arXiv subjects

David W. Hogg

Publications and source records attributed to David W. Hogg.

At least 109 records · Page 6Linked to original sources

arXiv2018

Inferring binary and trinary stellar populations in photometric and astrometric surveys

Multiple stellar systems are ubiquitous in the Milky Way, but are often unresolved and seen as single objects in spectroscopic, photometric, and astrometric surveys. Yet, modeling them is essential for developing a full understanding of large surveys such as Gaia, and connecting them to stellar and Galactic models. In this paper we address this problem by jointly fitting the Gaia and 2MASS photometric and astrometric data using a data-driven Bayesian hierarchical model that includes populations of binary and trinary systems. This allows us to classify observations into singles, binaries, and trinaries, in a robust and efficient manner, without resorting to external models. We are able to identify multiple systems and, in some cases, make strong predictions for the properties of its unresolved stars. We will be able to compare such predictions with Gaia Data Release 4, which will contain astrometric identification and analysis of binary systems.

astro-ph.SR↗

arXiv2018

Likelihood Non-Gaussianity in Large-Scale Structure Analyses

Standard present day large-scale structure (LSS) analyses make a major assumption in their Bayesian parameter inference --- that the likelihood has a Gaussian form. For summary statistics currently used in LSS, this assumption, even if the underlying density field is Gaussian, cannot be correct in detail. We investigate the impact of this assumption on two recent LSS analyses: the Beutler et al. (2017) power spectrum multipole ($P_\ell$) analysis and the Sinha et al. (2017) group multiplicity function ($ζ$) analysis. Using non-parametric divergence estimators on mock catalogs originally constructed for covariance matrix estimation, we identify significant non-Gaussianity in both the $P_\ell$ and $ζ$ likelihoods. We then use Gaussian mixture density estimation and Independent Component Analysis on the same mocks to construct likelihood estimates that approximate the true likelihood better than the Gaussian $pseudo$-likelihood. Using these likelihood estimates, we accurately estimate the true posterior probability distribution of the Beutler et al. (2017) and Sinha et al. (2017) parameters. Likelihood non-Gaussianity shifts the $fσ_8$ constraint by $-0.44σ$, but otherwise, does not significantly impact the overall parameter constraints of Beutler et al. (2017). For the $ζ$ analysis, using the pseudo-likelihood significantly underestimates the uncertainties and biases the constraints of Sinha et al. (2017) halo occupation parameters. For $\log M_1$ and $α$, the posteriors are shifted by $+0.43σ$ and $-0.51σ$ and broadened by $42\%$ and $66\%$, respectively. The divergence and likelihood estimation methods we present provide a straightforward framework for quantifying the impact of likelihood non-Gaussianity and deriving more accurate parameter constraints.

astro-ph.CO↗

arXiv2018

Discovery and Characterization of 3000+ Main-Sequence Binaries from APOGEE Spectra

We develop a data-driven spectral model for identifying and characterizing spatially unresolved multiple-star systems and apply it to APOGEE DR13 spectra of main-sequence stars. Binaries and triples are identified as targets whose spectra can be significantly better fit by a superposition of two or three model spectra, drawn from the same isochrone, than any single-star model. From an initial sample of $\sim$20,000 main-sequence targets, we identify $\sim$2,500 binaries in which both the primary and secondary star contribute detectably to the spectrum, simultaneously fitting for the velocities and stellar parameters of both components. We additionally identify and fit $\sim$200 triple systems, as well as $\sim$700 velocity-variable systems in which the secondary does not contribute detectably to the spectrum. Our model simplifies the process of simultaneously fitting single- or multi-epoch spectra with composite models and does not depend on a velocity offset between the two components of a binary, making it sensitive to traditionally undetectable systems with periods of hundreds or thousands of years. In agreement with conventional expectations, almost all the spectrally-identified binaries with measured parallaxes fall above the main sequence in the color-magnitude diagram. We find excellent agreement between spectrally and dynamically inferred mass ratios for the $\sim$600 binaries in which a dynamical mass ratio can be measured from multi-epoch radial velocities. We obtain full orbital solutions for 64 systems, including 14 close binaries within hierarchical triples. We make available catalogs of stellar parameters, abundances, mass ratios, and orbital parameters.

