Fitting and Phenomenology in Type Ia Supernova Cosmology: Generalized Likelihood Analyses for Multiple Evolving Populations and Observations of Near-Infrared Lightcurves including Host Galaxy Properties

In the late 1990s, Type Ia supernovae (SNeIa) led to the discovery that the Universe is expanding at an accelerating rate due to dark energy. Since then, many different tracers of acceleration have been used to characterize dark energy, but the source of cosmic acceleration has remained a mystery. To better understand dark energy, future surveys such as the ground-based Large Synoptic Survey Telescope and the space-based Wide-Field Infrared Survey Telescope will collect thousands of SNeIa to use as a primary dark energy probe. These large surveys will be systematics limited, which makes it imperative for our insight regarding systematics to dramatically increase over the next decade for SNeIa to continue to contribute to precision cosmology. I approach this problem by improving statistical methods in the likelihood analysis and collecting near infrared (NIR) SNeIa with their host galaxies to improve the nearby data set and search for additional systematics. Using more statistically robust methods to account for systematics within the likelihood function can increase accuracy in cosmological parameters with a minimal precision loss. Though a sample of at least 10,000 SNeIa is necessary to confirm multiple populations of SNeIa, the bias in cosmology is $\sim2~\sigma$ with only 2,500 SNeIa. This work focused on an example systematic (host galaxy correlations), but it can be generalized for any systematic that can be represented by a distribution of multiple Gaussians. The SweetSpot survey gathered 114 low-redshift, NIR SNeIa that will act as a crucial anchor sample for the future high redshift surveys. NIR observations are not as affected by dust contamination, which may lead to increased understanding of systematics seen in optical wavelengths. We obtained spatially resolved spectra for 32 SweetSpot host galaxies to test for local host galaxy correlations. For the first time, we probe global host galaxy correlations with NIR brightnesses from the current literature sample of SNeIa with host galaxy data from publicly available catalogs. We find inconclusive evidence that more massive galaxies host SNeIa that are brighter in the NIR than SNeIa hosted in less massive galaxies

The First Data Release from SweetSpot: 74 Supernovae in 36 Nights on WIYN+WHIRC

SweetSpot is a three-year National Optical Astronomy Observatory (NOAO) Survey program to observe Type Ia supernovae (SNe Ia) in the smooth Hubble flow with the WIYN High-resolution Infrared Camera (WHIRC) on the WIYN 3.5-m telescope. We here present data from the first half of this survey, covering the 2011B-2013B NOAO semesters, and consisting of 493 calibrated images of 74 SNe Ia observed in the rest-frame near-infrared (NIR) from $0.02 < z < 0.09$. Because many observed supernovae require host galaxy subtraction from templates taken in later semesters, this release contains only the 186 NIR ($JHK_s$) data points for the 33 SNe Ia that do not require host-galaxy subtraction. The sample includes 4 objects with coverage beginning before the epoch of B-band maximum and 27 beginning within 20 days of B-band maximum. We also provide photometric calibration between the WIYN+WHIRC and Two-Micron All Sky Survey (2MASS) systems along with light curves for 786 2MASS stars observed alongside the SNe Ia. This work is the first in a planned series of three SweetSpot Data Releases. Future releases will include the full set of images from all 3 years of the survey, including host-galaxy reference images and updated data processing and host-galaxy reference subtraction. SweetSpot will provide a well-calibrated sample that will help improve our ability to standardize distance measurements to SNe Ia, examine the intrinsic optical-NIR colors of SNe Ia at different epochs, explore nature of dust in other galaxies, and act as a stepping stone for more distant, potentially space-based surveys.Comment: Published in AJ. 10 tables. 11 figures. Lightcurve plots included as a figureset and available in source tarball. Data online at http://www.phyast.pitt.edu/~wmwv/SweetSpot/DR1_data

Are Type Ia Supernovae in Rest-frame H Brighter in More Massive Galaxies?

