arXiv subjects

David W. Hogg

Publications and source records attributed to David W. Hogg.

At least 127 records · Page 7Linked to original sources

arXiv2016

Data-driven, interpretable photometric redshifts trained on heterogeneous and unrepresentative data

We present a new method for inferring photometric redshifts in deep galaxy and quasar surveys, based on a data driven model of latent spectral energy distributions (SEDs) and a physical model of photometric fluxes as a function of redshift. This conceptually novel approach combines the advantages of both machine-learning and template-fitting methods by building template SEDs directly from the training data. This is made computationally tractable with Gaussian Processes operating in flux--redshift space, encoding the physics of redshift and the projection of galaxy SEDs onto photometric band passes. This method alleviates the need of acquiring representative training data or constructing detailed galaxy SED models; it requires only that the photometric band passes and calibrations be known or have parameterized unknowns. The training data can consist of a combination of spectroscopic and deep many-band photometric data, which do not need to entirely spatially overlap with the target survey of interest or even involve the same photometric bands. We showcase the method on the $i$-magnitude-selected, spectroscopically-confirmed galaxies in the COSMOS field. The model is trained on the deepest bands (from SUBARU and HST) and photometric redshifts are derived using the shallower SDSS optical bands only. We demonstrate that we obtain accurate redshift point estimates and probability distributions despite the training and target sets having very different redshift distributions, noise properties, and even photometric bands. Our model can also be used to predict missing photometric fluxes, or to simulate populations of galaxies with realistic fluxes and redshifts, for example. This method opens a new era in which photometric redshifts for large photometric surveys are derived using a flexible yet physical model of the data trained on all available surveys (spectroscopic and photometric).

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arXiv2016

A 14 $h^{-3}$ Gpc$^3$ study of cosmic homogeneity using BOSS DR12 quasar sample

The BOSS quasar sample is used to study cosmic homogeneity with a 3D survey in the redshift range $2.2<z<2.8$. We measure the count-in-sphere, $N(<\! r)$, i.e. the average number of objects around a given object, and its logarithmic derivative, the fractal correlation dimension, $D_2(r)$. For a homogeneous distribution $N(<\! r) \propto r^3$ and $D_2(r)=3$. Due to the uncertainty on tracer density evolution, 3D surveys can only probe homogeneity up to a redshift dependence, i.e. they probe so-called "spatial isotropy". Our data demonstrate spatial isotropy of the quasar distribution in the redshift range $2.2<z<2.8$ in a model-independent way, independent of any FLRW fiducial cosmology, resulting in $3-\langle D_2 \rangle < 1.7 \times 10^{-3}$ (2 $σ$) over the range $250<r<1200 \, h^{-1}$Mpc for the quasar distribution. If we assume that quasars do not have a bias much less than unity, this implies spatial isotropy of the matter distribution on large scales. Then, combining with the Copernican principle, we finally get homogeneity of the matter distribution on large scales. Alternatively, using a flat $Λ$CDM fiducial cosmology with CMB-derived parameters, and measuring the quasar bias relative to this $Λ$CDM model, our data provide a consistency check of the model, in terms of how homogeneous the Universe is on different scales. $D_2(r)$ is found to be compatible with our $Λ$CDM model on the whole $10<r<1200 \, h^{-1}$Mpc range. For the matter distribution we obtain $3-\langle D_2 \rangle < 5 \times 10^{-5}$ (2 $σ$) over the range $250<r<1200 \, h^{-1}$Mpc, consistent with homogeneity on large scales.

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arXiv2016

Hydrogen Emission from the Ionized Gaseous Halos of Low Redshift Galaxies

Using a sample of nearly half million galaxies, intersected by over 7 million lines of sight from the Sloan Digital Sky Survey Data Release 12, we trace H$α$ + [N{\small II}] emission from a galactocentric projected radius, $r_p$, of 5 kpc to more than 100 kpc. The emission flux surface brightness is $\propto r_p^{-1.9 \pm 0.4}$. We obtain consistent results using only the H$α$ or [N{\small II}] flux. We measure a stronger signal for the bluer half of the target sample than for the redder half on small scales, $r_p <$ 20 kpc. We obtain a $3σ$ detection of H$α$ + [N{\small II}] emission in the 50 to 100 kpc $r_p$ bin. The mean emission flux within this bin is $(1.10 \pm 0.35) \times 10^{-20}$ erg cm$^{-2}$ s$^{-1}$ Å$^{-1}$, which corresponds to $1.87 \times 10^{-20}$ erg cm$^{-2}$ s$^{-1}$ arcsec$^{-2}$ or 0.0033 Rayleigh. This detection is 34 times fainter than a previous strict limit obtained using deep narrow-band imaging. The faintness of the signal demonstrates why it has been so difficult to trace recombination radiation out to large radii around galaxies. This signal, combined with published estimates of n$_{\rm H}$, lead us to estimate the temperature of the gas to be 12,000 K, consistent with independent empirical estimates based on metal ion absorption lines and expectations from numerical simulations.

