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The radial velocity catalog from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) is unique in its simultaneously large volume and high precision as a result of its decade-long survey duration, multiplexing (600 fibers), and spectral resolution of $R \sim 22,500$. However, previous data reductions of APOGEE have not fully realized the potential radial velocity (RV) precision of the instrument. Here we present an RV catalog based on a new reduction of all 2.6 million visits of APOGEE DR17 and validate it against improved estimates for the theoretical RV performance. The core ideas of the new reduction are the simultaneous modeling of all components in the spectra, rather than a separate subtraction of point estimates for the sky, and a marginalization over stellar types, rather than a grid search for an optimum. We show that this catalog, when restricted to RVs measured with the same fiber, achieves noise-limited precision down to 30 m/s and delivers well-calibrated uncertainties. We also introduce a general method for calibrating fiber-to-fiber constant RV offsets and demonstrate its importance for high RV precision work in multi-fiber spectrographs. After calibration, we achieve 47 m/s RV precision on the combined catalog with RVs measured with different fibers. This degradation in precision relative to measurements with only a single fiber suggests that refining line spread function models should be a focus in SDSS-V to improve the fiber-unified RV catalog., Comment: 22 pages, 13 figures, SDSS-V Technical Paper, Submitted to AJ

We undertake a comprehensive investigation into the distribution of insitu stars within Milky Way-like galaxies, leveraging TNG50 simulations and comparing their predictions with data from the H3 survey. Our analysis reveals that 28% of galaxies demonstrate reasonable agreement with H3, while only 12% exhibit excellent alignment in their profiles, regardless of the specific spatial cut employed to define insitu stars. To uncover the underlying factors contributing to deviations between TNG50 and H3 distributions, we scrutinize correlation coefficients among internal drivers(e.g., virial radius, star formation rate [SFR]) and merger-related parameters (such as the effective mass-ratio, mean distance, average redshift, total number of mergers, average spin-ratio and maximum spin alignment between merging galaxies). Notably, we identify significant correlations between deviations from observational data and key parameters such as the median slope of virial radius, mean SFR values, and the rate of SFR change across different redshift scans. Furthermore, positive correlations emerge between deviations from observational data and parameters related to galaxy mergers. We validate these correlations using the Random Forest Regression method. Our findings underscore the invaluable insights provided by the H3 survey in unraveling the cosmic history of galaxies akin to the Milky Way, thereby advancing our understanding of galactic evolution and shedding light on the formation and evolution of Milky Way-like galaxies in cosmological simulations., Comment: 20 pages, 13 figures

Diffusion generative models have excelled at diverse image generation and reconstruction tasks across fields. A less explored avenue is their application to discriminative tasks involving regression or classification problems. The cornerstone of modern cosmology is the ability to generate predictions for observed astrophysical fields from theory and constrain physical models from observations using these predictions. This work uses a single diffusion generative model to address these interlinked objectives -- as a surrogate model or emulator for cold dark matter density fields conditional on input cosmological parameters, and as a parameter inference model that solves the inverse problem of constraining the cosmological parameters of an input field. The model is able to emulate fields with summary statistics consistent with those of the simulated target distribution. We then leverage the approximate likelihood of the diffusion generative model to derive tight constraints on cosmology by using the Hamiltonian Monte Carlo method to sample the posterior on cosmological parameters for a given test image. Finally, we demonstrate that this parameter inference approach is more robust to the addition of noise than baseline parameter inference networks., Comment: 14 pages, 10 figures

Shadow detection and removal is a challenging problem in the analysis of hyperspectral images. Yet, this step is crucial for analyzing data for remote sensing applications like methane detection. In this work, we develop a shadow detection and removal method only based on the spectrum of each pixel and the overall distribution of spectral values. We first introduce Iterative Logistic Regression (ILR) to learn a spectral basis in which shadows can be linearly classified. We then model the joint distribution of the mean radiance and the projection coefficients of the spectra onto the above basis as a parametric linear combination of Gaussians. We can then extract the maximum likelihood mixing parameter of the Gaussians to estimate the shadow coverage and to correct the shadowed spectra. Our correction scheme reduces correction artefacts at shadow borders. The shadow detection and removal method is applied to hyperspectral images from MethaneAIR, a precursor to the satellite MethaneSAT.

We make an in-depth analysis of different AGN jet models' signatures, inducing quiescence in galaxies with a halo mass of $10^{12} M_\odot$. Three jet models, including cosmic ray-dominant, hot thermal, and precessing kinetic jets, are studied at two energy flux levels each, compared to a jet-free, stellar feedback-only simulation. We examine the distribution of Mg II, O VI, and O VIII ions, alongside gas temperature and density profiles. Low-energy ions, like Mg II, concentrate in the ISM, while higher energy ions, e.g., O VIII, prevail at the AGN jet cocoon's edge. High-energy flux jets display an isotropic ion distribution with lower overall density. High-energy thermal or cosmic ray jets pressurize at smaller radii, significantly suppressing core density. The cosmic ray jet provides extra pressure support, extending cool and warm gas distribution. A break in the ion-to-mass ratio slope in O VI and O VIII is demonstrated in the ISM-to-CGM transition (between 10-30 kpc), growing smoothly towards the CGM at greater distances., Comment: 17 pages, 13 figures

Cosmological simulations play a crucial role in elucidating the effect of physical parameters on the statistics of fields and on constraining parameters given information on density fields. We leverage diffusion generative models to address two tasks of importance to cosmology -- as an emulator for cold dark matter density fields conditional on input cosmological parameters $\Omega_m$ and $\sigma_8$, and as a parameter inference model that can return constraints on the cosmological parameters of an input field. We show that the model is able to generate fields with power spectra that are consistent with those of the simulated target distribution, and capture the subtle effect of each parameter on modulations in the power spectrum. We additionally explore their utility as parameter inference models and find that we can obtain tight constraints on cosmological parameters., Comment: 7 pages, 5 figures, Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2023

Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due to the non-linear nature of the underlying physics. This paper presents novel machine learning models developed by the AstroAI team for the Ariel Data Challenge 2023, where one of the models secured the top position among 293 competitors. Leveraging Normalizing Flows, our models predict the posterior probability distribution of atmospheric parameters under different atmospheric assumptions. Moreover, we introduce an alternative model that exhibits higher performance potential than the winning model, despite scoring lower in the challenge. These findings highlight the need to reevaluate the evaluation metric and prompt further exploration of more efficient and accurate approaches for exoplanet atmosphere spectra analysis. Finally, we present recommendations to enhance the challenge and models, providing valuable insights for future applications on real observational data. These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds., Comment: Conference proceeding for the ECML PKDD 2023

