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1. Comprehensive High-resolution Chemical Spectroscopy of Barnard’s Star with SPIRou

Author

Farbod Jahandar, René Doyon, Étienne Artigau, Neil J. Cook, Charles Cadieux, David Lafrenière, Thierry Forveille, Jean-François Donati, Pascal Fouqué, Andrés Carmona, Ryan Cloutier, Paul Cristofari, Eric Gaidos, João Gomes da Silva, Lison Malo, Eder Martioli, J.-D. do Nascimento Jr., Stefan Pelletier, Thomas Vandal, and Kim Venn

Subjects
M dwarf stars, Low mass stars, Stellar atmospheres, Chemical abundances, Abundance ratios, Metallicity, Astrophysics, QB460-466
Abstract

Determination of fundamental parameters of stars impacts all fields of astrophysics, from galaxy evolution to constraining the internal structure of exoplanets. This paper presents a detailed spectroscopic analysis of Barnard’s star (otherwise known as Gl 699) that compares an exceptionally high-quality (an average signal-to-noise ratio of ∼1000 in the entire domain), high-resolution near-infrared (NIR) spectrum taken with Canada-France-Hawaii Telescope/SPIRou to PHOENIX-ACES stellar atmosphere models. The observed spectrum shows thousands of lines not identified in the models with a similarly large number of lines present in the model but not in the observed data. We also identify several other caveats, such as continuum mismatch, unresolved contamination, and spectral lines significantly shifted from their expected wavelengths; all of these can be a source of bias for the determination of abundance. Out of >10 ^4 observed lines in the NIR that could be used for chemical spectroscopy, we identify a short list of a few hundred lines that are reliable. We present a novel method for determining the effective temperature ( T _eff ) and overall metallicity of slowly rotating M dwarfs that uses several groups of lines as opposed to bulk spectral fitting methods. With this method, we infer T _eff = 3231 ± 21 K for Barnard's star, consistent with the value of 3238 ± 11 K inferred from the interferometric method. We also provide measurements of the abundance of 15 different elements for Barnard's star, including the abundances of four elements (K, O, Y, Th) never reported before for this star. This work emphasizes the need to improve current atmosphere models to fully exploit the NIR domain for chemical spectroscopy analysis.

Published
2024
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2. JWST/NIRCam Discovery of the First Y+Y Brown Dwarf Binary: WISE J033605.05–014350.4

Author

Per Calissendorff, Matthew De Furio, Michael Meyer, Loïc Albert, Christian Aganze, Mohamad Ali-Dib, Daniella C. Bardalez Gagliuffi, Frederique Baron, Charles A. Beichman, Adam J. Burgasser, Michael C. Cushing, Jacqueline Kelly Faherty, Clémence Fontanive, Christopher R. Gelino, John E. Gizis, Alexandra Z. Greenbaum, J. Davy Kirkpatrick, Sandy K. Leggett, Frantz Martinache, David Mary, Mamadou N’Diaye, Benjamin J. S. Pope, Thomas Roellig, Johannes Sahlmann, Anand Sivaramakrishnan, Daniel Peter Thorngren, Marie Ygouf, and Thomas Vandal

Subjects
Brown dwarfs, Y dwarfs, James Webb Space Telescope, Astrophysics, QB460-466
Abstract

We report the discovery of the first brown dwarf binary system with a Y dwarf primary, WISE J033605.05−014350.4, observed with NIRCam on JWST with the F150W and F480M filters. We employed an empirical point-spread function binary model to identify the companion, located at a projected separation of 0.″084, position angle of 295°, and with contrasts of 2.8 and 1.8 mag in F150W and F480M, respectively. At a distance of 10 pc based on its Spitzer parallax, and assuming a random inclination distribution, the physical separation is approximately 1 au. Evolutionary models predict for that an age of 1–5 Gyr, the companion mass is about 4–12.5 Jupiter masses around the 7.5–20 Jupiter mass primary, corresponding to a companion-to-host mass fraction of q = 0.61 ± 0.05. Under the assumption of a Keplerian orbit the period for this extreme binary is in the range of 5–9 yr. The system joins a small but growing sample of ultracool dwarf binaries with effective temperatures of a few hundreds of Kelvin. Brown dwarf binaries lie at the nexus of importance for understanding the formation mechanisms of these elusive objects, as they allow us to investigate whether the companions formed as stars or as planets in a disk around the primary.

Published
2023
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3. DeepEmSat: Deep Emulation for Satellite Data Mining

Author

Kate Duffy, Thomas Vandal, Shuang Li, Sangram Ganguly, Ramakrishna Nemani, and Auroop R. Ganguly

Subjects
remote sensing, machine learning, deep learning, atmospheric correction, emulator, Information technology, T58.5-58.64
Abstract

The growing volume of Earth science data available from climate simulations and satellite remote sensing offers unprecedented opportunity for scientific insight, while also presenting computational challenges. One potential area of impact is atmospheric correction, where physics-based numerical models retrieve surface reflectance information from top of atmosphere observations, and are too computationally intensive to be run in real time. Machine learning methods have demonstrated potential as fast statistical models for expensive simulations and for extracting credible insights from complex datasets. Here, we develop DeepEmSat: a deep learning emulator approach for atmospheric correction, and offer comparison against physics-based models to support the hypothesis that deep learning can make a contribution to the efficient processing of satellite images.

Published
2019
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11. Spectral Synthesis for Geostationary Satellite-to-Satellite Translation

Author

Weile Wang, Daniel McDuff, Thomas Vandal, Andrew Michaelis, Kate Duffy, and Ramakrishna R. Nemani

Subjects
medicine.medical_specialty, Computer science, Feature vector, Multispectral image, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Spectral bands, Translation (geometry), Autoencoder, Spectral imaging, ComputingMethodologies_PATTERNRECOGNITION, Computer Science::Computer Vision and Pattern Recognition, Geostationary orbit, medicine, General Earth and Planetary Sciences, Satellite, Electrical and Electronic Engineering, Physics::Atmospheric and Oceanic Physics, Remote sensing
Abstract

Earth-observing satellites carrying multispectral sensors are widely used to monitor the physical and biological states of the atmosphere, land, and oceans. These satellites have different vantage points above the Earth and different spectral imaging bands resulting in inconsistent imagery from one to another. This presents challenges in building downstream applications. What if we could generate synthetic bands for existing satellites from the union of all domains? We tackle the problem of generating synthetic spectral imagery for multispectral sensors as an unsupervised image-to-image translation problem modeled with a variational autoencoder (VAE) and generative adversarial network (GAN) architecture. Our approach introduces a novel shared spectral reconstruction loss to constrain the high-dimensional feature space of multispectral images. Simulated experiments performed by dropping one or more spectral bands show that cross-domain reconstruction outperforms measurements obtained from a second vantage point. Our proposed approach enables the synchronization of multispectral data and provides a basis for more homogeneous remote sensing datasets.

Published
2022

12. JWST/NIRCam discovery of the first Y+Y brown dwarf binary: WISE J033605.05$-$014350.4

Author

Per Calissendorff, Matthew De Furio, Michael Meyer, Loïc Albert, Christian Aganze, Mohamad Ali-Dib, Daniella C. Bardalez Gagliuffi, Frederique Baron, Charles A. Beichman, Adam J. Burgasser, Michael C. Cushing, Jacqueline Kelly Faherty, Clémence Fontanive, Christopher R. Gelino, John E. Gizis, Alexandra Z. Greenbaum, J. Davy Kirkpatrick, Sandy K. Leggett, Frantz Martinache, David Mary, Mamadou N’Diaye, Benjamin J. S. Pope, Thomas Roellig, Johannes Sahlmann, Anand Sivaramakrishnan, Daniel Peter Thorngren, Marie Ygouf, and Thomas Vandal

Subjects
Earth and Planetary Astrophysics (astro-ph.EP), Astrophysics - Solar and Stellar Astrophysics, Space and Planetary Science, FOS: Physical sciences, Astronomy and Astrophysics, Solar and Stellar Astrophysics (astro-ph.SR), Astrophysics - Earth and Planetary Astrophysics
Abstract

We report the discovery of the first brown dwarf binary system with a Y dwarf primary, WISE J033605.05$-$014350.4, observed with NIRCam on JWST with the F150W and F480M filters. We employed an empirical point spread function binary model to identify the companion, located at a projected separation of 84 milliarcseconds, position angle of 295 degrees, and with contrast of 2.8 and 1.8 magnitudes in F150W and F480M, respectively. At a distance of 10$\,$pc based on its Spitzer parallax, and assuming a random inclination distribution, the physical separation is approximately 1$\,$au. Evolutionary models predict for that an age of 1-5 Gyr, the companion mass is about 4-12.5 Jupiter masses around the 7.5-20 Jupiter mass primary, corresponding to a companion-to-host mass fraction of $q=0.61\pm0.05$. Under the assumption of a Keplerian orbit the period for this extreme binary is in the range of 5-9 years. The system joins a small but growing sample of ultracool dwarf binaries with effective temperatures of a few hundreds of Kelvin. Brown dwarf binaries lie at the nexus of importance for understanding the formation mechanisms of these elusive objects, as they allow us to investigate whether the companions formed as stars or as planets in a disk around the primary., Comment: 8 pages, 3 figures, 1 table. Accepted for publication in Astrophysical Journal Letters

Published
2023
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13. The Near Infrared Imager and Slitless Spectrograph for the James Webb Space Telescope -- IV. Aperture Masking Interferometry