K.A.P., M.W.-V., and L.G. were supported in part by the US National Science Foundation under grant AST-1311862. K.A. P. additionally acknowledges support from PITT PACC. K.A. P. was also supported in part by the Berkeley Center for Cosmological Physics and the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under contract No. DE-AC02-05CH11231 and U.S. Department of Energy Office of Science under contract No. DE-AC02-76SF00515. L.G. was additionally funded in part by the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 839090. We thank the referee, whose comments have improved this paper, and Saurabh Jha, Kyle Boone, and Ravi Gupta for useful conversations. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is funded by the National Aeronautics and Space Administration and operated by the California Institute of Technology. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions. SDSS-IV acknowledges support and resources from the Center for High-Performance Computing at the University of Utah. The SDSS website is www.sdss.org. SDSS-IV is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS Collaboration, including the Brazilian Participation Group, the Carnegie Institution for Science, Carnegie Mellon University, the Chilean Participation Group, the French Participation Group, Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofisica de Canarias, Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo, Lawrence Berkeley National Laboratory, Leibniz Institut fur Astrophysik Potsdam (AIP), Max-Planck-Institut fur Astronomie (MPIA Heidelberg), Max-Planck-Institut fur Astrophysik (MPA Garching), Max-Planck-Institut fur Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University, New York University, University of Notre Dame, Observatario Nacional/MCTI, The Ohio State University, Pennsylvania State University, Shanghai Astronomical Observatory, United Kingdom Participation Group, Universidad Nacional Autonoma de Mexico, University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University. This research uses services or data provided by the Astro Data Lab at NSF's National Optical-Infrared Astronomy Research Laboratory. NOIRLab is operated by the Association of Universities for Research in Astronomy (AURA), Inc., under a cooperative agreement with the National Science Foundation. The Legacy Surveys consist of three individual and complementary projects: the Dark Energy Camera Legacy Survey (DECaLS; Proposal ID #2014B-0404; PIs: David Schlegel and Arjun Dey), the Beijing-Arizona Sky Survey (BASS; NOAO Prop. ID #2015A-0801; PIs: Zhou Xu and Xiaohui Fan), and the Mayall z-band Legacy Survey (MzLS; Prop. ID #2016A-0453; PI: Arjun Dey). DECaLS, BASS, and MzLS together include data obtained, respectively, at the Blanco telescope, Cerro Tololo Inter-American Observatory, NSF's NOIRLab; the Bok telescope, Steward Observatory, University of Arizona; and the Mayall telescope, Kitt Peak National Observatory, NOIRLab. The Legacy Surveys project is honored to be permitted to conduct astronomical research on Iolkam Du'ag (Kitt Peak), a mountain with particular significance to the Tohono O'odham Nation. This project used data obtained with the Dark Energy Camera (DECam), which was constructed by the Dark Energy Survey (DES) collaboration. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, Center for Cosmology and Astro-Particle Physics at The Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo, Financiadora de Estudos e Projetos, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico and the Ministerio da Ciencia, Tecnologia e Inovacao, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnologicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenossische Technische Hochschule (ETH) Zurich, Fermi National Accelerator Laboratory, the University of Illinois at UrbanaChampaign, the Institut de Ciencies de l'Espai (IEEC/CSIC), the Institut de Fisica d'Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig Maximilians Universitat Munchen and the associated Excellence Cluster Universe, the University of Michigan, NSF's NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, and Texas A&M University. The Legacy Survey team makes use of data products from the Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE), which is a project of the Jet Propulsion Laboratory/California Institute of Technology. NEOWISE is funded by the National Aeronautics and Space Administration. The Legacy Surveys imaging of the DESI footprint is supported by the Director, Office of Science, Office of High Energy Physics of the U.S. Department of Energy under contract No. DE-AC02-05CH1123; by the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility under the same contract; and by the U.S. National Science Foundation, Division of Astronomical Sciences under contract No. AST-0950945 to NOAO. This