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Mapping the Diversity of Galaxy Spectra with Deep Unsupervised Machine Learning

ASTRONOMICAL JOURNAL 2022年第2期163卷 71页

作者： Teimoorinia, Hossen Archinuk, Finn Woo, Joanna Shishehchi, Sara Bluck, Asa F. L. NRC Herzberg Astron & Astrophys 5071 West Saanich Rd Victoria BC V9E 2E7 Canada Univ Victoria Dept Phys & Astron Victoria BC V8P 5C2 Canada Simon Fraser Univ Dept Phys Univ Dr Burnaby BC V5A 1S6 Canada Pegasystems Inc 1 Main St Cambridge MA 02142 USA Univ Cambridge Kavli Inst Cosmol Cavendish Lab Astrophys Grp Madingley Rd Cambridge CB3 0HA England

Modern spectroscopic surveys of galaxies such as MaNGA consist of millions of diverse spectra covering different regions of thousands of galaxies. We propose and implement a deep unsupervised machine-learning method to summarize the entire diversity of MaNGA spectra onto a 15 x 15 map (DESOM-1), where neighboring points on the map represent similar spectra. We demonstrate our method as an alternative to conventional full spectral fitting for deriving physical quantities and full probability distributions much more efficiently than traditional resource-intensive Bayesian methods. Since spectra are grouped by similarity, the distribution of spectra onto the map for a single galaxy, i.e., its "fingerprint," reveals the presence of distinct stellar populations within the galaxy, indicating smoother or episodic star formation histories. We further map the diversity of galaxy fingerprints onto a second map (DESOM-2). Using galaxy images and independent measures of galaxy morphology, we confirm that galaxies with similar fingerprints have similar morphologies and inclination angles. Since morphological information was not used in the mapping algorithm, relating galaxy morphology to the star formation histories encoded in the fingerprints is one example of how the DESOM maps can be used to make scientific inferences.

关键词： Galaxy abundances Galaxies astronomy data analysis Neural networks astronomy data modeling astronomy data visualization

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An Unambiguous Separation of Gamma-Ray Bursts into Two Classes from Prompt Emission Alone

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ASTROPHYSICAL JOURNAL LETTERS 2020年第2期896卷

作者： Jespersen, Christian K. Severin, Johann B. Steinhardt, Charles L. Vinther, Jonas Fynbo, Johan P. U. Selsing, Jonatan Watson, Darach Cosm Dawn Ctr DAWN Lyngbyvej 2 Copenhagen Denmark Univ Copenhagen Niels Bohr Inst Blegdamsvej 17 DK-2100 Copenhagen O Denmark

The duration of a gamma-ray burst (GRB) is a key indicator of its physical origin, with long bursts perhaps associated with the collapse of massive stars and short bursts with mergers of neutron stars. However, there is substantial overlap in the properties of both short and long GRBs and neither duration nor any other parameter so far considered completely separates the two groups. Here we unambiguously classify every GRB using a machine-learning dimensionality reduction algorithm, t-distributed stochastic neighborhood embedding, providing a catalog separating all Swift GRBs into two groups. Although the classification takes place only using prompt emission light curves, every burst with an associated supernova is found in the longer group and bursts with kilonovae in the short, suggesting along with the duration distributions that these two groups are truly long and short GRBs. Two bursts with a clear absence of a supernova belong to the longer class, indicating that these might have been direct-collapse black holes, a proposed phenomenon that may occur in the deaths of more massive stars.

关键词： Gamma-ray bursts astronomy data analysis astronomy data visualization Light curve classification Supernovae High energy astrophysics

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Unveiling the Hierarchical Structure of Open Star Clusters: The Perseus Double Cluster

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ASTROPHYSICAL JOURNAL 2020年第2期899卷

作者： Yu, Heng Shao, Zhengyi Diaferio, Antonaldo Li, Lu Beijing Normal Univ Dept Astron Beijing 100875 Peoples R China Chinese Acad Sci Shanghai Astron Observ 80 Nandan Rd Shanghai 200030 Peoples R China Key Lab Astrophys 100 Guilin Rd Shanghai 200234 Peoples R China Univ Torino Dipartimento Fis Via P Giuria 1 I-10125 Turin Italy Ist Nazl Fis Nucl INFN Sez Torino Via P Giuria 1 I-10125 Turin Italy Univ Chinese Acad Sci 19A Yuquan Rd Beijing 100049 Peoples R China