High-resolution 3D maps of interstellar dust are critical for probing the underlying physics shaping the structure of the interstellar medium, and for foreground correction of astrophysical observations affected by dust. We aim to construct a new 3D map of the spatial distribution of interstellar dust extinction out to a distance of 1.25 kpc from the Sun. We leveraged distance and extinction estimates to 54 million nearby stars derived from the Gaia BP/RP spectra. Using the stellar distance and extinction information, we inferred the spatial distribution of dust extinction. We modeled the logarithmic dust extinction with a Gaussian process in a spherical coordinate system via iterative charted refinement and a correlation kernel inferred in previous work. In total, our posterior has over 661 million degrees of freedom. We probed the posterior distribution using the variational inference method MGVI. Our 3D dust map has an angular resolution of up to 14' (Nside = 256), and we achieve parsec-scale distance resolution, sampling the dust in 516 logarithmically spaced distance bins spanning 69 pc to 1250 pc. We generated 12 samples from the variational posterior of the 3D dust distribution and release the samples alongside the mean 3D dust map and its corresponding uncertainty. Our map resolves the internal structure of hundreds of molecular clouds in the solar neighborhood and will be broadly useful for studies of star formation, Galactic structure, and young stellar populations. It is available for download in a variety of coordinate systems online and can also be queried via the publicly available dustmaps Python package., Comment: 27 pages, 24 figures, 1 table, to be published in Astronomy & Astrophysics (A&A)

A wide-field near-infrared survey of the Galactic disk and bulge/bar(s) is supported by a large representation of the community of Galactic astronomers. The combination of sensitivity, angular resolution and large field of view make Roman uniquely able to study the crowded and highly extincted lines of sight in the Galactic plane. A ~1000 deg2 survey of the bulge and inner Galactic disk would yield an impressive dataset of ~120 billion sources and map the structure of our Galaxy. The effort would foster subsequent expansions in numerous dimensions (spatial, depth, wavelengths, epochs). Importantly, the survey would benefit from early defintion by the community, namely because the Galactic disk is a complex environment, and different science goals will require trade offs., Comment: Submitted to the Roman Project on October 22 2021 in response to a call for white papers on early-definition Astrophysics opportunity

The Nancy Grace Roman Space Telescope is capable of delivering an unprecedented all-sky, high-spatial resolution, multi-epoch infrared map to the astronomical community. This opportunity arises in the midst of numerous ground- and space-based surveys that will provide extensive spectroscopy and imaging together covering the entire sky (such as Rubin/LSST, Euclid, UNIONS, SPHEREx, DESI, SDSS-V, GALAH, 4MOST, WEAVE, MOONS, PFS, UVEX, NEO Surveyor, etc.). Roman can uniquely provide uniform high-spatial-resolution (~0.1 arcsec) imaging over the entire sky, vastly expanding the science reach and precision of all of these near-term and future surveys. This imaging will not only enhance other surveys, but also facilitate completely new science. By imaging the full sky over two epochs, Roman can measure the proper motions for stars across the entire Milky Way, probing 100 times fainter than Gaia out to the very edge of the Galaxy. Here, we propose NANCY: a completely public, all-sky survey that will create a high-value legacy dataset benefiting innumerable ongoing and forthcoming studies of the universe. NANCY is a pure expression of Roman's potential: it images the entire sky, at high spatial resolution, in a broad infrared bandpass that collects as many photons as possible. The majority of all ongoing astronomical surveys would benefit from incorporating observations of NANCY into their analyses, whether these surveys focus on nearby stars, the Milky Way, near-field cosmology, or the broader universe., Comment: Submitted to the call for white papers for the Roman Core Community Survey (June 16th, 2023), and to the Bulletin of the AAS

We present an in-depth analysis of a newly proposed correlation function in visibility space, between the E and B modes of the linear polarization, hereafter the EB-correlation, for a set of time-averaged GRMHD simulations compared with the phase map from different semi-analytic models as well as the Event Horizon Telescope (EHT) 2017 data for M87* source. We demonstrate that the phase map of the time-averaged EB-correlation contains novel information that might be linked to the BH spin, accretion state and the electron temperature. A detailed comparison with a semi-analytic approach with different azimuthal expansion modes shows that to recover the morphology of the real/imaginary part of the correlation function and its phase, we require higher orders of these azimuthal modes. To extract the phase features, we propose to use the Zernike polynomial reconstruction developing an empirical metric to break degeneracies between models with different BH spins that are qualitatively similar. We use a set of different geometrical ring models with various magnetic and velocity field morphologies and show that both the image space and visibility based EB-correlation morphologies in MAD simulations can be explained with simple fluid and magnetic field geometries as used in ring models. SANEs by contrast are harder to model, demonstrating that the simple fluid and magnetic field geometries of ring models are not sufficient to describe them owing to higher Faraday Rotation depths. A qualitative comparison with the EHT data demonstrates that some of the features in the phase of EB-correlation might be well explained by the current models for BH spins as well as electron temperatures, while others may require a larger theoretical surveys., Comment: 26 pages, 21 Figures

Cosmological surveys must correct their observations for the reddening of extragalactic objects by Galactic dust. Existing dust maps, however, have been found to have spatial correlations with the large-scale structure of the Universe. Errors in extinction maps can propagate systematic biases into samples of dereddened extragalactic objects and into cosmological measurements such as correlation functions between foreground lenses and background objects and the primordial non-gaussianity parameter $f_{NL}$. Emission-based maps are contaminated by the cosmic infrared background, while maps inferred from stellar-reddenings suffer from imperfect removal of quasars and galaxies from stellar catalogs. Thus, stellar-reddening based maps using catalogs without extragalactic objects offer a promising path to making dust maps with minimal correlations with large-scale structure. We present two high-latitude integrated extinction maps based on stellar reddenings, with a point spread function of full-width half-maximum 6.1' and 15'. We employ a strict selection of catalog objects to filter out galaxies and quasars and measure the spatial correlation of our extinction maps with extragalactic structure. Our galactic extinction maps have reduced spatial correlation with large scale structure relative to most existing stellar-reddening based and emission-based extinction maps., Comment: 21 pages, 10 figures