publication makes use of data products from the Two Micron All Sky Survey, which is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. This research has made use of the NASA/IPAC Infrared Science Archive, which is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. Some of the data presented in this paper were obtained from the Mikulski Archive for Space Telescopes (MAST). STScI is operated by the Association of Universities for Research in Astronomy, Inc., under NASA contract NAS5-26555. Support for MAST for non-HST data is provided by the NASA Office of Space Science via grant NNX09AF08G and by other grants and contracts.We analyze 143 Type Ia supernovae (SNe Ia) observed in H band (1.6-1.8 mu m) and find that SNe Ia are intrinsically brighter in H band with increasing host galaxy stellar mass. We find that SNe Ia in galaxies more massive than 10(10)(.4)(3) M-circle dot are 0.13 +/- 0.04 mag brighter in H than SNe Ia in less massive galaxies. The same set of SNe Ia observed at optical wavelengths, after width-color-luminosity corrections, exhibit a 0.10 +/- 0.03 mag offset in the Hubble residuals. We observe an outlier population (vertical bar Delta H-max vertical bar > 0.5 mag) in the H band and show that removing the outlier population moves the mass threshold to 10(10.65) M-circle dot and reduces the step in H band to 0.08 +/- 0.04 mag, but the equivalent optical mass step is increased to 0.13 +/- 0.04 mag. We conclude that the outliers do not drive the brightness-host-mass correlation. Less massive galaxies preferentially host more higher-stretch SNe Ia, which are intrinsically brighter and bluer. It is only after correction for width-luminosity and color- luminosity relationships that SNe Ia have brighter optical Hubble residuals in more massive galaxies. Thus, finding that SNe Ia are intrinsically brighter in H in more massive galaxies is an opposite correlation to the intrinsic (prewidth-luminosity correction) optical brightness. If dust and the treatment of intrinsic color variation were the main driver of the host galaxy mass correlation, we would not expect a correlation of brighter H-band SNe Ia in more massive galaxies.National Science Foundation (NSF) AST-1311862PITT PACCBerkeley Center for Cosmological PhysicsUnited States Department of Energy (DOE) DE-AC02-05CH11231 DE-AC02-05CH1123 DE-AC02-76SF00515European Commission 839090National Aeronautics & Space Administration (NASA)Alfred P. Sloan FoundationUnited States Department of Energy (DOE)Participating InstitutionsCenter for High-Performance Computing at the University of UtahSDSS Collaboration, including the Brazilian Participation GroupCarnegie Institution for Science, Carnegie Mellon UniversityChilean Participation GroupFrench Participation GroupSmithsonian InstitutionHarvard-Smithsonian Center for AstrophysicsInstituto de Astrofisica de CanariasJohns Hopkins UniversityKavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of TokyoUnited States Department of Energy (DOE)Leibniz Institut fur Astrophysik Potsdam (AIP)Max-Planck-Institut fur Astronomie (MPIA Heidelberg) Max-Planck-Institut fur Astrophysik (MPA Garching) Max-Planck-Institut fur Extraterrestrische Physik (MPE)National Astronomical Observatories of ChinaNew Mexico State UniversityNew York UniversityUniversity of Notre DameObservatario Nacional/MCTIOhio State UniversityPennsylvania State UniversityShanghai Astronomical ObservatoryUnited Kingdom Participation GroupUniversidad Nacional Autonoma de MexicoUniversity of ArizonaUniversity of Colorado BoulderUniversity of OxfordUniversity of PortsmouthUniversity of UtahUniversity of VirginiaUniversity of WashingtonUniversity of WisconsinVanderbilt UniversityYale UniversityUnited States Department of Energy (DOE)National Science Foundation (NSF)Spanish GovernmentUK Research & Innovation (UKRI)Science & Technology Facilities Council (STFC)UK Research & Innovation (UKRI)Higher Education Funding Council for EnglandNational Center for Supercomputing Applications at the University of Illinois at Urbana-ChampaignKavli Institute of Cosmological Physics at the University of ChicagoOhio State UniversityMitchell Institute for Fundamental Physics and Astronomy at Texas AM UniversityFinanciadora de Inovacao e Pesquisa (Finep)Fundacao Carlos Chagas Filho de Amparo Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ)Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ)Spanish GovernmentGerman Research Foundation (DFG)Collaborating Institutions in the Dark Energy SurveyNational Energy Research Scientific Computing CenterUnited States Department of Energy (DOE)National Science Foundation (NSF) NSF - Directorate for Mathematical & Physical Sciences (MPS) AST-0950945Association of Universities for Research in Astronomy, Inc., under NASA NAS5-26555 National Aeronautics & Space Administration (NASA) NNX09AF08