We introduce a new kinematic method to investigate the structure of open star clusters. We adopt a hierarchical clustering algorithm that uses the celestial coordinates and the proper motions of the stars in the field of view of the cluster to estimate a proxy of the pairwise binding energy of the stars and arrange them in a binary tree. The cluster substructures and their members are identified by trimming the tree at two thresholds, according to the sigma-plateau method. Testing the algorithm on 100 mock catalogs shows that, on average, the membership of the identified clusters is (91.5 3.5)% complete and the fraction of unrelated stars is (10.4 2.0)%. We apply the algorithm to the stars in the field of view of the Perseus double cluster from the data Release 2 of Gaia. This approach identifies a single structure, Sub1, that separates into two substructures, Sub1-1 and Sub1-2. These substructures coincide withhPer and chi Per: the distributions of the proper motions and the color-magnitude diagrams of the members of Sub1-1 and Sub1-2 are fully consistent with those ofhPer and chi Per reported in the literature. These results suggest that our hierarchical clustering algorithm can be a powerful tool to unveil the complex kinematic information of star clusters.

关键词： Stellar kinematics stars: kinematics and dynamics Single-linkage hierarchical clustering Open star clusters astronomy data analysis astronomy data visualization

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Astronomical source finding services for the CIRASA visual analytic platform

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astronomy AND COMPUTING 2021年 37卷

作者： Riggi, S. Bordiu, C. Vitello, F. Tudisco, G. Sciacca, E. Magro, D. Sortino, R. Pino, C. Molinaro, M. Benedettini, M. Leurini, S. Bufano, F. Raciti, M. Becciani, U. INAF Osservatorio Astrofis Catania Via Santa Sofia 78 I-95123 Catania Italy CSIC Ctr Astrobiol INTA Ctra M-108Km 4 Madrid 28850 Spain INAF Ist Radioastron Via Gobetti 101 I-40127 Bologna Italy Univ Malta Inst Space Sci & Astron Msida M 2080 SD Malta Univ Catania Dept Elect Elect & Comp Engn Catania Italy INAF Osservatorio Astron Trieste Via GB Tiepolo 11 I-34143 Trieste Italy INAF Ist Astrofis & Planetol Spaziali Via Fosso del Cavaliere 100 I-00133 Rome Italy INAF Osservatorio Astron Cagliari Via Sci 5 I-09047 Selargius CA Italy

Innovative developments in data processing, archiving, analysis, and visualization are nowadays unavoidable to deal with the data deluge expected in next-generation facilities for radio astronomy, such as the Square Kilometre Array (SKA) and its precursors. In this context, the integration of source extraction and analysis algorithms into data visualization tools could significantly improve and speed up the cataloguing process of large area surveys, boosting astronomer productivity and shortening publication time. To this aim, we are developing a visual analytic platform (CIRASA) for advanced source finding and classification, integrating state-of-the-art tools, such as the CAESAR source finder, the ViaLactea Visual Analytic (VLVA) and Knowledge Base (VLKB). In this work, we present the project objectives and the platform architecture, focusing on the implemented source finding services. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Radio astronomy Galactic-plane Source-finding astronomy web services astronomy data visualization Distributed computing

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On the Three-dimensional Structure of Local Molecular Clouds

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ASTROPHYSICAL JOURNAL 2021年第1期919卷

