METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).Comment: proceedings of the International Astronomical Union, IAU-325 symposium, Cambridge University pres

Probability density estimation of photometric redshifts based on machine learning

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 784995

Machine Learning based Probability Density Functions of photometric redshifts and their application to cosmology in the era of dark Universe

The advent of wide, multiband multiepoch digital surveys of the sky has pushed astronomy in the big data era. Instruments, such as the Large Synoptic Survey Telescope or LSST, are in fact capable to produce up to 30 Terabytes of data per night. Such data streams imply that data acquisition, data reduction, data analysis and data interpretation, cannot be performed with traditional methods and that automatic procedures need to be implemented. In other words, Astronomy, like many other sciences, needs the adoption of what has been defined the fourth paradigm of modern science: the so called "data driven" or "Knowledge Discovery in Databases - KDD" (after the three older paradigms: theory, experimentation and simulations). With the words "Knowledge discovery" or "Data mining" we mean the extraction of useful information from a very large amount of data using automatic or semi-automatic techniques based on Machine Learning i.e. on algorithms built to teach the machines how to perform specific tasks typical of the human brain. This methodological revolution has led to the birth of the new discipline of Astroinformatics, which, besides the algorithms used to extract knowledge from data, covers also the proper acquisition and storage of the data, their pre-processing and analysis, as well as their distribu- tion to the community of users. This thesis takes place within the framework defined by this new discipline, since it describes the implementation and the application of a new machine learning method to the evaluation of photometric redshifts for the large samples of galaxies produced by the ongoing and future digital surveys of the extragalactic sky. Photometric redshifts (described in Section 1.1) are in fact fundamental for a huge variety of fundamental topics such as: fixing constraints to the dark matter and energy content of the Universe, mapping the galaxy color-redshift relationships, classifying astronomical sources, reconstructing the Large Scale Structure of the Universe through weak lensing, to quote just a few. Therefore, it comes as no surprise that in recent years a plethora of methods capable to calculate photo-z’s has been implemented based either on template models fitting and/or on empirical explorations of the photometric parameter space. Among the latter, many are based on machine learning but only a few allow the characterization of the results in terms of a reliable Probability Distribution Function (PDF). In fact, Machine learning based techniques while on the one hand are not explicitly dependent on the physical priors and are capable to produce accurate photo-z estimations within the photometric ranges covered by a spectroscopic training set, on the other are not easy to characterize in terms of a photo-z PDF, due to the fact that the analytical relation mapping the photometric parameters onto the redshift space is virtually unknown. In the course of my thesis I contributed to design, implement and test the innovative procedure METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) capable to provide reliable PDFs of the error distribution for empirical techniques. METAPHOR is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule) for the estimation of photo-z’s, with the possibility to easily replace the specific machine learning model chosen to predict photo-z’s, and of an algorithm for the calculation of individual source as well as of stacked objects sample PDFs. More in detail, my work in this context has been: i) the creation of software modules providing some of the functionalities of the entire method and finalised to obtain and analyze the results on all the datasets used so far (see the list of publications) and for the EUCLID contest (see below), ii) to fix the natural algorithms for improving some workflow facilities and, iii) the debugging of the whole procedure. The first application of METAPHOR was in the framework of the second internal Photo-z challenge of the Euclid consortium: a contest among different teams, aimed at establishing the best SED fitting and/or empirical methods, to be included in the official data flow processing pipelines for the mission. This contest lasted from September 2015 until the end of Jenuary 2016, and it was concluded with the releases of the results on the participants performances, in the middle of May 2016. Finally, the original workflow has been improved by adding other statistical estimators in order to better quantify the significance of the results. Through a comparison of the results obtained by METAPHOR and by the SED template fitting method Le-Phare on the SDSS- DR9 (Sloan Digital Sky Survey - Data Release 9) we verified the reliability of our PDF estimates using three different self-adaptive techniques, namely: MLPQNA, Random Forest and the standard K-Nearest Neighbors models. In order to further explore ways to improve the overall performances of photo-z methods, I also contributed to the implementation of an hybrid procedure based on the combination of SED template fitting estimates obtained with Le-Phare and of METAPHOR using as test data those extracted from the ESO (European Southern Observatory) KiDS (Kilo Degree Survey) Data Release 2. Always in the context of the KiDS survey, I was involved in the creation of a catalogue of ML photo-z’s and relative PDFs for the KiDS-DR3 (Data Release 3) survey, widely and exhaustively described in de Jong et al. (2017). A further work on KiDS DR3 data,Amaro et al. (2017), has been submitted to MNRAS. The main topic of this last work is to achieve a deeper analysis of photo-z PDFs obtained using different methods, two machine learning models (METAPHOR and ANNz2) and one based on SED fitting techniques (BPZ), through a direct comparison of both cumulative (stacked) and individual PDFs. The comparison has been made by discriminating between quantitative and qualitative estimators and using a special dummy PDF as benchmark to assess their capability to measure the quality of error estimation and invariance with respect to any type of error source. In fact, it is well known that, in absence of systematics, there are several factors affecting the photo-z reliability, such as photometric and internal errors of the methods as well as statistical biases. For the first time we implemented a ML based procedure capable to take into account also the intrinsic photometric uncertainties. By modifying the METAPHOR internal mechanism, I derived a dummy PDF method through which the individual PDFs, called dummy, are made up of a single number, e.g. 1 (the maximum probability) associated to the the redshift bin of chosen accuracy in which the only photo-z estimate for that source, falls. All the other redshift bins of a dummy PDF will be characterized by a probability identically equal to zero. Due to its intrinsic invariance to different sources of errors, the dummy method enables the possibility to compare PDF methods independently from the statistical estimator adopted. The results of this comparison, along with a discussion of the statistical estimators, have allowed us to conclude that, in order to assess the objective validity and quality of any photo-z PDF method, a combined set of statistical estimators is required. Finally, a natural application of photo-z PDFs is that involving the measurements of Weak Lensing (WL), i.e. the weak distortion of the galaxies images due to the inhomogeneities of the Universe Large Scale Structure (LSS, made up of voids, filaments, halos) along the line of sight. The shear or distortion of the galaxy shapes (ellipticities) due to the presence of matter between the observer and the lensed sources, is evaluated through the tangential component of the shear. The Excess Surface Density (i.e. a measurement of density distribution of the lenses), is proportional to the tangential shear, through a geometrical factor, which takes into account the angular diameter distances among observer, lens, and lensed galaxy source. Such distances in the geometrical factor are measured through photometric redshifts, or better through their full posterior probability distributions. Up to now, such distributions have been measured with template fitting methods: our Ma- chine Learning METAPHOR has been employed to make a preliminary comparative study on WL ESD, with respect to the SED fitter results. Furthermore, a confrontation between the ESD estimates obtained by using both METAPHOR PDFs and photo-z punctual estimates has been performed. The WL study outcome is very promising since we found that the use of punctual estimates and relative PDFs lead to indistinguishable results, at least to the required accuracy. Most importantly, we found a similar trend for the ESD results in the comparison of our Machine Learning method with a template fitter performance, despite all the limits of Machine Learning techniques (incompleteness of the training dataset, low reliability for results extrapolated outside the knowledge base) which become particularly relevant in WL studies