Diffuse interstellar bands (DIBs) are broad absorption features associated with interstellar dust and can serve as chemical and kinematic tracers. Conventional measurements of DIBs in stellar spectra are complicated by residuals between observations and best-fit stellar models. To overcome this, we simultaneously model the spectrum as a combination of stellar, dust, and residual components, with full posteriors on the joint distribution of the components. This decomposition is obtained by modeling each component as a draw from a high-dimensional Gaussian distribution in the data-space (the observed spectrum) -- a method we call "Marginalized Analytic Data-space Gaussian Inference for Component Separation" (MADGICS). We use a data-driven prior for the stellar component, which avoids missing stellar features not well-modeled by synthetic spectra. This technique provides statistically rigorous uncertainties and detection thresholds, which are required to work in the low signal-to-noise regime that is commonplace for dusty lines of sight. We reprocess all public Gaia DR3 RVS spectra and present an improved 8621 \r{A} DIB catalog, free of detectable stellar line contamination. We constrain the rest-frame wavelength to $8623.14 \pm 0.087$ \r{A} (vacuum), find no significant evidence for DIBs in the Local Bubble from the $1/6^{\rm{th}}$ of RVS spectra that are public, and show unprecedented correlation with kinematic substructure in Galactic CO maps. We validate the catalog, its reported uncertainties, and biases using synthetic injection tests. We believe MADGICS provides a viable path forward for large-scale spectral line measurements in the presence of complex spectral contamination., Comment: 28 pages, 27 figures, accepted at ApJ

Score-based generative models have emerged as alternatives to generative adversarial networks (GANs) and normalizing flows for tasks involving learning and sampling from complex image distributions. In this work we investigate the ability of these models to generate fields in two astrophysical contexts: dark matter mass density fields from cosmological simulations and images of interstellar dust. We examine the fidelity of the sampled cosmological fields relative to the true fields using three different metrics, and identify potential issues to address. We demonstrate a proof-of-concept application of the model trained on dust in denoising dust images. To our knowledge, this is the first application of this class of models to the interstellar medium., Comment: 8 pages, 3 figures, Accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022

We present the first large-scale 3D map of interstellar dust temperature. We build upon existing 3D reddening maps derived from starlight absorption (Bayestar19), covering 3/4 of the sky. Starting with the column density for each of 500 million 3D voxels, we propose a temperature and emissivity power-law slope ($\beta$) for each of them, and integrate along the line of sight to synthesize an emission map in five frequency bands observed by \emph{Planck} and \emph{IRAS}. The reconstructed emission map is constrained to match observations on a $10'$ scale, and does so with good fidelity. We produce 3D temperature maps at resolutions of $110', 55', $and $27'$. We assess performance on Cepheus, a dust cloud with two distinct components along the line of sight, and find distinct temperatures for the two components. We thus show that this methodology has enough precision to constrain clouds with different temperature along the line of sight up to $1-\sigma$ error. This would be an important result for dust frequency decorrelation foreground analysis for cosmic microwave background experiments, which would be impacted by a line-of-sight with varying temperature and magnetic field components. In addition to $T$ and $\beta$, we constrain the conversion factor between emission optical depth and reddening. This conversion factor is assumed to be constant in commonly used emission-based reddening maps. However, this work shows a factor of two variation that may prove significant for some applications., Comment: 11 Figures, 19 pages

We present a new $\texttt{python}$ package SARABANDE for measuring 3 & 4 Point Correlation Functions (3/4 PCFs) in $\mathcal{O}(N_{\rm g} \log N_{\rm g})$ time using Fast Fourier Transforms (FFTs), with $N_{\rm g}$ the number of grid points used for the FFT. SARABANDE can measure both projected and full 3 and 4 PCFs on gridded 2D and 3D datasets. The general technique is to generate suitable angular basis functions on an underlying grid, radially bin these to create kernels, and convolve these kernels with the original gridded data to obtain expansion coefficients about every point simultaneously. These coefficients are then combined to give us the 3/4 PCF as expanded in our basis. We apply SARABANDE to simulations of the Interstellar Medium (ISM) to show the results and scaling of calculating both the full and projected 3/4 PCFs., Comment: 16 Pages, 8 Figures, 8 Algorithms, 1 code package

In this white paper, we present the MegaMapper concept. The MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at $2

It is well known that the power spectrum is not able to fully characterize the statistical properties of non-Gaussian density fields. Recently, many different statistics have been proposed to extract information from non-Gaussian cosmological fields that perform better than the power spectrum. The Fisher matrix formalism is commonly used to quantify the accuracy with which a given statistic can constrain the value of the cosmological parameters. However, these calculations typically rely on the assumption that the likelihood of the considered statistic follows a multivariate Gaussian distribution. In this work we follow Sellentin & Heavens (2017) and use two different statistical tests to identify non-Gaussianities in different statistics such as the power spectrum, bispectrum, marked power spectrum, and wavelet scatering transform (WST). We remove the non-Gaussian components of the different statistics and perform Fisher matrix calculations with the \textit{Gaussianized} statistics using Quijote simulations. We show that constraints on the parameters can change by a factor of $\sim 2$ in some cases. We show with simple examples how statistics that do not follow a multivariate Gaussian distribution can achieve artificially tight bounds on the cosmological parameters when using the Fisher matrix formalism. We think that the non-Gaussian tests used in this work represent a powerful tool to quantify the robustness of Fisher matrix calculations and their underlying assumptions. We release the code used to compute the power spectra, bispectra, and WST that can be run on both CPUs and GPUs., Comment: 24 pages, 6 figures

Photometric pipelines struggle to estimate both the flux and flux uncertainty for stars in the presence of structured backgrounds such as filaments or clouds. However, it is exactly stars in these complex regions that are critical to understanding star formation and the structure of the interstellar medium. We develop a method, similar to Gaussian process regression, which we term local pixelwise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty on that prediction in order to improve estimates of flux and flux uncertainty. We show the validity of our model on synthetic data and real dust fields. We further demonstrate that the method is stable even in the crowded field limit. While we focus on optical-IR photometry, this method is not restricted to those wavelengths. We apply this technique to the 34 billion detections in the second data release of the Dark Energy Camera Plane Survey (DECaPS2). In addition to removing many $>3\sigma$ outliers and improving uncertainty estimates by a factor of $\sim 2-3$ on nebulous fields, we also show that our method is well-behaved on uncrowded fields. The entirely post-processing nature of our implementation of LPI photometry allows it to easily improve the flux and flux uncertainty estimates of past as well as future surveys., Comment: 22 pages, 15 pages, submitted to ApJ