astro-ph.SR↗

arXiv2017

Hack Weeks as a model for Data Science Education and Collaboration

Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are rapidly increasing in complexity. This gives rise to the need for scientific communities to adapt on shorter time scales than traditional university curricula allow for, and therefore requires new modes of knowledge transfer. The universal applicability of data science tools to a broad range of problems has generated new opportunities to foster exchange of ideas and computational workflows across disciplines. In recent years, hack weeks have emerged as an effective tool for fostering these exchanges by providing training in modern data analysis workflows. While there are variations in hack week implementation, all events consist of a common core of three components: tutorials in state-of-the-art methodology, peer-learning and project work in a collaborative environment. In this paper, we present the concept of a hack week in the larger context of scientific meetings and point out similarities and differences to traditional conferences. We motivate the need for such an event and present in detail its strengths and challenges. We find that hack weeks are successful at cultivating collaboration and the exchange of knowledge. Participants self-report that these events help them both in their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration and cultivate best practices.

physics.ed-ph↗

arXiv2017

Linear models for systematics and nuisances

The target of many astronomical studies is the recovery of tiny astrophysical signals living in a sea of uninteresting (but usually dominant) noise. In many contexts (i.e., stellar time-series, or high-contrast imaging, or stellar spectroscopy), there are structured components in this noise caused by systematic effects in the astronomical source, the atmosphere, the telescope, or the detector. More often than not, evaluation of the true physical model for these nuisances is computationally intractable and dependent on too many (unknown) parameters to allow rigorous probabilistic inference. Sometimes, housekeeping data---and often the science data themselves---can be used as predictors of the systematic noise. Linear combinations of simple functions of these predictors are often used as computationally tractable models that can capture the nuisances. These models can be used to fit and subtract systematics prior to investigation of the signals of interest, or they can be used in a simultaneous fit of the systematics and the signals. In this Note, we show that if a Gaussian prior is placed on the weights of the linear components, the weights can be marginalized out with an operation in pure linear algebra, which can (often) be made fast. We illustrate this model by demonstrating the applicability of a linear model for the non-linear systematics in K2 time-series data, where the dominant noise source for many stars is spacecraft motion and variability.

astro-ph.IM↗

arXiv2017

Data analysis recipes: Using Markov Chain Monte Carlo

Markov Chain Monte Carlo (MCMC) methods for sampling probability density functions (combined with abundant computational resources) have transformed the sciences, especially in performing probabilistic inferences, or fitting models to data. In this primarily pedagogical contribution, we give a brief overview of the most basic MCMC method and some practical advice for the use of MCMC in real inference problems. We give advice on method choice, tuning for performance, methods for initialization, tests of convergence, troubleshooting, and use of the chain output to produce or report parameter estimates with associated uncertainties. We argue that autocorrelation time is the most important test for convergence, as it directly connects to the uncertainty on the sampling estimate of any quantity of interest. We emphasize that sampling is a method for doing integrals; this guides our thinking about how MCMC output is best used.

astro-ph.IM↗

arXiv2017

A pixel-level model for event discovery in time-domain imaging

Difference imaging or image subtraction is a method that measures differential photometry by matching the pointing and point-spread function (PSF) between image frames. It is used for the detection of time-variable phenomena. Here we present a new category of method---CPM Difference Imaging, in which differences are not measured between matched images but instead between image frames and a data-driven predictive model that has been designed only to predict the pointing, PSF, and detector effects but not astronomical variability. In CPM Difference Imaging each pixel is modelled by the Causal Pixel Model (CPM) originally built for modeling Kepler data, in which pixel values are predicted by a linear combination of other pixels at the same epoch but far enough away such that these pixels are causally disconnected, astrophysically. It does not require that the user have any explicit model or description of the pointing or point-spread function of any of the images. Its principal drawback is that---in its current form---it requires an imaging campaign with many epochs and fairly stable telescope pointing. The method is applied to simulated data and also the K2 Campaign 9 microlensing data. We show that CPM Difference Imaging can detect variable objects and produce precise differentiate photometry in a crowded field. CPM Difference Imaging is capable of producing image differences at nearly photon-noise precision.

