1 code implementation • 28 May 2024 • David W. Hogg, Soledad Villar

The question in the title is being asked of all of the natural sciences; that is, we are calling on the scientific communities to take a step back and consider the role and value of ML in their fields; the (partial) answers we give here come from the particular perspective of physics.

no code implementations • 28 May 2024 • Valentina Tardugno Poleo, Nora Eisner, David W. Hogg

We build a one-dimensional convolutional neural network (CNN) to separate eclipsing binaries and other false positives from potential planet candidates, reducing the number of light curves that require human vetting.

1 code implementation • 21 May 2023 • Wilson Gregory, David W. Hogg, Ben Blum-Smith, Maria Teresa Arias, Kaze W. K. Wong, Soledad Villar

We use representation theory to quantify the dimension of the space of equivariant polynomial functions on 2-dimensional vector images.

no code implementations • 31 Jan 2023 • Soledad Villar, David W. Hogg, Weichi Yao, George A. Kevrekidis, Bernhard Schölkopf

We discuss links to causal modeling, and argue that the implementation of passive symmetries is particularly valuable when the goal of the learning problem is to generalize out of sample.

1 code implementation • 2 Apr 2022 • Soledad Villar, Weichi Yao, David W. Hogg, Ben Blum-Smith, Bianca Dumitrascu

Units equivariance (or units covariance) is the exact symmetry that follows from the requirement that relationships among measured quantities of physics relevance must obey self-consistent dimensional scalings.

2 code implementations • 7 Oct 2021 • Weichi Yao, Kate Storey-Fisher, David W. Hogg, Soledad Villar

Physical systems obey strict symmetry principles.

2 code implementations • NeurIPS 2021 • Soledad Villar, David W. Hogg, Kate Storey-Fisher, Weichi Yao, Ben Blum-Smith

There has been enormous progress in the last few years in designing neural networks that respect the fundamental symmetries and coordinate freedoms of physical law.

no code implementations • 29 Jan 2021 • Rodrigo Luger, Daniel Foreman-Mackey, Christina Hedges, David W. Hogg

The goal of this paper is twofold: (1) to explore the various degeneracies affecting the stellar light curve "inversion" problem and their effect on what can and cannot be learned from a stellar surface given unresolved photometric measurements; and (2) to motivate ensemble analyses of the light curves of many stars at once as a powerful data-driven alternative to common priors adopted in the literature.

Time Series Analysis Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics

no code implementations • 15 Jan 2021 • David W. Hogg, Soledad Villar

We emphasize that it is often valuable to choose far more parameters than data points, despite folk rules to the contrary: Suitably regularized models with enormous numbers of parameters generalize well and make good predictions for held-out data; over-fitting is not (mainly) a problem of having too many parameters.

no code implementations • 31 Dec 2020 • Dustin Lang, David W. Hogg

We show that the matched filter produces a maximum-likelihood estimate of the brightness of a purported point source, and this leads to a simple way to combine multiple images---taken through the same bandpass filter but with different noise levels and point-spread functions---to produce an optimal point source detection map.

Instrumentation and Methods for Astrophysics

1 code implementation • 23 Nov 2020 • Ningyuan Huang, David W. Hogg, Soledad Villar

This realization brought back the study of linear models for regression, including ordinary least squares (OLS), which, like deep learning, shows a "double-descent" behavior: (1) The risk (expected out-of-sample prediction error) can grow arbitrarily when the number of parameters $p$ approaches the number of samples $n$, and (2) the risk decreases with $p$ for $p>n$, sometimes achieving a lower value than the lowest risk for $p<n$.

no code implementations • 8 Sep 2020 • Adam Wheeler, Melissa Ness, David W. Hogg

Stars with peculiar element abundances are important markers of chemical enrichment mechanisms.