For decades we have known that the Sun lies within the Local Bubble, a cavity of low-density, high-temperature plasma surrounded by a shell of cold, neutral gas and dust. However, the precise shape and extent of this shell, the impetus and timescale for its formation, and its relationship to nearby star formation have remained uncertain, largely due to low-resolution models of the local interstellar medium. Leveraging new spatial and dynamical constraints from the Gaia space mission, here we report an analysis of the 3D positions, shapes, and motions of dense gas and young stars within 200 pc of the Sun. We find that nearly all the star-forming complexes in the solar vicinity lie on the surface of the Local Bubble and that their young stars show outward expansion mainly perpendicular to the bubble's surface. Tracebacks of these young stars' motions support a scenario where the origin of the Local Bubble was a burst of stellar birth and then death (supernovae) taking place near the bubble's center beginning 14 Myr ago. The expansion of the Local Bubble created by the supernovae swept up the ambient interstellar medium into an extended shell that has now fragmented and collapsed into the most prominent nearby molecular clouds, in turn providing robust observational support for the theory of supernova-driven star formation., Comment: Published in Nature on 12 January 2022. For data, interactive visualizations, and more information see https://sites.google.com/cfa.harvard.edu/local-bubble-star-formation

We leverage the 1 pc spatial resolution of the Leike et al. 2020 3D dust map to characterize the three-dimensional structure of nearby molecular clouds ($d \lesssim 400$ pc). We start by "skeletonizing" the clouds in 3D volume density space to determine their "spines," which we project on the sky to constrain cloud distances with $\approx 1\%$ uncertainty. For each cloud, we determine an average radial volume density profile around its 3D spine and fit the profiles using Gaussian and Plummer functions. The radial volume density profiles are well-described by a two-component Gaussian function, consistent with clouds having broad, lower-density outer envelopes and narrow, higher-density inner layers. The ratio of the outer to inner envelope widths is $\approx 3:1$. We hypothesize that these two components may be tracing a transition between atomic and diffuse molecular gas or between the unstable and cold neutral medium. Plummer-like models can also provide a good fit, with molecular clouds exhibiting shallow power-law wings with density, $n$, falling off like $n^{-2}$ at large radii. Using Bayesian model selection, we find that parameterizing the clouds' profiles using a single Gaussian is disfavored. We compare our results with 2D dust extinction maps, finding that the 3D dust recovers the total cloud mass from integrated approaches with fidelity, deviating only at higher levels of extinction ($A_V \gtrsim 2 - 3$ mag). The 3D cloud structure described here will enable comparisons with synthetic clouds generated in simulations, offering unprecedented insight into the origins and fates of molecular clouds in the interstellar medium., Comment: Published in ApJ. For data behind the figures see https://dataverse.harvard.edu/dataverse/3D_cloud_structure . For interactive gallery of local clouds see https://faun.rc.fas.harvard.edu/czucker/Paper_Figures/3D_Cloud_Topologies/gallery.html . Companion paper to Bialy et al. 2021 (arXiv:2109.09763). For press see https://www.cfa.harvard.edu/news/gigantic-cavity-space-sheds-new-light-how-stars-form

Wavelet scattering networks, which are convolutional neural networks (CNNs) with fixed filters and weights, are promising tools for image analysis. Imposing symmetry on image statistics can improve human interpretability, aid in generalization, and provide dimension reduction. In this work, we introduce a fast-to-compute, translationally invariant and rotationally equivariant wavelet scattering network (EqWS) and filter bank of wavelets (triglets). We demonstrate the interpretability and quantify the invariance/equivariance of the coefficients, briefly commenting on difficulties with implementing scale equivariance. On MNIST, we show that training on a rotationally invariant reduction of the coefficients maintains rotational invariance when generalized to test data and visualize residual symmetry breaking terms. Rotation equivariance is leveraged to estimate the rotation angle of digits and reconstruct the full rotation dependence of each coefficient from a single angle. We benchmark EqWS with linear classifiers on EMNIST and CIFAR-10/100, introducing a new second-order, cross-color channel coupling for the color images. We conclude by comparing the performance of an isotropic reduction of the scattering coefficients and RWST, a previous coefficient reduction, on an isotropic classification of magnetohydrodynamic simulations with astrophysical relevance., Comment: 20 pages, 14 figures, submitted to IEEE Signal Processing

The complex interplay of magnetohydrodynamics, gravity, and supersonic turbulence in the interstellar medium (ISM) introduces non-Gaussian structure that can complicate comparison between theory and observation. We show that the Wavelet Scattering Transform (WST), in combination with linear discriminant analysis (LDA), is sensitive to non-Gaussian structure in 2D ISM dust maps. WST-LDA classifies magnetohydrodynamic (MHD) turbulence simulations with up to a 97\% true positive rate in our testbed of 8 simulations with varying sonic and Alfv\'{e}nic Mach numbers. We present a side-by-side comparison with two other methods for non-Gaussian characterization, the Reduced Wavelet Scattering Transform (RWST) and the 3-Point Correlation Function (3PCF). We also demonstrate the 3D-WST-LDA and apply it to classification of density fields in position-position-velocity (PPV) space, where density correlations can be studied using velocity coherence as a proxy. WST-LDA is robust to common observational artifacts, such as striping and missing data, while also sensitive enough to extract the net magnetic field direction for sub-Alfv\'{e}nic turbulent density fields. We include a brief analysis of the effect of point spread functions and image pixelization on 2D-WST-LDA applied to density fields, which informs the future goal of applying WST-LDA to 2D or 3D all-sky dust maps to extract hydrodynamic parameters of interest., Comment: 21 pages, 13 figures, submitted to ApJ