astro-ph.IM↗

arXiv2017

Kronos & Krios: Evidence for accretion of a massive, rocky planetary system in a comoving pair of solar-type stars

We report and discuss the discovery of a comoving pair of bright solar-type stars, HD 240430 and HD 240429, with a significant difference in their chemical abundances. The two stars have an estimated 3D separation of $\approx 0.6$ pc ($\approx 0.01$ pc projected) at a distance of $r\approx 100$ pc with nearly identical three-dimensional velocities, as inferred from Gaia TGAS parallaxes and proper motions, and high-precision radial velocity measurements. Stellar parameters determined from high-resolution Keck HIRES spectra indicate that both stars are $\sim 4$ Gyr old. The more metal-rich of the two, HD 240430, shows an enhancement of refractory ($T_C>1200$ K) elements by $\approx 0.2$ dex and a marginal enhancement of (moderately) volatile elements ($T_C<1200$ K, C, N, O, Na, and Mn). This is the largest metallicity difference found in a wide binary pair yet. Additionally, HD 240430 shows an anomalously high surface lithium abundance ($A(\mathrm{Li})=2.75$), higher than its companion by $0.5$ dex. The proximity in phase-space and ages between the two stars suggests that they formed together with the same composition, at odds with the observed differences in metallicity and abundance patterns. We therefore suggest that the star HD~240430, "Kronos", accreted 15 $M_\oplus$ of rocky material after birth, selectively enhancing the refractory elements as well as lithium in its surface and convective envelope.

astro-ph.SR↗

arXiv2017

Red clump stars and Gaia: Calibration of the standard candle using a hierarchical probabilistic model

Distances to individual stars in our own Galaxy are critical in order to piece together the nature of its velocity and spatial structure. Core helium burning red clump (RC) stars have similar luminosities, are abundant throughout the Galaxy, and thus constitute good standard candles. We build a hierarchical probabilistic model to quantify the quality of RC stars as standard candles using parallax measurements from the first Gaia data release. A unique aspect of our methodology is to fully account for (and marginalize over) parallax, photometry, and dust corrections uncertainties, which leads to more robust results than standard approaches. We determine the absolute magnitude and intrinsic dispersion of the RC in 2MASS bands J, H, Ks, Gaia G band, and WISE bands W1, W2, W3, and W4. We find that the absolute magnitude of the RC is $-1.61 \pm$ 0.01 (in Ks), $+0.44 \pm$ 0.01 (in G) , $-0.93 \pm$ 0.01 (in J), $-1.46 \pm$ 0.01 (in H), $-1.68 \pm$ 0.02 (in W1), $-1.69\pm$ 0.02 (in W2), $-1.67 \pm$ 0.02 (in W3), $1.76 \pm$ 0.01 mag (in W4). The mean intrinsic dispersion is $\sim 0.17 \pm$ 0.03 mag across all bands (yielding a typical distance precision of $\sim$ 8%). Thus RC stars are reliable and precise standard candles. In addition, we have also re-calibrated the zero point of the absolute magnitude of the RC in each band, which provide a benchmark for future studies to estimate distances to RC stars. Finally, the parallax error shrinkage in the hierarchical model outlined in this work can be used to obtain more precise parallaxes than Gaia for the most distant RC stars across the Galaxy.

