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Measuring the physical properties of galaxies such as redshift frequently requires the use of Spectral Energy Distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space. Here we present a new method to estimate SEDs as a function of color from a small training set of template SEDs. We first cover the mathematical background behind the technique before demonstrating our ability to reconstruct spectra based upon colors and then compare to other common interpolation and extrapolation methods. When the photometric filters and spectra overlap we show reduction of error in the estimated spectra of over 65% compared to the more commonly used techniques. We also show an expansion of the method to wavelengths beyond the range of the photometric filters. Finally, we demonstrate the usefulness of our technique by generating 50 additional SED templates from an original set of 10 and applying the new set to photometric redshift estimation. We are able to reduce the photometric redshifts standard deviation by at least 22.0% and the outlier rejected bias by over 86.2% compared to original set for z $leq$ 3.
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We develop a method to estimate the dust attenuation curve of galaxies from full spectral fitting of their optical spectra. Motivated from previous studies, we separate the small-scale features from the large-scale spectral shape, by performing a moving average method to both the observed spectrum and the simple stellar population model spectra. The intrinsic dust-free model spectrum is then derived by fitting the observed ratio of the small-scale to large-scale (S/L) components with the S/L ratios of the SSP models. The selective dust attenuation curve is then determined by comparing the observed spectrum with the dust-free model spectrum. One important advantage of this method is that the estimated dust attenuation curve is independent of the shape of theoretical dust attenuation curves. We have done a series of tests on a set of mock spectra covering wide ranges of stellar age and metallicity. We show that our method is able to recover the input dust attenuation curve accurately, although the accuracy depends slightly on signal-to-noise ratio of the spectra. We have applied our method to a number of edge-on galaxies with obvious dust lanes from the ongoing MaNGA survey, deriving their dust attenuation curves and $E(B-V)$ maps, as well as dust-free images in $g$, $r$, and $i$ bands. These galaxies show obvious dust lane features in their original images, which largely disappear after we have corrected the effect of dust attenuation. The vertical brightness profiles of these galaxies become axis-symmetric and can well be fitted by a simple model proposed for the disk vertical structure. Comparing the estimated dust attenuation curve with the three commonly-adopted model curves, we find that the Calzetti curve provides the best description of the estimated curves for the inner region of galaxies, while the Milky Way and SMC curves work better for the outer region.
We present SPECULATOR - a fast, accurate, and flexible framework for emulating stellar population synthesis (SPS) models for predicting galaxy spectra and photometry. For emulating spectra, we use principal component analysis to construct a set of basis functions, and neural networks to learn the basis coefficients as a function of the SPS model parameters. For photometry, we parameterize the magnitudes (for the filters of interest) as a function of SPS parameters by a neural network. The resulting emulators are able to predict spectra and photometry under both simple and complicated SPS model parameterizations to percent-level accuracy, giving a factor of $10^3$-$10^4$ speed up over direct SPS computation. They have readily-computable derivatives, making them amenable to gradient-based inference and optimization methods. The emulators are also straightforward to call from a GPU, giving an additional order-of-magnitude speed-up. Rapid SPS computations delivered by emulation offers a massive reduction in the computational resources required to infer the physical properties of galaxies from observed spectra or photometry and simulate galaxy populations under SPS models, whilst maintaining the accuracy required for a range of applications.
In determining the distances to stars within the Milky Way galaxy, one often uses photometric or spectroscopic parallax. In these methods, the type of each individual star is determined, and the absolute magnitude of that star type is compared with the measured apparent magnitude to determine individual distances. In this article, we define the term statistical photometric parallax, in which statistical knowledge of the absolute magnitudes of stellar populations is used to determine the underlying density distributions of those stars. This technique has been used to determine the density distribution of the Milky Way stellar halo and its component tidal streams, using very large samples of stars from the Sloan Digital Sky Survey. Most recently, the volunteer computing platform MilkyWay@home has been used to find the best fit model parameters for the density of these halo stars.
State of the art radial velocity (RV) exoplanet searches are limited by the effects of stellar magnetic activity. Magnetically active spots, plage, and network regions each have different impacts on the observed spectral lines, and therefore on the apparent stellar RV. Differentiating the relative coverage, or filling factors, of these active regions is thus necessary to differentiate between activity-driven RV signatures and Doppler shifts due to planetary orbits. In this work, we develop a technique to estimate feature-specific magnetic filling factors on stellar targets using only spectroscopic and photometric observations. We demonstrate linear and neural network implementations of our technique using observations from the solar telescope at HARPS-N, the HK Project at the Mt. Wilson Observatory, and the Total Irradiance Monitor onboard SORCE. We then compare the results of each technique to direct observations by the Solar Dynamics Observatory (SDO). Both implementations yield filling factor estimates that are highly correlated with the observed values. Modeling the solar RVs using these filling factors reproduces the expected contributions of the suppression of convective blueshift and rotational imbalance due to brightness inhomogeneities. Both implementations of this technique reduce the overall activity-driven RMS RVs from 1.64 m/s to 1.02 m/s, corresponding to a 1.28 m/s reduction in the RMS variation. The technique provides an additional 0.41 m/s reduction in the RMS variation compared to traditional activity indicators.
With the dramatic rise in high-quality galaxy data expected from Euclid and Vera C. Rubin Observatory, there will be increasing demand for fast high-precision methods for measuring galaxy fluxes. These will be essential for inferring the redshifts of the galaxies. In this paper, we introduce Lumos, a deep learning method to measure photometry from galaxy images. Lumos builds on BKGnet, an algorithm to predict the background and its associated error, and predicts the background-subtracted flux probability density function. We have developed Lumos for data from the Physics of the Accelerating Universe Survey (PAUS), an imaging survey using 40 narrow-band filter camera (PAUCam). PAUCam images are affected by scattered light, displaying a background noise pattern that can be predicted and corrected for. On average, Lumos increases the SNR of the observations by a factor of 2 compared to an aperture photometry algorithm. It also incorporates other advantages like robustness towards distorting artifacts, e.g. cosmic rays or scattered light, the ability of deblending and less sensitivity to uncertainties in the galaxy profile parameters used to infer the photometry. Indeed, the number of flagged photometry outlier observations is reduced from 10% to 2%, comparing to aperture photometry. Furthermore, with Lumos photometry, the photo-z scatter is reduced by ~10% with the Deepz machine learning photo-z code and the photo-z outlier rate by 20%. The photo-z improvement is lower than expected from the SNR increment, however currently the photometric calibration and outliers in the photometry seem to be its limiting factor.
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