Spectral distortions of the cosmic microwave background (CMB) are sensitive to energy injection by exotic physics in the early universe. The proposed Primordial Inflation Explorer (PIXIE) mission has the raw sensitivity to provide meaningful limits on new physics, but only if foreground emission can be adequately modeled. We quantify the impact of interstellar dust on Compton $y$ and $\mu$ measurements by considering a range of grain size distributions and compositions constrained by theoretical and observational priors (Zelko & Finkbeiner 2020). We find that PIXIE can marginalize over a modest number of dust parameters and still recover $y$ and $\mu$ estimates, though with increased uncertainty. As more foreground components are included (synchrotron, free-free), the estimates of $y$ degrade, and measurement of $\mu$ in the range sometimes considered for the standard $\Lambda$CDM of $2\times10^{-8}$ becomes infeasible without ancillary low-frequency foreground information. An additional concern is dust absorption of the CMB monopole, a subtle effect that must be included. We quantify one form of model discrepancy error, finding that the error introduced by fitting our interstellar medium dust model with a modified blackbody is too large for CMB spectral distortions to be detectable. The greatest challenge may be the cosmic infrared background (CIB). We find that $\mu$ and $y$ are extremely sensitive to modeling choices for the CIB, and quantify biases expected for a range of assumptions., Comment: Published in the Astrophysical Journal. 25 pages, 12 figures, 10 tables. Updated to reflect the journal version. Corrected a typo in the Appendix Eq A6 and A9; updated the tables 5,6,7 (first column). There were no changes to the qualitative nature of the results

We study the effect of variations in dust size distribution and composition on the correlation between the spectral shape of extinction (parameterized by $R_{\textrm{V}}$) and far-infrared dust emissivity (parameterized by the power-law index $\beta$). Starting from the size distribution models proposed by Weingartner & Draine (2001), using the dust absorption and emission properties derived by Laor & Draine (1993) for carbonaceous and silicate grains, and by Li & Draine (2001) for PAH grains, we calculate the extinction and compare it with the reddening vector derived by Schlafly et al. (2016). An optimizer and an MCMC are used to explore the space of available parameters for the size distributions. We find that larger grains are correlated with high $R_{\textrm{V}}$. However, this trend is not enough to explain the emission-extinction correlation observed by Schlafly et al. (2016). For the $R_{\textrm{V}}-\beta$ correlation to arise, we need to impose explicit priors for the carbonaceous and silicate volume priors as functions of $R_{\textrm{V}}$. The results show that a composition with higher ratio of carbonaceous to silicate grains leads to higher $R_{\textrm{V}}$ and lower $\beta$. A relation between $E(\textrm{B}-\textrm{V})/\tau_{353}$ and $R_{\textrm{V}}$ is apparent, with possible consequences for the recalibration of emission-based dust maps as a function of $R_{\textrm{V}}$., Comment: Published in The Astrophysical Journal. 28 pages, 27 figures, 3 Tables; updated to correct some typos identified in the proof reading stage of the journal

The Smithsonian Astrophysical Observatory (SAO), a member of the Center for Astrophysics | Harvard and Smithsonian, is in discussions with the Space Applications Centre (SAC) of the Indian Space Research Organization (ISRO) and its partners in the newly formed Indian Sub-millimetre-wave Astronomy Alliance (ISAA), to collaborate in the construction of a sub-millimeter-wave astronomy observatory in the high altitude deserts of the Himalayas, initially at the 4500 m Indian Astronomical Observatory, Hanle. Two primary science goals are targeted. One is the mapping of the distribution of neutral atomic carbon, and the carbon monoxide (CO) molecule in higher energy states, in large parts of the Milky Way, and in selected external galaxies. Such studies would advance our understanding of molecular hydrogen present in the interstellar medium, but partly missed by existing observations; and characterize Galaxy-wide molecular cloud excitation conditions, through multi-level CO observations. Stars form in interstellar clouds of molecular gas and dust, and these observations would allow research into the formation and destruction processes of such molecular clouds and the life cycle of galaxies. As the second goal, the observatory would add a new location to the global Event Horizon Telescope (EHT) network, which lacks a station in the Himalayan longitudes. This addition would enhance the quality of the images synthesized by the EHT, support observations in higher sub-millimeter wave bands, sharpening its resolving ability, improve its dynamic imaging capability and add weather resilience to observing campaigns. In the broader context, this collaboration can be a starting point for a wider, mutually beneficial scientific exchange between the Indian and US astronomy communities, including a potential future EHT space component., Comment: Internal white paper prepared in support of the initiative, 32 pages, 7 figures

We develop a data-driven model to map stellar parameters (effective temperature, surface gravity and metallicity) accurately and precisely to broad-band stellar photometry. This model must, and does, simultaneously constrain the passband-specific dust reddening vector in the Milky Way. The model uses a neural network to learn the (de-reddened) absolute magnitude in one band and colors across many bands, given stellar parameters from spectroscopic surveys and parallax constraints from Gaia. To demonstrate the effectiveness of this approach, we train our model on a dataset with spectroscopic parameters from LAMOST, APOGEE and GALAH, Gaia parallaxes, and optical and near-infrared photometry from Gaia, Pan-STARRS~1, 2MASS and WISE. Testing the model on these datasets leads to an excellent fit and a precise - and by construction accurate - prediction of the color-magnitude diagrams in many bands. This flexible approach rigorously links spectroscopic and photometric surveys, and also results in an improved, stellar-temperature-dependent reddening vector. As such, it provides a simple and accurate method for predicting photometry in stellar evolutionary models. Our model will form a basis to infer stellar properties, distances and dust extinction from photometric data, which should be of great use in 3D mapping of the Milky Way. Our trained model may be obtained at https://doi.org/10.5281/zenodo.3902382., Comment: 30 pages, 13 figures. Revised version published in ApJ, including new Fig. 13

The Legacy Surveys, a combination of three ground-based imaging surveys, have mapped 16,000 deg$^2$ in three optical bands ($g$, $r$, and $z$) to a depth 1--$2$~mag deeper than the Sloan Digital Sky Survey (SDSS). Our work addresses one of the major challenges of wide-field imaging surveys conducted at ground-based observatories: the varying depth that results from varying observing conditions at Earth-bound sites. To mitigate these effects, two of the Legacy Surveys (the Dark Energy Camera Legacy Survey, or DECaLS; and the Mayall $z$-band Legacy Survey, or MzLS) employed a unique strategy to dynamically adjust the exposure times as rapidly as possible in response to the changing observing conditions. We present the tiling and observing strategies used by these surveys. We demonstrate that the tiling and dynamic observing strategies jointly result in a more uniform-depth survey that has higher efficiency for a given total observing time compared with the traditional approach of using fixed exposure times., Comment: v1: 14 pages, 3 tables and 5 figures; v2: 15 pages, 3 tables and 6 figures. Changes in response to referee comments; matches published version

