IA Scholar Query: Effectively Subsampled Quadratures for Least Squares Polynomial Approximations.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgWed, 28 Sep 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Flavour-universal search for heavy neutral leptons with a deep neural network-based displaced jet tagger with the CMS experiment
https://scholar.archive.org/work/gb63ulsuuzhixe7bhaqbgb7a5q
This thesis describes a search for long-lived heavy neutral leptons using a dataset of 137/fb collected during the 2016-2018 proton-proton runs with the CMS detector. The search uses a final state containing two leptons and at least one hadronic jet. This is the first analysis at the Large Hadron Collider which considers universal mixing between the Standard Model and heavy neutral lepton species. The search makes heavy use of a deep neural network-based displaced jet tagging algorithm, originally developed to target heavy long-lived gluino decays. The tagger was trained on both simulation and proton-proton collision data using the domain adaptation technique, which significantly improved the modelling of its output in simulation. The tagger has excellent performance for a range of long-lived particle lifetimes and generalises well to various flavours of displaced jets. In this analysis, the backgrounds are estimated in an entirely data-driven manner. No evidence for heavy neutral leptons is observed, and upper limits are set for a wide range of heavy neutral lepton mass, lifetime, and mixing scenarios. This is the most sensitive search for heavy neutral leptons in the 1–12 GeV mass range to date.Vilius Cepaitis, Alexander Tapper, Science And Technology Facilities Councilwork_gb63ulsuuzhixe7bhaqbgb7a5qWed, 28 Sep 2022 00:00:00 GMTEuclid preparation. XXV. The Euclid Morphology Challenge – Towards model-fitting photometry for billions of galaxies
https://scholar.archive.org/work/sr22ayy5wngrlbjvgln7trnn54
The ESA Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment of the Euclid Consortium; this pipeline will include a model-fitting algorithm, which will provide photometric and morphological estimates of paramount importance for the core science goals of the mission and for legacy science. The Euclid Morphology Challenge is a comparative investigation of the performance of five model-fitting software packages on simulated Euclid data, aimed at providing the baseline to identify the best suited algorithm to be implemented in the pipeline. In this paper we describe the simulated data set, and we discuss the photometry results. A companion paper (Euclid Collaboration: Bretonnière et al. 2022) is focused on the structural and morphological estimates. We created mock Euclid images simulating five fields of view of 0.48 deg2 each in the I_E band of the VIS instrument, each with three realisations of galaxy profiles (single and double Sérsic, and 'realistic' profiles obtained with a neural network); for one of the fields in the double Sérsic realisation, we also simulated images for the three near-infrared Y_E, J_E and H_E bands of the NISP-P instrument, and five Rubin/LSST optical complementary bands (u, g, r, i, and z). To analyse the results we created diagnostic plots and defined ad-hoc metrics. Five model-fitting software packages (DeepLeGATo, Galapagos-2, Morfometryka, ProFit, and SourceXtractor++) were compared, all typically providing good results. (cut)Euclid Collaboration: E. Merlin, M. Castellano, H. Bretonnière, M. Huertas-Company, U. Kuchner, D. Tuccillo, F. Buitrago, J. R. Peterson, C.J. Conselice, F. Caro, P. Dimauro, L. Nemani, A. Fontana, M. Kümmel, B. Häußler, W. G. Hartley, A. Alvarez Ayllon, E. Bertin, P. Dubath, F. Ferrari, L. Ferreira, R. Gavazzi, D. Hernández-Lang, G. Lucatelli, A. S. G. Robotham, M. Schefer, C. Tortora, N. Aghanim, A. Amara, L. Amendola, N. Auricchio, M. Baldi, R. Bender, C. Bodendorf, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, J. Carretero, F. J. Castander, S. Cavuoti, A. Cimatti, R. Cledassou, G. Congedo, L. Conversi, Y. Copin, L. Corcione, F. Courbin, M. Cropper, A. Da