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arXiv2016

Do fast stellar centroiding methods saturate the Cramér-Rao lower bound?

One of the most demanding tasks in astronomical image processing---in terms of precision---is the centroiding of stars. Upcoming large surveys are going to take images of billions of point sources, including many faint stars, with short exposure times. Real-time estimation of the centroids of stars is crucial for real-time PSF estimation, and maximal precision is required for measurements of proper motion. The fundamental Cramér-Rao lower bound sets a limit on the root-mean-squared-error achievable by optimal estimators. In this work, we aim to compare the performance of various centroiding methods, in terms of saturating the bound, when they are applied to relatively low signal-to-noise ratio unsaturated stars assuming zero-mean constant Gaussian noise. In order to make this comparison, we present the ratio of the root-mean-squared-errors of these estimators to their corresponding Cramér-Rao bound as a function of the signal-to-noise ratio and the full-width at half-maximum of faint stars. We discuss two general circumstances in centroiding of faint stars: (i) when we have a good estimate of the PSF, (ii) when we do not know the PSF. In the case that we know the PSF, we show that a fast polynomial centroiding after smoothing the image by the PSF can be as efficient as the maximum-likelihood estimator at saturating the bound. In the case that we do not know the PSF, we demonstrate that although polynomial centroiding is not as optimal as PSF profile fitting, it comes very close to saturating the Cramér-Rao lower bound in a wide range of conditions. We also show that the moment-based method of center-of-light never comes close to saturating the bound, and thus it does not deliver reliable estimates of centroids.

astro-ph.IM↗

arXiv2016

The population of long-period transiting exoplanets

The Kepler Mission has discovered thousands of exoplanets and revolutionized our understanding of their population. This large, homogeneous catalog of discoveries has enabled rigorous studies of the occurrence rate of exoplanets and planetary systems as a function of their physical properties. However, transit surveys like Kepler are most sensitive to planets with orbital periods much shorter than the orbital periods of Jupiter and Saturn, the most massive planets in our Solar System. To address this deficiency, we perform a fully automated search for long-period exoplanets with only one or two transits in the archival Kepler light curves. When applied to the $\sim 40,000$ brightest Sun-like target stars, this search produces 16 long-period exoplanet candidates. Of these candidates, 6 are novel discoveries and 5 are in systems with inner short-period transiting planets. Since our method involves no human intervention, we empirically characterize the detection efficiency of our search. Based on these results, we measure the average occurrence rate of exoplanets smaller than Jupiter with orbital periods in the range 2-25 years to be $2.0\pm0.7$ planets per Sun-like star.

astro-ph.EP↗

arXiv2016

The RAVE-on catalog of stellar atmospheric parameters and chemical abundances for chemo-dynamic studies in the Gaia era

The orbits, atmospheric parameters, chemical abundances, and ages of individual stars in the Milky Way provide the most comprehensive illustration of galaxy formation available. The Tycho-Gaia Astrometric Solution (TGAS) will deliver astrometric parameters for the largest ever sample of Milky Way stars, though its full potential cannot be realized without the addition of complementary spectroscopy. Among existing spectroscopic surveys, the RAdial Velocity Experiment (RAVE) has the largest overlap with TGAS ($\gtrsim$200,000 stars). We present a data-driven re-analysis of 520,781 RAVE spectra using The Cannon. For red giants, we build our model using high-fidelity APOGEE stellar parameters and abundances for stars that overlap with RAVE. For main-sequence and sub-giant stars, our model uses stellar parameters from the K2/EPIC. We derive and validate effective temperature $T_{\rm eff}$, surface gravity $\log{g}$, and chemical abundances of up to seven elements (O, Mg, Al, Si, Ca, Fe, Ni). We report a total of 1,685,851 elemental abundances with a typical precision of 0.07 dex, a substantial improvement over previous RAVE data releases. The synthesis of RAVE-on and TGAS is the most powerful data set for chemo-dynamic analyses of the Milky Way ever produced.