Solar and Stellar Astrophysics

1 code implementation • 19 Aug 2019 • Benjamin J. S. Pope, Timothy R. White, Will M. Farr, Jie Yu, Michael Greklek-McKeon, Daniel Huber, Conny Aerts, Suzanne Aigrain, Timothy R. Bedding, Tabetha Boyajian, Orlagh L. Creevey, David W. Hogg

While the Kepler Mission was designed to look at tens of thousands of faint stars (V > 12), brighter stars that saturated the detector are important because they can be and have been observed very accurately by other instruments.

Solar and Stellar Astrophysics Instrumentation and Methods for Astrophysics

2 code implementations • 2 Jan 2019 • Megan Bedell, David W. Hogg, Daniel Foreman-Mackey, Benjamin T. Montet, Rodrigo Luger

Here we propose a data-driven method to simultaneously extract precise RVs and infer the underlying stellar and telluric spectra using a linear model (in the log of flux).

Instrumentation and Methods for Astrophysics Earth and Planetary Astrophysics Solar and Stellar Astrophysics

1 code implementation • 11 May 2018 • Melissa K. Ness, Victor Silva Aguirre, Mikkel N. Lund, Matteo Cantiello, Daniel Foreman-Mackey, David W. Hogg, Ruth Angus

We find that this model, trained using 1000 stars, can be used to recover the temperature $T_{\rm eff}$ to $<$100 K, the surface gravity to $<$ 0. 1 dex, and the asteroseismic power-spectrum parameters $\rm \nu_{max}$ and $\rm \Delta{\nu}$ to $<11$ $\mu$Hz and $<0. 9$ $\mu$Hz ($\lesssim$ 15\%).

Solar and Stellar Astrophysics

1 code implementation • 25 Jan 2018 • Axel Widmark, Boris Leistedt, David W. Hogg

Multiple stellar systems are ubiquitous in the Milky Way, but are often unresolved and seen as single objects in spectroscopic, photometric, and astrometric surveys.

Solar and Stellar Astrophysics Astrophysics of Galaxies

1 code implementation • 17 Oct 2017 • David W. Hogg, Daniel Foreman-Mackey

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.

Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Computation

1 code implementation • 15 Jun 2017 • Lauren Anderson, David W. Hogg, Boris Leistedt, Adrian M. Price-Whelan, Jo Bovy

Usually this prior represents beliefs about the stellar density distribution of the Milky Way.

Astrophysics of Galaxies

2 code implementations • 7 Feb 2017 • Pierros Ntelis, Jean-Christophe Hamilton, Jean-Marc Le Goff, Nicolas Guillermo Busca, Eric Aubourg, Pierre Laurent, James Rich, Etienne Burtin, Hélion du Mas des Bourboux, Nathalie Palanque Delabrouille, Christophe Yeche, David W. Hogg, Adam Myers, Jeremy Tinker, Julian Bautista, Timothée Delubac, Graziano Rossi, Donald P. Schneider Rita Toheiro, Mariana Vargas-Magaña

Defining the scale of transition to homogeneity as the scale at which $\mathcal{D}_2(r)$ reaches 3 within $1\%$, i. e. $\mathcal{D}_2(r)>2. 97$ 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 $\Lambda$CDM model $\mathcal{R}_H=62. 0\ h^{-1}\ \mathrm{Mpc}$.

Cosmology and Nongalactic Astrophysics

2 code implementations • 24 Oct 2016 • Adrian M. Price-Whelan, David W. Hogg, Daniel Foreman-Mackey, Hans-Walter Rix

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.

Solar and Stellar Astrophysics Earth and Planetary Astrophysics

1 code implementation • 20 Jun 2016 • Karl D. Gordon, Morgan Fouesneau, Heddy Arab, Kirill Tchernyshyov, Daniel R. Weisz, Julianne J. Dalcanton, Benjamin F. Williams, Eric F. Bell, Luciana Bianchi, Martha Boyer, Yumi Choi, Andrew Dolphin, Leo Girardi, David W. Hogg, Jason S. Kalirai, Maria Kapala, Alexia R. Lewis, Hans-Walter Rix, Karin Sandstrom, Evan D. Skillman

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.