Silva, H. Degaudenzi, J. Dinis, M. Douspis, F. Dubath, C.A.J. Duncan, X. Dupac, S. Dusini, S. Farrens, S. Ferriol, M. Frailis, E. Franceschi, P. Franzetti, S. Galeotta, B. Garilli, B. Gillis, C. Giocoli, A. Grazian, F. Grupp, S.V.H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, P. Hudelot, K. Jahnke, S. Kermiche, A. Kiessling, T. Kitching, R. Kohley, M. Kunz, H. Kurki-Suonio, S. Ligori, P. B. Lilje, I. Lloro, O. Mansutti, O. Marggraf, K. Markovic, F. Marulli, R. Massey, H.J McCracken, E. Medinaceli, M. Melchior, M. Meneghetti, G. Meylan, M. Moresco, L. Moscardini, E. Munari, S.M. Niemi, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, W.J. Percival, G. Polenta, M. Poncet, L. Popa, L. Pozzetti, F. Raison, R. Rebolo, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, E. Rossetti, R. Saglia, D. Sapone, B. Sartoris, P. Schneider, A. Secroun, G. Seidel, C. Sirignano, G. Sirri, J. Skottfelt, J.-L. Starck, P. Tallada-Crespí, A.N. Taylor, I. Tereno, R. Toledo-Moreo, I. Tutusaus, L. Valenziano, T. Vassallo, Y. Wang, J. Weller, A. Zacchei, G. Zamorani, J. Zoubian, S. Andreon, S. Bardelli, A. Boucaud, C. Colodro-Conde, D. Di Ferdinando, J. Graciá-Carpio, V. Lindholm, N. Mauri, S. Mei, C. Neissner, V. Scottez, A. Tramacere, E. Zucca, C. Baccigalupi, A. Balaguera-Antolínez, M. Ballardini, F. Bernardeau, A. Biviano, S. Borgani, A.S. Borlaff, C. Burigana, R. Cabanac, A. Cappi, C.S. Carvalho, S. Casas, G. Castignani, A.R. Cooray, J. Coupon, H.M. Courtois, O. Cucciati, S. Davini, G. De Lucia, G. Desprez, J.A. Escartin, S. Escoffier, M. Farina, K. Ganga, J. Garcia-Bellido, K. George, G. Gozaliasl, H. Hildebrandt, I. Hook, O. Ilbert, S. Ilic, B. Joachimi, V. Kansal, E. Keihanen, C.C. Kirkpatrick, A. Loureiro, J. Macias-Perez, M. Magliocchetti, G. Mainetti, R. Maoli, S. Marcin, M. Martinelli, N. Martinet, S. Matthew, M. Maturi, R.B. Metcalf, P. Monaco, G. Morgante, S. Nadathur, A.A. Nucita, L. Patrizii, V. Popa, C. Porciani, D. Potter, A. Pourtsidou, M. Pöntinen, P. Reimberg, A.G. Sánchez, Z. Sakr, M. Schirmer, M. Sereno, J. Stadel, R. Teyssier, C. Valieri, J. Valiviita, S.E. van Mierlo, A. Veropalumbo, M. Viel, J. R. Weaver, D. Scottwork_sr22ayy5wngrlbjvgln7trnn54Mon, 26 Sep 2022 00:00:00 GMTSN 2021hpr and its two siblings in the Cepheid calibrator galaxy NGC 3147: A hierarchical BayeSN analysis of a Type Ia supernova trio, and a Hubble constant constraint
https://scholar.archive.org/work/jczqvadhnvgizbcbz2whdplmky
To improve Type Ia supernova (SN Ia) standardisability, the consistency of distance estimates to siblings – SNe in the same host galaxy – should be investigated. We present Young Supernova Experiment Pan-STARRS-1 grizy photometry of SN 2021hpr, the third spectroscopically confirmed SN Ia in the high-stellar-mass Cepheid-calibrator galaxy NGC 3147. We analyse NGC 3147's trio of SN Ia siblings: SNe 1997bq, 2008fv and 2021hpr, using a new version of the BayeSN model of SN Ia spectral-energy distributions, retrained simultaneously using optical-NIR BgVrizYJH (0.35–1.8 μm) data. The distance estimates to each sibling are consistent, with a sample standard deviation ≲0.01 mag, much smaller than the total intrinsic scatter in the training sample: σ_0≈0.09 mag. Fitting normal SN Ia siblings in three additional galaxies, we estimate a ≈90 siblings' intrinsic scatter is smaller than σ_0. We build a new hierarchical model that fits light curves of siblings in a single galaxy simultaneously; this yields more precise estimates of the common distance and the dust parameters. Fitting the trio for a common dust law shape yields R_V=2.69±0.52. Our work motivates future hierarchical modelling of more siblings, to tightly constrain their intrinsic scatter, and better understand SN-host correlations. Finally, we estimate the Hubble constant, using a Cepheid distance to NGC 3147, the siblings trio, and 109 Hubble flow (0.01 < z_CMB < 0.08) SNe Ia; marginalising over the siblings' and population's intrinsic scatters, and the peculiar velocity dispersion, yields H_0=77.9±6.5 km s^-1Mpc^-1.Sam M. Ward, Stephen Thorp, Kaisey S. Mandel, Suhail Dhawan, David O. Jones, Kirsty Taggart, Ryan J. Foley, Gautham Narayan, Kenneth C. Chambers, David A. Coulter, Kyle W. Davis, Thomas de Boer, Kaylee de Soto, Nicholas Earl, Alex Gagliano, Hua Gao, Jens Hjorth, Mark E. Huber, Luca Izzo, Danial Langeroodi, Eugene A. Magnier, Peter McGill, Armin Rest, César Rojas-Bravo, Radosław Wojtakwork_jczqvadhnvgizbcbz2whdplmkyWed, 21 Sep 2022 00:00:00 GMTDark Energy Survey Year 3 Results: Redshift Calibration of the MagLim Lens Sample from the combination of SOMPZ and clustering and its impact on Cosmology