Active learning with RESSPECT: Resource allocation for extragalactic astronomical transients

The authors would like to thank David Kirkby and Connor Sheere for insightful discussions. This work is part of the Recommendation System for Spectroscopic Followup (RESSPECT) project, governed by an inter-collaboration agreement signed between the Cosmostatistics Initiative (COIN) and the LSST Dark Energy Science Collaboration (DESC). This research is supported in part by the HPI Research Center in Machine Learning and Data Science at UC Irvine. EEOI and SS acknowledge financial support from CNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky Surveys. SGG and AKM acknowledge support by FCT under Project CRISP PTDC/FIS-AST-31546/2017. This work was partly supported by the Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of Houston. DOJ is supported by a Gordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa Cruz. Support for this work was provided by NASA through the NASA Hubble Fellowship grant HF2-51462.001 awarded by the Space Telescope Science Institute, which is operated by the Association of Universities for Research in Astronomy, Inc., for NASA, under contract NAS5-26555. BQ is supported by the International Gemini Observatory, a program of NSF's NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation, on behalf of the Gemini partnership of Argentina, Brazil, Canada, Chile, the Republic of Korea, and the United States of America. AIM acknowledges support from the Max Planck Society and the Alexander von Humboldt Foundation in the framework of the Max Planck-Humboldt Research Award endowed by the Federal Ministry of Education and Research. L.G. was funded by the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 839090. This work has been partially supported by the Spanish grant PGC2018-095317-B-C21 within the European Funds for Regional Development (FEDER).The recent increase in volume and complexity of available astronomical data has led to a wide use of supervised machine learning techniques. Active learning strategies have been proposed as an alternative to optimize the distribution of scarce labeling resources. However, due to the specific conditions in which labels can be acquired, fundamental assumptions, such as sample representativeness and labeling cost stability cannot be fulfilled. The Recommendation System for Spectroscopic followup (RESSPECT) project aims to enable the construction of optimized training samples for the Rubin Observatory Legacy Survey of Space and Time (LSST), taking into account a realistic description of the astronomical data environment. In this work, we test the robustness of active learning techniques in a realistic simulated astronomical data scenario. Our experiment takes into account the evolution of training and pool samples, different costs per object, and two different sources of budget. Results show that traditional active learning strategies significantly outperform random sampling. Nevertheless, more complex batch strategies are not able to significantly overcome simple uncertainty sampling techniques. Our findings illustrate three important points: 1) active learning strategies are a powerful tool to optimize the label-acquisition task in astronomy, 2) for upcoming large surveys like LSST, such techniques allow us to tailor the construction of the training sample for the first day of the survey, and 3) the peculiar data environment related to the detection of astronomical transients is a fertile ground that calls for the development of tailored machine learning algorithms.HPI Research Center in Machine Learning and Data Science at UC IrvineCNRS 2017 MOMENTUM grant under the project Active Learning for Large Scale Sky SurveysFCT under Project CRISP PTDC/FIS-AST-31546/2017Hewlett Packard Enterprise Data Science Institute (HPE DSI) at the University of HoustonGordon and Betty Moore Foundation postdoctoral fellowship at the University of California, Santa CruzSpace Telescope Science InstituteNational Aeronautics & Space Administration (NASA) HF2-51462.001 NAS5-26555International Gemini Observatory, a program of NSF's NOIRLabNational Science Foundation (NSF)Max Planck SocietyFoundation CELLEXAlexander von Humboldt FoundationEuropean Commission 839090Spanish grant within the European Funds for Regional Development (FEDER) PGC2018-095317-B-C2