Author

Anand Sivaramakrishnan, Peter Tuthill, James P. Lloyd, Alexandra Z. Greenbaum, Deepashri Thatte, Rachel A. Cooper, Thomas Vandal, Jens Kammerer, Joel Sanchez-Bermudez, Benjamin J. S. Pope, Dori Blakely, Loïc Albert, Neil J. Cook, Doug Johnstone, André R. Martel, Kevin Volk, Anthony Soulain, Étienne Artigau, David Lafrenière, Chris J. Willott, Sébastien Parmentier, K. E. Saavik Ford, Barry McKernan, M. Begoña Vila, Neil Rowlands, René Doyon, Mathilde Beaulieu, Louis Desdoigts, Alexander W. Fullerton, Matthew De Furio, Paul Goudfrooij, Sherie T. Holfeltz, Stephanie LaMassa, Michael Maszkiewicz, Michael R. Meyer, Marshall D. Perrin, Laurent Pueyo, Johannes Sahlmann, Sangmo Tony Sohn, Paula S. Teixeira, and Sheng-hai Zheng

Subjects
Space and Planetary Science, FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM)
Abstract

The James Webb Space Telescope's Near Infrared Imager and Slitless Spectrograph (JWST-NIRISS) flies a 7-hole non-redundant mask (NRM), the first such interferometer in space, operating at 3-5 \micron~wavelengths, and a bright limit of $\simeq 4$ magnitudes in W2. We describe the NIRISS Aperture Masking Interferometry (AMI) mode to help potential observers understand its underlying principles, present some sample science cases, explain its operational observing strategies, indicate how AMI proposals can be developed with data simulations, and how AMI data can be analyzed. We also present key results from commissioning AMI. Since the allied Kernel Phase Imaging (KPI) technique benefits from AMI operational strategies, we also cover NIRISS KPI methods and analysis techniques, including a new user-friendly KPI pipeline. The NIRISS KPI bright limit is $\simeq 8$ W2 magnitudes. AMI (and KPI) achieve an inner working angle of $\sim 70$ mas that is well inside the $\sim 400$ mas NIRCam inner working angle for its circular occulter coronagraphs at comparable wavelengths., 30 pages, 10 figures

Published
2022

14. TOI-1452 b: SPIRou and TESS Reveal a Super-Earth in a Temperate Orbit Transiting an M4 Dwarf

Author

Charles Cadieux, René Doyon, Mykhaylo Plotnykov, Guillaume Hébrard, Farbod Jahandar, Étienne Artigau, Diana Valencia, Neil J. Cook, Eder Martioli, Thomas Vandal, Jean-François Donati, Ryan Cloutier, Norio Narita, Akihiko Fukui, Teruyuki Hirano, François Bouchy, Nicolas B. Cowan, Erica J. Gonzales, David R. Ciardi, Keivan G. Stassun, Luc Arnold, Björn Benneke, Isabelle Boisse, Xavier Bonfils, Andrés Carmona, Pía Cortés-Zuleta, Xavier Delfosse, Thierry Forveille, Pascal Fouqué, João Gomes da Silva, Jon M. Jenkins, Flavien Kiefer, Ágnes Kóspál, David Lafrenière, Jorge H. C. Martins, Claire Moutou, J.-D. do Nascimento, Merwan Ould-Elhkim, Stefan Pelletier, Joseph D. Twicken, Luke G. Bouma, Scott Cartwright, Antoine Darveau-Bernier, Konstantin Grankin, Masahiro Ikoma, Taiki Kagetani, Kiyoe Kawauchi, Takanori Kodama, Takayuki Kotani, David W. Latham, Kristen Menou, George Ricker, Sara Seager, Motohide Tamura, Roland Vanderspek, Noriharu Watanabe, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Canada-France-Hawaii Telescope Corporation (CFHT), National Research Council of Canada (NRC)-Centre National de la Recherche Scientifique (CNRS)-University of Hawai'i [Honolulu] (UH), Laboratoire d'Astrophysique de Marseille (LAM), Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France, and ANR-18-CE31-0019,SPlaSH,Recherche de planètes habitables avec SPIRou(2018)

Subjects
Earth and Planetary Astrophysics (astro-ph.EP), [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], Exoplanets, FOS: Physical sciences, Astronomy and Astrophysics, M dwarf stars, Astrophysics - Solar and Stellar Astrophysics, Space and Planetary Science, [SDU]Sciences of the Universe [physics], Super Earths, Solar and Stellar Astrophysics (astro-ph.SR), Astrophysics - Earth and Planetary Astrophysics
Abstract

Exploring the properties of exoplanets near or inside the radius valley provides insights on the transition from the rocky super-Earths to the larger, hydrogen-rich atmosphere mini-Neptunes. Here, we report the discovery of TOI-1452 b, a transiting super-Earth ($R_{\rm p} = 1.67 \pm 0.07$ R$_{\oplus}$) in an 11.1--day temperate orbit ($T_{\rm eq} = 326 \pm 7$ K) around the primary member ($H = 10.0$, $T_{\rm eff} = 3185 \pm 50$ K) of a nearby visual binary M dwarf. The transits were first detected by TESS, then successfully isolated between the two $3.2^{\prime\prime}$ companions with ground-based photometry from OMM and MuSCAT3. The planetary nature of TOI-1452 b was established through high-precision velocimetry with the near-infrared SPIRou spectropolarimeter as part of the ongoing SPIRou Legacy Survey. The measured planetary mass ($4.8 \pm 1.3$ M$_{\oplus}$) and inferred bulk density ($5.6^{+1.8}_{-1.6}$ g/cm$^3$) is suggestive of a rocky core surrounded by a volatile-rich envelope. More quantitatively, the mass and radius of TOI-1452 b, combined with the stellar abundance of refractory elements (Fe, Mg and Si) measured by SPIRou, is consistent with a core mass fraction of $18\pm6$ % and a water mass fraction of $22^{+21}_{-13}$%. The water world candidate TOI-1452 b is a prime target for future atmospheric characterization with JWST, featuring a Transmission Spectroscopy Metric similar to other well-known temperate small planets such as LHS 1140 b and K2-18 b. The system is located near Webb's northern Continuous Viewing Zone, implying that is can be followed at almost any moment of the year., Published in The Astronomical Journal

Published
2022
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15. Line-by-line velocity measurements, an outlier-resistant method for precision velocimetry

Author

Étienne Artigau, Charles Cadieux, Neil J. Cook, René Doyon, Thomas Vandal, Jean-François Donati, Claire Moutou, Xavier Delfosse, Pascal Fouqué, Eder Martioli, François Bouchy, Jasmine Parsons, Andres Carmona, Xavier Dumusque, Nicola Astudillo-Defru, Xavier Bonfils, Lucille Mignon, Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), and Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France

Subjects
Earth and Planetary Astrophysics (astro-ph.EP), Astrophysics - Solar and Stellar Astrophysics, Space and Planetary Science, [SDU]Sciences of the Universe [physics], FOS: Physical sciences, Astronomy and Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Solar and Stellar Astrophysics (astro-ph.SR), Astrophysics - Earth and Planetary Astrophysics
Abstract

We present a new algorithm for precision radial velocity (pRV) measurements, a line-by-line (LBL) approach designed to handle outlying spectral information in a simple but efficient manner. The effectiveness of the LBL method is demonstrated on two datasets, one obtained with SPIRou on Barnard's star, and the other with HARPS on Proxima Centauri. In the near-infrared, the LBL provides a framework for m/s-level accuracy in pRV measurements despite the challenges associated with telluric absorption and sky emission lines. We confirm with SPIRou measurements spanning 2.7 years that the candidate super-Earth on a 233-day orbit around Barnard's star is an artifact due to a combination of time-sampling and activity. The LBL analysis of the Proxima Centauri HARPS post-upgrade data alone easily recovers the Proxima b signal and also provides a 2-sigma detection of the recently confirmed 5-day Proxima d planet, but argues against the presence of the candidate Proxima c with a period of 1900 days. We provide evidence that the Proxima c signal is associated with small, unaccounted systematic effects affecting the HARPS-TERRA template matching RV extraction method for long-period signals. Finally, the LBL framework provides a very effective activity indicator, akin to the full width at half maximum derived from the cross-correlation function, from which we infer a rotation period of $92.1^{+4.2}_{-3.5}$ days for Proxima., Accepted for publication in AJ

Published
2022
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16. Performance of near-infrared high-contrast imaging methods with JWST from commissioning

Author

Jens Kammerer, Julien H. Girard, Aarynn Carter, Marshall D. Perrin, Rachel Cooper, Deepashri Thatte, Thomas Vandal, Jarron Leisenring, Jason Wang, William Balmer, Anand Sivaramakrishnan, Laurent Pueyo, Kimberly Ward-Duong, Ben Sunnquist, and Jéa Adams Redai

Subjects
Earth and Planetary Astrophysics (astro-ph.EP), FOS: Physical sciences, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Astrophysics - Earth and Planetary Astrophysics
Abstract

