Name | Talk Title | Abstract |
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Amir Aghamoussa | Time delay estimation of strong lens systems |
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Yun-Young Choi | |
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Scott Daniel | Simulating LSST | The LSST Simulations team is developing software tools to simulate both the behavior of LSST and the data it will collect. It is our hope that these tools will aid both in the development of new data processing and analysis techniques as well as in answering the question "what is the most effective way to schedule the survey to deliver on our stated science goals?" This talk will enumerate the simulation tools available to the scientific community and highlight the ways in which non-members of the LSST project can and are using them to aid in the development of the LSST survey.
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Tim Eifler | The LSST Awakens - statistical power of LSST Year 1 data |
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Farhang Habibi | Object Classification in SDSS DR12 | LSST will observe ~10 billions of stars and the same number of galaxies. Since it is a non-spectroscopic survey, it is crucial to make a method to automatically separate different celestial objects from each other. Here, we use spectroscopy-photometry sample of SDSS DR12 to automatically classify stars, galaxies and QSOs using their magnitude, colour index and apparent angular size. We compute the classification efficiency of the whole sample and show that even a basic classifier can significantly separate these three different objects.
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Myungshin Im | Infrared Medium-deep Survey and Prospects of LSST | Infrared Medium-deep Survey (IMS) is a moderately deep NIR imaging survey over 120 sq. deg. of the sky (YJ ~23 AB mag), with accompanying deep imaging data in optical (ugriz ~ 25 AB mag). With IMS, we have been studying high redshift quasars out to z ~ 7, large scale structures of galaxies out to z~ 1.2, and energetic transients such as supernovae and GRBs, in order to understand the growth and the evolution of galaxies and supermassive black holes over cosmic history. The IMS depth (only 6 out of 200 visits of LSST) and the field of view (1/100 of LSST) make IMS as a nice testbed for LSST. In this talk, we will present key IMS results, and discuss future prospects of LSST.
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James Jee | LSST weak-lensing systematics |
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Elise Jennings | New statistical frameworks & methods for precision cosmology |
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Steven Kahn | The Large Synoptic Survey Telescope: General Overview and Current Status | The Large Synoptic Survey Telescope (LSST) is a large aperture, wide field ground-based telescope designed to provide a six color, time-domain imaging survey of the entire southern hemisphere of sky. Over ten years, LSST will make ~ a thousand visits to every part of the southern sky. The resulting database will enable a rich variety of scientific investigations ranging from studies of moving bodies in the solar system to precision measurements of the expansion history of the universe as a whole. LSST is presently under construction with funding from both the National Science Foundation and the Department of Energy in the United States. I will review the overall design of this facility, comment on its current status, and highlight some of the exciting science that we can expect when LSST becomes operational in 2022.
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Alex Kim | Opportunities in Type Ia Supernova Cosmology with LSST | LSST surveys will discover and generate light curves of an unprecedented and unmatched ~100,000 Type Ia supernovae (SNe Ia), which can be used to measure the expansion history of the Universe. Maximizing the science yield from the LSST supernova sample requires optimization of the observing strategy, including in the analysis transients without spectroscopic classification, and identifying synergy with other observing resources. Much work is being dedicated to addressing these research questions, and new contributions can have an important impact on the direction of SN Ia cosmology in the upcoming decade.
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Sang Chul Kim | KMTNet Supernova Program and the Future | Using the Korea Microlensing Telescope Network (KMTNet) of three identical 1.6 m optical telescopes each with wide field-of-view (2 deg X 2 deg) in Chile(CTIO), South Africa(SAAO) and Australia(SSO), we have initiated KMTNet Supernova Program (KSP) from 2015 October. KSP aims at searching for supernovae, other optical transients and related sources using the unique 24-hour coverage on the same target. We will introduce KMTNet facilities and KSP, in relation to future large surveys.
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Benjamin L'Huillier | Halo interactions in the Horizon Run 4 Simulation |
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Eric Linder | Strong Lensing: Cosmology, Data Challenges, and Followup | Strong lensing is a highly complementary geometrical dark energy probe. 1) I will outline its leverage, including some new results on double source lens systems (yet another probe). 2) I will discuss statistical challenges of time delay estimation, including the new Time Delay Challenge 2, and survey optimization. 3) I will pose some interesting open questions in time domain science, including maximizing LSST’s reach through other facilities such as monitoring by KMTNet and spectroscopy by DESI.
