Accelerating Stellar Photometric Distance Estimates with Neural Networks
Building on the Bayesian approach to estimating stellar distances from broadband photometry, we show that the computation can be accelerated by about an order of magnitude by using neural networks. Focusing on the case of the ugrizy filter complement for Rubin’s Legacy Survey of Space and Time (LSST...
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IOP Publishing
2025-01-01
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| Series: | The Astronomical Journal |
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| Online Access: | https://doi.org/10.3847/1538-3881/addf51 |
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| author | Karlo Mrakovčić Željko Ivezić Lovro Palaversa |
| author_facet | Karlo Mrakovčić Željko Ivezić Lovro Palaversa |
| author_sort | Karlo Mrakovčić |
| collection | DOAJ |
| description | Building on the Bayesian approach to estimating stellar distances from broadband photometry, we show that the computation can be accelerated by about an order of magnitude by using neural networks. Focusing on the case of the ugrizy filter complement for Rubin’s Legacy Survey of Space and Time (LSST), we show that the Bayesian approach is equivalent to mapping from a 10-dimensional space of five measured colors and their uncertainties to a three-dimensional space of absolute magnitude, metallicity, and interstellar dust extinction along the line of sight. Once the neural network is trained, this mapping is faster by more than an order of magnitude compared to the Bayesian approach, for both optimized grid search and Markov chain Monte Carlo implementation methods. We have developed and tested a pipeline that achieves significant acceleration by first running the Bayesian method on 5%–10% of the sample, then using it to train a neural network, and finally processing the entire sample with the resulting neural network. This computation is done in patches of about 10 deg ^2 due to the variation of Bayesian priors across the sky. We present an analysis of pipeline performance, including speed and biases as functions of input stellar parameters and signal-to-noise ratio, using TRILEGAL-based simulated LSST catalogs by P. Dal Tio et al. We intend to run this pipeline on LSST data releases and make its outputs publicly available. |
| format | Article |
| id | doaj-art-ebf5019bb80b4e7da74e159a43e084bc |
| institution | Kabale University |
| issn | 1538-3881 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | The Astronomical Journal |
| spelling | doaj-art-ebf5019bb80b4e7da74e159a43e084bc2025-08-20T03:50:12ZengIOP PublishingThe Astronomical Journal1538-38812025-01-0117027210.3847/1538-3881/addf51Accelerating Stellar Photometric Distance Estimates with Neural NetworksKarlo Mrakovčić0https://orcid.org/0009-0009-8154-3827Željko Ivezić1https://orcid.org/0000-0001-5250-2633Lovro Palaversa2https://orcid.org/0000-0003-3710-0331Faculty of Physics, University of Rijeka , Radmile Matejčić 2, 51000 Rijeka, Croatia ; karlo.mrakovcic@uniri.hrDepartment of Astronomy and the DiRAC Institute, University of Washington , 3910 15th Avenue NE, Seattle, WA 98195, USA; Ruđer Bošković Institute , Bijenička cesta 54, 10000 Zagreb, CroatiaRuđer Bošković Institute , Bijenička cesta 54, 10000 Zagreb, CroatiaBuilding on the Bayesian approach to estimating stellar distances from broadband photometry, we show that the computation can be accelerated by about an order of magnitude by using neural networks. Focusing on the case of the ugrizy filter complement for Rubin’s Legacy Survey of Space and Time (LSST), we show that the Bayesian approach is equivalent to mapping from a 10-dimensional space of five measured colors and their uncertainties to a three-dimensional space of absolute magnitude, metallicity, and interstellar dust extinction along the line of sight. Once the neural network is trained, this mapping is faster by more than an order of magnitude compared to the Bayesian approach, for both optimized grid search and Markov chain Monte Carlo implementation methods. We have developed and tested a pipeline that achieves significant acceleration by first running the Bayesian method on 5%–10% of the sample, then using it to train a neural network, and finally processing the entire sample with the resulting neural network. This computation is done in patches of about 10 deg ^2 due to the variation of Bayesian priors across the sky. We present an analysis of pipeline performance, including speed and biases as functions of input stellar parameters and signal-to-noise ratio, using TRILEGAL-based simulated LSST catalogs by P. Dal Tio et al. We intend to run this pipeline on LSST data releases and make its outputs publicly available.https://doi.org/10.3847/1538-3881/addf51Neural networksDistance measureDistance indicatorsStellar distanceExtinctionInterstellar extinction |
| spellingShingle | Karlo Mrakovčić Željko Ivezić Lovro Palaversa Accelerating Stellar Photometric Distance Estimates with Neural Networks The Astronomical Journal Neural networks Distance measure Distance indicators Stellar distance Extinction Interstellar extinction |
| title | Accelerating Stellar Photometric Distance Estimates with Neural Networks |
| title_full | Accelerating Stellar Photometric Distance Estimates with Neural Networks |
| title_fullStr | Accelerating Stellar Photometric Distance Estimates with Neural Networks |
| title_full_unstemmed | Accelerating Stellar Photometric Distance Estimates with Neural Networks |
| title_short | Accelerating Stellar Photometric Distance Estimates with Neural Networks |
| title_sort | accelerating stellar photometric distance estimates with neural networks |
| topic | Neural networks Distance measure Distance indicators Stellar distance Extinction Interstellar extinction |
| url | https://doi.org/10.3847/1538-3881/addf51 |
| work_keys_str_mv | AT karlomrakovcic acceleratingstellarphotometricdistanceestimateswithneuralnetworks AT zeljkoivezic acceleratingstellarphotometricdistanceestimateswithneuralnetworks AT lovropalaversa acceleratingstellarphotometricdistanceestimateswithneuralnetworks |