Astrophysics of Galaxies

1 code implementation • 17 Jun 2016 • Gabriela Calistro Rivera, Elisabeta Lusso, Joseph F. Hennawi, David W. Hogg

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.

Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

2 code implementations • 29 Feb 2016 • Pierre Laurent, Jean-Marc Le Goff, Etienne Burtin, Jean-Christophe Hamilton, David W. Hogg, Adam Myers, Pierros Ntelis, Isabelle Pâris, James Rich, Eric Aubourg, Julian Bautista, Timothée Delubac, Hélion du Mas des Bourboux, Sarah Eftekharzadeh, Nathalie Palanque Delabrouille, Patrick Petitjean, Graziano Rossi, Donald P. Schneider, Christophe Yeche

For the matter distribution we obtain $3-\langle D_2 \rangle < 5 \times 10^{-5}$ (2 $\sigma$) over the range $250<r<1200 \, h^{-1}$Mpc, consistent with homogeneity on large scales.

Cosmology and Nongalactic Astrophysics

no code implementations • 8 Aug 2015 • Dun Wang, David W. Hogg, Dan Foreman-Mackey, Bernhard Schölkopf

Here we present the Causal Pixel Model (CPM) for Kepler data, a data-driven model intended to capture variability but preserve transit signals.

Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics

1 code implementation • 12 May 2015 • Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest.

no code implementations • 16 Feb 2015 • Daniel Foreman-Mackey, Benjamin T. Montet, David W. Hogg, Timothy D. Morton, Dun Wang, Bernhard Schölkopf

For all planet candidates, we present posterior distributions on the properties of each system based strictly on the transit observables.

Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics

no code implementations • 29 Jan 2015 • Melissa Ness, David W. Hogg, Hans-Walter Rix, Anna Y. Q. Ho, Gail Zasowski

New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge.

Solar and Stellar Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics

no code implementations • 5 Jun 2014 • Dustin Lang, David W. Hogg, Bernhard Scholkopf

We describe a system that builds a high dynamic-range and wide-angle image of the night sky by combining a large set of input images.

2 code implementations • 24 Mar 2014 • Sivaram Ambikasaran, Daniel Foreman-Mackey, Leslie Greengard, David W. Hogg, Michael O'Neil

In many cases, such as regression using Gaussian processes, the covariance matrix is of the form $C = \sigma^2 I + K$, where $K$ is computed using a specified covariance kernel which depends on the data and additional parameters (hyperparameters).

Numerical Analysis Instrumentation and Methods for Astrophysics Statistics Theory Statistics Theory

1 code implementation • 25 Nov 2012 • Brendon J. Brewer, Daniel Foreman-Mackey, David W. Hogg

We present and implement a probabilistic (Bayesian) method for producing catalogs from images of stellar fields.

Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Applications

22 code implementations • 16 Feb 2012 • Daniel Foreman-Mackey, David W. Hogg, Dustin Lang, Jonathan Goodman

The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample).

Instrumentation and Methods for Astrophysics Computational Physics Computation

8 code implementations • 27 Aug 2010 • David W. Hogg, Jo Bovy, Dustin Lang

We go through the many considerations involved in fitting a model to data, using as an example the fit of a straight line to a set of points in a two-dimensional plane.

Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability

4 code implementations • 12 Oct 2009 • Dustin Lang, David W. Hogg, Keir Mierle, Michael Blanton, Sam Roweis

We have built a reliable and robust system that takes as input an astronomical image, and returns as output the pointing, scale, and orientation of that image (the astrometric calibration or WCS information).

Instrumentation and Methods for Astrophysics

1 code implementation • 17 Dec 2004 • Sanjoy Mahajan, David W. Hogg

Most introductory physics textbooks neglect air resistance in situations where an astute student can observe that it dominates the dynamics.

Physics Education

2 code implementations • 11 May 1999 • David W. Hogg

Formulae for the line-of-sight and transverse comoving distances, proper motion distance, angular diameter distance, luminosity distance, k-correction, distance modulus, comoving volume, lookback time, age, and object intersection probability are all given, some with justifications.

astro-ph

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