For the past 150 years, the prevailing view of the local Interstellar Medium (ISM) was based on a peculiarity known as the Gould's Belt, an expanding ring of young stars, gas, and dust, tilted about 20$^\circ$ to the Galactic plane. Still, the physical relation between local gas clouds has remained practically unknown because the distance accuracy to clouds is of the same order or larger than their sizes. With the advent of large photometric surveys and the Gaia satellite astrometric survey this situation has changed. Here we report the 3-D structure of all local cloud complexes. We find a narrow and coherent 2.7 kpc arrangement of dense gas in the Solar neighborhood that contains many of the clouds thought to be associated with the Gould Belt. This finding is inconsistent with the notion that these clouds are part of a ring, disputing the Gould Belt model. The new structure comprises the majority of nearby star-forming regions, has an aspect ratio of about 1:20, and contains about 3 million solar masses of gas. Remarkably, the new structure appears to be undulating and its 3-D distribution is well described by a damped sinusoidal wave on the plane of the Milky Way, with an average period of about 2 kpc and a maximum amplitude of about 160 pc. Our results represent a first step in the revision of the local gas distribution and Galactic structure and offer a new, broader context to studies on the transformation of molecular gas into stars., Comment: Published in Nature on 7 January 2020. For data, interactive visualizations, and more information see: http://tinyurl.com/radwave (https://sites.google.com/cfa.harvard.edu/radcliffewave/)

Accurate distances to local molecular clouds are critical for understanding the star and planet formation process, yet distance measurements are often obtained inhomogeneously on a cloud-by-cloud basis. We have recently developed a method which combines stellar photometric data with Gaia DR2 parallax measurements in a Bayesian framework to infer the distances of nearby dust clouds to a typical accuracy of $\sim5\%$. After refining the technique to target lower latitudes and incorporating deep optical data from DECam in the southern Galactic plane, we have derived a catalog of distances to molecular clouds in Reipurth (2008, Star Formation Handbook, vols I and II) which contains a large fraction of the molecular material in the solar neighborhood. Comparison with distances derived from maser parallax measurements towards the same clouds shows our method produces consistent distances with $\lesssim10\%$ scatter for clouds across our entire distance spectrum (150 pc $-$ 2.5 kpc). We hope this catalog of homogeneous distances will serve as a baseline for future work., Comment: Accepted for publication in Astronomy & Astrophysics. Full catalog of distances available at https://doi.org/10.7910/DVN/07L7YZ or the CDS

Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields (Portillo et al. 2017). We extend our work to multiple bands, achieving greater sensitivity ($\sim$ 0.4 mag) and greater speed (500x) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, both in terms of recovering accurate posteriors in the catalog space, and in directly deblending sources. When applied to Sloan Digital Sky Survey (SDSS) observations of M2, taking Hubble Space Telescope data as truth, our joint fit on $r$ and $i$ band data goes $\sim0.4$ mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20\% for F606W$\leq 20$. Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 magnitudes deeper using the same data, and maintains a lower false discovery rate down to F606W$\sim 20.5$. Given recent improvements in computational speed, multiband PCAT shows promise in application to large-scale surveys and is a plausible framework for joint analysis of multi-instrument observational data., Comment: 25 pages, 15 figures. Incorporated comments from referee process

We present a new three-dimensional map of dust reddening, based on Gaia parallaxes and stellar photometry from Pan-STARRS 1 and 2MASS. This map covers the sky north of a declination of -30 degrees, out to a distance of several kiloparsecs. This new map contains three major improvements over our previous work. First, the inclusion of Gaia parallaxes dramatically improves distance estimates to nearby stars. Second, we incorporate a spatial prior that correlates the dust density across nearby sightlines. This produces a smoother map, with more isotropic clouds and smaller distance uncertainties, particularly to clouds within the nearest kiloparsec. Third, we infer the dust density with a distance resolution that is four times finer than in our previous work, to accommodate the improvements in signal-to-noise enabled by the other improvements. As part of this work, we infer the distances, reddenings and types of 799 million stars. We obtain typical reddening uncertainties that are ~30% smaller than those reported in the Gaia DR2 catalog, reflecting the greater number of photometric passbands that enter into our analysis. Our 3D dust map can be accessed at https://doi.org/10.7910/DVN/2EJ9TX or through the Python package "dustmaps," and can be queried interactively at http://argonaut.skymaps.info. Our catalog of stellar parameters can be accessed at https://doi.org/10.7910/DVN/AV9GXO., Comment: 43 pages, 19 figures

Many surveys use maximum-likelihood (ML) methods to fit models when extracting photometry from images. We show these ML estimators systematically overestimate the flux as a function of the signal-to-noise ratio and the number of model parameters involved in the fit. This bias is substantially worse for resolved sources: while a 1% bias is expected for a 10$\sigma$ point source, a 10$\sigma$ resolved galaxy with a simplified Gaussian profile suffers a 2.5% bias. This bias also behaves differently depending how multiple bands are used in the fit: simultaneously fitting all bands leads the flux bias to become roughly evenly distributed between them, while fixing the position in "non-detection" bands (i.e. forced photometry) gives flux estimates in those bands that are biased low, compounding a bias in derived colors. We show that these effects are present in idealized simulations, outputs from the Hyper Suprime-Cam fake object pipeline (SynPipe), and observations from Sloan Digital Sky Survey Stripe 82. Prescriptions to correct for the ML bias in flux, and its uncertainty, are provided., Comment: 35 pages, 13 figures, accepted to AJ; code and data available online at https://github.com/joshspeagle/phot_bias

We present a uniform catalog of accurate distances to local molecular clouds informed by the Gaia DR2 data release. Our methodology builds on that of Schlafly et al. (2014). First, we infer the distance and extinction to stars along sightlines towards the clouds using optical and near-infrared photometry. When available, we incorporate knowledge of the stellar distances obtained from Gaia DR2 parallax measurements. We model these per-star distance-extinction estimates as being caused by a dust screen with a 2-D morphology derived from Planck at an unknown distance, which we then fit for using a nested sampling algorithm. We provide updated distances to the Schlafly et al. (2014) sightlines towards the Dame et al. (2001) and Magnani et al. (1985) clouds, finding good agreement with the earlier work. For a subset of 27 clouds, we construct interactive pixelated distance maps to further study detailed cloud structure, and find several clouds which display clear distance gradients and/or are comprised of multiple components. We use these maps to determine robust average distances to these clouds. The characteristic combined uncertainty on our distances is approximately 5-6%, though this can be higher for clouds at farther distances, due to the limitations of our single-cloud model., Comment: Accepted for publication in The Astrophysical Journal. Many figures are interactive; click the link in the figure captions of the PDF to access, or see the online version of the published article