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arXiv2016

Chemical tagging can work: Identification of stellar phase-space structures purely by chemical-abundance similarity

Chemical tagging promises to use detailed abundance measurements to identify spatially separated stars that were in fact born together (in the same molecular cloud), long ago. This idea has not yielded much practical success, presumably because of the noise and incompleteness in chemical-abundance measurements. We have succeeded in substantially improving spectroscopic measurements with The Cannon, which has now delivered 15 individual abundances for ~100,000 stars observed as part of the APOGEE spectroscopic survey, with precisions around 0.04 dex. We test the chemical-tagging hypothesis by looking at clusters in abundance space and confirming that they are clustered in phase space. We identify (by the k-means algorithm) overdensities of stars in the 15-dimensional chemical-abundance space delivered by The Cannon, and plot the associated stars in phase space. We use only abundance-space information (no positional information) to identify stellar groups. We find that clusters in abundance space are indeed clusters in phase space. We recover some known phase-space clusters and find other interesting structures. This is the first-ever project to identify phase-space structures at survey-scale by blind search purely in abundance space; it verifies the precision of the abundance measurements delivered by The Cannon; the prospects for future data sets appear very good.

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arXiv2016

The Panchromatic Hubble Andromeda Treasury XV. The BEAST: Bayesian Extinction and Stellar Tool

We present the Bayesian Extinction And Stellar Tool (BEAST), a probabilistic approach to modeling the dust extinguished photometric spectral energy distribution of an individual star while accounting for observational uncertainties common to large resolved star surveys. Given a set of photometric measurements and an observational uncertainty model, the BEAST infers the physical properties of the stellar source using stellar evolution and atmosphere models and constrains the line of sight extinction using a newly developed mixture model that encompasses the full range of dust extinction curves seen in the Local Group. The BEAST is specifically formulated for use with large multi-band surveys of resolved stellar populations. Our approach accounts for measurement uncertainties and any covariance between them due to stellar crowding (both systematic biases and uncertainties in the bias) and absolute flux calibration, thereby incorporating the full information content of the measurement. We illustrate the accuracy and precision possible with the BEAST using data from the Panchromatic Hubble Andromeda Treasury. While the BEAST has been developed for this survey, it can be easily applied to similar existing and planned resolved star surveys.

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arXiv2016

AGNfitter: A Bayesian MCMC approach to fitting spectral energy distributions of AGN

We present AGNfitter, a publicly available open-source algorithm implementing a fully Bayesian Markov Chain Monte Carlo method to fit the spectral energy distributions (SEDs) of active galactic nuclei (AGN) from the sub-mm to the UV, allowing one to robustly disentangle the physical processes responsible for their emission. AGNfitter makes use of a large library of theoretical, empirical, and semi-empirical models to characterize both the nuclear and host galaxy emission simultaneously. The model consists of four physical emission components: an accretion disk, a torus of AGN heated dust, stellar populations, and cold dust in star forming regions. AGNfitter determines the posterior distributions of numerous parameters that govern the physics of AGN with a fully Bayesian treatment of errors and parameter degeneracies, allowing one to infer integrated luminosities, dust attenuation parameters, stellar masses, and star formation rates. We tested AGNfitter's performace on real data by fitting the SEDs of a sample of 714 X-ray selected AGN from the XMM-COSMOS survey, spectroscopically classified as Type1 (unobscured) and Type2 (obscured) AGN by their optical-UV emission lines. We find that two independent model parameters, namely the reddening of the accretion disk and the column density of the dusty torus, are good proxies for AGN obscuration, allowing us to develop a strategy for classifying AGN as Type1 or Type2, based solely on an SED-fitting analysis. Our classification scheme is in excellent agreement with the spectroscopic classification, giving a completeness fraction of $\sim 86\%$ and $\sim 70\%$, and an efficiency of $\sim 80\%$ and $\sim 77\%$, for Type1 and Type2 AGNs, respectively.

