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Found 35 papers, 25 papers with code
Is machine learning good or bad for the natural sciences?
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.
NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning
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.
GeometricImageNet: Extending convolutional neural networks to vector and tensor images
We use representation theory to quantify the dimension of the space of equivariant polynomial functions on 2-dimensional vector images.
Towards fully covariant machine learning
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.
Dimensionless machine learning: Imposing exact units equivariance
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.
A simple equivariant machine learning method for dynamics based on scalars
Physical systems obey strict symmetry principles.
Scalars are universal: Equivariant machine learning, structured like classical physics
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.
Mapping stellar surfaces I: Degeneracies in the rotational light curve problem
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
Fitting very flexible models: Linear regression with large numbers of parameters
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.
Principled point-source detection in collections of astronomical images
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
Dimensionality reduction, regularization, and generalization in overparameterized regressions
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 $p<n$.
An unsupervised method for identifying $X$-enriched stars directly from spectra: Li in LAMOST
Stars with peculiar element abundances are important markers of chemical enrichment mechanisms.
Solar and Stellar Astrophysics
The K2 Bright Star Survey I: Methodology and Data Release
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
Wobble: A Data-driven Analysis Technique for Time-series Stellar Spectra
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
Inference of stellar parameters from brightness variations
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
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.
Solar and Stellar Astrophysics Astrophysics of Galaxies
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.
Instrumentation and Methods for Astrophysics Data Analysis, Statistics and Probability Computation
Improving \textsl{Gaia} parallax precision with a data-driven model of stars
Usually this prior represents beliefs about the stellar density distribution of the Milky Way.
Astrophysics of Galaxies
Exploring cosmic homogeneity with the BOSS DR12 galaxy sample
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
The Joker: A custom Monte Carlo sampler for binary-star and exoplanet radial velocity data
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
The Panchromatic Hubble Andromeda Treasury XV. The BEAST: Bayesian Extinction and Stellar Tool
We present the Bayesian Extinction And Stellar Tool (BEAST), a probabilistic approach to modeling the dust extinguished photometric spectral energy distribution of an individual star while accounting for observational uncertainties common to large resolved star surveys.
Astrophysics of Galaxies
AGNfitter: A Bayesian MCMC approach to fitting spectral energy distributions of AGN
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
A 14 $h^{-3}$ Gpc$^3$ study of cosmic homogeneity using BOSS DR12 quasar sample
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
A Causal, Data-Driven Approach to Modeling the Kepler Data
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
Removing systematic errors for exoplanet search via latent causes
We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest.
A systematic search for transiting planets in the K2 data
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
The Cannon: A data-driven approach to stellar label determination
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
Towards building a Crowd-Sourced Sky Map
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.
Fast Direct Methods for Gaussian Processes
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
Probabilistic Catalogs for Crowded Stellar Fields
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
emcee: The MCMC Hammer
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
Data analysis recipes: Fitting a model to data
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
Astrometry.net: Blind astrometric calibration of arbitrary astronomical images
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
Introductory physics: The new scholasticism
Most introductory physics textbooks neglect air resistance in situations where an astute student can observe that it dominates the dynamics.
Physics Education
Distance measures in cosmology
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