https://scholar.archive.org/work/hmasezuxwrcvrptwjimfpwpd7e
We present an alternative calibration of the MagLim lens sample redshift distributions from the Dark Energy Survey (DES) first three years of data (Y3). The new calibration is based on a combination of a Self-Organising Maps based scheme and clustering redshifts to estimate redshift distributions and inherent uncertainties, which is expected to be more accurate than the original DES Y3 redshift calibration of the lens sample. We describe in detail the methodology, we validate it on simulations and discuss the main effects dominating our error budget. The new calibration is in fair agreement with the fiducial DES Y3 redshift distributions calibration, with only mild differences (<3σ) in the means and widths of the distributions. We study the impact of this new calibration on cosmological constraints, analysing DES Y3 galaxy clustering and galaxy-galaxy lensing measurements, assuming a ΛCDM cosmology. We obtain Ω_ m = 0.30± 0.04, σ_8 = 0.81± 0.07 and S_8 = 0.81± 0.04, which implies a ∼ 0.4σ shift in the Ω_-S_8 plane compared to the fiducial DES Y3 results, highlighting the importance of the redshift calibration of the lens sample in multi-probe cosmological analyses.G. Giannini, A. Alarcon, M. Gatti, A. Porredon, M. Crocce, G. M. Bernstein, R. Cawthon, C. Sánchez, C. Doux, J. Elvin-Poole, M. Raveri, J. Myles, A. Amon, S. Allam, O. Alves, F. Andrade-Oliveira, E. Baxter, K. Bechtol, M. R. Becker, J. Blazek, H. Camacho, A. Campos, A. Carnero Rosell, M. Carrasco Kind, A. Choi, J. Cordero, J. De Vicente, J. DeRose, H. T. Diehl, S. Dodelson, A. Drlica-Wagner, K. Eckert, S. Everett, X. Fang, A. Farahi, P. Fosalba, O. Friedrich, D. Gruen, R. A. Gruendl, J. Gschwend, I. Harrison, W. G. Hartley, E. M. Huff, M. Jarvis, E. Krause, N. Kuropatkin, P. Lemos, N. MacCrann, J. McCullough, J. Muir, S. Pandey, J. Prat, M. Rodriguez-Monroy, A. J. Ross, E. S. Rykoff, S. Samuroff, L. F. Secco, I. Sevilla-Noarbe, E. Sheldon, M. A. Troxel, D. L. Tucker, N. Weaverdyck, B. Yanny, B. Yin, Y. Zhang, T. M. C. Abbott, M. Aguena, D. Bacon, E. Bertin, S. Bocquet, D. Brooks, D. L. Burke, J. Carretero, F. J. Castander, M. Costanzi, L. N. da Costa, M. E. S. Pereira, S. Desai, P. Doel, I. Ferrero, B. Flaugher, D. Friedel, J. Frieman, J. García-Bellido, D. W. Gerdes, G. Gutierrez, S. R. Hinton, D. L. Hollowood, K. Honscheid, D. J. James, S. Kent, K. Kuehn, O. Lahav, C. Lidman, M. Lima, P. Melchior, J. Mena-Fernández, F. Menanteau, R. Miquel, R. L. C. Ogando, M. Paterno, F. Paz-Chinchón, A. Pieres, A. A. Plazas Malagón, A. Roodman, E. Sanchez, V. Scarpine, M. Smith, E. Suchyta, M. E. C. Swanson, G. Tarle, D. Thomas, C. To, M. Vincenziwork_hmasezuxwrcvrptwjimfpwpd7eTue, 13 Sep 2022 00:00:00 GMTFast Algorithms for Monotone Lower Subsets of Kronecker Least Squares Problems
https://scholar.archive.org/work/zq5atlvbhjcehgemgcrqhghnaa
Approximate solutions to large least squares problems can be computed efficiently using leverage score-based row-sketches, but directly computing the leverage scores, or sampling according to them with naive methods, still requires an expensive manipulation and processing of the design matrix. In this paper we develop efficient leverage score-based sampling methods for matrices with certain Kronecker product-type structure; in particular we consider matrices that are monotone lower column subsets of Kronecker product matrices. Our discussion is general, encompassing least squares problems on infinite domains, in which case matrices formally have infinitely many rows. We briefly survey leverage score-based sampling guarantees from the numerical linear algebra and approximation theory communities, and follow this with efficient algorithms for sampling when the design matrix has Kronecker-type structure. Our numerical examples confirm that sketches based on exact leverage score sampling for our class of structured matrices achieve superior residual compared to approximate leverage score sampling methods.Osman Asif Malik, Yiming Xu, Nuojin Cheng, Stephen Becker, Alireza Doostan, Akil Narayanwork_zq5atlvbhjcehgemgcrqhghnaaTue, 13 Sep 2022 00:00:00 GMTIs Monte Carlo a bad sampling strategy for learning smooth functions in high dimensions?