The James Webb Space Telescope (JWST) will revolutionize the field of high-contrast imaging and enable both the direct detection of Saturn-mass planets and the characterization of substellar companions in the mid-infrared. While JWST will feature unprecedented sensitivity, angular resolution will be the key factor when competing with ground-based telescopes. Here, we aim to characterize the performance of several extreme angular resolution imaging techniques available with JWST in the 3-5 micron regime based on data taken during commissioning. Firstly, we introduce custom tools to simulate, reduce, and analyze NIRCam and MIRI coronagraphy data and use these tools to extract companion detection limits from on-sky NIRCam round and bar mask coronagraphy observations. Secondly, we present on-sky NIRISS aperture masking interferometry (AMI) and kernel phase imaging (KPI) observations from which we extract companion detection limits using the publicly available fouriever tool. Scaled to a total integration time of one hour and a target of the brightness of AB Dor, we find that NIRISS AMI and KPI reach contrasts of $\sim$7-8 mag at $\sim$70 mas and $\sim$9 mag at $\sim$200 mas. Beyond $\sim$250 mas, NIRCam coronagraphy reaches deeper contrasts of $\sim$13 mag at $\sim$500 mas and $\sim$15 mag at $\sim$2 arcsec. While the bar mask performs $\sim$1 mag better than the round mask at small angular separations $\lesssim$0.75 arcsec, it is the other way around at large angular separations $\gtrsim$1.5 arcsec. Moreover, the round mask gives access to the full 360 deg field-of-view which is beneficial for the search of new companions. We conclude that already during the instrument commissioning, JWST high-contrast imaging in the L- and M-bands performs close to its predicted limits., Comment: 14 pages, 8 figures, proceeding of SPIE Astronomical Telescopes + Instrumentation 2022

Published
2022
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17. The Science Performance of JWST as Characterized in Commissioning