We present a color-based method for identifying red giants based on Pan-STARRS grz and WISE W1 and W2 photometry. We utilize a subsample of bright stars with precise parallaxes from Gaia DR2 to verify that the color-based selection reliably separates dwarfs from giants. The selection is conservative in the sense that contamination is small (~30%) but not all giants are included (the selection primarily identifies K giants). The color-based selection can be applied to stars brighter than $W1\approx16$, more than two magnitudes fainter than techniques relying on shallower 2MASS photometry. Many streams and clouds are visible in the resulting sky maps, especially when binned by Gaia DR2 proper motions, including the Sagittarius stream, the Hercules-Aquila Cloud, the Eastern Banded Structure, Monoceros, and the Virgo Overdensity. In addition to the characterization of new and known stellar streams, we expect that this method for selecting red giants will enable detailed analysis of the diffuse stellar halo to distances exceeding 100 kpc., Comment: 7 pages, 6 figures, ApJL Accepted

SPHEREx is a proposed NASA MIDEX mission selected for Phase A study. SPHEREx would carry out the first all-sky spectral survey in the near infrared. At the end of its two-year mission, SPHEREx would obtain 0.75-to-5$\mu$m spectra of every 6.2 arcsec pixel on the sky, with spectral resolution R>35 and a 5-$\sigma$ sensitivity AB$>$19 per spectral/spatial resolution element. More details concerning SPHEREx are available at http://spherex.caltech.edu. The SPHEREx team has proposed three specific science investigations to be carried out with this unique data set: cosmic inflation, interstellar and circumstellar ices, and the extra-galactic background light. Though these three themes are undoubtedly compelling, they are far from exhausting the scientific output of SPHEREx. Indeed, SPHEREx would create a unique all-sky spectral database including spectra of very large numbers of astronomical and solar system targets, including both extended and diffuse sources. These spectra would enable a wide variety of investigations, and the SPHEREx team is dedicated to making the data available to the community to enable these investigations, which we refer to as Legacy Science. To that end, we have sponsored two workshops for the general scientific community to identify the most interesting Legacy Science themes and to ensure that the SPHEREx data products are responsive to their needs. In February of 2016, some 50 scientists from all fields met in Pasadena to develop these themes and to understand their implications for the SPHEREx mission. The 2016 workshop highlighted many synergies between SPHEREx and other contemporaneous astronomical missions, facilities, and databases. Consequently, in January 2018 we convened a second workshop at the Center for Astrophysics in Cambridge to focus specifically on these synergies. This white paper reports on the results of the 2018 SPHEREx workshop., Comment: 50 pages, 24 figures, more details at http://spherex.caltech.edu

We present a simple but effective technique for measuring angular variation in $R_V$ across the sky. We divide stars from the Pan-STARRS1 catalog into Healpix pixels and determine the posterior distribution of reddening and $R_V$ for each pixel using two independent Monte Carlo methods. We find the two methods to be self-consistent in the limits where they are expected to perform similarly. We also find some agreement with high-precision photometric studies of $R_V$ in Perseus and Ophiuchus, as well as with a map of reddening near the Galactic plane based on stellar spectra from APOGEE. While current studies of $R_V$ are mostly limited to isolated clouds, we have developed a systematic method for comparing $R_V$ values for the majority of observable dust. This is a proof of concept for a more rigorous Galactic reddening map.x, Comment: 21 pages, 20 figures

We present a new technique to determine distances to major star-forming regions across the Perseus Molecular Cloud, using a combination of stellar photometry, astrometric data, and $\rm ^{12} CO$ spectral-line maps. Incorporating the Gaia DR2 parallax measurements when available, we start by inferring the distance and reddening to stars from their Pan-STARRS1 and 2MASS photometry, based on a technique presented in Green et al. 2014; Green et al. 2015 and implemented in their 3D "Bayestar" dust map of three-quarters of the sky. We then refine the Green et al. technique by using the velocity slices of a CO spectral cube as dust templates and modeling the cumulative distribution of dust along the line of sight towards these stars as a linear combination of the emission in the slices. Using a nested sampling algorithm, we fit these per-star distance-reddening measurements to find the distances to the CO velocity slices towards each star-forming region. This results in distance estimates explicitly tied to the velocity structure of the molecular gas. We determine distances to the B5, IC348, B1, NGC1333, L1448, and L1451 star-forming regions and find that individual clouds are located between $\approx 275-300$ pc, with typical combined uncertainties of $\approx 5\%$. We find that the velocity gradient across Perseus corresponds to a distance gradient of about 25 pc, with the eastern portion of the cloud farther away than the western portion. We determine an average distance to the complex of $294\pm 17$ pc, about 60 pc higher than the distance derived to the western portion of the cloud using parallax measurements of water masers associated with young stellar objects. The method we present is not limited to the Perseus Complex, but may be applied anywhere on the sky with adequate CO data in the pursuit of more accurate 3D maps of molecular clouds in the solar neighborhood and beyond., Comment: Accepted for publication in The Astrophysical Journal

We present a new 3D map of interstellar dust reddening, covering three quarters of the sky (declinations greater than -30 degrees) out to a distance of several kiloparsecs. The map is based on high-quality stellar photometry of 800 million stars from Pan-STARRS 1 and 2MASS. We divide the sky into sightlines containing a few hundred stars each, and then infer stellar distances and types, along with the line-of-sight dust distribution. Our new map incorporates a more accurate average extinction law and an additional 1.5 years of Pan-STARRS 1 data, tracing dust to greater extinctions and at higher angular resolutions than our previous map. Out of the plane of the Galaxy, our map agrees well with 2D reddening maps derived from far-infrared dust emission. After accounting for a 15% difference in scale, we find a mean scatter of 10% between our map and the Planck far-infrared emission-based dust map, out to a depth of 0.8 mag in E(r-z), with the level of agreement varying over the sky. Our map can be downloaded at http://argonaut.skymaps.info, or by its DOI: 10.7910/DVN/LCYHJG., Comment: Submitted to MNRAS. 17 pages, 16 figures

