arXiv subjects

E. E. O. Ishida

Publications and source records attributed to E. E. O. Ishida.

At least 37 records · Page 2Linked to original sources

arXiv2021

Anomaly detection in the Zwicky Transient Facility DR3

We present results from applying the SNAD anomaly detection pipeline to the third public data release of the Zwicky Transient Facility (ZTF DR3). The pipeline is composed of 3 stages: feature extraction, search of outliers with machine learning algorithms and anomaly identification with followup by human experts. Our analysis concentrates in three ZTF fields, comprising more than 2.25 million objects. A set of 4 automatic learning algorithms was used to identify 277 outliers, which were subsequently scrutinised by an expert. From these, 188 (68%) were found to be bogus light curves -- including effects from the image subtraction pipeline as well as overlapping between a star and a known asteroid, 66 (24%) were previously reported sources whereas 23 (8%) correspond to non-catalogued objects, with the two latter cases of potential scientific interest (e. g. 1 spectroscopically confirmed RS Canum Venaticorum star, 4 supernovae candidates, 1 red dwarf flare). Moreover, using results from the expert analysis, we were able to identify a simple bi-dimensional relation which can be used to aid filtering potentially bogus light curves in future studies. We provide a complete list of objects with potential scientific application so they can be further scrutinised by the community. These results confirm the importance of combining automatic machine learning algorithms with domain knowledge in the construction of recommendation systems for astronomy. Our code is publicly available at https://github.com/snad-space/zwad

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arXiv2020

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 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 which aimed to catalyze the development of robust classifiers under LSST-like conditions of a non-representative training set for a large photometric test set of imbalanced classes. Over 1,000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between Sep 28, 2018 and Dec 17, 2018, ultimately identifying three winners in February 2019. 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 multi-layer 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.

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arXiv2019

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 https://doi.org/10.5281/zenodo.2612896. 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.

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arXiv2019

Gaia DR2 unravels incompleteness of nearby cluster population: New open clusters in the direction of Perseus

Open clusters (OCs) are popular tracers of the structure and evolutionary history of the Galactic disk. The OC population is often considered to be complete within 1.8 kpc of the Sun. The recent Gaia Data Release 2 (DR2) allows the latter claim to be challenged. We perform a systematic search for new OCs in the direction of Perseus using precise and accurate astrometry from Gaia DR2. We implement a coarse-to-fine search method. First, we exploit spatial proximity using a fast density-aware partitioning of the sky via a k-d tree in the spatial domain of Galactic coordinates, (l, b). Secondly, we employ a Gaussian mixture model in the proper motion space to quickly tag fields around OC candidates. Thirdly, we apply an unsupervised membership assignment method, UPMASK, to scrutinise the candidates. We visually inspect colour-magnitude diagrams to validate the detected objects. Finally, we perform a diagnostic to quantify the significance of each identified overdensity in proper motion and in parallax space We report the discovery of 41 new stellar clusters. This represents an increment of at least 20% of the previously known OC population in this volume of the Milky Way. We also report on the clear identification of NGC 886, an object previously considered an asterism. This letter challenges the previous claim of a near-complete sample of open clusters up to 1.8 kpc. Our results reveal that this claim requires revision, and a complete census of nearby open clusters is yet to be found.

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arXiv2019

Optimizing spectroscopic follow-up strategies for supernova photometric classification with active learning

