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Abstract We present near-infraredJHKphotometry for the resolved stellar populations in 13 nearby galaxies: NGC 6822, IC 1613, NGC 3109, Sextans B, Sextans A, NGC 300, NGC 55, NGC 7793, NGC 247, NGC 5253, Cen A, NGC 1313, and M83, acquired from the 6.5 m Baade–Magellan telescope. We measure distances to each galaxy using the J-region asymptotic giant branch (JAGB) method, a new standard candle that leverages the constant luminosities of color-selected, carbon-rich AGB stars. While only single-epoch, random-phase photometry is necessary to derive JAGB distances, our photometry is time-averaged over multiple epochs, thereby decreasing the contribution of the JAGB stars’ intrinsic variability to the measured dispersions in their observed luminosity functions. To cross-validate these distances, we also measure near-infrared tip of the red giant branch (TRGB) distances to these galaxies. The residuals obtained from subtracting the distance moduli from the two methods yield an rms scatter ofσJAGB−TRGB= ±0.07 mag. Therefore, all systematics in the JAGB method and TRGB method (e.g., crowding, differential reddening, star formation histories) must be contained within these ±0.07 mag bounds for this sample of galaxies because the JAGB and TRGB distance indicators are drawn from entirely distinct stellar populations and are thus affected by these systematics independently. Finally, the composite JAGB star luminosity function formed from this diverse sample of galaxies is well described by a Gaussian function with a modal value ofMJ= –6.20 ± 0.003 mag (stat), indicating that the underlying JAGB star luminosity function of a well-sampled full star formation history is highly symmetric and Gaussian based on over 6700 JAGB stars in the composite sample.
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Photometry on Structured Backgrounds: Local Pixel-wise Infilling by Regression
Abstract 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 pixel-wise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty of 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. In addition to removing many >3σoutliers and improving uncertainty estimates by a factor of ∼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.
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- Award ID(s):
- 2019786
- PAR ID:
- 10369014
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 933
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 155
- Size(s):
- Article No. 155
- Sponsoring Org:
- National Science Foundation
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