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because it is difficult to accurately assess the contribution from different sources of errors, namely internal to the method itself and from the photometric features defining the available parameter space. Second, because the problem of defining a robust statistical method, always able to quantify and qualify the PDF estimation validity, is still an open issue. We present a comparison among PDFs obtained using three different methods on the same data set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution template fitting method, BPZ. The photometric data were extracted from the KiDS (Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical evaluation of both individual and stacked PDFs was done through quantitative and qualitative estimators, including a dummy PDF, useful to verify whether different statistical estimators can correctly assess PDF quality. We conclude that, in order to quantify the reliability and accuracy of any zphot PDF method, a combined set of statistical estimators is required.Comment: Accepted for publication by MNRAS, 20 pages, 14 figure

Gastrointestinal cytomegalovirus disease in a patient with pemphigus vulgaris treated with corticosteroid and mycophenolate mofetil

Pemphigus vulgaris is an autoimmune disease characterized by the formation of suprabasal intra-epidermal blisters on the skin and mucosal surfaces. Infectious diseases are the main cause of death in patients with pemphigus due to the disrupture of the physiological skin barrier, immune dysregulation, and the use of immunosuppressive medications leaving the patient prone to acquire opportunistic infections. We report the case of a 67-year-old woman diagnosed with pemphigus vulgaris, who was irregularly taking prednisone and mycophenolate mofetil. She was hospitalized because of a 1-month history of watery diarrhea and oral ulcers. Unfortunately, the patient died suddenly on the ward. The autopsy revealed a bilateral saddle pulmonary embolism, Gram-positive cocci bronchopneumonia, and gastrointestinal cytomegalovirus infection, causing extensive gastrointestinal mucosal ulcer

Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of identifying and rejecting potential outliers, can increase the precision of zphot estimates and of their cumulative PDF, without sacrificing much in terms of completeness of the sample. We provide a way to assess rejection through proper cuts on the shape descriptors of a PDF, such as the width and the height of the maximum PDF's peak. In this work we tested these rejection criteria to galaxies with photometry extracted from the Kilo Degree Survey (KiDS) ESO Data Release 4, proving that such approach could lead to significant improvements to the zphot quality: e.g., for the clipped sample showing the best trade-off between precision and completeness, we achieve a reduction in outliers fraction of $\simeq 75\%$ and an improvement of $\simeq 6\%$ for NMAD, with respect to the original data set, preserving the $\simeq 93\%$ of its content.Comment: Preprint version of the manuscript to appear in the Volume "Intelligent Astrophysics" of the series "Emergence, Complexity and Computation", Book eds. I. Zelinka, D. Baron, M. Brescia, Springer Nature Switzerland, ISSN: 2194-728