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Ashish Mahabal | Exploring and exploiting surveys jointly | The number of projects surveying big and small parts of the sky has exploded. In order to make sense of the resulting datasets we are starting to see more forays into informatics. We will broadly survey some of the machine learning and statistical methods specifically with transients in mind. The surveys differ in many ways including wavelengths, apertures, cadence, co-added depth and so on. As a result many surveys tend to create their own training samples, and really start producing meaningful results after a good while. Domain Adaptation methods can help jump-start such work. We will discuss these methods using a pilot done on variable sources from Catalina Realtime Transients Survey (CRTS), with CRTS data combined with those from Lincoln Near-Earth Asteroid Research (LINEAR), and Palomar Transient Factory (PTF). The technique can be used on any set of surveys provided there is an overlap in some set of observables/quantifiers, and we will briefly discuss this in the context of LSST and its pathfinders.
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Hong-Kyu Moon | Potential Synergy between KMTNet and LSST in Solar System Studies |
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Changbom Park | Future Survey Astronomy in Korea |
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Hyunbae Park | Cosmic Microwave Background Temperature Anisotropy from the Kinetic Sunyaev-Zel'dovich Effect |
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Graziano Rossi | Data Mining Challenges and Opportunities in Cosmology | TBD
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Eduardo Rozo | Cosmology with Galaxy Clusters in the Era of DESI and LSST |
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Cristiano Sabiu | Anisotropic Baryon Oscillations and Cosmic Distances | In the era of large spectroscopic surveys like DESI, I introduce a consistent model independent methodology for constraining the geometrical distances measures using the anisotropic clustering of galaxies. These meausrements can help in the future to constrain or break the standard model of LCDM.
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Michael Schneider | |
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Arman Shafieloo | Search for evidences beyond the standard model of cosmology |
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Min-Su Shin | Applications of machine learning and big data analytics in the Korean astronomy community | I will summarize the past research of Koreancommunity in exploiting new machine learning algorithms and big data analytics technology to analyze big astronomical data. I will also introduce current research activities to prepare for the LSST big data era, focusing on time-series analysis and photo-z estimation.
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Beth Willman | Community Science Opportunities with LSST |
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Soung-Chul Yang | Properties of Nearby Galaxies as Revealed by RR Lyrae Variables |
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Hu Zhan | A Chinese Space-Borne Optical Survey | Recently, China Manned Space Agency announced one of its key science projects -- the large-scale multiband imaging and slitless spectroscopy survey. It will be carried out by a 2m-class telescope in the same orbit as the Chinese space station, and the telescope can dock with the space station for maintenance or repair as needed. The plan is to cover roughly 40% of the whole sky in at least 6 wide band filters from 250nm to 1000nm, reaching an average depth of AB 25.5 mag (point source, 5-sigma). Low resolution slitless spectra will be taken at the same time over the same area reaching a broad band depth of AB 22-23 mag. Deeper exposures would be made over selected areas across the sky. In this talk I will give a brief introduction to the project and discuss the expected performance from simulations.
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Yi Zheng | Study on the mapping of dark matter clustering from real space to redshift space | The mapping of dark matter clustering from real space to redshift space introduces the anisotropic property to the measured density power spectrum in redshift space, known as the Redshift Space Distortion effect. The mapping formula is intrinsically non-linear, which is complicated by the higher order polynomials due to indefinite cross correlations between the density and velocity fields, and the Finger--of--God effect due to the randomness of the peculiar velocity field. Whilst the full higher order polynomials remain unknown, the other systematics can be controlled consistently within the same order truncation in the expansion of the mapping formula, as shown in this paper. The systematic due to the unknown non--linear density and velocity fields is removed by separately measuring all terms in the expansion directly using simulations. The uncertainty caused by the velocity randomness is controlled by splitting the FoG term into two pieces, 1) the non--local FoG term being independent of the separation vector between two different points, and 2) the local FoG term appearing as an indefinite polynomials which is expanded in the same order as all other perturbative polynomials. Using 100 realizations of simulations, we find that the best fitted non--local FoG function is Gaussian, with only one scale--independent free parameter, and that our new mapping formulation accurately reproduces the observed 2-dimensional density power spectrum in redshift space at the smallest scales by far, up to $k\sim 0.3\hompc$, considering the resolution of future experiments.
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Yoonyoung Kim | Active Asteroids and Our Contributions to the Research Field |
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