We present the first application of the angle-dependent 3-Point Correlation Function (3PCF) to the density fields magnetohydrodynamic (MHD) turbulence simulations intended to model interstellar (ISM) turbulence. Previous work has demonstrated that the angle-averaged bispectrum, the 3PCF's Fourier-space analog, is sensitive to the sonic and Alfv\'enic Mach numbers of turbulence. Here we show that introducing angular information via multipole moments with respect to the triangle opening angle offers considerable additional discriminatory power on these parameters. We exploit a fast, order $N_{\rm g} \log N_{\rm g}$ ($N_{\rm g}$ the number of grid cells used for a Fourier Transform) 3PCF algorithm to study a suite of MHD turbulence simulations with 10 different combinations of sonic and Alfv\'enic Mach numbers over a range from sub to super-sonic and sub to super-Alfv\'{e}nic. The 3PCF algorithm's speed for the first time enables full quantification of the time-variation of our signal: we study 9 timeslices for each condition, demonstrating that the 3PCF is sufficiently time-stable to be used as an ISM diagnostic. In future, applying this framework to 3-D dust maps will enable better treatment of dust as a cosmological foreground as well as reveal conditions in the ISM that shape star formation., Comment: 19 pages, 16 figures; version accepted by ApJ

Strong lensing is a sensitive probe of the small-scale density fluctuations in the Universe. We implement a novel approach to modeling strongly lensed systems using probabilistic cataloging, which is a transdimensional, hierarchical, and Bayesian framework to sample from a metamodel (union of models with different dimensionality) consistent with observed photon count maps. Probabilistic cataloging allows us to robustly characterize modeling covariances within and across lens models with different numbers of subhalos. Unlike traditional cataloging of subhalos, it does not require model subhalos to improve the goodness of fit above the detection threshold. Instead, it allows the exploitation of all information contained in the photon count maps, for instance, when constraining the subhalo mass function. We further show that, by not including these small subhalos in the lens model, fixed-dimensional inference methods can significantly mismodel the data. Using a simulated Hubble Space Telescope (HST) dataset, we show that the subhalo mass function can be probed even when many subhalos in the sample catalogs are individually below the detection threshold and would be absent in a traditional catalog. With the planned Wide Field Infrared Space Telescope (WFIRST), simultaneous probabilistic cataloging of dark subhalos in high-resolution, deep strong lens images has the potential to constrain the subhalo mass function at even lower masses.

Cataloging is challenging in crowded fields because sources are extremely covariant with their neighbors and blending makes even the number of sources ambiguous. We present the first optical probabilistic catalog, cataloging a crowded (~0.1 sources per pixel brighter than 22nd magnitude in F606W) Sloan Digital Sky Survey r band image from M2. Probabilistic cataloging returns an ensemble of catalogs inferred from the image and thus can capture source-source covariance and deblending ambiguities. By comparing to a traditional catalog of the same image and a Hubble Space Telescope catalog of the same region, we show that our catalog ensemble better recovers sources from the image. It goes more than a magnitude deeper than the traditional catalog while having a lower false discovery rate brighter than 20th magnitude. We also present an algorithm for reducing this catalog ensemble to a condensed catalog that is similar to a traditional catalog, except it explicitly marginalizes over source-source covariances and nuisance parameters. We show that this condensed catalog has a similar completeness and false discovery rate to the catalog ensemble. Future telescopes will be more sensitive, and thus more of their images will be crowded. Probabilistic cataloging performs better than existing software in crowded fields and so should be considered when creating photometric pipelines in the Large Synoptic Space Telescope era., Comment: 29 pages, 27 figures; changed prior on number of sources, added discussion on use of posterior sample catalogs in Section 4.2, added discussion of convergence in Section 5.4, expanded Introduction and Discussion, added cross-checks on assumed flux function index and PSF width in Appendices D and E; version to be published in the Astrophysical Journal

We present the details of the photometric and astrometric calibration of the Pan-STARRS1 $3\pi$ Survey. The photometric goals were to reduce the systematic effects introduced by the camera and detectors, and to place all of the observations onto a photometric system with consistent zero points over the entire area surveyed, the ~30,000 square degrees north of $\delta$ = -30 degrees. The astrometric calibration compensates for similar systematic effects so that positions, proper motions, and parallaxes are reliable as well. The Pan-STARRS Data Release 2 (DR2) astrometry is tied to the Gaia DR1 release., Comment: Pan-STARRS Public Data Release 2 : Paper V

DESI (Dark Energy Spectroscopic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations (BAO) and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. To trace the underlying dark matter distribution, spectroscopic targets will be selected in four classes from imaging data. We will measure luminous red galaxies up to $z=1.0$. To probe the Universe out to even higher redshift, DESI will target bright [O II] emission line galaxies up to $z=1.7$. Quasars will be targeted both as direct tracers of the underlying dark matter distribution and, at higher redshifts ($ 2.1 < z < 3.5$), for the Ly-$\alpha$ forest absorption features in their spectra, which will be used to trace the distribution of neutral hydrogen. When moonlight prevents efficient observations of the faint targets of the baseline survey, DESI will conduct a magnitude-limited Bright Galaxy Survey comprising approximately 10 million galaxies with a median $z\approx 0.2$. In total, more than 30 million galaxy and quasar redshifts will be obtained to measure the BAO feature and determine the matter power spectrum, including redshift space distortions.

DESI (Dark Energy Spectropic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. The DESI instrument is a robotically-actuated, fiber-fed spectrograph capable of taking up to 5,000 simultaneous spectra over a wavelength range from 360 nm to 980 nm. The fibers feed ten three-arm spectrographs with resolution $R= \lambda/\Delta\lambda$ between 2000 and 5500, depending on wavelength. The DESI instrument will be used to conduct a five-year survey designed to cover 14,000 deg$^2$. This powerful instrument will be installed at prime focus on the 4-m Mayall telescope in Kitt Peak, Arizona, along with a new optical corrector, which will provide a three-degree diameter field of view. The DESI collaboration will also deliver a spectroscopic pipeline and data management system to reduce and archive all data for eventual public use.