Rejection Criteria Based on Outliers in the KiDS Photometric Redshifts and PDF Distributions Derived by Machine Learning

The Probability Density Function (PDF) provides an estimate of the photometric redshift (zphot) prediction error. It is crucial for current and future sky surveys, characterized by strict requirements on the zphot precision, reliability and completeness. The present work stands on the assumption that properly defined rejection criteria, capable of identifying and rejecting potential outliers, can increase the precision of zphot estimates and of their cumulative PDF, without sacrificing much in terms of completeness of the sample. We provide a way to assess rejection through proper cuts on the shape descriptors of a PDF, such as the width and the height of the maximum PDF's peak. In this work we tested these rejection criteria to galaxies with photometry extracted from the Kilo Degree Survey (KiDS) ESO Data Release 4, proving that such approach could lead to significant improvements to the zphot quality: e.g., for the clipped sample showing the best trade-off between precision and completeness, we achieve a reduction in outliers fraction of {\$}{\$}{\backslash}simeq 75{\backslash}{\%}{\$}{\$}≃75{\%}and an improvement of {\$}{\$}{\backslash}simeq 6{\backslash}{\%}{\$}{\$}≃6{\%}for NMAD, with respect to the original data set, preserving the {\$}{\$}{\backslash}simeq 93{\backslash}{\%}{\$}{\$}≃93{\%}of its content

Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry

In the era of large sky surveys, photometric redshifts (photo-z) represent crucial information for galaxy evolution and cosmology studies. In this work, we propose a new Machine Learning (ML) tool called Galaxy morphoto-Z with neural Networks (GaZNet-1), which uses both images and multi-band photometry measurements to predict galaxy redshifts, with accuracy, precision and outlier fraction superior to standard methods based on photometry only. As a first application of this tool, we estimate photo-z of a sample of galaxies in the Kilo-Degree Survey (KiDS). GaZNet-1 is trained and tested on $\sim140 000$ galaxies collected from KiDS Data Release 4 (DR4), for which spectroscopic redshifts are available from different surveys. This sample is dominated by bright (MAG$\_$AUTO$<21$) and low redshift ($z < 0.8$) systems, however, we could use $\sim$ 6500 galaxies in the range $0.8 < z < 3$ to effectively extend the training to higher redshift. The inputs are the r-band galaxy images plus the 9-band magnitudes and colours, from the combined catalogs of optical photometry from KiDS and near-infrared photometry from the VISTA Kilo-degree Infrared survey. By combining the images and catalogs, GaZNet-1 can achieve extremely high precision in normalized median absolute deviation (NMAD=0.014 for lower redshift and NMAD=0.041 for higher redshift galaxies) and low fraction of outliers ($0.4$\% for lower and $1.27$\% for higher redshift galaxies). Compared to ML codes using only photometry as input, GaZNet-1 also shows a $\sim 10-35$% improvement in precision at different redshifts and a $\sim$ 45% reduction in the fraction of outliers. We finally discuss that, by correctly separating galaxies from stars and active galactic nuclei, the overall photo-z outlier fraction of galaxies can be cut down to $0.3$\%.Comment: Accepted for publication in A&

Gastrointestinal cytomegalovirus disease in a patient with pemphigus vulgaris treated with corticosteroid and mycophenolate mofetil

Pemphigus vulgaris is an autoimmune disease characterized by the formation of suprabasal intra-epidermal blisters on the skin and mucosal surfaces. Infectious diseases are the main cause of death in patients with pemphigus due to the disrupture of the physiological skin barrier, immune dysregulation, and the use of immunosuppressive medications leaving the patient prone to acquire opportunistic infections. We report the case of a 67-year-old woman diagnosed with pemphigus vulgaris, who was irregularly taking prednisone and mycophenolate mofetil. She was hospitalized because of a 1-month history of watery diarrhea and oral ulcers. Unfortunately, the patient died suddenly on the ward. The autopsy revealed a bilateral saddle pulmonary embolism, Gram-positive cocci bronchopneumonia, and gastrointestinal cytomegalovirus infection, causing extensive gastrointestinal mucosal ulcer

Abordaje odontológico integral, a través de la terapéutica cannábica, en pacientes con riesgo médico del Centro de Alta Complejidad 2022 FOLP-UNLP

Introducción. En el marco regulatorio de la ley nacional Argentina 27.350, de investigación médica y científica del uso medicinal de la planta de cannabis y sus derivados se organizó un equipo de trabajo multidisciplinario en el Centro de Alta Complejidad FOLP UNLP, con una nueva perspectiva, que atiende pacientes con patologías complejas cuyos síntomas dificultan la atención odontológica integral, agravando así su salud. En odontología el abordaje de pacientes con: ECNE, TEA, retraso madurativo es muy dificultoso. Sumado a la ansiedad y angustia que pueden presentar ante la consulta odontológica.Facultad de Odontologí