Author

Jane Rigby, Marshall Perrin, Michael McElwain, Randy Kimble, Scott Friedman, Matt Lallo, René Doyon, Lee Feinberg, Pierre Ferruit, Alistair Glasse, Marcia Rieke, George Rieke, Gillian Wright, Chris Willott, Knicole Colon, Stefanie Milam, Susan Neff, Christopher Stark, Jeff Valenti, Jim Abell, Faith Abney, Yasin Abul-Huda, D. Scott Acton, Evan Adams, David Adler, Jonathan Aguilar, Nasif Ahmed, Loïc Albert, Stacey Alberts, David Aldridge, Marsha Allen, Martin Altenburg, Javier Álvarez-Márquez, Catarina Alves de Oliveira, Greg Andersen, Harry Anderson, Sara Anderson, Ioannis Argyriou, Amber Armstrong, Santiago Arribas, Etienne Artigau, Amanda Arvai, Charles Atkinson, Gregory Bacon, Thomas Bair, Kimberly Banks, Jaclyn Barrientes, Bruce Barringer, Peter Bartosik, William Bast, Pierre Baudoz, Thomas Beatty, Katie Bechtold, Tracy Beck, Eddie Bergeron, Matthew Bergkoetter, Rachana Bhatawdekar, Stephan Birkmann, Ronald Blazek, Claire Blome, Anthony Boccaletti, Torsten Böker, John Boia, Nina Bonaventura, Nicholas Bond, Kari Bosley, Ray Boucarut, Matthew Bourque, Jeroen Bouwman, Gary Bower, Charles Bowers, Martha Boyer, Larry Bradley, Greg Brady, Hannah Braun, David Breda, Pamela Bresnahan, Stacey Bright, Christopher Britt, Asa Bromenschenkel, Brian Brooks, Keira Brooks, Bob Brown, Matthew Brown, Patricia Brown, Andy Bunker, Matthew Burger, Howard Bushouse, Steven Cale, Alex Cameron, Peter Cameron, Alicia Canipe, James Caplinger, Francis Caputo, Mihai Cara, Larkin Carey, Stefano Carniani, Maria Carrasquilla, Margaret Carruthers, Michael Case, Riggs Catherine, Don Chance, George Chapman, Stéphane Charlot, Brian Charlow, Pierre Chayer, Bin Chen, Brian Cherinka, Sarah Chichester, Zack Chilton, Taylor Chonis, Mark Clampin, Charles Clark, Kerry Clark, Dan Coe, Benee Coleman, Brian Comber, Tom Comeau, Dennis Connolly, James Cooper, Rachel Cooper, Eric Coppock, Matteo Correnti, Christophe Cossou, Alain Coulais, Laura Coyle, Misty Cracraft, Mirko Curti, Steven Cuturic, Katherine Davis, Michael Davis, Bruce Dean, Amy DeLisa, Wim deMeester, Nadia Dencheva, Nadezhda Dencheva, Joseph DePasquale, Jeremy Deschenes, Örs Hunor Detre, Rosa Diaz, Dan Dicken, Audrey DiFelice, Matthew Dillman, William Dixon, Jesse Doggett, Tom Donaldson, Rob Douglas, Kimberly DuPrie, Jean Dupuis, John Durning, Nilufar Easmin, Weston Eck, Chinwe Edeani, Eiichi Egami, Ralf Ehrenwinkler, Jonathan Eisenhamer, Michael Eisenhower, Michelle Elie, James Elliott, Kyle Elliott, Tracy Ellis, Michael Engesser, Nestor Espinoza, Odessa Etienne, Mireya Etxaluze, Patrick Falini, Matthew Feeney, Malcolm Ferry, Joseph Filippazzo, Brian Fincham, Mees Fix, Nicolas Flagey, Michael Florian, Jim Flynn, Erin Fontanella, Terrance Ford, Peter Forshay, Ori Fox, David Franz, Henry Fu, Alexander Fullerton, Sergey Galkin, Anthony Galyer, Macarena García Marín, Jonathan P. Gardner, Lisa Gardner, Dennis Garland, Bruce Garrett, Danny Gasman, Andras Gaspar, Daniel Gaudreau, Peter Gauthier, Vincent Geers, Paul Geithner, Mario Gennaro, Giovanna Giardino, Julien Girard, Mark Giuliano, Kirk Glassmire, Adrian Glauser, Stuart Glazer, John Godfrey, David Golimowski, David Gollnitz, Fan Gong, Shireen Gonzaga, Michael Gordon, Karl Gordon, Paul Goudfrooij, Thomas Greene, Matthew Greenhouse, Stefano Grimaldi, Andrew Groebner, Timothy Grundy, Pierre Guillard, Irvin Gutman, Kong Q. Ha, Peter Haderlein, Andria Hagedorn, Kevin Hainline, Craig Haley, Maryam Hami, Forrest Hamilton, Heidi Hammel, Carl Hansen, Tom Harkins, Michael Harr, Jessica Hart, Quyen Hart, George Hartig, Ryan Hashimoto, Sujee Haskins, William Hathaway, Keith Havey, Brian Hayden, Karen Hecht, Chris Heller-Boyer, Caroline Henriques, Alaina Henry, Karl Hermann, Scarlin Hernandez, Brigette Hesman, Brian Hicks, Bryan Hilbert, Dean Hines, Melissa Hoffman, Sherie Holfeltz, Bryan J. Holler, Jennifer Hoppa, Kyle Hott, Joseph M. Howard, Rick Howard, Alexander Hunter, David Hunter, Brendan Hurst, Bernd Husemann, Leah Hustak, Luminita Ilinca Ignat, Garth Illingworth, Sandra Irish, Wallace Jackson, Amir Jahromi, Peter Jakobsen, LeAndrea James, Bryan James, William Januszewski, Ann Jenkins, Hussein Jirdeh, Phillip Johnson, Timothy Johnson, Vicki Jones, Ron Jones, Danny Jones, Olivia Jones, Ian Jordan, Margaret Jordan, Sarah Jurczyk, Alden Jurling, Catherine Kaleida, Phillip Kalmanson, Jens Kammerer, Huijo Kang, Shaw-Hong Kao, Diane Karakla, Patrick Kavanagh, Doug Kelly, Sarah Kendrew, Herbert Kennedy, Deborah Kenny, Ritva Keski-kuha, Charles Keyes, Richard Kidwell, Wayne Kinzel, Jeff Kirk, Mark Kirkpatrick, Danielle Kirshenblat, Pamela Klaassen, Bryan Knapp, J. Scott Knight, Perry Knollenberg, Robert Koehler, Anton Koekemoer, Aiden Kovacs, Trey Kulp, Nimisha Kumari, Mark Kyprianou, Stephanie La Massa, Aurora Labador, Alvaro Labiano, Pierre-Olivier Lagage, Charles-Philippe Lajoie, Matthew Lallo, May Lam, Tracy Lamb, Scott Lambros, Richard Lampenfield, James Langston, Kirsten Larson, David Law, Jon Lawrence, David Lee, Jarron Leisenring, Kelly Lepo, Michael Leveille, Nancy Levenson, Marie Levine, Zena Levy, Dan Lewis, Hannah Lewis, Mattia Libralato, Paul Lightsey, Miranda Link, Lily Liu, Amy Lo, Alexandra Lockwood, Ryan Logue, Chris Long, Douglas Long, Charles Loomis, Marcos Lopez-Caniego, Jose Lorenzo Alvarez, Jennifer Love-Pruitt, Adrian Lucy, Nora Luetzgendorf, Peiman Maghami, Roberto Maiolino, Melissa Major, Sunita Malla, Eliot Malumuth, Elena Manjavacas, Crystal Mannfolk, Amanda Marrione, Anthony Marston, André Martel, Marc Maschmann, Gregory Masci, Michaela Masciarelli, Michael Maszkiewicz, John Mather, Kenny McKenzie, Brian McLean, Matthew McMaster, Katie Melbourne, Marcio Meléndez, Michael Menzel, Kaiya Merz, Michele Meyett, Luis Meza, Cherie Miskey, Karl Misselt, Christopher Moller, Jane Morrison, Ernie Morse, Harvey Moseley, Gary Mosier, Matt Mountain, Julio Mueckay, Michael Mueller, Susan Mullally, Jess Murphy, Katherine Murray, Claire Murray, David Mustelier, James Muzerolle, Matthew Mycroft, Richard Myers, Kaila Myrick, Shashvat Nanavati, Elizabeth Nance, Omnarayani Nayak, Bret Naylor, Edmund Nelan, Bryony Nickson, Alethea Nielson, Maria Nieto-Santisteban, Nikolay Nikolov, Alberto Noriega-Crespo, Brian O’Shaughnessy, Brian O’Sullivan, William Ochs, Patrick Ogle, Brenda Oleszczuk, Joseph Olmsted, Shannon Osborne, Richard Ottens, Beverly Owens, Camilla Pacifici, Alyssa Pagan, James Page, Sang Park, Keith Parrish, Polychronis Patapis, Lee Paul, Tyler Pauly, Cheryl Pavlovsky, Andrew Pedder, Matthew Peek, Maria Pena-Guerrero, Konstantin Penanen, Yesenia Perez, Michele Perna, Beth Perriello, Kevin Phillips, Martin Pietraszkiewicz, Jean-Paul Pinaud, Norbert Pirzkal, Joseph Pitman, Aidan Piwowar, Vera Platais, Danielle Player, Rachel Plesha, Joe Pollizi, Ethan Polster, Klaus Pontoppidan, Blair Porterfield, Charles Proffitt, Laurent Pueyo, Christine Pulliam, Brian Quirt, Irma Quispe Neira, Rafael Ramos Alarcon, Leah Ramsay, Greg Rapp, Robert Rapp, Bernard Rauscher, Swara Ravindranath, Timothy Rawle, Michael Regan, Timothy A. Reichard, Carl Reis, Michael E. Ressler, Armin Rest, Paul Reynolds, Timothy Rhue, Karen Richon, Emily Rickman, Michael Ridgaway, Christine Ritchie, Hans-Walter Rix, Massimo Robberto, Gregory Robinson, Michael Robinson, Orion Robinson, Frank Rock, David Rodriguez, Bruno Rodriguez Del Pino, Thomas Roellig, Scott Rohrbach, Anthony Roman, Fred Romelfanger, Perry Rose, Anthony Roteliuk, Marc Roth, Braden Rothwell, Neil Rowlands, Arpita Roy, Pierre Royer, Patricia Royle, Chunlei Rui, Peter Rumler, Joel Runnels, Melissa Russ, Zafar Rustamkulov, Grant Ryden, Holly Ryer, Modhumita Sabata, Derek Sabatke, Elena Sabbi, Bridget Samuelson, Benjamin Sapp, Bradley Sappington, B. Sargent, Arne Sauer, Silvia Scheithauer, Everett Schlawin, Joseph Schlitz, Tyler Schmitz, Analyn Schneider, Jürgen Schreiber, Vonessa Schulze, Ryan Schwab, John Scott, Kenneth Sembach, Clare Shanahan, Bryan Shaughnessy, Richard Shaw, Nanci Shawger, Christopher Shay, Evan Sheehan, Sharon Shen, Allan Sherman, Bernard Shiao, Hsin-Yi Shih, Irene Shivaei, Matthew Sienkiewicz, David Sing, Marco Sirianni, Anand Sivaramakrishnan, Joy Skipper, G. C. Sloan, Christine Slocum, Steven Slowinski, Erin Smith, Eric Smith, Denise Smith, Corbett Smith, Gregory Snyder, Warren Soh, Sangmo Tony Sohn, Christian Soto, Richard Spencer, Scott Stallcup, John Stansberry, Carl Starr, Elysia Starr, Alphonso Stewart, Massimo Stiavelli, Amber Straughn, David Strickland, Jeff Stys, Francis Summers, Fengwu Sun, Ben Sunnquist, Daryl Swade, Michael Swam, Robert Swaters, Robby Swoish, Joanna M. Taylor, Rolanda Taylor, Maurice Te Plate, Mason Tea, Kelly Teague, Randal Telfer, Tea Temim, Deepashri Thatte, Christopher Thompson, Linda Thompson, Shaun Thomson, Tuomo Tikkanen, William Tippet, Connor Todd, Sharon Toolan, Hien Tran, Edwin Trejo, Justin Truong, Chris Tsukamoto, Samuel Tustain, Harrison Tyra, Leonardo Ubeda, Kelli Underwood, Michael Uzzo, Julie Van Campen, Thomas Vandal, Bart Vandenbussche, Begoña Vila, Kevin Volk, Glenn Wahlgren, Mark Waldman, Chanda Walker, Michel Wander, Christine Warfield, Gerald Warner, Matthew Wasiak, Mitchell Watkins, Andrew Weaver, Mark Weilert, Nick Weiser, Ben Weiss, Sarah Weissman, Alan Welty, Garrett West, Lauren Wheate, Elizabeth Wheatley, Thomas Wheeler, Rick White, Kevin Whiteaker, Paul Whitehouse, Jennifer Whiteleather, William Whitman, Christina Williams, Christopher Willmer, Scott Willoughby, Andrew Wilson, Gregory Wirth, Emily Wislowski, Erin Wolf, David Wolfe, Schuyler Wolff, Bill Workman, Ray Wright, Carl Wu, Rai Wu, Kristen Wymer, Kayla Yates, Christopher Yeager, Jared Yeates, Ethan Yerger, Jinmi Yoon, Alice Young, Susan Yu, Dean Zak, Peter Zeidler, Julia Zhou, Thomas Zielinski, Cristian Zincke, Stephanie Zonak, Rigby, J., Perrin, M., Mcelwain, M., Kimble, R., Friedman, S., Lallo, M., Doyon, R., Feinberg, L., Ferruit, P., Glasse, A., Rieke, M., Rieke, G., Wright, G., Willott, C., Colon, K., Milam, S., Neff, S., Stark, C., Valenti, J., Abell, J., Abney, F., Abul-Huda, Y., Scott Acton, D., Adams, E., Adler, D., Aguilar, J., Ahmed, N., Albert, L., Alberts, S., Aldridge, D., Allen, M., Altenburg, M., Alvarez-Marquez, J., Alves de Oliveira, C., Andersen, G., Anderson, H., Anderson, S., Argyriou, I., Armstrong, A., Arribas, S., Artigau, E., Arvai, A., Atkinson, C., Bacon, G., Bair, T., Banks, K., Barrientes, J., Barringer, B., Bartosik, P., Bast, W., Baudoz, P., Beatty, T., Bechtold, K., Beck, T., Bergeron, E., Bergkoetter, M., Bhatawdekar, R., Birkmann, S., Blazek, R., Blome, C., Boccaletti, A., Boker, T., Boia, J., Bonaventura, N., Bond, N., Bosley, K., Boucarut, R., Bourque, M., Bouwman, J., Bower, G., Bowers, C., Boyer, M., Bradley, L., Brady, G., Braun, H., Breda, D., Bresnahan, P., Bright, S., Britt, C., Bromenschenkel, A., Brooks, B., Brooks, K., Brown, B., Brown, M., Brown, P., Bunker, A., Burger, M., Bushouse, H., Cale, S., Cameron, A., Cameron, P., Canipe, A., Caplinger, J., Caputo, F., Cara, M., Carey, L., Carniani, S., Carrasquilla, M., Carruthers, M., Case, M., Catherine, R., Chance, D., Chapman, G., Charlot, S., Charlow, B., Chayer, P., Chen, B., Cherinka, B., Chichester, S., Chilton, Z., Chonis, T., Clampin, M., Clark, C., Clark, K., Coe, D., Coleman, B., Comber, B., Comeau, T., Connolly, D., Cooper, J., Cooper, R., Coppock, E., Correnti, M., Cossou, C., Coulais, A., Coyle, L., Cracraft, M., Curti, M., Cuturic, S., Davis, K., Davis, M., Dean, B., Delisa, A., Demeester, W., Dencheva, N., Depasquale, J., Deschenes, J., Hunor Detre, I., Diaz, R., Dicken, D., Difelice, A., Dillman, M., Dixon, W., Doggett, J., Donaldson, T., Douglas, R., Duprie, K., Dupuis, J., Durning, J., Easmin, N., Eck, W., Edeani, C., Egami, E., Ehrenwinkler, R., Eisenhamer, J., Eisenhower, M., Elie, M., Elliott, J., Elliott, K., Ellis, T., Engesser, M., Espinoza, N., Etienne, O., Etxaluze, M., Falini, P., Feeney, M., Ferry, M., Filippazzo, J., Fincham, B., Fix, M., Flagey, N., Florian, M., Flynn, J., Fontanella, E., Ford, T., Forshay, P., Fox, O., Franz, D., Fu, H., Fullerton, A., Galkin, S., Galyer, A., Garcia Marin, M., Gardner, J. P., Gardner, L., Garland, D., Garrett, B., Gasman, D., Gaspar, A., Gaudreau, D., Gauthier, P., Geers, V., Geithner, P., Gennaro, M., Giardino, G., Girard, J., Giuliano, M., Glassmire, K., Glauser, A., Glazer, S., Godfrey, J., Golimowski, D., Gollnitz, D., Gong, F., Gonzaga, S., Gordon, M., Gordon, K., Goudfrooij, P., Greene, T., Greenhouse, M., Grimaldi, S., Groebner, A., Grundy, T., Guillard, P., Gutman, I., Ha, K. Q., Haderlein, P., Hagedorn, A., Hainline, K., Haley, C., Hami, M., Hamilton, F., Hammel, H., Hansen, C., Harkins, T., Harr, M., Hart, J., Hart, Q., Hartig, G., Hashimoto, R., Haskins, S., Hathaway, W., Havey, K., Hayden, B., Hecht, K., Heller-Boyer, C., Henriques, C., Henry, A., Hermann, K., Hernandez, S., Hesman, B., Hicks, B., Hilbert, B., Hines, D., Hoffman, M., Holfeltz, S., Holler, B. J., Hoppa, J., Hott, K., Howard, J. M., Howard, R., Hunter, A., Hunter, D., Hurst, B., Husemann, B., Hustak, L., Ilinca Ignat, L., Illingworth, G., Irish, S., Jackson, W., Jahromi, A., Jakobsen, P., James, L., James, B., Januszewski, W., Jenkins, A., Jirdeh, H., Johnson, P., Johnson, T., Jones, V., Jones, R., Jones, D., Jones, O., Jordan, I., Jordan, M., Jurczyk, S., Jurling, A., Kaleida, C., Kalmanson, P., Kammerer, J., Kang, H., Kao, S. -H., Karakla, D., Kavanagh, P., Kelly, D., Kendrew, S., Kennedy, H., Kenny, D., Keski-Kuha, R., Keyes, C., Kidwell, R., Kinzel, W., Kirk, J., Kirkpatrick, M., Kirshenblat, D., Klaassen, P., Knapp, B., Scott Knight, J., Knollenberg, P., Koehler, R., Koekemoer, A., Kovacs, A., Kulp, T., Kumari, N., Kyprianou, M., La Massa, S., Labador, A., Labiano, A., Lagage, P. -O., Lajoie, C. -P., Lam, M., Lamb, T., Lambros, S., Lampenfield, R., Langston, J., Larson, K., Law, D., Lawrence, J., Lee, D., Leisenring, J., Lepo, K., Leveille, M., Levenson, N., Levine, M., Levy, Z., Lewis, D., Lewis, H., Libralato, M., Lightsey, P., Link, M., Liu, L., Lo, A., Lockwood, A., Logue, R., Long, C., Long, D., Loomis, C., Lopez-Caniego, M., Lorenzo Alvarez, J., Love-Pruitt, J., Lucy, A., Luetzgendorf, N., Maghami, P., Maiolino, R., Major, M., Malla, S., Malumuth, E., Manjavacas, E., Mannfolk, C., Marrione, A., Marston, A., Martel, A., Maschmann, M., Masci, G., Masciarelli, M., Maszkiewicz, M., Mather, J., Mckenzie, K., Mclean, B., Mcmaster, M., Melbourne, K., Melendez, M., Menzel, M., Merz, K., Meyett, M., Meza, L., Miskey, C., Misselt, K., Moller, C., Morrison, J., Morse, E., Moseley, H., Mosier, G., Mountain, M., Mueckay, J., Mueller, M., Mullally, S., Murphy, J., Murray, K., Murray, C., Mustelier, D., Muzerolle, J., Mycroft, M., Myers, R., Myrick, K., Nanavati, S., Nance, E., Nayak, O., Naylor, B., Nelan, E., Nickson, B., Nielson, A., Nieto-Santisteban, M., Nikolov, N., Noriega-Crespo, A., O'Shaughnessy, B., O'Sullivan, B., Ochs, W., Ogle, P., Oleszczuk, B., Olmsted, J., Osborne, S., Ottens, R., Owens, B., Pacifici, C., Pagan, A., Page, J., Park, S., Parrish, K., Patapis, P., Paul, L., Pauly, T., Pavlovsky, C., Pedder, A., Peek, M., Pena-Guerrero, M., Penanen, K., Perez, Y., Perna, M., Perriello, B., Phillips, K., Pietraszkiewicz, M., Pinaud, J. -P., Pirzkal, N., Pitman, J., Piwowar, A., Platais, V., Player, D., Plesha, R., Pollizi, J., Polster, E., Pontoppidan, K., Porterfield, B., Proffitt, C., Pueyo, L., Pulliam, C., Quirt, B., Quispe Neira, I., Ramos Alarcon, R., Ramsay, L., Rapp, G., Rapp, R., Rauscher, B., Ravindranath, S., Rawle, T., Regan, M., Reichard, T. A., Reis, C., Ressler, M. E., Rest, A., Reynolds, P., Rhue, T., Richon, K., Rickman, E., Ridgaway, M., Ritchie, C., Rix, H. -W., Robberto, M., Robinson, G., Robinson, M., Robinson, O., Rock, F., Rodriguez, D., Rodriguez Del Pino, B., Roellig, T., Rohrbach, S., Roman, A., Romelfanger, F., Rose, P., Roteliuk, A., Roth, M., Rothwell, B., Rowlands, N., Roy, A., Royer, P., Royle, P., Rui, C., Rumler, P., Runnels, J., Russ, M., Rustamkulov, Z., Ryden, G., Ryer, H., Sabata, M., Sabatke, D., Sabbi, E., Samuelson, B., Sapp, B., Sappington, B., Sargent, B., Sauer, A., Scheithauer, S., Schlawin, E., Schlitz, J., Schmitz, T., Schneider, A., Schreiber, J., Schulze, V., Schwab, R., Scott, J., Sembach, K., Shanahan, C., Shaughnessy, B., Shaw, R., Shawger, N., Shay, C., Sheehan, E., Shen, S., Sherman, A., Shiao, B., Shih, H. -Y., Shivaei, I., Sienkiewicz, M., Sing, D., Sirianni, M., Sivaramakrishnan, A., Skipper, J., Sloan, G. C., Slocum, C., Slowinski, S., Smith, E., Smith, D., Smith, C., Snyder, G., Soh, W., Tony Sohn, S., Soto, C., Spencer, R., Stallcup, S., Stansberry, J., Starr, C., Starr, E., Stewart, A., Stiavelli, M., Straughn, A., Strickland, D., Stys, J., Summers, F., Sun, F., Sunnquist, B., Swade, D., Swam, M., Swaters, R., Swoish, R., Taylor, J. M., Taylor, R., Te Plate, M., Tea, M., Teague, K., Telfer, R., Temim, T., Thatte, D., Thompson, C., Thompson, L., Thomson, S., Tikkanen, T., Tippet, W., Todd, C., Toolan, S., Tran, H., Trejo, E., Truong, J., Tsukamoto, C., Tustain, S., Tyra, H., Ubeda, L., Underwood, K., Uzzo, M., Van Campen, J., Vandal, T., Vandenbussche, B., Vila, B., Volk, K., Wahlgren, G., Waldman, M., Walker, C., Wander, M., Warfield, C., Warner, G., Wasiak, M., Watkins, M., Weaver, A., Weilert, M., Weiser, N., Weiss, B., Weissman, S., Welty, A., West, G., Wheate, L., Wheatley, E., Wheeler, T., White, R., Whiteaker, K., Whitehouse, P., Whiteleather, J., Whitman, W., Williams, C., Willmer, C., Willoughby, S., Wilson, A., Wirth, G., Wislowski, E., Wolf, E., Wolfe, D., Wolff, S., Workman, B., Wright, R., Wu, C., Wu, R., Wymer, K., Yates, K., Yeager, C., Yeates, J., Yerger, E., Yoon, J., Young, A., Yu, S., Zak, D., Zeidler, P., Zhou, J., Zielinski, T., Zincke, C., Zonak, S., and bibliotheque, la.