作者： Zucker, Catherine Goodman, Alyssa Alves, Joao Bialy, Shmuel Koch, Eric W. Speagle, Joshua S. Foley, Michael M. Finkbeiner, Douglas Leike, Reimar Ensslin, Torsten Peek, Joshua E. G. Edenhofer, Gordian Harvard & Smithsonian Ctr Astrophys 60 Garden St Cambridge MA 02138 USA Univ Vienna Dept Astrophys Turkenschanzstr 17 A-1180 Vienna Austria Univ Alberta Dept Phys 4-183 CCIS Edmonton AB T6G 2E1 Canada Univ Toronto Dept Stat Sci Toronto ON M5S 3G3 Canada Univ Toronto David A Dunlap Dept Astron & Astrophys Toronto ON M5S 3H4 Canada Univ Toronto Dunlap Inst Astron & Astrophys Toronto ON M5S 3H4 Canada Max Planck Inst Astrophys Karl Schwarzschildstr 1 D-85748 Garching Germany Ludwig Maximilians Univ Munchen Geschwister Scholl Pl 1 D-80539 Munich Germany Space Telescope Sci Inst 3700 San Martin Dr Baltimore MD 21218 USA Johns Hopkins Univ Dept Phys & Astron Baltimore MD 21218 USA

We leverage the 1 pc spatial resolution of the Leike et al. three-dimensional (3D) dust map to characterize the 3D structure of nearby molecular clouds (d less than or similar to 400 pc). We start by "skeletonizing" the clouds in 3D volume density space to determine their "spines," which we project on the sky to constrain cloud distances with approximate to 1% uncertainty. For each cloud, we determine an average radial volume density profile around its 3D spine and fit the profiles using Gaussian and Plummer functions. The radial volume density profiles are well described by a two-component Gaussian function, consistent with clouds having broad, lower-density outer envelopes and narrow, higher-density inner layers. The ratio of the outer to inner envelope widths is approximate to 3:1. We hypothesize that these two components may be tracing a transition between atomic and diffuse molecular gas or between the unstable and cold neutral medium. Plummer-like models can also provide a good fit, with molecular clouds exhibiting shallow power-law wings with density, n, falling off like n (-2) at large radii. Using Bayesian model selection, we find that parameterizing the clouds' profiles using a single Gaussian is disfavored. We compare our results with two-dimensional dust extinction maps, finding that the 3D dust recovers the total cloud mass from integrated approaches with fidelity, deviating only at higher levels of extinction (A ( V ) greater than or similar to 2-3 mag). The 3D cloud structure described here will enable comparisons with synthetic clouds generated in simulations, offering unprecedented insight into the origins and fates of molecular clouds in the interstellar medium.

关键词： Molecular clouds Solar neighborhood Interstellar reddening astronomy data visualization

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The Per-Tau Shell: A Giant Star-forming Spherical Shell Revealed by 3D Dust Observations

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ASTROPHYSICAL JOURNAL LETTERS 2021年第1期919卷 L5-L5页

作者： Bialy, Shmuel Zucker, Catherine Goodman, Alyssa Foley, Michael M. Alves, Joao Semenov, Vadim A. Benjamin, Robert Leike, Reimar Ensslin, Torsten Harvard Smithsonian Ctr Astrophys 60 Garden St Cambridge MA 02138 USA Harvard Univ Radcliffe Inst Adv Study 10 Garden St Cambridge MA 02138 USA Univ Vienna Dept Astrophys Turken Schanzstr 17 A-1180 Vienna Austria Univ Wisconsin Dept Phys 800 West Main St Whitewater WI 53190 USA Max Planck Inst Astrophys Karl Schwarzschildstr 1 D-85748 Garching Germany Ludwig Maximilians Univ Munchen Geschwister SchollPl 1 D-80539 Munich Germany

A major question in the field of star formation is how molecular clouds form out of the diffuse interstellar medium (ISM). Recent advances in 3D dust mapping are revolutionizing our view of the structure of the ISM. Using the highest-resolution 3D dust map to date, we explore the structure of a nearby star-forming region, which includes the well-known Perseus and Taurus molecular clouds. We reveal an extended near-spherical shell, 156 pc in diameter (hereafter called the "Per-Tau Shell"), in which the Perseus and Taurus clouds are embedded. We also find a large ring structure at the location of Taurus (hereafter called the "Tau Ring"). We discuss a formation scenario for the Per-Tau Shell, in which previous stellar and supernova feedback events formed a large expanding shell, where the swept-up ISM has condensed to form both the shell and the Perseus and Taurus molecular clouds within it. We present auxiliary observations of H i, H alpha, Al-26, and X-rays that further support this scenario, and estimate the Per-Tau Shell's age to be approximate to 6-22 Myrs. The Per-Tau shell offers the first 3D observational view of a phenomenon long-hypothesized theoretically, molecular cloud formation and star formation triggered by previous stellar and supernova feedback.

