1,720,965 research outputs found
Case studies of interpretable machine learning in astrophysics
Get PDFn this work, variable stars (especially RR Lyrae) light curves and the pursuit of Intermediate Mass Black Holes (IMBHs) within globular clusters (GCs), are explored through a data-driven approach using interpretable machine learning (ML) techniques.
We initiate the study with the development of an inherently interpretable classifier, utilizing L1 penalization to induce simplicity through sparsity in the model. This approach ensures a straightforward interpretation of astronomical data, providing a transparent model that facilitates the extraction of valuable insights. This penalized classifier, which reaches sparsity in the light-curve features, with a limited trade-off in accuracy performs well both on the Catalina Sky Survey validation set and, remarkably, also on the different ASAS/ASAS-SN light curve test set.
Following this, we apply the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to analyze light curves of RR Lyrae and Scuti stars, uncovering the underlying dynamical systems from observational light curves from the Catalina Sky Survey. This method yields a sparse and interpretable representation. The success rate depends systematically on variable type, with possible implications for variable star classification; however it does not obviously depend on amplitude or period. Successful models can be reduced to the generalized Lienard equation . For the equation can be solved exactly, and it admits both periodic and non-periodic solutions. We find a condition on the coefficients of the general equation for the presence of a limit cycle, which is also observed numerically in several instances.
In addition, we employ Dynamic Mode Decomposition (DMD) to investigate the modes of variable stars within the Omega Centauri cluster. This data-driven technique provides insights into the diverse modes of stellar variability, contributing to our understanding of the complex dynamics of these stars.
In the context of IMBH detection in GCs, we apply two ML models: CORELS, an inherently interpretable model, and XGBoost, a black box model elucidated post hoc using local, model-agnostic explanation rules known as anchors. By training these models on simulated GC data and subsequently applying them to actual observational data, we emphasize the importance of interpretability in scientific investigations. Our results demonstrate that simpler, interpretable models can indeed attain accuracy comparable to their more complex counterparts (for the relevant metrics), an approach of significant importance in the field of astronomy where comprehending the model’s decision-making process is crucial for establishing trust and facilitating further scientific exploration for domain-field experts.
Our findings challenge the prevalent assumption that complexity is a necessary condition for accuracy, highlighting the existence of interpretable models within the set of accurate predictive models.
In the domains of variable stars and identifying intermediate-mass black holes within globular clusters, we demonstrate that the application of machine learning tools can be both reliable and insightful when guided by models that are both interpretable and straightforward.
This aligns with the immediate call for transparency and human-understandability in ML applications, extending beyond astronomy and into the broader scientific community
Measuring the spectral index of turbulent gas with deep learning from projected density maps
Get PDFTurbulence plays a key role in star formation in molecular clouds, affecting star cluster primordial properties. As modelling present-day objects hinges on our understanding of their initial conditions, better constraints on turbulence can result in windfalls in Galactic archaeology, star cluster dynamics, and star formation. Observationally, constraining the spectral index of turbulent gas usually involves computing spectra from velocity maps. Here, we suggest that information on the spectral index might be directly inferred from column density maps (possibly obtained by dust emission/absorption) through deep learning. We generate mock density maps from a large set of adaptive mesh refinement turbulent gas simulations using the hydro-simulation code RAMSES. We train a convolutional neural network (CNN) on the resulting images to predict the turbulence index, optimize hyperparameters in validation and test on a holdout set. Our adopted CNN model achieves a mean squared error of 0.024 in its predictions on our holdout set, over underlying spectral indexes ranging from 3 to 4.5. We also perform robustness tests by applying our model to altered holdout set images, and to images obtained by running simulations at different resolutions. This preliminary result on simulated density maps encourages further developments on real data, where observational biases and other issues need to be taken into account
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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