Subjects
Science & Technology, Observatories, Infrared astronomy, Astronomical instrumentation, INFRARED SPECTROGRAPH NIRSPEC, FOS: Physical sciences, Astronomy and Astrophysics, Astronomy & Astrophysics, Settore FIS/05 - Astronomia e Astrofisica, Space and Planetary Science, Physical Sciences, Astrophysics - Instrumentation and Methods for Astrophysics, [PHYS.ASTR] Physics [physics]/Astrophysics [astro-ph], Instrumentation and Methods for Astrophysics (astro-ph.IM)
Abstract

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies., Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb293

Published
2022
Full Text
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18. APERO: A PipelinE to Reduce Observations—Demonstration with SPIRou

Author

Neil James Cook, Étienne Artigau, René Doyon, Melissa Hobson, Eder Martioli, François Bouchy, Claire Moutou, Andres Carmona, Chris Usher, Pascal Fouqué, Luc Arnold, Xavier Delfosse, Isabelle Boisse, Charles Cadieux, Thomas Vandal, Jean-François Donati, Ariane Deslières, Institut d'Astrophysique de Paris (IAP), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), Institut de Planétologie et d'Astrophysique de Grenoble (IPAG), Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire des Sciences de l'Univers de Grenoble (OSUG ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France -Institut national des sciences de l'Univers (INSU - CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Université Grenoble Alpes (UGA)-Météo-France, Canada-France-Hawaii Telescope Corporation (CFHT), National Research Council of Canada (NRC)-Centre National de la Recherche Scientifique (CNRS)-University of Hawai'i [Honolulu] (UH), Laboratoire d'Astrophysique de Marseille (LAM), and Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Earth and Planetary Astrophysics (astro-ph.EP), Radial velocity, Near infrared astronomy, FOS: Physical sciences, Astronomy and Astrophysics, Astronomy data reduction, Astrophysics - Solar and Stellar Astrophysics, [SDU]Sciences of the Universe [physics], Space and Planetary Science, Calibration, Spectropolarimetry, Astrophysics - Instrumentation and Methods for Astrophysics, Instrumentation and Methods for Astrophysics (astro-ph.IM), Spectroscopy, Solar and Stellar Astrophysics (astro-ph.SR), Astrophysics - Earth and Planetary Astrophysics
Abstract

