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We present an automated radar data analysis algorithm developed to calculate probability distributions of meteor- and meteoroid parameters for head echoes detected with the Middle and Upper atmosphere (MU) radar in Shigaraki, Japan. The algorithm utilizes direct Monte Carlo simulations of uncertainties, with Bayesian Markov-chain Monte Carlo estimation of meteor model parameters and N-body propagation of distributions to perform orbit determination. The implementation has been validated using raw data simulations and a comparison with previous analysis methods. The concepts are applicable on a wide range of possible head echo measurements with other radar systems. The generated probability distributions provide quantitative reliability, which enables improved statistical studies and investigating the origins of detected meteoroids. The methodology section is highly detailed in order for the methods to be reproducible and provide a solid reference foundation for future studies. One such study is presented in a companion paper called ‘High-altitude meteors detected by the interferometric MU radar’.

ACKNOWLEDGEMENTS This publication is part of the project ‘Contribution of the UGR to the PLATO2.0 space mission. Phases C / D-1’, funded by MCNI/AEI/PID2019-107061GB-C64. DPO acknowledges MNRAS for the opportunity to publish his first scientific article, in particular to the assistant editor, Bella Lock, for her comments. He also appreciates the comments of the anonymous reviewer of this article. They have undoubtedly contributed to improving it. He also acknowledges all the co-authors of this work, especially to JCS and AGH, his thesis project directors, because without them, he would not have been able to publish this work. And he also appreciates the patience of his wife, Pilar, and his son, Fernando, for all the time they have allowed him to get to this point. AGH acknowledges funding support from Spanish public funds for research under project PID2019-107061GB-C64 by the Spanish Ministry of Science and Education, and from ‘European Regional Development Fund/Junta de Andalucía-Consejería de Economía y Conocimiento’ under project E-FQM-041-UGR18 by Universidad de Granada. JPG acknowledges funding support from Spanish public funds for research from project PID2019-107061GB-C63 from the ‘Programas Estatales de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i y de I+D + i Orientada a los Retos de la Sociedad’, as well as from the State Agency for Research of the Spanish MCIU through the ‘Center of Excellence Severo Ochoa’ award to the Instituto de Astrofísica de Andalucía (SEV-2017-0709). SBF received financial support from the Spanish State Research Agency (AEI) Projects No. PID2019-107061GB-C64. He also thanks the resources received from the PLATO project collaboration with Centro de Astrobiología (PID2019-107061GB-C61). This paper includes data collected with the TESS mission, obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the TESS mission is provided by the NASA Explorer Program. STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS 5-26555., DATA AVAILABILITY Tables of the most significant peaks, corresponding to each of the 11 δ Sct stars of our sample, extracted with MM, are available in VizieR DataBase. In them, we can find the values of the frequencies, amplitudes, phases, corresponding errors, and S/N of the extracted peaks., In this work, we aim at constraining the age of the young open cluster Melotte 20, known as α Per, using seismic indices. The method consists of the following steps: (1) Extract the frequency content of a sample of stars in the field of an open cluster. (2) Search for possible regularities in the frequency spectra of δ Sct stars candidates, using different techniques, such as the Fourier transform, the autocorrelation function, the histogram of frequency differences and the échelle diagram. (3) Constrain the age of the selected stars by both the physical parameters and seismic indices by comparing them with a grid of asteroseismic models representative of δ Sct stars. (4) Find possible common ages between these stars to determine the age of the cluster. We performed the pulsation analysis with MultiModes, a rapid, accurate and powerful open-source code, which is presented in this paper. The result is that the age of α Per could be between 96 and 100 Myr. This is an improvement over different techniques in the past. We therefore show that space astroseismology is capable of taking important steps in the dating of young open clusters., Project 'Contribution of the UGR MCNI/AEI/PID2019-107061GB-C64, Spanish public funds for research PID2019-107061GB-C64, Spanish Ministry of Science and Education, European Regional Development Fund/Junta de Andalucia-Consejeria de Economia y Conocimiento' E-FQM-041-UGR18, Universidad de Granada, Spanish public funds for research from project PID2019-107061GB-C63, Programas Estatales de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i y de I+D + i Orientada a los Retos de la Sociedad', State Agency for Research of the Spanish MCIU through the `Center of Excellence Severo Ochoa' award SEV-2017-0709, PID2019-107061GB-C64, PID2019-107061GB-C61, National Aeronautics & Space Administration (NASA), Association of Universities for Research in Astronomy, Inc., under NASA NAS 5-26555

Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful semantic representations of galaxies that are useful for new tasks on which the models were never trained. We exploit these representations to outperform several recent approaches at practical tasks crucial for investigating large galaxy samples. The first task is identifying galaxies of similar morphology to a query galaxy. Given a single galaxy assigned a free text tag by humans (e.g. "#diffuse"), we can find galaxies matching that tag for most tags. The second task is identifying the most interesting anomalies to a particular researcher. Our approach is 100% accurate at identifying the most interesting 100 anomalies (as judged by Galaxy Zoo 2 volunteers). The third task is adapting a model to solve a new task using only a small number of newly-labelled galaxies. Models fine-tuned from our representation are better able to identify ring galaxies than models fine-tuned from terrestrial images (ImageNet) or trained from scratch. We solve each task with very few new labels; either one (for the similarity search) or several hundred (for anomaly detection or fine-tuning). This challenges the longstanding view that deep supervised methods require new large labelled datasets for practical use in astronomy. To help the community benefit from our pretrained models, we release our fine-tuning code Zoobot. Zoobot is accessible to researchers with no prior experience in deep learning., 20 pages plus appendix. Accepted to MNRAS (open-access DOI below). Code, documentation, pretrained models: https://github.com/mwalmsley/zoobot (PyTorch and TensorFlow)

We introduce a new software package for modeling the point-spread function (PSF) of astronomical images, called Piff (PSFs In the Full FOV), which we apply to the first three years (known as Y3) of the Dark Energy Survey (DES) data. We describe the relevant details about the algorithms used by Piff to model the PSF, including how the PSF model varies across the field of view (FOV). Diagnostic results show that the systematic errors from the PSF modeling are very small over the range of scales that are important for the DES Y3 weak lensing analysis. In particular, the systematic errors from the PSF modeling are significantly smaller than the corresponding results from the DES year one (Y1) analysis. We also briefly describe some planned improvements to Piff that we expect to further reduce the modeling errors in future analyses., Comment: 19 pages, accepted/published by MNRAS. See https://www.darkenergysurvey.org/des-year-3-cosmology-results-papers/ for the full DES Y3 cosmology release

The stellar occultation technique provides competitive accuracy in determining the sizes, shapes, astrometry, etc., of the occulting body, comparable to in-situ observations by spacecraft. With the increase in the number of known Solar System objects expected from the LSST, the highly precise astrometric catalogues, such as Gaia, and the improvement of ephemerides, occultations observations will become more common with a higher number of chords in each observation. In the context of the Big Data era, we developed SORA, an open-source python library to reduce and analyse stellar occultation data efficiently. It includes routines from predicting such events up to the determination of Solar System bodies' sizes, shapes, and positions., Comment: 15 pages, 7 figures

We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the $griz$ bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for $10,699$ test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code BAGPIPES, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under $6$ min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed GALPRO, a highly intuitive and efficient Python package to rapidly generate multivariate PDFs on-the-fly. GALPRO is documented and available for researchers to use in their cosmology and galaxy evolution studies., Comment: 18 pages, 8 figures, Accepted by MNRAS

Made available in DSpace on 2020-12-12T02:46:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-06-11 Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object. These groups are mainly identified in proper elements or frequencies domains. Because of robotic telescope surveys, the number of known asteroids has increased from ∼eq10000 in the early 1990s to more than 750000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a, e, sin (i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand-alone and ensemble approaches. The extremely randomized trees (ExtraTree) method had the highest precision, enabling to retrieve up to 97 per cent of family members identified with standard HCM. School of Natural Sciences and Engineering São Paulo State University (UNESP) National Space Research Institute (INPE) Division of Space Mechanics and Control São Paulo State University (UNESP) School of Natural Sciences and Engineering São Paulo State University (UNESP) São Paulo State University (UNESP)