Rapid: Early classification of explosive transients using deep learning

We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we present the first method specifically designed to provide early classifications of astronomical time-series data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPID well-suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of transients from the ZTF data stream. We have made RAPID available as an open-source software package (this https URL) for machine learning-based alert-brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds

Coding culture: challenges and recommendations for comparative cultural databases

Considerable progress in explaining cultural evolutionary dynamics has been made by applying rigorous models from the natural sciences to historical and ethnographic information collected and accessed using novel digital platforms. Initial results have clarified several long-standing debates in cultural evolutionary studies, such as population origins, the role of religion in the evolution of complex societies and the factors that shape global patterns of language diversity. However, future progress requires recognition of the unique challenges posed by cultural data. To address these challenges, standards for data collection, organisation and analysis must be improved and widely adopted. Here, we describe some major challenges to progress in the construction of large comparative databases of cultural history, including recognising the critical role of theory, selecting appropriate units of analysis, data gathering and sampling strategies, winning expert buy-in, achieving reliability and reproducibility in coding, and ensuring interoperability and sustainability of the resulting databases. We conclude by proposing a set of practical guidelines to meet these challenges

The internal kinematic of star-forming regions in interacting galaxies

Star formation processes can be found in colliding galaxies, where the gas compression can trigger the formation of giant star-forming regions. We present the results from a detailed kinematic analysis in a sample of HII regions located in three strongly interacting galaxies. The velocity dispersion and the luminosity of the multiple-components analyzed in the emission-line profiles suggest that these star-forming objects correspond to giant complexes. In addition, the star formation rates and the ionization state in these regions revealed the presence of ongoing star formation events.Fil: Firpo, V.. Gemini Observatory; Chile. Universidad de La Serena; ChileFil: Muthukrishna, D.. Institute Of Astronomy; Reino UnidoFil: Campuzano Castro, Federico. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Bosch, Guillermo Luis. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Hägele, Guillermo Federico. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Torres Flores, S.. Universidad de La Serena; ChileFil: Cardaci, Monica Viviana. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaII workshop on Chemical Abundances in Gaseous Nebulae: Open problems in Nebular AstrophysicsSao Jose dos CamposBrasilUniversidade do Vale do Paraíb

Chemodynamics in Blue Compact Dwarf galaxies: II Zw 33 and Mrk 600

Realizamos un estudio cinemático de cinco regiones de formación estelar pertenecientes a dos galaxias enanas compactas azules: II Zw 33 and Mrk 600, utilizando datos espectroscópicos de alta resolución obtenidos con el espectrógrafo MIKE del telescopio Clay. Los espectros presentan líneas de emisión con perfiles muy complejos revelando multiples componentes Gaussianas. Con el propósito de analizar la cinemática de estas regiones de formación estelar, desarrollamos un programa en Python, basado en el paquete de minimización de mínimos cuadrados no lineal y ajuste de curvas (LMFit), y utilizamos un método estadístico, para analizar la bondad de la inclusión de una nueva componente gaussiana en el modelo. Se estimaron las propiedades físicas del gas (densidades y temperaturas electrónicas), abundancias químicas iónicas y totales de diversas especies atómicas y el grado de ionización en cada región de formación estelar.We performed a kinematic study of five star-formation regions belonging to two Blue Compact Dwarf galaxies: IIZw33 and Mrk600, using high resolution spectroscopic data obtained with the MIKE spectrograph mounted at the Clay telescope. The spectra present very complex emission-line profiles showing multiple Gaussian components. In order to analyze the kinematics of these star-forming regions we developed a Python code, based on the Non-Linear Least-Square Minimization and Curve-Fitting (LMFit) package, and used a statistical method, to analyze the goodness of the inclusion of a new Gaussian in the model. We estimated the physical conditions (electron densities and temperatures), ionic and total chemical abundances of several atomic species and the ionization degree on each star-forming region.Fil: Campuzano Castro, Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Hägele, Guillermo Federico. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Bosch, Guillermo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Firpo, V.. Gemini Observatory; ChileFil: Cardaci, Monica Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Astrofísica La Plata. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas. Instituto de Astrofísica La Plata; ArgentinaFil: Muthukrishna, D.. University of Cambridge; Reino UnidoFil: Morrell, Nidia Irene. Las Campanas Observatory; ChileSegunda Reunión Binacional entre la Sociedad Chilena de Astronomía (SOCHIAS)La SerenaChileSociedad Chilena de AstronomíaAsociación Argentina de Astronomí

The Photometric LSST Astronomical Time-series Classification Challenge PLAsTiCC: Selection of a Performance Metric for Classification Probabilities Balancing Diverse Science Goals

Classification of transient and variable light curves is an essential step in using astronomical observations to develop an understanding of the underlying physical processes from which they arise. However, upcoming deep photometric surveys, including the Large Synoptic Survey Telescope (LSST), will produce a deluge of low signal-to-noise data for which traditional type estimation procedures are inappropriate. Probabilistic classification is more appropriate for such data but is incompatible with the traditional metrics used on deterministic classifications. Furthermore, large survey collaborations like LSST intend to use the resulting classification probabilities for diverse science objectives, indicating a need for a metric that balances a variety of goals. We describe the process used to develop an optimal performance metric for an open classification challenge that seeks to identify probabilistic classifiers that can serve many scientific interests. The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) aims to identify promising techniques for obtaining classification probabilities of transient and variable objects by engaging a broader community beyond astronomy. Using mock classification probability submissions emulating realistically complex archetypes of those anticipated of PLAsTiCC, we compare the sensitivity of two metrics of classification probabilities under various weighting schemes, finding that both yield results that are qualitatively consistent with intuitive notions of classification performance. We thus choose as a metric for PLAsTiCC a weighted modification of the cross-entropy because it can be meaningfully interpreted in terms of information content. Finally, we propose extensions of our methodology to ever more complex challenge goals and suggest some guiding principles for approaching the choice of a metric of probabilistic data products

Behavioral Modernity and the Cultural Transmission of Structured Information: The Semantic Axelrod Model

Cultural transmission models are coming to the fore in explaining increases in the Paleolithic toolkit richness and diversity. During the later Paleolithic, technologies increase not only in terms of diversity but also in their complexity and interdependence. As Mesoudi and O'Brien (2008) have shown, selection broadly favors social learning of information that is hierarchical and structured, and multiple studies have demonstrated that teaching within a social learning environment can increase fitness. We believe that teaching also provides the scaffolding for transmission of more complex cultural traits. Here, we introduce an extension of the Axelrod (1997} model of cultural differentiation in which traits have prerequisite relationships, and where social learning is dependent upon the ordering of those prerequisites. We examine the resulting structure of cultural repertoires as learning environments range from largely unstructured imitation, to structured teaching of necessary prerequisites, and we find that in combination with individual learning and innovation, high probabilities of teaching prerequisites leads to richer cultural repertoires. Our results point to ways in which we can build more comprehensive explanations of the archaeological record of the Paleolithic as well as other cases of technological change.Comment: 24 pages, 7 figures. Submitted to "Learning Strategies and Cultural Evolution during the Paleolithic", edited by Kenichi Aoki and Alex Mesoudi, and presented at the 79th Annual Meeting of the Society for American Archaeology, Austin TX. Revised 5/14/1

Models and simulations for the photometric lsst astronomical time series classification challenge (Plasticc)

We describe the simulated data sample for the "Photometric LSST Astronomical Time Series Classification Challenge" (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the Large Synoptic Survey Telescope (LSST), a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to 2018 December 17, and included 1,094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at this https URL. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models