astro-ph.GA↗

arXiv2016

Constructing Polynomial Spectral Models for Stars

Stellar spectra depend on the stellar parameters and on dozens of photospheric elemental abundances. Simultaneous fitting of these $\mathcal{N}\sim 10-40$ model labels to observed spectra has been deemed unfeasible, because the number of ab initio spectral model grid calculations scales exponentially with $\mathcal{N}$. We suggest instead the construction of a polynomial spectral model (PSM) of order $\mathcal{O}$ for the model flux at each wavelength. Building this approximation requires a minimum of only ${\mathcal{N}+\mathcal{O}\choose\mathcal{O}}$ calculations: e.g. a quadratic spectral model ($\mathcal{O}=2$) to fit $\mathcal{N}=20$ labels simultaneously, can be constructed from as few as $231$ ab initio spectral model calculations; in practice, a somewhat larger number ($\sim 300-1000$) of randomly chosen models lead to a better performing PSM. Such a PSM can be a good approximation only over a portion of label space, which will vary case by case. Yet, taking the APOGEE survey as an example, a single quadratic PSM provides a remarkably good approximation to the exact ab initio spectral models across much of this survey: for random labels within that survey the PSM approximates the flux to within $10^{-3}$, and recovers the abundances to within $\sim 0.02$ dex rms of the exact models. This enormous speed-up enables the simultaneous many-label fitting of spectra with computationally expensive ab initio models for stellar spectra, such as non-LTE models. A PSM also enables the simultaneous fitting of observational parameters, such as the spectrum's continuum or line-spread function.

astro-ph.SR↗

arXiv2016

A Causal, Data-Driven Approach to Modeling the Kepler Data

Astronomical observations are affected by several kinds of noise, each with its own causal source; there is photon noise, stochastic source variability, and residuals coming from imperfect calibration of the detector or telescope. The precision of NASA Kepler photometry for exoplanet science---the most precise photometric measurements of stars ever made---appears to be limited by unknown or untracked variations in spacecraft pointing and temperature, and unmodeled stellar variability. Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals. The CPM works at the pixel level so that it can capture very fine-grained information about the variation of the spacecraft. The CPM predicts each target pixel value from a large number of pixels of other stars sharing the instrument variabilities while not containing any information on possible transits in the target star. In addition, we use the target star's future and past (auto-regression). By appropriately separating, for each data point, the data into training and test sets, we ensure that information about any transit will be perfectly isolated from the model. The method has four hyper-parameters (the number of predictor stars, the auto-regressive window size, and two L2-regularization amplitudes for model components), which we set by cross-validation. We determine a generic set of hyper-parameters that works well for most of the stars and apply the method to a corresponding set of target stars. We find that we can consistently outperform (for the purposes of exoplanet detection) the Kepler Pre-search Data Conditioning (PDC) method for exoplanet discovery.

astro-ph.EP↗

arXiv2016

Chaotic Dispersal of Tidal Debris

Several long, dynamically cold stellar streams have been observed around the Milky Way Galaxy, presumably formed from the tidal disruption of globular clusters. In integrable potentials---where all orbits are regular---tidal debris phase-mixes close to the orbit of the progenitor system. However, the Milky Way's dark matter halo is expected not to be fully integrable; an appreciable fraction of orbits will be chaotic. This paper examines the influence of chaos on the phase-space morphology of cold tidal streams. Streams even in weakly chaotic regions look very different from those in regular regions. We find that streams can be sensitive to chaos on a much shorter time-scale than any standard prediction (from the Lyapunov or frequency-diffusion times). For example, on a weakly chaotic orbit with a chaotic timescale predicted to be $>$1000 orbital periods ($>$1000 Gyr), the resulting stellar stream is, after just a few 10's of orbits, substantially more diffuse than any formed on a nearby but regular orbit. We find that the enhanced diffusion of the stream stars can be understood by looking at the variance in orbital frequencies of orbit ensembles centered around the parent (progenitor) orbit. Our results suggest that long, cold streams around our Galaxy must exist only on regular (or very nearly regular) orbits; they potentially provide a map of the regular regions of the Milky Way potential. This suggests a promising new direction for the use of tidal streams to constrain the distribution of dark matter around our Galaxy.

astro-ph.GA↗

arXiv2016

The Cannon 2: A data-driven model of stellar spectra for detailed chemical abundance analyses