astro-ph.GA↗

arXiv2017

Improving \textsl{Gaia} parallax precision with a data-driven model of stars

Converting a noisy parallax measurement into a posterior belief over distance requires inference with a prior. Usually this prior represents beliefs about the stellar density distribution of the Milky Way. However, multi-band photometry exists for a large fraction of the \textsl{\small{Gaia}} \textsl{\small{TGAS}} Catalog and is incredibly informative about stellar distances. Here we use \textsl{\small{2MASS}} colors for 1.4 million \textsl{\small{TGAS}} stars to build a noise-deconvolved empirical prior distribution for stars in color--magnitude space. This model contains no knowledge of stellar astrophysics or the Milky Way, but is precise because it accurately generates a large number of noisy parallax measurements under an assumption of stationarity; that is, it is capable of combining the information from many stars. We use the Extreme Deconvolution (\textsl{\small{XD}}) algorithm---an Empirical Bayes approximation to a full hierarchical model of the true parallax and photometry of every star---to construct this prior. The prior is combined with a \textsl{\small{TGAS}} likelihood to infer a precise photometric parallax estimate and uncertainty (and full posterior) for every star. Our parallax estimates are more precise than the \textsl{\small{TGAS}} catalog entries by a median factor of 1.2 (14% are more precise by a factor >2) and are more precise than previous Bayesian distance estimates that use spatial priors. We validate our parallax inferences using members of the Milky Way star cluster M67, which is not visible as a cluster in the \textsl{\small{TGAS}} parallax estimates, but appears as a cluster in our posterior parallax estimates. Our results, including a parallax posterior pdf for each of 1.4 million \textsl{\small{TGAS}} stars, are available in companion electronic tables.

astro-ph.GA↗

arXiv2017

Masses and Ages for 230,000 LAMOST Giants, via Their Carbon and Nitrogen Abundances

We measure carbon and nitrogen abundances to $\lesssim$ 0.1 dex for 450,000 giant stars from their low-resolution (R$\sim$1800) LAMOST DR2 survey spectra. We use these [C/M] and [N/M] measurements, together with empirical relations based on the APOKASC sample, to infer stellar masses and implied ages for 230,000 of these objects to 0.08 dex and 0.2 dex respectively. We use The Cannon, a data-driven approach to spectral modeling, to construct a predictive model for LAMOST spectra. Our reference set comprises 8125 stars observed in common between the APOGEE and LAMOST surveys, taking seven APOGEE DR12 labels (parameters) as ground truth: Teff, logg, [M/H], [$α$/M], [C/M], [N/M], and Ak. We add seven colors to the Cannon model, based on the g, r, i, J, H, K, W1, and W2 magnitudes from APASS, 2MASS & WISE, which improves our constraints on Teff and logg by up to 20% and on Ak by up to 70%. Cross-validation of the model demonstrates that, for high-SNR objects, our inferred labels agree with the APOGEE values to within 50 K in temperature, 0.04 magnitudes in Ak, and < 0.1 dex in logg, [M/H], [C/M], [N/M], and [$α$/M]. We apply the model to 450,000 giants in LAMOST DR2 that have not been observed by APOGEE. This demonstrates that precise individual abundances can be measured from low-resolution spectra, and represents the largest catalog of [C/M], [N/M], masses and ages to date. As as result, we greatly increase the number and sky coverage of stars with mass and age estimates.

astro-ph.SR↗

arXiv2017

Exploring cosmic homogeneity with the BOSS DR12 galaxy sample

In this study, we probe the transition to cosmic homogeneity in the Large Scale Structure (LSS) of the Universe using the CMASS galaxy sample of BOSS spectroscopic survey which covers the largest effective volume to date, $3\ h^{-3}\ \mathrm{Gpc}^3$ at $0.43 \leq z \leq 0.7$. We study the scaled counts-in-spheres, $\mathcal{N}( 2.97$ for $r>\mathcal{R}_H$, we find $\mathcal{R}_H = (63.3\pm0.7) \ h^{-1}\ \mathrm{Mpc}$, in agreement at the percentage level with the predictions of the $Λ$CDM model $\mathcal{R}_H=62.0\ h^{-1}\ \mathrm{Mpc}$. Thanks to the large cosmic depth of the survey, we investigate the redshift evolution of the transition to homogeneity scale and find agreement with the $Λ$CDM prediction. Finally, we find that $\mathcal{D}_2$ is compatible with $3$ at scales larger than $300\ h^{-1}\ $Mpc in all redshift bins. These results consolidate the Cosmological Principle and represent a precise consistency test of the $ΛCDM$ model.

