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- Villarroel, Brian, et al.
(författare)
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The VASCO project : 100 red transients and their follow up
- 2020
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Ingår i: Proceedings of the International Astronautical Congress, IAC. - : International Astronautical Federation, IAF.
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Konferensbidrag (refereegranskat)abstract
- The Vanishing & Appearing Sources during a Century of Observations (VASCO) project investigates astronomical surveys spanning a 70 years time interval, searching for unusual and exotic transients. We present herein the VASCO Citizen Science Project, that uses three different approaches to the identification of unusual transients in a given set of candidates: hypothesis-driven, exploratory-driven and machine learning-driven (which is of particular benefit for SETI searches). To address the big data challenge, VASCO combines methods from the Virtual Observatory, a user-aided machine learning and visual inspection through citizen science. In this article, we demonstrate the citizen science project, the new and improved candidate selection process and give a progress report. We also present the VASCO citizen science network led by amateur astronomy associations mainly located in Algeria, Cameroon and Nigeria. At the moment of writing, the citizen science project has carefully examined 12,000 candidate image pairs in the data, and has so far identified 713 objects classified as “vanished”. The most interesting candidates will be followed up with optical and infrared imaging, together with the observations by the most potent radio telescopes.
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| 4. |
- Falck, Tillmann, et al.
(författare)
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Least-Squares Support Vector Machines for the identification of Wiener-Hammerstein systems
- 2012
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Ingår i: Control Engineering Practice. - : Elsevier BV. - 0967-0661 .- 1873-6939. ; 20:11, s. 1165-1174
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Tidskriftsartikel (refereegranskat)abstract
- This paper considers the identification of Wiener-Hammerstein systems using Least-Squares Support Vector Machines based models. The power of fully black-box NARX-type models is evaluated and compared with models incorporating information about the structure of the systems. For the NARX models it is shown how to extend the kernel-based estimator to large data sets. For the structured model the emphasis is on preserving the convexity of the estimation problem through a suitable relaxation of the original problem. To develop an empirical understanding of the implications of the different model design choices, all considered models are compared on an artificial system under a number of different experimental conditions. The obtained results are then validated on the Wiener-Hammerstein benchmark data set and the final models are presented. It is illustrated that black-box models are a suitable technique for the identification of Wiener-Hammerstein systems. The incorporation of structural information results in significant improvements in modeling performance.
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| 5. |
- Giri, Sambit K., et al.
(författare)
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Constraining Lyman continuum escape using Machine Learning
- 2018
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Ingår i: Peering towards Cosmic Dawn. - : Cambridge University Press. - 9781107192461 ; , s. 254-258
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Konferensbidrag (refereegranskat)abstract
- The James Webb Space Telescope (JWST) will observe the rest-frame ultraviolet/optical spectra of galaxies from the epoch of reionization (EoR) in unprecedented detail. While escaping into the intergalactic medium, hydrogen-ionizing (Lyman continuum; LyC) photons from the galaxies will contribute to the bluer end of the UV slope and make nebular emission lines less prominent. We present a method to constrain leakage of the LyC photons using the spectra of high redshift (z greater than or similar to 6) galaxies. We simulate JWST/NIRSpec observations of galaxies at z = 6-9 by matching the fluxes of galaxies observed in the Frontier Fields observations of galaxy cluster MACS-J0416. Our method predicts the escape fraction f(esc) with a mean absolute error Delta f(esc) approximate to 0.14. The method also predicts the redshifts of the galaxies with an error approximate to 0.0003.
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| 6. |
- Giri, Sambit K., et al.
(författare)
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Identifying reionization-epoch galaxies with extreme levels of Lyman continuum leakage in James Webb Space Telescope surveys
- 2020
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Ingår i: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 491:4, s. 5277-5286
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Tidskriftsartikel (refereegranskat)abstract
- The James Webb Space Telescope (JWST) NIRSpec instrument will allow rest-frame ultraviolet/optical spectroscopy of galaxies in the epoch of reionization (EoR). Some galaxies may exhibit significant leakage of hydrogen-ionizing photons into the intergalactic medium, resulting in faint nebular emission lines. We present a machine learning framework for identifying cases of very high hydrogen-ionizing photon escape from galaxies based on the data quality expected from potential NIRSpec observations of EoR galaxies in lensed fields. We train our algorithm on mock samples of JWST/NIRSpec data for galaxies at redshifts z = 6-10. To make the samples more realistic, we combine synthetic galaxy spectra based on cosmological galaxy simulations with observational noise relevant for z greater than or similar to 6 objects of a brightness similar to EoR galaxy candidates uncovered in Frontier Fields observations of galaxy cluster Abell-2744 and MACS-J0416. We find that ionizing escape fractions (f(esc)) of galaxies brighter than m(AB,1500) approximate to 27 mag may be retrieved with mean absolute error Delta f(esc) approximate to 0.09(0.12) for 24 h (1.5 h) JWST/NIRSpec exposures at resolution R = 100. For 24 h exposure time, even fainter galaxies (m(AB,1500) < 28.5 mag) can be processed with Delta f(esc) approximate to 0.14. This framework simultaneously estimates the redshift of these galaxies with a relative error less than 0.03 for both 24 (m(AB,1500) < 28.5 mag) and 1.5 h (m(AB,1500) < 27 mag) exposure times. We also consider scenarios where just a minor fraction of galaxies attain high f(esc) and present the conditions required for detecting a subpopulation of high-f(esc) galaxies within the data set.
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| 8. |
- Huang, Xiaolin, et al.
(författare)
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Asymmetric nu-tube support vector regression
- 2014
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Ingår i: Computational Statistics & Data Analysis. - : Elsevier BV. - 0167-9473 .- 1872-7352. ; 77, s. 371-382
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Tidskriftsartikel (refereegranskat)abstract
- Finding a tube of small width that covers a certain percentage of the training data samples is a robust way to estimate a location: the values of the data samples falling outside the tube have no direct influence on the estimate. The well-known nu-tube Support Vector Regression (nu-SVR) is an effective method for implementing this idea in the context of covariates. However, the nu-SVR considers only one possible location of this tube: it imposes that the amount of data samples above and below the tube are equal. The method is generalized such that those outliers can be divided asymmetrically over both regions. This extension gives an effective way to deal with skewed noise in regression problems. Numerical experiments illustrate the computational efficacy of this extension to the nu-SVR.
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