https://scholar.archive.org/work/dudcxpagujbahesuqxkzgivhiq
This paper concerns the approximation of smooth, high-dimensional functions from limited samples using polynomials. This task lies at the heart of many applications in computational science and engineering – notably, those arising from parametric modelling and uncertainty quantification. It is common to use Monte Carlo (MC) sampling in such applications, so as not to succumb to the curse of dimensionality. However, it is well known this strategy is theoretically suboptimal. There are many polynomial spaces of dimension n for which the sample complexity scales log-quadratically in n. This well-documented phenomenon has led to a concerted effort to design improved, in fact, near-optimal strategies, whose sample complexities scale log-linearly, or even linearly in n. Paradoxically, in this work we show that MC is actually a perfectly good strategy in high dimensions. We first document this phenomenon via several numerical examples. Next, we present a theoretical analysis that resolves this paradox for holomorphic functions of infinitely-many variables. We show that there is a least-squares scheme based on m MC samples whose error decays algebraically fast in m/log(m), with a rate that is the same as that of the best n-term polynomial approximation. This result is non-constructive, since it assumes knowledge of a suitable polynomial space in which to perform the approximation. We next present a compressed sensing-based scheme that achieves the same rate, except for a larger polylogarithmic factor. This scheme is practical, and numerically it performs as well as or better than well-known adaptive least-squares schemes. Overall, our findings demonstrate that MC sampling is eminently suitable for smooth function approximation when the dimension is sufficiently high. Hence the benefits of improved sampling strategies are generically limited to lower-dimensional settings.Ben Adcock, Simone Brugiapagliawork_dudcxpagujbahesuqxkzgivhiqThu, 08 Sep 2022 00:00:00 GMTGalaxy and Mass Assembly (GAMA): Probing galaxy-group correlations in redshift space with the halo streaming model
https://scholar.archive.org/work/r3koi2j7hnaf5pisrpc322kcvy
We have studied the galaxy-group cross-correlations in redshift space for the Galaxy And Mass Assembly (GAMA) Survey. We use a set of mock GAMA galaxy and group catalogues to develop and test a novel 'halo streaming' model for redshift-space distortions. This treats 2-halo correlations via the streaming model, plus an empirical 1-halo term derived from the mocks, allowing accurate modelling into the nonlinear regime. In order to probe the robustness of the growth rate inferred from redshift-space distortions, we divide galaxies by colour, and divide groups according to their total stellar mass, calibrated to total mass via gravitational lensing. We fit our model to correlation data, to obtain estimates of the perturbation growth rate, fσ_8, validating parameter errors via the dispersion between different mock realizations. In both mocks and real data, we demonstrate that the results are closely consistent between different subsets of the group and galaxy populations, considering the use of correlation data down to some minimum projected radius, r_ min. For the mock data, we can use the halo streaming model to below r_ min = 5h^-1 Mpc, finding that all subsets yield growth rates within about 3 data, the results are limited by cosmic variance: fσ_8=0.29± 0.10 at an effective redshift of 0.20; but there is every reason to expect that this method will yield precise constraints from larger datasets of the same type, such as the DESI bright galaxy survey.Qianjun Hang, John A. Peacock, Shadab Alam, Yan-Chuan Cai, Katarina Kraljic, Marcel van Daalen, M. Bilicki, B.W. Holwerda, J.Lovedaywork_r3koi2j7hnaf5pisrpc322kcvyWed, 07 Sep 2022 00:00:00 GMTEssays on Macroeconomic Policy and the Prices of Financial Assets
https://scholar.archive.org/work/axlzshgegzfgfoc7py6vfkgxve
My dissertation investigates what information embedded in financial prices reveals about questions relevant for macroeconomic policy. The first two chapters examine implicit government guarantees in the U.S. life insurance industry. My third chapter provides novel evidence on a phenomenon at the intersection of asset pricing and monetary policy. To what extent do investors view life insurers as "too big to fail?" Chapter 1 provides new evidence on this question using a natural experiment from the 2008-09 financial crisis. The analysis examines market reactions to a U.S. Treasury announcement that raised expectations about government backstops for the life insurance industry. I find that a subset of large life insurers benefited from significant protection against bankruptcy, with implied risk neutral probabilities of a government rescue ranging from 21% to 37% at the one-year horizon. Rescue probabilities exhibit a broadly downward sloping term structure, suggesting that investors expected the protection to subside with time. Cross-sectional differences are also informative about which insurers would later be designated systemically important by regulators. Chapter 2 builds on the findings presented in Chapter 1. In this chapter, I study the long-term dynamics of implicit government backstops for the U.S. life insurance industry and how this protection affects moral hazard. I structurally estimate a partial equilibrium model of life insurers protected in part by emergency bailouts. The estimates imply that for the 2001-20 period the investor expectation of the (physical) probability of a bailout is 9.1% for large insurers. Indicative of prominent time-variation, the standard deviation of these bailout probabilities is 10.3%. A counterfactual analysis reveals limited average levels of moral hazard in risk-taking practices. Structural estimates for small life insurers imply more modest support. Overall, the results are consistent with the presence of a significant and time-varying "too big to fail" subsidy for larg [...]James Caldera, University, Mywork_axlzshgegzfgfoc7py6vfkgxveTue, 06 Sep 2022 00:00:00 GMTModel-independent measurement of charm mixing parameters in B̅→ D^0 ( → K_S^0 π^+ π^-) μ^- ν̅_μ X decays