We report a framework for spectroscopic follow-up design for optimizing supernova photometric classification. The strategy accounts for the unavoidable mismatch between spectroscopic and photometric samples, and can be used even in the beginning of a new survey -- without any initial training set. The framework falls under the umbrella of active learning (AL), a class of algorithms that aims to minimize labelling costs by identifying a few, carefully chosen, objects which have high potential in improving the classifier predictions. As a proof of concept, we use the simulated data released after the Supernova Photometric Classification Challenge (SNPCC) and a random forest classifier. Our results show that, using only 12\% the number of training objects in the SNPCC spectroscopic sample, this approach is able to double purity results. Moreover, in order to take into account multiple spectroscopic observations in the same night, we propose a semi-supervised batch-mode AL algorithm which selects a set of $N=5$ most informative objects at each night. In comparison with the initial state using the traditional approach, our method achieves 2.3 times higher purity and comparable figure of merit results after only 180 days of observation, or 800 queries (73% of the SNPCC spectroscopic sample size). Such results were obtained using the same amount of spectroscopic time necessary to observe the original SNPCC spectroscopic sample, showing that this type of strategy is feasible with current available spectroscopic resources. The code used in this work is available in the COINtoolbox: https://github.com/COINtoolbox/ActSNClass .

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arXiv2018

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 thousands of galaxies. Our software is made publicly available as an user-friendly package developed in Python, R and via an interactive web application (https://cosmostatisticsinitiative.shinyapps.io/CosmoPhotoz). This software allows users to apply a set of GLMs to their own photometric catalogues and generates publication quality plots with minimum effort from the user. 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.

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arXiv2018

Spatial field reconstruction with INLA: Application to IFU galaxy data

Astronomical observations of extended sources, such as cubes of integral field spectroscopy (IFS), encode auto-correlated spatial structures that cannot be optimally exploited by standard methodologies. This work introduces a novel technique to model IFS datasets, which treats the observed galaxy properties as realizations of an unobserved Gaussian Markov random field. The method is computationally efficient, resilient to the presence of low-signal-to-noise regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian inference, the Integrated Nested Laplace Approximation (INLA). As a case study, we analyse 721 IFS data cubes of nearby galaxies from the CALIFA and PISCO surveys, for which we retrieve the maps of the following physical properties: age, metallicity, mass and extinction. The proposed Bayesian approach, built on a generative representation of the galaxy properties, enables the creation of synthetic images, recovery of areas with bad pixels, and an increased power to detect structures in datasets subject to substantial noise and/or sparsity of sampling. A snippet code to reproduce the analysis of this paper is available in the COIN toolbox, together with the field reconstructions of the CALIFA and PISCO samples.

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arXiv2017

cosmoabc: Likelihood-free inference via Population Monte Carlo Approximate Bayesian Computation

Approximate Bayesian Computation (ABC) enables parameter inference for complex physical systems in cases where the true likelihood function is unknown, unavailable, or computationally too expensive. It relies on the forward simulation of mock data and comparison between observed and synthetic catalogues. Here we present cosmoabc, a Python ABC sampler featuring a Population Monte Carlo (PMC) variation of the original ABC algorithm, which uses an adaptive importance sampling scheme. The code is very flexible and can be easily coupled to an external simulator, while allowing to incorporate arbitrary distance and prior functions. As an example of practical application, we coupled cosmoabc with the numcosmo library and demonstrate how it can be used to estimate posterior probability distributions over cosmological parameters based on measurements of galaxy clusters number counts without computing the likelihood function. cosmoabc is published under the GPLv3 license on PyPI and GitHub and documentation is available at http://goo.gl/SmB8EX

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arXiv2017

On the realistic validation of photometric redshifts, or why Teddy will never be Happy

Two of the main problems encountered in the development and accurate validation of photometric redshift (photo-z) techniques are the lack of spectroscopic coverage in feature space (e.g. colours and magnitudes) and the mismatch between photometric error distributions associated with the spectroscopic and photometric samples. Although these issues are well known, there is currently no standard benchmark allowing a quantitative analysis of their impact on the final photo-z estimation. In this work, we present two galaxy catalogues, Teddy and Happy, built to enable a more demanding and realistic test of photo-z methods. Using photometry from the Sloan Digital Sky Survey and spectroscopy from a collection of sources, we constructed datasets which mimic the biases between the underlying probability distribution of the real spectroscopic and photometric sample. We demonstrate the potential of these catalogues by submitting them to the scrutiny of different photo-z methods, including machine learning (ML) and template fitting approaches. Beyond the expected bad results from most ML algorithms for cases with missing coverage in feature space, we were able to recognize the superiority of global models in the same situation and the general failure across all types of methods when incomplete coverage is convoluted with the presence of photometric errors - a data situation which photo-z methods were not trained to deal with up to now and which must be addressed by future large scale surveys. Our catalogues represent the first controlled environment allowing a straightforward implementation of such tests. The data are publicly available within the COINtoolbox (https://github.com/COINtoolbox/photoz_catalogues).

