40 pages, 17 figures, submitted to ApJ, the PROVABGS SED modeling pipeline is publicly available at this https URL
The PRObabilistic Value-Added Bright Galaxy Survey (PROVABGS) catalog will provide measurements of galaxy properties, such as stellar mass ($M_*$), star formation rate (${\rm SFR}$), stellar metallicity ($Z_{\rm MW}$), and stellar age ($t_{\rm age, MW}$), for >10 million galaxies of the DESI Bright Galaxy Survey. Full posterior distributions of the galaxy properties will be inferred using state-of-the-art Bayesian spectral energy distribution (SED) modeling of DESI spectroscopy and Legacy Surveys photometry. In this work, we present the SED model, Bayesian inference framework, and methodology of PROVABGS. Furthermore, we apply the PROVABGS SED modeling on realistic synthetic DESI spectra and photometry, constructed using the L-GALAXIES semi-analytic model. We compare the inferred galaxy properties to the true galaxy properties of the simulation using a hierarchical Bayesian framework to quantify accuracy and precision. Overall, we accurately infer the true $M_*$, ${\rm SFR}$, $Z_{\rm MW}$, and $t_{\rm age, MW}$ of the simulated galaxies. However, the priors on galaxy properties induced by the SED model have a significant impact on the posteriors. They impose a ${\rm SFR}{>}10^{-1} M_\odot/{\rm yr}$ lower bound on ${\rm SFR}$, a ${\sim}0.3$ dex bias on $\log Z_{\rm MW}$ for galaxies with low spectral signal-to-noise, and $t_{\rm age, MW} < 8\,{\rm Gyr}$ upper bound on stellar age. This work also demonstrates that a joint analysis of spectra and photometry significantly improves the constraints on galaxy properties over photometry alone and is necessary to mitigate the impact of the priors. With the methodology presented and validated in this work, PROVABGS will maximize information extracted from DESI observations and provide a probabilistic value-added galaxy catalog that will extend current galaxy studies to new regimes and unlock cutting-edge probabilistic analyses.
16 pages, 4 figures, published in PSJ
Events which meet certain criteria from star tracker images onboard the Juno spacecraft have been proposed to be due to interplanetary dust particle impacts on its solar arrays. These events have been suggested to be caused by particles with diameters larger than 10 micrometers. Here, we compare the reported event rates to expected dust impact rates using dynamical meteoroid models for the four most abundant meteoroid/dust populations in the inner solar system. We find that the dust impact rates predicted by dynamical meteoroid models are not compatible with either the Juno observations in terms of the number of star tracker events per day, or with the variations of dust flux on Juno's solar panels with time and position in the solar system. For example, the rate of star tracker events on Juno's anti-sunward surfaces is the largest during a period during which Juno is expected to experience the peak impact fluxes on the opposite, sunward hemisphere. We also investigate the hypothesis of dust leaving the Martian Hill sphere originating either from the surface of Mars itself or from one of its moons. We do not find such a hypothetical source to be able to reproduce the star tracker event rate variations observed by Juno. We conclude that the star tracker events observed by Juno are unlikely to be the result of instantaneous impacts from the Zodiacal Cloud.
12 pages, 6 figures, under review
We present an approach for using machine learning to automatically discover the governing equations and hidden properties of real physical systems from observations. We train a "graph neural network" to simulate the dynamics of our solar system's Sun, planets, and large moons from 30 years of trajectory data. We then use symbolic regression to discover an analytical expression for the force law implicitly learned by the neural network, which our results showed is equivalent to Newton's law of gravitation. The key assumptions that were required were translational and rotational equivariance, and Newton's second and third laws of motion. Our approach correctly discovered the form of the symbolic force law. Furthermore, our approach did not require any assumptions about the masses of planets and moons or physical constants. They, too, were accurately inferred through our methods. Though, of course, the classical law of gravitation has been known since Isaac Newton, our result serves as a validation that our method can discover unknown laws and hidden properties from observed data. More broadly this work represents a key step toward realizing the potential of machine learning for accelerating scientific discovery.
19 pages, 14 figures and 3 tables. Submitted to MNRAS. Comments welcome
12 pages, 6 figures (+2 in Appendix). Submitted to MNRAS
Submitted to ApJL on 12/29/2021, still waiting for the referee report. 7 pages, 3 figures
17 pages and 12 figures, response to referee submitted
12 pages, 1 appendix, comments welcome!
18 pages
v1: 9 pages, 7 figures, 2 tables
4 pages, 1 figure, to appear in the proceedings of Astronomical Data Analysis Software and Systems XXXI published by ASP
HDR, Universit\'e Paris-Saclay. Textbook-like document. 353 pages, 165 figures, 31 tables, 796 references. Comments are welcome
51 pages, 29 figures, 22 tables, submitted to AAS Journals
18 pages, 11 figures. Accepted for publication in MNRAS
11 pages, 5 figures, published in ApJL
30 pages
23 pages, 9 figures, 6 tables, accepted for publication in MNRAS
8 pages; accepted for publication in A&A
21 pages, 6 figures, 5 tables, published in Frontiers in Astronomy and Space Science
34 pages, 23 figures, Submitted to APJ
27 pages, 20 figures, Submitted to APJ
33+10 pages, 4 appendices, 8 figures
22 pages, 6 figures, accepted for publication in PASJ
20 pages, 16 figures, submitted to ApJ
18 pages, 14 figures. Submitted to MNRAS
21 pages, 16 figures, 3 tables
15 pages, 15 figures
26 pages, 13 figures, accepted in A&A in January 2022
Accepted for publication in ApJ
16 pages, 10 figures, submitted to Astronomy and Astrophysics
15 pages, 9 figures, regular paper
22 pages, 8 figures
22 pages, six figures, accepted by AJ
Accepted for publication in ApJ Letters
20 pages, 3 figures, 11 tables, 9 equations, Conference on Complex Systems CCS2017, Cancun, Mexico, Sattelite meeting "Efficiency in complex systems"
39 pages + references, 4 figures
17 pages, 4 figures
19 pages, 15 figures, Submitted to/Accepted by ApJS
5 pages, 3 figures, 1 ancillary Mathematica file