https://scholar.archive.org/work/gsficndgandibo5qpvaqvgc6j4
A measurement of charm mixing and CP-violating parameters is reported, using B̅→ D^0 ( → K_S^0 π^+ π^-) μ^- ν̅_μ X decays reconstructed in proton-proton collisions collected by the LHCb experiment during the years 2016 to 2018, corresponding to an integrated luminosity of 5.4 fb^-1. The measured mixing and CP-violating parameters are x_ CP = [ 4.29 ± 1.48 ± 0.26 ] × 10^-3 , y_ CP = [ 12.61 ± 3.12 ± 0.83 ] × 10^-3 , Δ x = [ -0.77 ± 0.93 ± 0.28 ] × 10^-3 , Δ y = [ 3.01 ± 1.92 ± 0.26 ] × 10^-3 . The results are complementary to and consistent with previous measurements. A combination with the recent LHCb analysis of D^*+→ D^0 ( → K_S^0 π^+ π^-) π^+ decays is reported.LHCb Collaborationwork_gsficndgandibo5qpvaqvgc6j4Tue, 30 Aug 2022 00:00:00 GMTOn the reconstruction of functions from values at subsampled quadrature points
https://scholar.archive.org/work/5bpr7qyyjnfxvpajwq3ktgx5n4
This paper is concerned with function reconstruction from samples. The sampling points used in several approaches are (1) structured points connected with fast algorithms or (2) unstructured points coming from, e.g., an initial random draw to achieve an improved information complexity. We connect both approaches and propose a subsampling of structured points in an offline step. In particular, we start with structured quadrature points (QMC), which provide stable L_2 reconstruction properties. The subsampling procedure consists of a computationally inexpensive random step followed by a deterministic procedure to further reduce the number of points while keeping its information. In these points functions (belonging to a RKHS of bounded functions) will be sampled and reconstructed from whilst achieving state of the art error decay. We apply our general findings on the d-dimensional torus to subsample rank-1 lattices, where it is known that full rank-1 lattices loose half the optimal order of convergence (expressed in terms of the size of the lattice). In contrast to that, our subsampled version regains the optimal rate since many of the lattice points are not needed. Moreover, we utilize fast and memory efficient Fourier algorithms in order to compute the approximation. Numerical experiments in higher dimensions support our findings.Felix Bartel and Lutz Kämmerer and Daniel Potts and Tino Ullrichwork_5bpr7qyyjnfxvpajwq3ktgx5n4Mon, 29 Aug 2022 00:00:00 GMTComparison of airborne measurements of NO, NO2, HONO, NOy, and CO during FIREX-AQ
https://scholar.archive.org/work/odbbuvvm6zcotjqbbke53ko47m
Abstract. We present a comparison of fast-response instruments installed onboard the NASA DC-8 aircraft that measured nitrogen oxides (NO and NO2), nitrous acid (HONO), total reactive odd nitrogen (measured both as the total (NOy) and from the sum of individually measured species (ΣNOy)), and carbon monoxide (CO) in the troposphere during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign. By targeting smoke from summertime wildfires, prescribed fires, and agricultural burns across the continental United States, FIREX-AQ provided a unique opportunity to investigate measurement accuracy in concentrated plumes where hundreds of species coexist. Here, we compare NO measurements by chemiluminescence (CL) and laser-induced fluorescence (LIF); NO2 measurements by CL, LIF, and cavity-enhanced spectroscopy (CES); HONO measurements by CES and iodide-adduct chemical ionization mass spectrometry (CIMS); and CO measurements by tunable diode laser absorption spectrometry (TDLAS) and integrated cavity output spectroscopy (ICOS). Additionally, total NOy measurements using the CL instrument were compared with ΣNOy (= NO + NO2 + HONO + nitric acid (HNO3) + acyl peroxy nitrates (APNs) + submicrometer particulate nitrate (pNO3)). Other NOy species were not included in ΣNOy as they either contributed minimally to it (e.g., C1–C5 alkyl nitrates, nitryl chloride (ClNO2), dinitrogen pentoxide (N2O5)) or were not measured during FIREX-AQ (e.g., higher oxidized alkyl nitrates, nitrate (NO3), non-acyl peroxynitrates, coarse-mode aerosol nitrate). The aircraft instrument intercomparisons demonstrate the following points: (1) NO measurements by CL and LIF agreed well within instrument uncertainties but with potentially reduced time response for the CL instrument; (2) NO2 measurements by LIF and CES agreed well within instrument uncertainties, but CL NO2 was on average 10 % higher; (3) CES and CIMS HONO measurements were highly correlated in each fire plume transect, but the correlation slope of CES vs. CIMS for all 1 Hz data during FIREX-AQ was 1.8, which we attribute to a reduction in the CIMS sensitivity to HONO in high-temperature environments; (4) NOy budget closure was demonstrated