We have shown that data-driven models are effective for inferring physical attributes of stars (labels; Teff, logg, [M/H]) from spectra, even when the signal-to-noise ratio is low. Here we explore whether this is possible when the dimensionality of the label space is large (Teff, logg, and 15 abundances: C, N, O, Na, Mg, Al, Si, S, K, Ca, Ti, V, Mn, Fe, Ni) and the model is non-linear in its response to abundance and parameter changes. We adopt ideas from compressed sensing to limit overall model complexity while retaining model freedom. The model is trained with a set of 12,681 red-giant stars with high signal-to-noise spectroscopic observations and stellar parameters and abundances taken from the APOGEE Survey. We find that we can successfully train and use a model with 17 stellar labels. Validation shows that the model does a good job of inferring all 17 labels (typical abundance precision is 0.04 dex), even when we degrade the signal-to-noise by discarding ~50% of the observing time. The model dependencies make sense: the spectral derivatives with respect to abundances correlate with known atomic lines, and we identify elements belonging to atomic lines that were previously unknown. We recover (anti-)correlations in abundance labels for globular cluster stars, consistent with the literature. However we find the intrinsic spread in globular cluster abundances is 3--4 times smaller than previously reported. We deliver 17 labels with associated errors for 87,563 red giant stars, as well as open-source code to extend this work to other spectroscopic surveys.

astro-ph.SR↗

arXiv2016

Campaign 9 of the $K2$ Mission: Observational Parameters, Scientific Drivers, and Community Involvement for a Simultaneous Space- and Ground-based Microlensing Survey

$K2$'s Campaign 9 ($K2$C9) will conduct a $\sim$3.7 deg$^{2}$ survey toward the Galactic bulge from 7/April through 1/July of 2016 that will leverage the spatial separation between $K2$ and the Earth to facilitate measurement of the microlens parallax $π_{\rm E}$ for $\gtrsim$127 microlensing events. These will include several that are planetary in nature as well as many short-timescale microlensing events, which are potentially indicative of free-floating planets (FFPs). These satellite parallax measurements will in turn allow for the direct measurement of the masses of and distances to the lensing systems. In this white paper we provide an overview of the $K2$C9 space- and ground-based microlensing survey. Specifically, we detail the demographic questions that can be addressed by this program, including the frequency of FFPs and the Galactic distribution of exoplanets, the observational parameters of $K2$C9, and the array of resources dedicated to concurrent observations. Finally, we outline the avenues through which the larger community can become involved, and generally encourage participation in $K2$C9, which constitutes an important pathfinding mission and community exercise in anticipation of $WFIRST$.

astro-ph.EP↗

arXiv2016

State of the Field: Extreme Precision Radial Velocities

The Second Workshop on Extreme Precision Radial Velocities defined circa 2015 the state of the art Doppler precision and identified the critical path challenges for reaching 10 cm/s measurement precision. The presentations and discussion of key issues for instrumentation and data analysis and the workshop recommendations for achieving this precision are summarized here. Beginning with the HARPS spectrograph, technological advances for precision radial velocity measurements have focused on building extremely stable instruments. To reach still higher precision, future spectrometers will need to produce even higher fidelity spectra. This should be possible with improved environmental control, greater stability in the illumination of the spectrometer optics, better detectors, more precise wavelength calibration, and broader bandwidth spectra. Key data analysis challenges for the precision radial velocity community include distinguishing center of mass Keplerian motion from photospheric velocities, and the proper treatment of telluric contamination. Success here is coupled to the instrument design, but also requires the implementation of robust statistical and modeling techniques. Center of mass velocities produce Doppler shifts that affect every line identically, while photospheric velocities produce line profile asymmetries with wavelength and temporal dependencies that are different from Keplerian signals. Exoplanets are an important subfield of astronomy and there has been an impressive rate of discovery over the past two decades. Higher precision radial velocity measurements are required to serve as a discovery technique for potentially habitable worlds and to characterize detections from transit missions. The future of exoplanet science has very different trajectories depending on the precision that can ultimately be achieved with Doppler measurements.