The Sloan Digital Sky Survey Reverberation Mapping Project: Technical Overview

The Sloan Digital Sky Survey Reverberation Mapping project (SDSS-RM) is a dedicated multi-object RM experiment that has spectroscopically monitored a sample of 849 broad-line quasars in a single 7 deg$^2$ field with the SDSS-III BOSS spectrograph. The RM quasar sample is flux-limited to i_psf=21.7 mag, and covers a redshift range of 0.1<z<4.5. Optical spectroscopy was performed during 2014 Jan-Jul dark/grey time, with an average cadence of ~4 days, totaling more than 30 epochs. Supporting photometric monitoring in the g and i bands was conducted at multiple facilities including the CFHT and the Steward Observatory Bok telescopes in 2014, with a cadence of ~2 days and covering all lunar phases. The RM field (RA, DEC=14:14:49.00, +53:05:00.0) lies within the CFHT-LS W3 field, and coincides with the Pan-STARRS 1 (PS1) Medium Deep Field MD07, with three prior years of multi-band PS1 light curves. The SDSS-RM 6-month baseline program aims to detect time lags between the quasar continuum and broad line region (BLR) variability on timescales of up to several months (in the observed frame) for ~10% of the sample, and to anchor the time baseline for continued monitoring in the future to detect lags on longer timescales and at higher redshift. SDSS-RM is the first major program to systematically explore the potential of RM for broad-line quasars at z>0.3, and will investigate the prospects of RM with all major broad lines covered in optical spectroscopy. SDSS-RM will provide guidance on future multi-object RM campaigns on larger scales, and is aiming to deliver more than tens of BLR lag detections for a homogeneous sample of quasars. We describe the motivation, design and implementation of this program, and outline the science impact expected from the resulting data for RM and general quasar science.Comment: 25 pages, submitted to ApJS; project website at http://www.sdssrm.or

The Sloan Digital Sky Survey Reverberation Mapping project : photometric g and i light curves

Funding: P.H. acknowledges the support of the Natural Sciences and Engineering Research Council of Canada (NSERC), funding reference number 2017-05983. C.J.G., W.N.B., and D.P.S. acknowledge support from NSF grant AST-1517113. Y.S. acknowledges support from an Alfred P. Sloan Research Fellowship and NSF grant AST-1715579. L.C.H. acknowledges the National Key R&D Program of China (2016YFA0400702) and the National Science Foundation of China (11721303, 11991052). J.V.H.S. and K.H. acknowledge support from a STFC grant ST/R000824/1. C.S.K. is supported by NSF grants AST-1814440 and AST-1908570.The Sloan Digital Sky Survey (SDSS) Reverberation Mapping program monitors 849 active galactic nuclei (AGNs) both spectroscopically and photometrically. The photometric observations used in this work span over 4 yr and provide an excellent baseline for variability studies of these objects. We present the photometric light curves from 2014 to 2017 obtained by the Steward Observatory's Bok telescope and the Canada-France-Hawaii telescope with MegaCam. We provide details on the data acquisition and processing of the data from each telescope, the difference imaging photometry used to produce the light curves, and the calculation of a variability index to quantify each AGN's variability. We find that the Welch-Stetson J index provides a useful characterization of AGN variability and can be used to select AGNs for further study.PostprintPeer reviewe