astro-ph.CO↗

arXiv2017

Comoving stars in Gaia DR1: An abundance of very wide separation co-moving pairs

The primary sample of the {\it Gaia} Data Release 1 is the Tycho-Gaia Astrometric Solution (TGAS): $\approx$ 2 million Tycho-2 sources with improved parallaxes and proper motions relative to the initial catalog. This increased astrometric precision presents an opportunity to find new binary stars and moving groups. We search for high-confidence comoving pairs of stars in TGAS by identifying pairs of stars consistent with having the same 3D velocity using a marginalized likelihood ratio test to discriminate candidate comoving pairs from the field population. Although we perform some visualizations using (bias- corrected) inverse parallax as a point estimate of distance, the likelihood ratio is computed with a probabilistic model that includes the covariances of parallax and proper motions and marginalizes the (unknown) true distances and 3D velocities of the stars. We find 13,085 comoving star pairs among 10,606 unique stars with separations as large as 10 pc (our search limit). Some of these pairs form larger groups through mutual comoving neighbors: many of these pair networks correspond to known open clusters and OB associations, but we also report the discovery of several new comoving groups. Most surprisingly, we find a large number of very wide ($>1$ pc) separation comoving star pairs, the number of which increases with increasing separation and cannot be explained purely by false-positive contamination. Our key result is a catalog of high-confidence comoving pairs of stars in TGAS. We discuss the utility of this catalog for making dynamical inferences about the Galaxy, testing stellar atmosphere models, and validating chemical abundance measurements.

astro-ph.SR↗

arXiv2017

Approximate Bayesian Computation in Large Scale Structure: constraining the galaxy-halo connection

Standard approaches to Bayesian parameter inference in large scale structure assume a Gaussian functional form (chi-squared form) for the likelihood. This assumption, in detail, cannot be correct. Likelihood free inferences such as Approximate Bayesian Computation (ABC) relax these restrictions and make inference possible without making any assumptions on the likelihood. Instead ABC relies on a forward generative model of the data and a metric for measuring the distance between the model and data. In this work, we demonstrate that ABC is feasible for LSS parameter inference by using it to constrain parameters of the halo occupation distribution (HOD) model for populating dark matter halos with galaxies. Using specific implementation of ABC supplemented with Population Monte Carlo importance sampling, a generative forward model using HOD, and a distance metric based on galaxy number density, two-point correlation function, and galaxy group multiplicity function, we constrain the HOD parameters of mock observation generated from selected "true" HOD parameters. The parameter constraints we obtain from ABC are consistent with the "true" HOD parameters, demonstrating that ABC can be reliably used for parameter inference in LSS. Furthermore, we compare our ABC constraints to constraints we obtain using a pseudo-likelihood function of Gaussian form with MCMC and find consistent HOD parameter constraints. Ultimately our results suggest that ABC can and should be applied in parameter inference for LSS analyses.

astro-ph.CO↗

arXiv2017

The Joker: A custom Monte Carlo sampler for binary-star and exoplanet radial velocity data

Given sparse or low-quality radial-velocity measurements of a star, there are often many qualitatively different stellar or exoplanet companion orbit models that are consistent with the data. The consequent multimodality of the likelihood function leads to extremely challenging search, optimization, and MCMC posterior sampling over the orbital parameters. Here we create a custom Monte Carlo sampler for sparse or noisy radial-velocity measurements of two-body systems that can produce posterior samples for orbital parameters even when the likelihood function is poorly behaved. The six standard orbital parameters for a binary system can be split into four non-linear parameters (period, eccentricity, argument of pericenter, phase) and two linear parameters (velocity amplitude, barycenter velocity). We capitalize on this by building a sampling method in which we densely sample the prior pdf in the non-linear parameters and perform rejection sampling using a likelihood function marginalized over the linear parameters. With sparse or uninformative data, the sampling obtained by this rejection sampling is generally multimodal and dense. With informative data, the sampling becomes effectively unimodal but too sparse: in these cases we follow the rejection sampling with standard MCMC. The method produces correct samplings in orbital parameters for data that include as few as three epochs. The Joker can therefore be used to produce proper samplings of multimodal pdfs, which are still informative and can be used in hierarchical (population) modeling. We give some examples that show how the posterior pdf depends sensitively on the number and time coverage of the observations and their uncertainties.

astro-ph.SR↗

arXiv2017

Hierarchical probabilistic inference of the color-magnitude diagram and shrinkage of stellar distance uncertainties