With the maturation of near-infrared high-resolution spectroscopy, especially when used for precision radial velocity, data reduction has faced unprecedented challenges in terms of how one goes from raw data to calibrated, extracted, and corrected data with required precisions of thousandths of a pixel. Here we present APERO (A PipelinE to Reduce Observations), specifically focused on SPIRou, the near-infrared spectropolarimeter on the Canada--France--Hawaii Telescope (SPectropolarim\`etre InfraROUge, CFHT). In this paper, we give an overview of APERO and detail the reduction procedure for SPIRou. APERO delivers telluric-corrected 2D and 1D spectra as well as polarimetry products. APERO enables precise stable radial velocity measurements on sky (via the LBL algorithm), good to at least ~2 m/s over the current 5-year lifetime of SPIRou., Comment: Accepted for publication in PASP. 55 pages, 29 figures, 10 pages of Appendices

Published
2022

19. Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow

Author

Ramakrishna R. Nemani and Thomas Vandal

Subjects
Severe weather, Computer Networks and Communications, Optical flow, Mesoscale meteorology, Bilinear interpolation, Tracking (particle physics), Computer Science Applications, Upsampling, Artificial Intelligence, Geostationary orbit, Environmental science, Satellite imagery, Software, Remote sensing
Abstract

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral, and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary (GEO) satellites has hemispheric coverage at 10-15-min intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery. We apply this technique to 16 bands of the GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full-disk hemispheric snapshots of different spatial resolutions from 10 to 1 min. Experiments show the effectiveness of task-specific optical flow and multiscale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Finally, we demonstrate strong performance in capturing variability during convective precipitation events.

Published
2021

20. Where Is the Water? Jupiter-like C/H Ratio but Strong H 2 O Depletion Found on τ Boötis b Using SPIRou

Author

Neil Cook, Charles Cadieux, Claire Moutou, David Lafrenière, J. H. C. Martins, Antoine Darveau-Bernier, Guillaume Hébrard, Stefan Pelletier, Simon Delisle, Romain Allart, Pascal Fouqué, Jean-François Donati, Björn Benneke, Caroline Piaulet, Anne Boucher, Louis-Philippe Coulombe, Étienne Artigau, René Doyon, Thomas Vandal, Eder Martioli, Xavier Delfosse, Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), ANR-18-CE31-0019,SPlaSH,Recherche de planètes habitables avec SPIRou(2018), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), and Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Physics, 010308 nuclear & particles physics, Astronomy and Astrophysics, Astrophysics, Molecular spectroscopy, 01 natural sciences, Astronomical instrumentation, Jupiter, 13. Climate action, Space and Planetary Science, [SDU]Sciences of the Universe [physics], 0103 physical sciences, 010303 astronomy & astrophysics, ComputingMilieux_MISCELLANEOUS, Astrophysics - Earth and Planetary Astrophysics
Abstract

The present-day envelope of gaseous planets is a relic of how these giant planets originated and evolved. Measuring their elemental composition therefore presents a powerful opportunity to answer long-standing questions regarding planet formation. Obtaining precise observational constraints on the elemental inventory of giant exoplanets has, however, remained challenging due to the limited simultaneous wavelength coverage of current space-based instruments. Here, we present thermal emission observations of the non-transiting hot Jupiter $\tau$ Boo b using the new wide wavelength coverage (0.95$-$2.50$\,\mu$m) and high spectral resolution ($R=70\,000$) SPIRou spectrograph. By combining a total of 20 hours of SPIRou data obtained over five nights in a full atmospheric retrieval framework designed for high-resolution data, we constrain the abundances of all the major oxygen- and carbon-bearing molecules and recover a non-inverted temperature structure using a new free-shape, nonparametric TP profile retrieval approach. We find a volume mixing ratio of log(CO)$\,\,=-2.46_{-0.29}^{+0.25}$ and a highly depleted water abundance of less than $0.0072$ times the value expected for a solar composition envelope. Combined with upper limits on the abundances of CH$_4$, CO$_2$, HCN, TiO, and C$_2$H$_2$, this results in a gas-phase C/H ratio of 5.85$_{-2.82}^{+4.44}\times\,$solar, consistent with the value of Jupiter, and an envelope C/O ratio robustly greater than 0.60, even when taking into account the oxygen that may be sequestered out of the gas-phase. Combined, the inferred super-solar C/H, O/H, and C/O ratios on $\tau$ Boo b support a formation scenario beyond the water snowline in a disk enriched in CO due to pebble drift., Comment: 27 pages, 14 figures, 3 tables, Accepted for publication in The Astronomical Journal

Published
2021

21. Quantum-assisted associative adversarial network: applying quantum annealing in deep learning

Author

Max Wilson, Eleanor Rieffel, Thomas Vandal, and Tad Hogg

Subjects
FOS: Computer and information sciences, Computer Science::Machine Learning, Computer Science - Machine Learning, Theoretical computer science, Computer science, Boltzmann machine, FOS: Physical sciences, Machine Learning (stat.ML), 01 natural sciences, Machine Learning (cs.LG), 010305 fluids & plasmas, Theoretical Computer Science, Statistics - Machine Learning, Artificial Intelligence, 0103 physical sciences, Feature (machine learning), Graphical model, 010306 general physics, Quantum computer, Quantum Physics, business.industry, Applied Mathematics, Deep learning, Quantum annealing, Generative model, Computational Theory and Mathematics, Artificial intelligence, Quantum Physics (quant-ph), business, Software, MNIST database
Abstract

Generative models have the capacity to model and generate new examples from a dataset and have an increasingly diverse set of applications driven by commercial and academic interest. In this work, we present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted by the discriminator. A quantum processor can be used to sample from the model to train the Boltzmann machine. This novel hybrid quantum-classical algorithm joins a growing family of algorithms that use a quantum processor sampling subroutine in deep learning, and provides a scalable framework to test the advantages of quantum-assisted learning. For the latent space model, fully connected, symmetric bipartite and Chimera graph topologies are compared on a reduced stochastically binarized MNIST dataset, for both classical and quantum sampling methods. The quantum-assisted associative adversarial network successfully learns a generative model of the MNIST dataset for all topologies. Evaluated using the Fréchet inception distance and inception score, the quantum and classical versions of the algorithm are found to have equivalent performance for learning an implicit generative model of the MNIST dataset. Classical sampling is used to demonstrate the algorithm on the LSUN bedrooms dataset, indicating scalability to larger and color datasets. Though the quantum processor used here is a quantum annealer, the algorithm is general enough such that any quantum processor, such as gate model quantum computers, may be substituted as a sampler.

Published
2021

22. Corrigendum: Data Science for Weather Impacts on Crop Yield

Author

Venkata Shashank Konduri, Sanmay Ganguly, Thomas Vandal, and Auroop R. Ganguly

Subjects
Global and Planetary Change, Food security, lcsh:TP368-456, Ecology, Crop yield, lcsh:TX341-641, Agricultural engineering, food security, crop yield, Horticulture, Management, Monitoring, Policy and Law, pairwise dependence, lcsh:Food processing and manufacture, nonlinear regression, Environmental science, weather indices, Nonlinear regression, lcsh:Nutrition. Foods and food supply, Agronomy and Crop Science, Food Science
Published
2020
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23. New Dynamical Mass Estimates of the beta Pictoris Planetary System Through Gaussian Process Stellar Activity Modelling

Author

René Doyon, Julien Rameau, and Thomas Vandal

Subjects
Physics, symbols.namesake, symbols, Astrophysics::Solar and Stellar Astrophysics, Beta Pictoris, Astrophysics::Earth and Planetary Astrophysics, Astrophysics, Planetary system, Gaussian process, Astrophysics::Galaxy Astrophysics
Abstract

Nearly 15 years of radial velocity (RV) monitoring and direct imaging (DI) enabled the detection oftwo giant planets orbiting the young, nearby star β Pictoris. The δ Scuti pulsations of the star, overwhelming planetary signals, need however to be carefully suppress. In this talk, we propose a new and independent analysis of the system, making use of all available data, including photometric light curve from the ground and space, long term RV and DI monitoring. We demonstrate how all data can be consistently modelled in a Bayesian framework. We show how modern and physically motivated kernels for Gaussian Process can effectively model complex stellar activity. Using further carefull statistical treatment of the data to extend the monitoring, we detect both planets from RV data only for the first time. To characterize the system more accurately, we also perform a joint fit of all available relative astrometry and RV data. We provide precise orbital parameters and discuss the whole system architecture. The inferred dynamical mass measurements for both planets are also compared to mass-luminosity evolutionary tracks. This work opens the path towards a precise characterization of young planetary systems combing photometry, spectroscopy, and astrometry.

