Probing the stellar initial mass function with high-z supernovae

The first supernovae (SNe) will soon be visible at the edge of the observable universe, revealing the birthplaces of Population III stars. With upcoming near-infrared missions, a broad analysis of the detectability of high-z SNe is paramount. We combine cosmological and radiationtransport simulations, instrument specifications and survey strategies to create synthetic observations of primeval core-collapse (CC), Type IIn and pair-instability (PI) SNe with the James Webb Space Telescope (JWST). We show that a dedicated observational campaign with theJWST can detect up to ~15 PI explosions, ~300 CC SNe, but less than one Type IIn explosion per year, depending on the Population III star formation history. Our synthetic survey also shows that ≈1-2 × 102 SNe detections, depending on the accuracy of the classification, are sufficient to discriminate between a Salpeter and flat mass distribution for high-redshift stars with a confidence level greater than 99.5 per cent. We discuss how the purity of the sample affects our results and how supervised learning methods may help to discriminate between CC and PI SNe. © 2014 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society

Breaking the color-reddening degeneracy in type Ia supernovae

A new method to study the intrinsic color and luminosity of type Ia supernovae (SNe Ia) is presented. A metric space built using principal component analysis (PCA) on spectral series SNe Ia between -12.5 and +17.5 days from B maximum is used as a set of predictors. This metric space is built to be insensitive to reddening. Hence, it does not predict the part of color excess due to dust-extinction. At the same time, the rich variability of SN Ia spectra is a good predictor of a large fraction of the intrinsic color variability. Such metric space is a good predictor of the epoch when the maximum in the B-V color curve is reached. Multivariate Partial Least Square (PLS) regression predicts the intrinsic B band light-curve and the intrinsic B-V color curve up to a month after maximum. This allows to study the relation between the light curves of SNe Ia and their spectra. The total-to-selective extinction ratio RV in the host-galaxy of SNe Ia is found, on average, to be consistent with typical Milky-Way values. This analysis shows the importance of collecting spectra to study SNe Ia, even with large sample publicly available. Future automated surveys as LSST will provide a large number of light curves. The analysis shows that observing accompaning spectra for a significative number of SNe will be important even in the case of "normal" SNe Ia

The overlooked potential of Generalized Linear Models in astronomy-II: Gamma regression and photometric redshifts

Machine learning techniques offer a precious tool box for use within astronomy to solve problems involving so-called big data. They provide a means to make accurate predictions about a particular system without prior knowledge of the underlying physical processes of the data. In this article, and the companion papers of this series, we present the set of Generalized Linear Models (GLMs) as a fast alternative method for tackling general astronomical problems, including the ones related to the machine learning paradigm. To demonstrate the applicability of GLMs to inherently positive and continuous physical observables, we explore their use in estimating the photometric redshifts of galaxies from their multi-wavelength photometry. Using the gamma family with a log link function we predict redshifts from the PHoto-z Accuracy Testing simulated catalogue and a subset of the Sloan Digital Sky Survey from Data Release 10. We obtain fits that result in catastrophic outlier rates as low as ~1% for simulated and ~2% for real data. Moreover, we can easily obtain such levels of precision within a matter of seconds on a normal desktop computer and with training sets that contain merely tho nds of galaxies. Our software is made publicly available as a user-friendly package developed in Python, R and via an interactive web application. This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort. By facilitating their ease of use to the astronomical community, this paper series aims to make GLMs widely known and to encourage their implementation in future large-scale projects, such as the Large Synoptic Survey Telescope

Exploring the spectroscopic diversity of type Ia supernovae with DRACULA: a machine learning approach

The existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. One major challenge inevitably met when trying to infer the existence of one or more subclasses is the time consuming, and subjective, process of subclass definition. In this work, we show how machine learning tools facilitate the automatic discovery of sub-populations of SNIa; to that end we introduce the DRACULA Python package (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy). Our approach is divided in three steps: (i) Transfer Learning, which takes advantage of all available spectra (even those without an epoch estimate) as an information source, (ii) dimensionality reduction through Deep Learning and (iii) unsupervised learning (clustering) using K-Means. Results match a previously suggested classification scheme, showing that the proposed method is able to grasp the main spectral features behind the definition of such subclasses. Moreover, our methodology is capable of automatically identifying a hierarchical structure of spectral features. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa sub-classes, followed by 91bg-like events. In this context, SNIa spectra are described by a space of 4 dimensions + 1 for the time evolution of objects. We interpreted this as evidence that the progenitor system and the explosion mechanism should be described by a small number of initial physical parameters. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of subclasses (and outliers). DRACULA is publicly available within COINtoolbox (https://github.com/COINtoolbox/DRACULA)

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

Knowledge of risk factors, beliefs and practices of female healthcare professionals towards breast cancer in a tertiary institution in Lagos, Nigeria

<p>Abstract</p> <p>Background</p> <p>Breast cancer is the leading female malignancy in Nigeria. Screening for early detection has led to reduction in mortality from the disease. It is known that attitudes of physicians and motivation by community nurses influence uptake of screening methods by women. This study aims to investigate knowledge of breast cancer risk factors, beliefs about treatment and practice of screening methods among a cohort of female healthcare professionals in Lagos, Nigeria.</p> <p>Methods</p> <p>A cross-sectional study was conducted using a self-administered questionnaire to assess the knowledge of breast cancer risk factors, beliefs about treatment and practice of screening methods among 207 female doctors, nurses and other healthcare professionals working in a university teaching hospital in Lagos, Nigeria. Stratified random sampling method was employed. Chi square test, analysis of variance and Mantel-Haenszel test were performed in data analysis using SPSS v10.0 and Epi Info version 6 statistical packages.</p> <p>Results</p> <p>Female doctors obtained a mean knowledge score of 74% and were the only professional group that had satisfactory knowledge of risk factors. Majority (86%) believed that early breast cancer is curable while half of participants believed that prayer can make breast cancer disappear from the affected breast. Eighty three percent practice breast self-examination (BSE) once a month and only 8% have ever had a mammogram. Age, knowledge of risk factors, profession and beliefs were not significantly associated with rate of BSE in this study.</p> <p>Conclusion</p> <p>Results from this study suggest the need for continuing medical education programmes aimed at improving knowledge of breast cancer among female healthcare providers other than doctors.</p

Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)

Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set

Massive stars exploding in a He-rich circumstellar medium - VII. The metamorphosis of ASASSN-15ed from a narrow line Type Ibn to a normal Type Ib Supernova

We present the results of the spectroscopic and photometric monitoring campaign of ASASSN-15ed. The transient was discovered quite young by the All Sky Automated Survey for SuperNovae (ASAS-SN) survey. Amateur astronomers allowed us to sample the photometric SN evolution around maximum light, which we estimate to have occurred on JD = 2457087.4 ± 0.6 in the r band. Its apparent r-band magnitude at maximum was r = 16.91 ± 0.10, providing an absolute magnitude Mr ≈ −20.04 ± 0.20, which is slightly more luminous than the typical magnitudes estimated for Type Ibn SNe. The post-peak evolution was well monitored, and the decline rate (being in most bands around 0.1 mag d−1 during the first 25 d after maximum) is marginally slower than the average decline rates of SNe Ibn during the same time interval. The object was initially classified as a Type Ibn SN because early-time spectra were characterized by a blue continuum with superimposed narrow P-Cygni lines of He I, suggesting the presence of a slowly moving (1200–1500 km s−1), He-rich circumstellar medium. Later on, broad P-Cygni He I lines became prominent. The inferred velocities, as measured from the minimum of the broad absorption components, were between 6000 and 7000 km s−1. As we attribute these broad features to the SN ejecta, this is the first time we have observed the transition of a Type Ibn SN to a Type Ib SN