关键词： Interstellar medium Molecular clouds Solar neighborhood Stellar feedback Superbubbles astronomy data visualization

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Touching the Stars: Using High-resolution 3D Printing to Visualize Stellar Nurseries

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ASTROPHYSICAL JOURNAL LETTERS 2021年第1期918卷 L3-L3页

作者： Imara, Nia Forbes, John C. Weaver, James C. Univ Calif Santa Cruz 1156 High St Santa Cruz CA 95064 USA Ctr Computat Astrophys 162 5th Ave New York NY 10010 USA Harvard Univ Sch Engn & Appl Sci Cambridge MA 02138 USA

Owing to their intricate variable density architecture, and as a principal site of star formation, molecular clouds represent one of the most functionally significant, yet least understood features of our universe. To unravel the intrinsic structural complexity of molecular clouds, here we leverage the power of high-resolution bitmap-based three-dimensional (3D) printing, which provides the opportunity to visualize astrophysical structures in a way that uniquely taps into the human brain's ability to recognize patterns suppressed in 2D representations. Using a new suite of nine simulations, each representing different physical extremes in the turbulent interstellar medium, as our source data, our workflow permits the unambiguous visualization of features in the 3D-printed models, such as quasi-planar structures, that are frequently obscured in traditional renderings and animations. Our bitmap-based 3D printing approach thus faithfully reproduces the subtle density gradient distribution within molecular clouds in a tangible, intuitive, and visually stunning manner. While laying the groundwork for the intuitive analysis of other structurally complex astronomical data sets, our 3D-printed models also serve as valuable tools in educational and public outreach endeavors.

关键词： Interstellar medium Giant molecular clouds astronomy data visualization

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Measuring Solar Differential Rotation with an Iterative Phase Correlation Method

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ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES 2021年第1期252卷

作者： Hrazdira, Zdenek Druckmueller, Miloslav Habbal, Shadia Brno Univ Technol Inst Math Fac Mech Engn Brno 61669 Czech Republic Univ Hawaii Inst Astron 2680 Woodlawn Dr Honolulu HI 96822 USA

A reliable inference of the differential rotation rate of the solar photosphere is essential for models of the solar interior. The work presented here is based on a novel iterative phase correlation technique, which relies on the measurement of the local shift, at the central meridian, between two images separated by a given time interval. Consequently, it does not require any specific reference features, such as sunspots or supergranules, nor extended observations spanning several months. The reliability of the method is demonstrated by applying it to high spatial and temporal resolution continuum images of the solar photosphere, at 6173 A, acquired by the Solar Dynamics Observatory Helioseismic and Magnetic Imager over one complete Carrington rotation. The data selected covers the time period of 2020 January 1 to February 2. The method was applied to one day, and to the full time interval. The differential rotation rate derived using this feature-independent technique yields values that fall in the middle of the range of those published to date. Most importantly, the method is suited for the production of detailed rotation maps of the solar photosphere. It also enables the visual and quantitative identification of the north-south asymmetry in the solar differential rotation rate, when present.

关键词： Solar rotation Solar differential rotation Astronomical techniques Astronomical models Analytical mathematics Computational methods Direct imaging Astrometry Astronomical methods astronomy software astronomy data analysis astronomy data visualization

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Assessment of Astronomical Images Using Combined Machine-learning Models

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ASTRONOMICAL JOURNAL 2020年第4期159卷 170-170页

作者： Teimoorinia, H. Kavelaars, J. J. Gwyn, S. D. J. Durand, D. Rolston, K. Ouellette, A. NRC Herzberg Astron & Astrophys 5071 West Saanich Rd Victoria BC V9E 2E7 Canada Univ Victoria Dept Phys & Astron Victoria BC V8P 5C2 Canada