Published
2020

24. GeoNEX: A Geostationary Earth Observatory at NASA Earth Exchange: Earth Monitoring from Operational Geostationary Satellite Systems

Author

Kazuhito Ichii, Andrew Michaelis, Tsengdar Lee, Weile Wang, Jong-Min Yeom, Alexei Lyapustin, Jia Zhang, Thomas Vandal, Satya Kalluri, Ramakrishna R. Nemani, Hirofumi Hashimoto, Shuang Li, Hideaki Takenaka, and Atsushi Higuchi

Subjects
010504 meteorology & atmospheric sciences, Emergency management, business.industry, Cloud cover, Multispectral image, 0211 other engineering and technologies, Cloud computing, 02 engineering and technology, 01 natural sciences, Pipeline (software), Pipeline transport, Observatory, Geostationary orbit, Environmental science, business, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, FY-4, GK-2A) carries sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. When combined, data from various currently operating/planned geostationary platforms provide a geo-ring of hyper-temporal (5-10 minutes), multispectral observations at spatial resolutions as high as 500 m. These high frequency observations offer exciting new possibilities for monitoring our planet, including better retrievals of geophysical variables by overcoming cloud cover, enabling studies of diurnally varying phenomena in the atmosphere, land, and the oceans, and support operational decision-making in agriculture, hydrology and disaster management. The NASA Earth Exchange (NEX) team, in collaboration with scientists from JAXA, KARI, NOAA and other international institutions, created the GeoNEX (www.nasa.gov/geonex) pipeline to integrate data from all available geostationary platforms and produce and distribute spatially, temporally, and radiometrically consistent data for the earth science community. We envision various institutions adapting the Geo component (e.g., GeoNOAA, GeoKARI, GeoChiba, GeoJAXA, GeoCMA) and customizing the pipeline and downstream products to serve the local/regional research and applied science communities.

Published
2020

25. Dynamical Mass Estimates of the $\beta$ Pictoris Planetary System Through Gaussian Process Stellar Activity Modelling

Author

Julien Rameau, René Doyon, and Thomas Vandal

Subjects
Physics, Astronomy and Astrophysics, Astrometry, Astrophysics, Planetary system, Exoplanet, Radial velocity, symbols.namesake, Astrophysics - Solar and Stellar Astrophysics, Space and Planetary Science, symbols, Orbit determination, Gaussian process, Astrophysics - Earth and Planetary Astrophysics
Abstract

Nearly 15 years of radial velocity (RV) monitoring and direct imaging enabled the detection of two giant planets orbiting the young, nearby star $\beta$ Pictoris. The $\delta$ Scuti pulsations of the star, overwhelming planetary signals, need to be carefully suppressed. In this work, we independently revisit the analysis of the RV data following a different approach than in the literature to model the activity of the star. We show that a Gaussian Process (GP) with a stochastically driven damped harmonic oscillator kernel can model the $\delta$ Scuti pulsations. It provides similar results as parametric models but with a simpler framework, using only 3 hyperparameters. It also enables to model poorly sampled RV data, that were excluded from previous analysis, hence extending the RV baseline by nearly five years. Altogether, the orbit and the mass of both planets can be constrained from RV only, which was not possible with the parametric modelling. To characterize the system more accurately, we also perform a joint fit of all available relative astrometry and RV data. Our orbital solutions for $\beta$ Pic b favour a low eccentricity of $0.029^{+0.061}_{-0.024}$ and a relatively short period of $21.1^{+2.0}_{-0.8}$ yr. The orbit of $\beta$ Pic c is eccentric with $0.206^{+0.074}_{-0.063}$ with a period of $3.36\pm0.03$ yr. We find model-independent masses of $11.7\pm1.4$ and $8.5\pm0.5$ M$_{Jup}$ for $\beta$ Pic b and c, respectively, assuming coplanarity. The mass of $\beta$ Pic b is consistent with the hottest start evolutionary models, at an age of $25\pm3$ Myr. A direct direction of $\beta$ Pic c would provide a second calibration measurement in a coeval system., Comment: 25 pages, 18 figures, Accepted to AJ, Typos Corrected

Published
2020

26. Data Science for Weather Impacts on Crop Yield

Author

Sanmay Ganguly, Thomas Vandal, Venkata Shashank Konduri, and Auroop R. Ganguly

Subjects
Computer science, Yield (finance), Context (language use), lcsh:TX341-641, Scientific literature, Horticulture, Management, Monitoring, Policy and Law, pairwise dependence, Extreme weather, Econometrics, Global and Planetary Change, Food security, Ecology, lcsh:TP368-456, business.industry, Public sector, Regression analysis, food security, crop yield, lcsh:Food processing and manufacture, nonlinear regression, weather indices, Pairwise comparison, business, Agronomy and Crop Science, lcsh:Nutrition. Foods and food supply, Food Science
Abstract

Private businesses in sectors, such as food, energy, and retail, as well as public sector and federal agencies are interested in the predictive understanding of weather impacts on crop yield, which is an important aspect of food security. Scientific literature has mainly examined how crop yield is impacted by growing season-averaged weather indices. Although a few studies did consider weather extremes in their analysis, their scope was either restricted to measuring their conditional relationship with yield or the extreme event types considered were limited. Selection of regression models, whether the more commonly used linear approaches or nonlinear methods, have not been appropriately justified in this context. Here, we develop data-driven methods to examine two inter-related hypotheses for improved scientific understanding and enhanced predictive modeling. The first hypothesis, that extreme weather indices have a statistically significant information content in them is found to be valid based on linear and nonlinear methods for pairwise dependence. The second hypothesis, examines the value addition of nonlinear regression methods, and suggests that linear approaches may not alone be adequate. The results of this study can inform scientific understanding, generation and relevance of indices and end-to-end risk assessment systems in the context of climate impacts on crop yield. An immediate application may be in the context of NASA Earth Exchange (NEX) which facilitates the generation and dissemination of impacts relevant weather data and indices using a multitude of satellite-derived data sets and model outputs.

Published
2020

27. High-Dimensional Similarity Search with Quantum-Assisted Variational Autoencoder

Author

Walter Vinci, Thomas Vandal, Max Wilson, Nicholas Gao, Eleanor Rieffel, and Ramakrishna R. Nemani

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Quantum Physics, Quantum machine learning, Computer science, Nearest neighbor search, FOS: Physical sciences, Hamming distance, Machine Learning (stat.ML), 02 engineering and technology, Autoencoder, Machine Learning (cs.LG), Euclidean distance, Statistics - Machine Learning, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Quantum algorithm, Adiabatic process, Quantum Physics (quant-ph), Algorithm, Quantum, Linear search, Quantum computer
Abstract

Recent progress in quantum algorithms and hardware indicates the potential importance of quantum computing in the near future. However, finding suitable application areas remains an active area of research. Quantum machine learning is touted as a potential approach to demonstrate quantum advantage within both the gate-model and the adiabatic schemes. For instance, the Quantum-assisted Variational Autoencoder (QVAE) has been proposed as a quantum enhancement to the discrete VAE. We extend on previous work and study the real-world applicability of a QVAE by presenting a proof-of-concept for similarity search in large-scale high-dimensional datasets. While exact and fast similarity search algorithms are available for low dimensional datasets, scaling to high-dimensional data is non-trivial. We show how to construct a space-efficient search index based on the latent space representation of a QVAE. Our experiments show a correlation between the Hamming distance in the embedded space and the Euclidean distance in the original space on the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset.Further, we find real-world speedups compared to linear search and demonstrate memory-efficient scaling to half a billion data points.

Published
2020
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28. Intercomparison of machine learning methods for statistical downscaling: the case of daily and extreme precipitation

Author

Evan Kodra, Auroop R. Ganguly, and Thomas Vandal

Subjects
FOS: Computer and information sciences, Atmospheric Science, 010504 meteorology & atmospheric sciences, Computer science, 0207 environmental engineering, Machine Learning (stat.ML), Feature selection, 02 engineering and technology, Machine learning, computer.software_genre, 01 natural sciences, Statistics - Machine Learning, Predictability, 020701 environmental engineering, 0105 earth and related environmental sciences, Artificial neural network, business.industry, Statistical model, Autoencoder, Support vector machine, 13. Climate action, Ordinary least squares, Artificial intelligence, business, computer, Downscaling
Abstract

Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning have not been explored. In this paper, we compare four fundamental statistical methods, Bias Correction Spatial Disaggregation (BCSD), Ordinary Least Squares, Elastic-Net, and Support Vector Machine, with three more advanced machine learning methods, Multi-task Sparse Structure Learning (MSSL), BCSD coupled with MSSL, and Convolutional Neural Networks to downscale daily precipitation in the Northeast United States. Metrics to evaluate of each method's ability to capture daily anomalies, large scale climate shifts, and extremes are analyzed. We find that linear methods, led by BCSD, consistently outperform non-linear approaches. The direct application of state-of-the-art machine learning methods to statistical downscaling does not provide improvements over simpler, longstanding approaches., 20 pages, 6 figures, 3 tables