for all flights within the combined instrument uncertainties of 25 %. However, we used a fluid dynamic flow model to estimate that average pNO3 sampling fraction through the NOy inlet in smoke was variable from one flight to another and ranged between 0.36 and 0.99, meaning that approximately 0 %–24 % on average of the total measured NOy in smoke may have been unaccounted for and may be due to unmeasured species such as organic nitrates; (5) CO measurements by ICOS and TDLAS agreed well within combined instrument uncertainties, but with a systematic offset that averaged 2.87 ppbv; and (6) integrating smoke plumes followed by fitting the integrated values of each plume improved the correlation between independent measurements.Ilann Bourgeois, Jeff Peischl, J. Andrew Neuman, Steven S. Brown, Hannah M. Allen, Pedro Campuzano-Jost, Matthew M. Coggon, Joshua P. DiGangi, Glenn S. Diskin, Jessica B. Gilman, Georgios I. Gkatzelis, Hongyu Guo, Hannah A. Halliday, Thomas F. Hanisco, Christopher D. Holmes, L. Gregory Huey, Jose L. Jimenez, Aaron D. Lamplugh, Young Ro Lee, Jakob Lindaas, Richard H. Moore, Benjamin A. Nault, John B. Nowak, Demetrios Pagonis, Pamela S. Rickly, Michael A. Robinson, Andrew W. Rollins, Vanessa Selimovic, Jason M. St. Clair, David Tanner, Krystal T. Vasquez, Patrick R. Veres, Carsten Warneke, Paul O. Wennberg, Rebecca A. Washenfelder, Elizabeth B. Wiggins, Caroline C. Womack, Lu Xu, Kyle J. Zarzana, Thomas B. Ryersonwork_odbbuvvm6zcotjqbbke53ko47mMon, 29 Aug 2022 00:00:00 GMTSKYSURF: Constraints on Zodiacal Light and Extragalactic Background Light through Panchromatic HST All-Sky Surface-Brightness Measurements: I. Survey Overview and Methods
https://scholar.archive.org/work/vtnx2e5sifg5zeowikfwm5vza4
We give an overview and describe the rationale, methods, and testing of the Hubble Space Telescope (HST) Archival Legacy project "SKYSURF." SKYSURF uses HST's unique capability as an absolute photometer to measure the 0.2-1.7 μm sky surface brightness (SB) from 249,861 WFPC2, ACS, and WFC3 exposures in 1400 independent HST fields. SKYSURF's panchromatic dataset is designed to constrain the discrete and diffuse UV to near-IR sky components: Zodiacal Light (ZL; inner Solar System), Kuiper Belt Objects (KBOs; outer Solar System), Diffuse Galactic Light (DGL), and the discrete plus diffuse Extragalactic Background Light (EBL). We outline SKYSURF's methods to: (1) measure sky-SB levels between its detected objects; (2) measure the integrated discrete EBL, most of which comes from AB≃17-22 mag galaxies; and (3) estimate how much diffuse light may exist in addition to the extrapolated discrete galaxy counts. Simulations of HST WFC3/IR images with known sky-values and gradients, realistic cosmic ray (CR) distributions, and star plus galaxy counts were processed with nine different algorithms to measure the "Lowest Estimated Sky-SB" (LES) in each image between the discrete objects. The best algorithms recover the inserted LES values within 0.2 and within 0.2-0.4 non-standard re-processing of these HST images that includes restoring the lowest sky-level from each visit into each drizzled image. We provide a proof of concept of our methods from the WFC3/IR F125W images, where any residual diffuse light that HST sees in excess of the Kelsall et al. (1998) Zodiacal model prediction does not depend on the total object flux that each image contains. This enables us to present our first SKYSURF results on diffuse light in Carleton et al. (2022).Rogier A. Windhorstwork_vtnx2e5sifg5zeowikfwm5vza4Thu, 25 Aug 2022 00:00:00 GMTA Mass-Magnitude Relation for Low-mass Stars Based on Dynamical Measurements of Thousands of Binary Star Systems
https://scholar.archive.org/work/dbkmushiwzeztbcwnyvacyupc4
Stellar mass is a fundamental parameter that is key to our understanding of stellar formation and evolution, as well as the characterization of nearby exoplanet companions. Historically, stellar masses have been derived from long-term observations of visual or spectroscopic binary star systems. While advances in high-resolution imaging have enabled observations of systems with shorter orbital periods, stellar mass measurements remain challenging, and relatively few have been precisely measured. We present a new statistical approach to measuring masses for populations of stars. Using Gaia astrometry, we analyze the relative orbital motion of >3,800 wide binary systems comprising low-mass stars to establish a Mass-Magnitude relation in the Gaia G_RP band spanning the absolute magnitude range 14.5>M_G_RP>4.0, corresponding to a mass range of 0.08 M_⊙≲ M≲1.0 M_⊙. This relation is directly applicable to >30 million stars in the Gaia catalog. Based on comparison to existing Mass-Magnitude relations calibrated for 2MASS K_s magnitudes, we estimate that the internal precision of our mass estimates is ∼10%. We use this relation to estimate masses for a volume-limited sample of ∼18,200 stars within 50 pc of the Sun and the present-day field mass function for stars with M≲ 1.0 M_⊙, which we find peaks at 0.16 M_⊙. We investigate a volume-limited sample of wide binary systems with early K dwarf primaries, complete for binary mass ratios q>0.2, and measure the distribution of q at separations >100 au. We find that our distribution of q is not uniformly distributed, rather decreasing towards q=1.0.Mark R. Giovinazzi, Cullen H. Blakework_dbkmushiwzeztbcwnyvacyupc4Thu, 25 Aug 2022 00:00:00 GMTArbitrary-order asymptotic expansions of Gaussian quadrature rules with classical and generalised weight functions
