Juan Cabral

Juan B. Cabral, Alvaro Roy Schachner

Multicriteria decision-making methods exhibit critical dependence on the choice of normalization techniques, where different selections can alter 20-40% of the final rankings. Current practice is characterized by the ad-hoc selection of methods without systematic robustness evaluation. We present a framework that addresses this methodological sensitivity through automated exploration of the scaling transformation space. The implementation leverages the existing Scikit-Criteria infrastructure to automatically generate all possible methodological combinations and provide robust comparative analysis.We apply this approach in an evaluation dataset of cryptocurrencies with 6 methodological scenarios, showing a range of correlation between methods, explicitly quantifying the methodological sensitivity limits.

Paula V. Romero Jure, Sergio Masuelli, Juan Bautista Cabral

In this paper we present the development of a dataset consisting of 91 Multi-band Cloud and Moisture Product Full-Disk (MCMIPF) from the Advanced Baseline Imager (ABI) on board GOES-16 geostationary satellite with 91 temporally and spatially corresponding CLDCLASS products from the CloudSat polar satellite. The products are diurnal, corresponding to the months of January and February 2019 and were chosen such that the products from both satellites can be co-located over South America. The CLDCLASS product provides the cloud type observed for each of the orbit's steps and the GOES-16 multiband images contain pixels that can be co-located with these data. We develop an algorithm that returns a product in the form of a table that provides pixels from multiband images labelled with the type of cloud observed in them. These labelled data conformed in this particular structure are very useful to perform supervised learning. This was corroborated by training a simple linear artificial neural network based on the work of Gorooh et al. (2020), which gave good results, especially for the classification of deep convective clouds.

Agustín Borda, Juan Bautista Cabral, Gonzalo Giarda et al.

In Multi-Criteria Decision Analysis, Rank Reversals are a serious problem that can greatly affect the results of a Multi-Criteria Decision Method against a particular set of alternatives. It is therefore useful to have a mechanism that allows one to measure the performance of a method on a set of alternatives. This idea could be taken further to build a global ranking of the effectiveness of different methods to solve a problem. In this paper, we present three tests that detect the presence of Rank Reversals, along with their implementation in the Scikit-Criteria library. We also address the complications that arise when implementing these tests for general scenarios and the design considerations we made to handle them. We close with a discussion about how these additions could play a major role in the judgment of multi-criteria decision methods for problem solving.

Martin Beroiz, Juan B. Cabral, Bruno Sanchez

We present an algorithm implemented in the astroalign Python module for image registration in astronomy. Our module does not rely on WCS information and instead matches 3-point asterisms (triangles) on the images to find the most accurate linear transformation between the two. It is especially useful in the context of aligning images prior to stacking or performing difference image analysis. Astroalign can match images of different point-spread functions, seeing, and atmospheric conditions.

J. B. Cabral, B. Sánchez, F. Ramos et al.

Machine learning algorithms are highly useful for the classification of time series data in astronomy in this era of peta-scale public survey data releases. These methods can facilitate the discovery of new unknown events in most astrophysical areas, as well as improving the analysis of samples of known phenomena. Machine learning algorithms use features extracted from collected data as input predictive variables. A public tool called Feature Analysis for Time Series (FATS) has proved an excellent workhorse for feature extraction, particularly light curve classification for variable objects. In this study, we present a major improvement to FATS, which corrects inconvenient design choices, minor details, and documentation for the re-engineering process. This improvement comprises a new Python package called "feets", which is important for future code-refactoring for astronomical software tools.

Juan B. Cabral, Bruno Sánchez, Martín Beroiz et al.

Data processing pipelines represent an important slice of the astronomical software library that include chains of processes that transform raw data into valuable information via data reduction and analysis. In this work we present Corral, a Python framework for astronomical pipeline generation. Corral features a Model-View-Controller design pattern on top of an SQL Relational Database capable of handling: custom data models; processing stages; and communication alerts, and also provides automatic quality and structural metrics based on unit testing. The Model-View-Controller provides concept separation between the user logic and the data models, delivering at the same time multi-processing and distributed computing capabilities. Corral represents an improvement over commonly found data processing pipelines in Astronomy since the design pattern eases the programmer from dealing with processing flow and parallelization issues, allowing them to focus on the specific algorithms needed for the successive data transformations and at the same time provides a broad measure of quality over the created pipeline. Corral and working examples of pipelines that use it are available to the community at https://github.com/toros-astro.