchez-Sáez, Paula; Ramírez, Mauricio; Grandón, Daniela; Pineda-García, Jonathan; Chabour-Barra, Francisca; Silva-Farfán, Javier
    The Deep Learning Identification of Galaxy Hosts in Transients (DELIGHT, Förster et al. 2022, submitted) is a library created by the ALeRCE broker to automatically identify host galaxies of transient candidates using multi-resolution images and a convolutional neural network (you can test it with our example notebook, that you can run in Colab). The initial idea for DELIGHT started as a project proposed for the La Serena School of Data Science in 2021. You can install it using pip install astro-delight, but we recommend cloning this repository and pip install . from there. The library has a class with several methods that allow you to get the most likely host coordinates starting from given transient coordinates. In order to do this, the delight object needs a list of object identifiers and coordinates (oid, ra, dec). With this information, it downloads PanSTARRS images centered around the position of the transients (2 arcmin x 2 arcmin), gets their WCS solutions, creates the multi-resolution images, does some extra preprocessing of the data, and finally predicts the position of the hosts using a multi-resolution image and a convolutional neural network. It can also estimate the host's semi-major axis if requested taking advantage of the multi-resolution images. Note that DELIGHT's prediction time is currently dominated by the time to download PanSTARRS images using the panstamps service. In the future, we expect that there will be services that directly provide multi-resolution images, which should be more lightweight with no significant loss of information. Once these images are obtained, the processing times are only milliseconds per host. If you cannot install some of the dependencies, e.g. tensorflow, you can try running DELIGHT directly from Google Colab (test in this link). Github link: https://github.com/fforster/delight PyPi link: https://pypi.org/project/astro-delight/...
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    Differentiable-path integrals in quantum mechanics
    (2015) Koch, Benjamin; Reyes, Ignacio; Koch, Benjamin; Reyes, Ignacio; Koch, Benjamin; Reyes, Ignacio; Koch, Benjamin; Reyes, Ignacio
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    Módulos interactivos en línea de semiología médica. Una herramienta para estandarizar el aprendizaje clínico
    (2016) Gonzalez, Alejandro; Vargas, Bryan; Gonzalez, Vicente; Reyes, Ignacio; Sarfatis, Alberto
    Background: The learning process for medical semiology depends on multidisciplinary teaching activities, including simulation tools. These tools should achieve a standardization level aiming at a same level of basic knowledge in each student. Aim: To evaluate an interactive online semiology learning tool. Material and Methods: An interactive online learning method for medical semiology was developed. It focused mainly on physical examination and incorporated audiovisual and self-explanatory elements, to strengthen the acquisition of skills and basic knowledge for each standardized clinical learning simulation session. Subsequently, a satisfaction survey was conducted. Also the performance of students in a clinical examination was compared with that of students of the previous year. Results: Student satisfaction was outstanding, and there was a significant improvement in the performance on the final exam. Conclusions: The use of interactive self-learning online content for medical semiology provides an effective tool to improve student learning.