https://scholar.archive.org/work/r27srz6fpjd6dg4c274rosynka
Gaussian quadrature rules are a classical tool for the numerical approximation of integrals with smooth integrands and positive weight functions. We derive and expicitly list asymptotic expressions for the points and weights of Gaussian quadrature rules for three general classes of positive weight functions: analytic functions on a bounded interval with algebraic singularities at the endpoints, analytic weight functions on the halfline with exponential decay at infinity and an algebraic singularity at the finite endpoint, and analytic functions on the real line with exponential decay in both directions at infinity. The results include the Gaussian rules of classical orthogonal polynomials (Legendre, Jacobi, Laguerre and Hermite) as special cases. We present experiments indicating the range of the number of points at which these expressions achieve high precision. We provide an algorithm that can compute arbitrarily many terms in these expansions for the classical cases, and many though not all terms for the generalized cases.Peter Opsomer, Daan Huybrechswork_r27srz6fpjd6dg4c274rosynkaWed, 24 Aug 2022 00:00:00 GMTThe Saddle-Point Accountant for Differential Privacy
https://scholar.archive.org/work/dpbanhpaknebhf3zw3wsd5eqa4
We introduce a new differential privacy (DP) accountant called the saddle-point accountant (SPA). SPA approximates privacy guarantees for the composition of DP mechanisms in an accurate and fast manner. Our approach is inspired by the saddle-point method – a ubiquitous numerical technique in statistics. We prove rigorous performance guarantees by deriving upper and lower bounds for the approximation error offered by SPA. The crux of SPA is a combination of large-deviation methods with central limit theorems, which we derive via exponentially tilting the privacy loss random variables corresponding to the DP mechanisms. One key advantage of SPA is that it runs in constant time for the n-fold composition of a privacy mechanism. Numerical experiments demonstrate that SPA achieves comparable accuracy to state-of-the-art accounting methods with a faster runtime.Wael Alghamdi, Shahab Asoodeh, Flavio P. Calmon, Juan Felipe Gomez, Oliver Kosut, Lalitha Sankar, Fei Weiwork_dpbanhpaknebhf3zw3wsd5eqa4Sat, 20 Aug 2022 00:00:00 GMTA Framework and Benchmark for Deep Batch Active Learning for Regression
https://scholar.archive.org/work/vxddcsrry5fxnfn3he4wv3mcg4
The acquisition of labels for supervised learning can be expensive. In order to improve the sample-efficiency of neural network regression, we study active learning methods that adaptively select batches of unlabeled data for labeling. We present a framework for constructing such methods out of (network-dependent) base kernels, kernel transformations and selection methods. Our framework encompasses many existing Bayesian methods based on Gaussian Process approximations of neural networks as well as non-Bayesian methods. Additionally, we propose to replace the commonly used last-layer features with sketched finite-width Neural Tangent Kernels, and to combine them with a novel clustering method. To evaluate different methods, we introduce an open-source benchmark consisting of 15 large tabular regression data sets. Our proposed method outperforms the state-of-the-art on our benchmark, scales to large data sets, and works out-of-the-box without adjusting the network architecture or training code. We provide open-source code that includes efficient implementations of all kernels, kernel transformations, and selection methods, and can be used for reproducing our results.David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwartwork_vxddcsrry5fxnfn3he4wv3mcg4Thu, 18 Aug 2022 00:00:00 GMTSeismic fragility analysis using stochastic polynomial chaos expansions
https://scholar.archive.org/work/3kotdqzgrbdw7dok3hz5qgkmsi
Within the performance-based earthquake engineering (PBEE) framework, the fragility model plays a pivotal role. Such a model represents the probability that the engineering demand parameter (EDP) exceeds a certain safety threshold given a set of selected intensity measures (IMs) that characterize the earthquake load. The-state-of-the art methods for fragility computation rely on full non-linear time-history analyses. Within this perimeter, there are two main approaches: the first relies on the selection and scaling of recorded ground motions; the second, based on random vibration theory, characterizes the seismic input with a parametric stochastic ground motion model (SGMM). The latter case has the great advantage that the problem of seismic risk analysis is framed as a forward uncertainty quantification problem. However, running classical full-scale Monte Carlo simulations is intractable because of the prohibitive computational cost of typical finite element models. Therefore, it is of great interest to define fragility models that link an EDP of interest with the SGMM parameters -- which are regarded as IMs in this context. The computation of such fragility models is a challenge on its own and, despite few recent studies, there is still an important research gap in this domain. This study tackles this computational challenge by using stochastic polynomial chaos expansions to represent the statistical dependence of EDP on IMs. More precisely, this surrogate model estimates the full conditional probability distribution of EDP conditioned on IMs. We compare the proposed approach with some state-of-the-art methods in two case studies. The numerical results show that the new method prevails its competitors in estimating both the conditional distribution and the fragility functions.X. Zhu, M. Broccardo, B. Sudretwork_3kotdqzgrbdw7dok3hz5qgkmsiTue, 16 Aug 2022 00:00:00 GMTCharacterization of JWST science performance from commissioning