astro-ph.IM↗

arXiv2015

Spectroscopic determination of masses (and implied ages) for red giants

The mass of a star is arguably its most fundamental parameter. For red giant stars, tracers luminous enough to be observed across the Galaxy, mass implies a stellar evolution age. It has proven to be extremely difficult to infer ages and masses directly from red giant spectra using existing methods. From the KEPLER and APOGEE surveys, samples of several thousand stars exist with high-quality spectra and asteroseismic masses. Here we show that from these data we can build a data-driven spectral model using The Cannon, which can determine stellar masses to $\sim$ 0.07 dex from APOGEE DR12 spectra of red giants; these imply age estimates accurate to $\sim$ 0.2 dex (40 percent). We show that The Cannon constrains these ages foremost from spectral regions with CN absorption lines, elements whose surface abundances reflect mass-dependent dredge-up. We deliver an unprecedented catalog of 80,000 giants (including 20,000 red-clump stars) with mass and age estimates, spanning the entire disk (from the Galactic center to R $\sim$ 20 kpc). We show that the age information in the spectra is not simply a corollary of the birth-material abundances [Fe/H] and [$α$/Fe], and that even within a mono-abundance population of stars, there are age variations that vary sensibly with Galactic position. Such stellar age constraints across the Milky Way open up new avenues in Galactic archeology.

astro-ph.SR↗

arXiv2015

Finding, characterizing and classifying variable sources in multi-epoch sky surveys: QSOs and RR Lyrae in PS1 3$π$ data

In area and depth, the Pan-STARRS1 (PS1) 3$π$ survey is unique among many-epoch, multi-band surveys and has enormous potential for all-sky identification of variable sources. PS1 has observed the sky typically seven times in each of its five bands ($grizy$) over 3.5 years, but unlike SDSS not simultaneously across the bands. Here we develop a new approach for quantifying statistical properties of non-simultaneous, sparse, multi-color lightcurves through light-curve structure functions, effectively turning PS1 into a $\sim 35$-epoch survey. We use this approach to estimate variability amplitudes and timescales $(ω_r, τ)$ for all point-sources brighter than $r_{\mathrm{P1}}=21.5$ mag in the survey. With PS1 data on SDSS Stripe 82 as ``ground truth", we use a Random Forest Classifier to identify QSOs and RR Lyrae based on their variability and their mean PS1 and WISE colors. We find that, aside from the Galactic plane, QSO and RR Lyrae samples of purity $\sim$75\% and completeness $\sim$92\% can be selected. On this basis we have identified a sample of $\sim 1,000,000$ QSO candidates, as well as an unprecedentedly large and deep sample of $\sim$150,000 RR Lyrae candidates with distances from $\sim$10 kpc to $\sim$120 kpc. Within the Draco dwarf spheroidal, we demonstrate a distance precision of 6\% for RR Lyrae candidates. We provide a catalog of all likely variable point sources and likely QSOs in PS1, a total of $25.8\times 10^6$ sources.

astro-ph.GA↗

arXiv2015

SDSS-IV/MaNGA: Spectrophotometric Calibration Technique

Mapping Nearby Galaxies at Apache Point Observatory (MaNGA), one of three core programs in the Sloan Digital Sky Survey-IV (SDSS-IV), is an integral-field spectroscopic (IFS) survey of roughly 10,000 nearby galaxies. It employs dithered observations using 17 hexagonal bundles of 2 arcsec fibers to obtain resolved spectroscopy over a wide wavelength range of 3,600-10,300A. To map the internal variations within each galaxy, we need to perform accurate {\it spectral surface photometry}, which is to calibrate the specific intensity at every spatial location sampled by each individual aperture element of the integral field unit. The calibration must correct only for the flux loss due to atmospheric throughput and the instrument response, but not for losses due to the finite geometry of the fiber aperture. This requires the use of standard star measurements to strictly separate these two flux loss factors (throughput versus geometry), a difficult challenge with standard single-fiber spectroscopy techniques due to various practical limitations. Therefore, we developed a technique for spectral surface photometry using multiple small fiber-bundles targeting standard stars simultaneously with galaxy observations. We discuss the principles of our approach and how they compare to previous efforts, and we demonstrate the precision and accuracy achieved. MaNGA's relative calibration between the wavelengths of H$α$ and H$β$ has a root-mean-square (RMS) of 1.7%, while that between [NII] $λ$6583A and [OII] $λ$3727A has an RMS of 4.7%. Using extinction-corrected star formation rates and gas-phase metallicities as an illustration, this level of precision guarantees that flux calibration errors will be sub-dominant when estimating these quantities. The absolute calibration is better than 5% for more than 89% of MaNGA's wavelength range.

astro-ph.IM↗