Published
2018

29. TOI–1278 B: SPIRou Unveils a Rare Brown Dwarf Companion in Close-in Orbit around an M Dwarf

Author

David Lafrenière, J. H. C. Martins, François Bouchy, Michael B. Lund, Thierry Forveille, Isabelle Boisse, Étienne Artigau, Pedro Figueira, Giuseppe Marino, Jean-François Donati, Neil Cook, Lison Malo, Julien Morin, Antonio Frasca, Xavier Bonfils, Claire Moutou, Thomas Vandal, George R. Ricker, Nuno C. Santos, Farbod Jahandar, Jason F. Rowe, Luc Arnold, P. Cortés-Zuleta, Stefan Pelletier, Eder Martioli, Guillermo Torres, Charles Cadieux, Guillaume Hébrard, Jonathan Gagné, Andres Carmona, Riccardo Papini, Pascal Fouqué, Eric B. Ting, Jon M. Jenkins, David W. Latham, Joshua N. Winn, Sara Seager, David R. Ciardi, René Doyon, Xavier Delfosse, Michael Fausnaugh, João Gomes da Silva, Institut de recherche en astrophysique et planétologie (IRAP), Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS), ANR-18-CE31-0019,SPlaSH,Recherche de planètes habitables avec SPIRou(2018), Institut national des sciences de l'Univers (INSU - CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Université de Montréal (UdeM), Institut d'Astrophysique de Paris (IAP), and Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Star (game theory), Brown dwarf, FOS: Physical sciences, Astrophysics, 01 natural sciences, law.invention, Telescope, law, 0103 physical sciences, Transit (astronomy), 010303 astronomy & astrophysics, Solar and Stellar Astrophysics (astro-ph.SR), ComputingMilieux_MISCELLANEOUS, Earth and Planetary Astrophysics (astro-ph.EP), Physics, [SDU.ASTR]Sciences of the Universe [physics]/Astrophysics [astro-ph], 05 social sciences, 050301 education, Astronomy and Astrophysics, Radial velocity, Photometry (astronomy), Orbit, Astrophysics - Solar and Stellar Astrophysics, [SDU]Sciences of the Universe [physics], Space and Planetary Science, Eccentricity (mathematics), 0503 education, Astrophysics - Earth and Planetary Astrophysics
Abstract

We present the discovery of an $18.5\pm0.5$M$_{\rm Jup}$ brown dwarf (BD) companion to the M0V star TOI-1278. The system was first identified through a percent-deep transit in TESS photometry; further analysis showed it to be a grazing transit of a Jupiter-sized object. Radial velocity (RV) follow-up with the SPIRou near-infrared high-resolution velocimeter and spectropolarimeter in the framework of the 300-night SPIRou Legacy Survey (SLS) carried out at the Canada-France-Hawaii Telescope (CFHT) led to the detection of a Keplerian RV signal with a semi-amplitude of $2306\pm10$ m/s in phase with the 14.5-day transit period, having a slight but non-zero eccentricity. The intermediate-mass ratio ($M_\star/M_{\rm{comp}} \sim31$) is unique for having such a short separation ($0.095\pm0.001$ AU) among known M-dwarf systems. Interestingly, M dwarf-brown dwarf systems with similar mass ratios exist with separations of tens to thousands of AUs., Comment: accepted for publication in The Astronomical Journal

Published
2021

30. First Provisional Land Surface Reflectance Product from Geostationary Satellite Himawari-8 AHI

Author

Weile Wang, Alexei Lyapustin, Yujie Wang, Ramakrishna R. Nemani, Kazuhito Ichii, Shuang Li, Thomas Vandal, Jing Yao, Jun Xiong, Lexiang Qian, and Hirofumi Hashimoto

Subjects
010504 meteorology & atmospheric sciences, Pixel, 0211 other engineering and technologies, Atmospheric correction, 02 engineering and technology, Land cover, 01 natural sciences, Reflectivity, FluxNet, Product (mathematics), Geostationary orbit, General Earth and Planetary Sciences, Environmental science, Bidirectional reflectance distribution function, 021101 geological & geomatics engineering, 0105 earth and related environmental sciences, Remote sensing
Abstract

A provisional surface reflectance (SR) product from the Advanced Himawari Imager (AHI) on-board the new generation geostationary satellite (Himawari-8) covering the period between July 2015 and December 2018 is made available to the scientific community. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm is used in conjunction with time series Himawari-8 AHI observations to generate 1-km gridded and tiled land SR every 10 minutes during day time. This Himawari-8 AHI SR product includes retrieved atmospheric properties (e.g., aerosol optical depth at 0.47µm and 0.51µm), spectral surface reflectance (AHI bands 1–6), parameters of the RTLS BRDF model, and quality assurance flags. Product evaluation shows that Himawari-8 AHI data on average yielded 35% more cloud-free, valid pixels in a single day when compared to available data from the low earth orbit (LEO) satellites Terra/Aqua with MODIS sensor. Comparisons of Himawari-8 AHI SR against corresponding MODIS SR products (MCD19A1) over a variety of land cover types with the similar viewing geometry show high consistency between them, with correlation coefficients (r) being 0.94 and 0.99 for red and NIR bands, respectively. The high-frequency geostationary data are expected to facilitate studies of ecosystems on daily to diurnal time scales, complementing observations from networks such as the FLUXNET.

Published
2019

31. Generating High Resolution Climate Change Projections through Single Image Super-Resolution: An Abridged Version

Author

Auroop R. Ganguly, Sanmay Ganguly, Ramakrishna R. Nemani, Andrew Michaelis, Evan Kodra, and Thomas Vandal

Subjects
010504 meteorology & atmospheric sciences, Computer science, 0208 environmental biotechnology, Climate change, High resolution, Image processing, 02 engineering and technology, 01 natural sciences, Superresolution, 020801 environmental engineering, Earth system science, Precipitation, Single image, 0105 earth and related environmental sciences, Remote sensing, Downscaling
Abstract

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework with multi-scale input channels for statistical downscaling of climate variables. A comparison of DeepSD to four state-of-the-art methods downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.

Published
2018

32. Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep Learning

Author

Evan Kodra, Ramakrishna R. Nemani, Sangram Ganguly, Jennifer G. Dy, Auroop R. Ganguly, and Thomas Vandal

Subjects
FOS: Computer and information sciences, 010504 meteorology & atmospheric sciences, Calibration (statistics), Computer science, Computer Science - Artificial Intelligence, Gaussian, 0208 environmental biotechnology, Bayesian probability, Machine Learning (stat.ML), 02 engineering and technology, 01 natural sciences, Machine Learning (cs.LG), symbols.namesake, Statistics - Machine Learning, Econometrics, Uncertainty quantification, Risk management, 0105 earth and related environmental sciences, business.industry, Deep learning, 020801 environmental engineering, Computer Science - Learning, Artificial Intelligence (cs.AI), 13. Climate action, Log-normal distribution, symbols, Artificial intelligence, business, Downscaling
Abstract

Deep Learning (DL) methods have been transforming computer vision with innovative adaptations to other domains including climate change. For DL to pervade Science and Engineering (S&E) applications where risk management is a core component, well-characterized uncertainty estimates must accompany predictions. However, S&E observations and model-simulations often follow heavily skewed distributions and are not well modeled with DL approaches, since they usually optimize a Gaussian, or Euclidean, likelihood loss. Recent developments in Bayesian Deep Learning (BDL), which attempts to capture uncertainties from noisy observations, aleatoric, and from unknown model parameters, epistemic, provide us a foundation. Here we present a discrete-continuous BDL model with Gaussian and lognormal likelihoods for uncertainty quantification (UQ). We demonstrate the approach by developing UQ estimates on `DeepSD', a super-resolution based DL model for Statistical Downscaling (SD) in climate applied to precipitation, which follows an extremely skewed distribution. We find that the discrete-continuous models outperform a basic Gaussian distribution in terms of predictive accuracy and uncertainty calibration. Furthermore, we find that the lognormal distribution, which can handle skewed distributions, produces quality uncertainty estimates at the extremes. Such results may be important across S&E, as well as other domains such as finance and economics, where extremes are often of significant interest. Furthermore, to our knowledge, this is the first UQ model in SD where both aleatoric and epistemic uncertainties are characterized., 10 Pages

Published
2018

33. DeepSD

Author

Thomas Vandal, Andrew Michaelis, Evan Kodra, Sanmay Ganguly, Auroop R. Ganguly, and Ramakrishna R. Nemani

Subjects
FOS: Computer and information sciences, 010504 meteorology & atmospheric sciences, Computer science, business.industry, Computer Vision and Pattern Recognition (cs.CV), Deep learning, Reliability (computer networking), 0208 environmental biotechnology, Climate system, Computer Science - Computer Vision and Pattern Recognition, Climate change, Statistical model, Image processing, 02 engineering and technology, 01 natural sciences, 020801 environmental engineering, Earth system science, Precipitation, Artificial intelligence, Predictability, business, 0105 earth and related environmental sciences, Remote sensing, Downscaling
Abstract

The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios., 9 pages, 5 Figures, 2 Tables

Published
2017

34. Statistical Downscaling in Climate with State-of-the-Art Scalable Machine Learning

Author

Auroop R. Ganguly, Thomas Vandal, and Udit Bhatia

Subjects
Computer science, business.industry, Deep learning, Dimensionality reduction, Multi-task learning, Climate change, Machine learning, computer.software_genre, Covariate, Scalability, Artificial intelligence, Adaptation (computer science), business, computer, Downscaling
Abstract

The adaptation of infrastructure to events including hydrological and weather extremes, which may be exacerbated by climate change, largely depends on high-resolution climate projections decades and centuries into the future. The downscaling of a wide range of general circulation models (GCMs) is imperative to understanding climate change at a local scale. In parallel of developing frameworks allowing for credible validation, the statistics and machine learning communities are working on specific methods for covariate selection and prediction on various climate applications. State-of-the-art machine learning methods that are pushing the boundaries in fields such as computer vision, natural language processing, and speech recognition should be carefully considered for incorporation into statistical downscaling (SD). More specifically, the deep learning models being developed, focusing on dimensionality reduction and spatiotemporal data, have potential to improve over the current generation of SD models. Utilizing multitask learning (MTL) to take advantage of spatially dependent observations may allow for more generalizable models.

Published
2017

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