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arXiv2016

A metric space for type Ia supernova spectra: a new method to assess explosion scenarios

Over the past years type Ia supernovae (SNe Ia) have become a major tool to determine the expansion history of the Universe, and considerable attention has been given to, both, observations and models of these events. However, until now, their progenitors are not known. The observed diversity of light curves and spectra seems to point at different progenitor channels and explosion mechanisms. Here, we present a new way to compare model predictions with observations in a systematic way. Our method is based on the construction of a metric space for SN Ia spectra by means of linear Principal Component Analysis (PCA), taking care of missing and/or noisy data, and making use of Partial Least Square regression (PLS) to find correlations between spectral properties and photometric data. We investigate realizations of the three major classes of explosion models that are presently discussed: delayed-detonation Chandrasekhar-mass explosions, sub-Chandrasekhar-mass detonations, and double-degenerate mergers, and compare them with data. We show that in the PC space all scenarios have observed counterparts, supporting the idea that different progenitors are likely. However, all classes of models face problems in reproducing the observed correlations between spectral properties and light curves and colors. Possible reasons are briefly discussed.

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arXiv2016

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 identification of subtypes of SNeIa through the establishment of a hierarchical group structure in the continuous space of spectral diversity formed by these objects. Using Deep Learning, we were capable of performing such identification in a 4 dimensional feature space (+1 for time evolution), while the standard Principal Component Analysis barely achieves similar results using 15 principal components. This is evidence that the progenitor system and the explosion mechanism can be described by a small number of initial physical parameters. As a proof of concept, we show that our results are in close agreement with a previously suggested classification scheme and that our proposed method can grasp the main spectral features behind the definition of such subtypes. This allows the confirmation of the velocity of lines as a first order effect in the determination of SNIa subtypes, followed by 91bg-like events. Given the expected data deluge in the forthcoming years, our proposed approach is essential to allow a quick and statistically coherent identification of SNeIa subtypes (and outliers). All tools used in this work were made publicly available in the Python package Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy (DRACULA) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).

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arXiv2016

The supernova impostor PSN J09132750+7627410 and its progenitor

We report the results of our follow-up campaign of the supernova impostor PSN J09132750+7627410, based on optical data covering $\sim250\,\rm{d}$. From the beginning, the transient shows prominent narrow Balmer lines with P-Cygni profiles, with a blue-shifted absorption component becoming more prominent with time. Along the $\sim3\,\rm{months}$ of the spectroscopic monitoring, broad components are never detected in the hydrogen lines, suggesting that these features are produced in slowly expanding material. The transient reaches an absolute magnitude $M_r=-13.60\pm0.19\,\rm{mag}$ at maximum, a typical luminosity for supernova impostors. Amateur astronomers provided $\sim4\,\rm{years}$ of archival observations of the host galaxy, NGC 2748. The detection of the quiescent progenitor star in archival images obtained with the Hubble Space Telescope suggests it to be an $18-20$\msun white-yellow supergiant.

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arXiv2016

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.

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arXiv2015

Probing the stellar initial mass function with high-$z$ supernovae

The first supernovae 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-$z$ supernovae is paramount. We combine cosmological and radiation transport simulations, instrument specifications, and survey strategies to create synthetic observations of primeval core-collapse, Type IIn and pair-instability supernovae with the James Webb Space Telescope ($JWST$). We show that a dedicated observational campaign with the $JWST$ can detect up to $\sim 15$ pair-instability explosions, $\sim 300$ core-collapse supernovae, but less than one Type IIn explosion per year, depending on the Population III star formation history. Our synthetic survey also shows that $\approx 1-2 \times10^2$ supernovae 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.