https://scholar.archive.org/work/u3jys742frfjlden2dm7vctkwq
This document characterizes the actual science performance of the James Webb Space Telescope (JWST), as known on 12 July 2022. Following six months of commissioning to prepare JWST for science operations, the observatory is now fully capable of achieving the discoveries for which it was built. A key part of commissioning activities was characterizing the on-orbit performance of the observatory. Here we summarize how the performance of the spacecraft, telescope, science instruments, and ground system differ from pre-launch expectations. 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.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 Alvarez Marquez, 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 Boeker, 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 Garcia Marin, Jonathan 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 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 Ortega, Pierre-Olivier Lagage, Charles-Phillipe 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 Pennanen, 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, Gregory Sloan, Christine Slocum, Steven Slowinski, Erin Smith, Eric Smith, Denise Smith, Corbett Smith, Gregory Snyder, Warren Soh, 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 Zonakwork_u3jys742frfjlden2dm7vctkwqTue, 16 Aug 2022 00:00:00 GMTRaman Spectroscopy for Mars: An Interdisciplinary Approach
https://scholar.archive.org/work/4qdbhgxl3vbcdp6s6pesddx6wu
The Raman Laser Spectrometer (RLS) aboard the Rosalind Franklin ExoMars rover is among the first instances of Raman spectroscopy, a technique well-established in the study of mineralogy and molecular structures, being used in the search for life on Mars. This thesis is focused on increasing the scientific return of the ExoMars RLS in the particular context of (presumed) challenging mixed mineralogy samples of exobiological interest including lacustrine sediments and evaporite minerals; more specifically, clay (phyllosilicate) minerals and nitrate salts. To accomplish this goal the thesis provides an end-to-end assessment of the capabilities of the RLS, with insights into how interdisciplinary approaches can increase its scientific return. The interdisciplinary approach adopted sought to maintain continuity between field observation, sample processing and spectral analysis using terrestrial analogues and flight equivalent hardware. This approach also forms an accessible framework for replicating ExoMars RLS processes and includes an in-depth review of calibrating an RLS equivalent repositioning state. The stage can be positioned to within 20μm and return to a previous position within 5μm. Constructed and tested using off-the-shelf hardware, it is well suited to research groups seeking to add positioning capabilities to analogue ExoMars systems. The Tecopa Basin, a former terminal basin in the Mojave Desert, California, was selected as sufficiently analogous to the proposed Oxia Planum rover landing site. The location provided samples with smectite clays which are abundant at Oxia Planum and subsurface evaporates, notably biologically accessible nitrogen which is a prerequisite for known forms of life but yet to be directly observed on Mars. Fourteen well documented and contextualised samples were taken from the basin with efforts made to first understand their stratigraphic contexts and relations to other samples in the data set. Sample contents analysed with RLS flight equivalent hardware, and ExoMars sample hand [...]Oliver J. Hardywork_4qdbhgxl3vbcdp6s6pesddx6wuMon, 15 Aug 2022 00:00:00 GMTVoid BAO measurements on quasars from eBOSS
https://scholar.archive.org/work/envmnhwpzvgcdaz2xwedo55tay
We present the clustering of voids based on the quasar (QSO) sample of the extended Baryon Oscillation Spectroscopic Survey Data Release 16 in configuration space. We define voids as overlapping empty circumspheres computed by Delaunay tetrahedra spanned by quartets of quasars, allowing for an estimate of the depth of underdense regions. To maximise the BAO signal-to-noise ratio, we consider only voids with radii larger than 36h^-1Mpc. Our analysis shows a negative BAO peak in the cross-correlation of QSOs and voids. The joint BAO measurement of the QSO auto-correlation and the corresponding cross-correlation with voids shows an improvement in 70% of the QSO mocks with an average improvement of ∼5%. However, on the SDSS data, we find no improvement compatible with cosmic variance. For both mocks and data, adding voids does not introduce any bias. We find under the flat ΛCDM assumption, a distance joint measurement on data at the effective redshift z_ eff=1.48 of D_V(z_ eff)=26.297±0.547. A forecast of a DESI-like survey with 1000 boxes with a similar effective volume recovers the same results as for light-cone mocks with an average of 4.8% improvement in 68% of the boxes.A. Tamone, C. Zhao, D. Forero-Sánchez, A. Variu, C.-H. Chuang, F.-S. Kitaura, J.-P. Kneib, C. Taowork_envmnhwpzvgcdaz2xwedo55tayFri, 12 Aug 2022 00:00:00 GMT