We present a two-component machine-learning-based approach for classifying astronomical images by data quality via an examination of sources detected in the images and image pixel values from representative sources within those images. The first component, which uses a clustering algorithm, creates a proper and small fraction of the image pixels to determine the quality of the observation. The representative images (and associated tables) are similar to 800 times smaller than the original images, significantly reducing the time required to train our algorithm. The useful information in the images is preserved, permitting them to be classified into different categories, but the required storage is reduced. The second component, which is a deep neural network model, classifies the representative images. Using ground-based telescope imaging data, we demonstrate that the method can be used to separate "usable" images from those that present some problems for scientific projects-such as images that were taken in suboptimal conditions. This method uses two different data sets as input to a deep model and provides better performance than if we only used the images' pixel information. The method may be used in cases where large and complex data sets should be examined using deep models. Our automated classification approach achieves 97% agreement when compared to classification generated via manual image inspection. We compare our method with traditional results and show that the method improves the results by about 10%, and also presents more comprehensive outcomes.

关键词： astronomy data analysis Convolutional neural networks Neural networks astronomy data modeling astronomy data visualization

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A Comparison of Flare Forecasting Methods. IV. Evaluating Consecutive-day Forecasting Patterns

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ASTROPHYSICAL JOURNAL 2020年第2期890卷 1-1页

作者： Park, Sung-Hong Leka, K. D. Kusano, Kanya Andries, Jesse Barnes, Graham Bingham, Suzy Bloomfield, D. Shaun McCloskey, Aoife E. Delouille, Veronique Falconer, David Gallagher, Peter T. Georgoulis, Manolis K. Kubo, Yuki Lee, Kangjin Lee, Sangwoo Lobzin, Vasily Mun, JunChul Murray, Sophie A. Nageem, Tarek A. M. Hamad Qahwaji, Rami Sharpe, Michael Steenburgh, R. A. Steward, Graham Terkildsen, Michael Nagoya Univ Inst Space Earth Environm Res Nagoya Aichi Japan Northwest Res Associates Boulder CO USA Royal Observ Belgium Solar Terr Ctr Excellence Brussels Belgium Met Off Exeter Devon England Northumbria Univ Newcastle Upon Tyne Tyne & Wear England Trinity Coll Dublin Sch Phys Dublin Ireland NASA NSSTC Huntsville AL USA Dublin Inst Adv Studies Sch Cosm Phys Dublin Ireland Georgia State Univ Dept Phys & Astron Atlanta GA 30303 USA Acad Athens Res Ctr Astron & Appl Math Athens Greece Natl Inst Informat & Commun Technol Tokyo Japan Elect & Telecommun Res Inst Daejeon South Korea Kyung Hee Univ Sch Space Res Yongin South Korea SELab Inc Seoul South Korea Bur Meteorol Space Weather Serv Sydney NSW Australia Natl Radio Res Agcy Korean Space Weather Ctr Jeju South Korea Univ Bradford Bradford W Yorkshire England NOAA NWS NCEP Space Weather Predict Ctr Boulder CO USA

A crucial challenge to successful flare prediction is forecasting periods that transition between "flare-quiet" and "flare-active." Building on earlier studies in this series in which we describe the methodology, details, and results of flare forecasting comparison efforts, we focus here on patterns of forecast outcomes (success and failure) over multiday periods. A novel analysis is developed to evaluate forecasting success in the context of catching the first event of flare-active periods and, conversely, correctly predicting declining flare activity. We demonstrate these evaluation methods graphically and quantitatively as they provide both quick comparative evaluations and options for detailed analysis. For the testing interval 2016-2017, we determine the relative frequency distribution of two-day dichotomous forecast outcomes for three different event histories (i.e., event/event, no-event/event, and event/no-event) and use it to highlight performance differences between forecasting methods. A trend is identified across all forecasting methods that a high/low forecast probability on day 1 remains high/low on day 2, even though flaring activity is transitioning. For M-class and larger flares, we find that explicitly including persistence or prior flare history in computing forecasts helps to improve overall forecast performance. It is also found that using magnetic/modern data leads to improvement in catching the first-event/first-no-event transitions. Finally, 15% of major (i.e., M-class or above) flare days over the testing interval were effectively missed due to a lack of observations from instruments away from the Earth-Sun line.

关键词： The Sun Solar magnetic fields Solar flares Solar activity Solar active region magnetic fields Sunspots Solar x-ray flares astronomy data analysis astronomy data visualization Astrostatistics tools

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