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arXiv2015

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). 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 M(r) ~ -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 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), 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. 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.

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arXiv2015

Massive stars exploding in a He-rich circumstellar medium - VIII. PSN J07285387+3349106, a highly reddened supernova Ibn

We present spectroscopic and photometric observations for the Type Ibn supernova (SN) dubbed PSN J07285387+3349106. Using data provided by amateur astronomers, we monitored the photometric rise of the SN to maximum light, occurred on 2015 February 18.8 UT (JD(max,V) = 2457072.0 +- 0.8). PSN J07285387+3349106 exploded in the inner region of an infrared luminous galaxy, and is the most reddened SN Ibn discovered so far. We apply multiple methods to derive the total reddening to the SN, and determine a total colour excess E(B-V)(tot) = 0.99 +- 0.48 mag. Accounting for the reddening correction, which is affected by a large uncertainty, we estimate a peak absolute magnitude of M(V) = -20.30 +- 1.50. The spectra are dominated by continuum emission at early phases, and He I lines with narrow P-Cygni profiles are detected. We also identify weak Fe III and N II features. All these lines show an absorption component which is blue-shifted by about 900-1000 km/s. The spectra also show relatively broad He I line wings with low contrast, which extend to above 3000 km/s. From about 2 weeks past maximum, broad lines of O I, Mg II and the Ca II near-infrared triplet are identified. The composition and the expansion velocity of the circumstellar material, and the presence of He I and alpha-elements in the SN ejecta indicate that PSN J07285387+3349106 was produced by the core-collapse of a stripped-envelope star. We suggest that the precursor was WNE-type Wolf-Rayet star in its dense, He-rich circumstellar cocoon.

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arXiv2015

The Overlooked Potential of Generalized Linear Models in Astronomy-III: Bayesian Negative Binomial Regression and Globular Cluster Populations

In this paper, the third in a series illustrating the power of generalized linear models (GLMs) for the astronomical community, we elucidate the potential of the class of GLMs which handles count data. The size of a galaxy's globular cluster population $N_{\rm GC}$ is a prolonged puzzle in the astronomical literature. It falls in the category of count data analysis, yet it is usually modelled as if it were a continuous response variable. We have developed a Bayesian negative binomial regression model to study the connection between $N_{\rm GC}$ and the following galaxy properties: central black hole mass, dynamical bulge mass, bulge velocity dispersion, and absolute visual magnitude. The methodology introduced herein naturally accounts for heteroscedasticity, intrinsic scatter, errors in measurements in both axes (either discrete or continuous), and allows modelling the population of globular clusters on their natural scale as a non-negative integer variable. Prediction intervals of 99% around the trend for expected $N_{\rm GC}$comfortably envelope the data, notably including the Milky Way, which has hitherto been considered a problematic outlier. Finally, we demonstrate how random intercept models can incorporate information of each particular galaxy morphological type. Bayesian variable selection methodology allows for automatically identifying galaxy types with different productions of GCs, suggesting that on average S0 galaxies have a GC population 35% smaller than other types with similar brightness.

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arXiv2015

Using gamma regression for photometric redshifts of survey galaxies

Machine learning techniques offer a plethora of opportunities in tackling big data within the astronomical community. We present the set of Generalized Linear Models as a fast alternative for determining photometric redshifts of galaxies, a set of tools not commonly applied within astronomy, despite being widely used in other professions. With this technique, we achieve catastrophic outlier rates of the order of ~1%, that can be achieved in a matter of seconds on large datasets of size ~1,000,000. To make these techniques easily accessible to the astronomical community, we developed a set of libraries and tools that are publicly available.

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