We present a hierarchical probabilistic model for improving geometric stellar distance estimates using color--magnitude information. This is achieved with a data driven model of the color--magnitude diagram, not relying on stellar models but instead on the relative abundances of stars in color--magnitude cells, which are inferred from very noisy magnitudes and parallaxes. While the resulting noise-deconvolved color--magnitude diagram can be useful for a range of applications, we focus on deriving improved stellar distance estimates relying on both parallax and photometric information. We demonstrate the efficiency of this approach on the 1.4 million stars of the Gaia TGAS sample that also have APASS magnitudes. Our hierarchical model has 4~million parameters in total, most of which are marginalized out numerically or analytically. We find that distance estimates are significantly improved for the noisiest parallaxes and densest regions of the color--magnitude diagram. In particular, the average distance signal-to-noise ratio and uncertainty improve by 19~percent and 36~percent, respectively, with 8~percent of the objects improving in SNR by a factor greater than 2. This computationally efficient approach fully accounts for both parallax and photometric noise, and is a first step towards a full hierarchical probabilistic model of the Gaia data.

astro-ph.SR↗

arXiv2017

Galactic Doppelganger: The chemical similarity among field stars and among stars with a common birth origin

We explore to which extent stars within Galactic disk open clusters resemble each other in the high-dimensional space of their photospheric element abundances, and contrast this with pairs of field stars. Our analysis is based on abundances for 20 elements, homogeneously derived from APOGEE spectra (with carefully quantified uncertainties, with a median value of $\sim 0.03$ dex). We consider 90 red giant stars in seven open clusters and find that most stars within a cluster have abundances in most elements that are indistinguishable (in a $χ^2$-sense) from those of the other members, as expected for stellar birth siblings. An analogous analysis among pairs of $>1000$ field stars shows that highly significant abundance differences in the 20-dimensional space can be established for the vast majority of these pairs, and that the APOGEE-based abundance measurements have high discriminating power. However, pairs of field stars whose abundances are indistinguishable even at 0.03~dex precision exist: $\sim 0.3$ percent of all field star pairs, and $\sim 1.0$ percent of field star pairs at the same (solar) metallicity [Fe/H] = $0 \pm 0.02$. Most of these pairs are presumably not birth siblings from the same cluster, but rather doppelganger. Our analysis implies that 'chemical tagging' in the strict sense, identifying birth siblings for typical disk stars through their abundance similarity alone, will not work with such data. However, our approach shows that abundances have extremely valuable information for probabilistic chemo-orbital modeling and combined with velocities, we have identified new cluster members from the field.

astro-ph.SR↗

arXiv2017

Label Transfer from APOGEE to LAMOST: Precise Stellar Parameters for 450,000 LAMOST Giants

In this era of large-scale stellar spectroscopic surveys, measurements of stellar attributes ("labels," i.e. parameters and abundances) must be made precise and consistent across surveys. Here, we demonstrate that this can be achieved by a data-driven approach to spectral modeling. With The Cannon, we transfer information from the APOGEE survey to determine precise Teff, log g, [Fe/H], and [$α$/M] from the spectra of 450,000 LAMOST giants. The Cannon fits a predictive model for LAMOST spectra using 9952 stars observed in common between the two surveys, taking five labels from APOGEE DR12 as ground truth: Teff, log g, [Fe/H], [α/M], and K-band extinction $A_k$. The model is then used to infer Teff, log g, [Fe/H], and [$α$/M] for 454,180 giants, 20% of the LAMOST DR2 stellar sample. These are the first [$α$/M] values for the full set of LAMOST giants, and the largest catalog of [$α$/M] for giant stars to date. Furthermore, these labels are by construction on the APOGEE label scale; for spectra with S/N > 50, cross-validation of the model yields typical uncertainties of 70K in Teff, 0.1 in log g, 0.1 in [Fe/H], and 0.04 in [$α$/M], values comparable to the broadly stated, conservative APOGEE DR12 uncertainties. Thus, by using "label transfer" to tie low-resolution (LAMOST R $\sim$ 1800) spectra to the label scale of a much higher-resolution (APOGEE R $\sim$ 22,500) survey, we substantially reduce the inconsistencies between labels measured by the individual survey pipelines. This demonstrates that label transfer with The Cannon can successfully bring different surveys onto the same physical scale.

astro-ph.SR↗