33 pages, 12 figures, for a video summarizing the results, see this https URL
We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotationally, translationally, and permutation invariant and have no scale cutoff. By training on galaxy catalogs that only contain the 3D positions and radial velocities of approximately $1,000$ galaxies in tiny volumes of $(25~h^{-1}{\rm Mpc})^3$, our models achieve a precision of approximately $12$% when inferring the value of $\Omega_{\rm m}$. To test the robustness of our models, we evaluated their performance on galaxy catalogs from thousands of hydrodynamic simulations, each with different efficiencies of supernova and AGN feedback, run with five different codes and subgrid models, including IllustrisTNG, SIMBA, Astrid, Magneticum, and SWIFT-EAGLE. Our results demonstrate that our models are robust to astrophysics, subgrid physics, and subhalo/galaxy finder changes. Furthermore, we test our models on 1,024 simulations that cover a vast region in parameter space - variations in 5 cosmological and 23 astrophysical parameters - finding that the model extrapolates really well. Including both positions and velocities are key to building robust models, and our results indicate that our networks have likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than, at least, $~\sim10~h^{-1}{\rm kpc}$.
13 pages, 6 figures, submitted to ApJ. Key results are shown in Figures 4-6
Recent observations have reignited interest in a population of dwarf galaxies that are large and diffuse for their mass, often called ultra-diffuse galaxies. However, the origin and evolution of these mass-size outliers and the role of environment are still unclear. Using the exquisitely deep and wide Hyper Suprime-Cam Strategic Survey images, we search for ultra-puffy galaxies, defined as being $1.5\sigma$ larger than the average size for their mass, around Milky-Way--like galaxies. We present the size and radial distributions of mass-size outliers, and derive their quenched fraction to explore the impact of environment. Surprisingly, despite being outliers in size, the ultra-puffy galaxies have a similar quenched fraction as normal-sized satellites of Milky Way analogs in both observations and simulations, suggesting that quenching is not tied to being a mass-size outlier. The quenched fraction is higher for the ultra-puffy galaxies associated with redder hosts as well as those that are closer to the host in projection. In contrast, the ultra-diffuse galaxies are overall redder and more quiescent compared with normal satellites. We show that the classic definition of ultra-diffuse galaxies is heavily weighted towards quenched galaxies and thus cannot be used for a study of quenching of mass-size outliers.
The latest High Altitude Water Cherenkov (HAWC) point-like source catalog up to 56 TeV reported the detection of two sources in the region of the Galactic plane at galactic longitude 52\deg < l < 55\deg, 3HWC J1930+188 and 3HWC J1928+178. The first one is associated with a known TeV source, the supernova remnant SNR G054.1+00.3. It was discovered by one of the currently operating Imaging Atmospheric Cherenkov Telescope (IACT), the Very Energetic Radiation Imaging Telescope Array System (VERITAS), detected by the High Energy Stereoscopic System (H.E.S.S.), and identified as a composite SNR. However, the source 3HWC J1928+178, discovered by HAWC and coincident with the pulsar PSR J1928+1746, was not detected by any IACT despite their long exposure on the region, until a recent new analysis of H.E.S.S. data was able to confirm it. Moreover, no X-ray counterpart has been detected from this pulsar. We present a multicomponent fit of this region using the latest HAWC data. This reveals an additional new source, HAWC J1932+192, which is potentially associated with the pulsar PSR J1932+1916, whose gamma-ray emission could come from the acceleration of particles in its pulsar wind nebula. In the case of 3HWC J1928+178, several possible explanations are explored, in a attempt to unveil the origins of the very-high-energy gamma-ray emission.
22 pages, 25 figures, accepted for publication in RDTM
Purpose: We present the five-year in-orbit background evolution of Insight-HXMT since the launch, as well as the effects of the background model in data analysis. Methods: The backgrounds of the three main payloads, i.e., Low-Energy Telescope, Medium-Energy Telescope and High-Energy Telescope, are described, respectively. The evolution of the background over time is obtained by simply comparing the background in every year during the in-orbit operation of Insight-HXMT. Results: The major observational characteristics of the Insight-HXMT in-orbit background are presented, including the light curve, spectrum, geographical distribution, and long-term evolution. The systematic error in background estimation is investigated for every year. Conclusion: The observational characteristics of the five-year in-orbit background are consistent with our knowledge of the satellite design and the space environment, and the background model is still valid for the latest observations of Insight-HXMT.
32 pages, 13 figures, summary video: this https URL
We discover analytic equations that can infer the value of $\Omega_{\rm m}$ from the positions and velocity moduli of halo and galaxy catalogues. The equations are derived by combining a tailored graph neural network (GNN) architecture with symbolic regression. We first train the GNN on dark matter halos from Gadget N-body simulations to perform field-level likelihood-free inference, and show that our model can infer $\Omega_{\rm m}$ with $\sim6\%$ accuracy from halo catalogues of thousands of N-body simulations run with six different codes: Abacus, CUBEP$^3$M, Gadget, Enzo, PKDGrav3, and Ramses. By applying symbolic regression to the different parts comprising the GNN, we derive equations that can predict $\Omega_{\rm m}$ from halo catalogues of simulations run with all of the above codes with accuracies similar to those of the GNN. We show that by tuning a single free parameter, our equations can also infer the value of $\Omega_{\rm m}$ from galaxy catalogues of thousands of state-of-the-art hydrodynamic simulations of the CAMELS project, each with a different astrophysics model, run with five distinct codes that employ different subgrid physics: IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE. Furthermore, the equations also perform well when tested on galaxy catalogues from simulations covering a vast region in parameter space that samples variations in 5 cosmological and 23 astrophysical parameters. We speculate that the equations may reflect the existence of a fundamental physics relation between the phase-space distribution of generic tracers and $\Omega_{\rm m}$, one that is not affected by galaxy formation physics down to scales as small as $10~h^{-1}{\rm kpc}$.
16 pages, 12 figures, Submitted to ApJ
73 pages, 16 Figures, 2 Tables. Invited review to be published in the Special Issue "Binary Evolution in Galactic Nuclei", eds. Roberto Capuzzo-Dolcetta and Adrian Hamers, accepted for publication in the journal "Universe"
8 pages, 4 figures. Submitted to A&A
5 pages, 1 figure. Submitted to MNRAS Letters
48 pages, 21 figures, accepted for publication in The Astrophysical Journal Supplement Series. Multipanel figures for all 45 galaxies will be available in the online version of the published paper (and here: this https URL ). Data products will be available as a MAST HLSP soon (placeholder DOI: 10.17909/pbe1-m743)
Published in Galaxies Special Issue "The Symbiosis between Radio Source and Galaxy Evolution" O'Dea & Baum (guest editors), 20 pages, 8 figures; this https URL
Accepted by Icarus
36 pages, 28 figures, accepted by ApJ
Submitted to MNRAS
Submitted to MNRAS
23 pages, 13 figures, 2 tables, submitted to AAS
28 pages, 9 figures
11 pages, 13 figures
23 pages, 15 figures, accepted for publication in ApJ
7 pages, 1 figure
8 pages, 4 figures
Invited review at 2022 RR Lyrae and Cepheids conference on La Palma. 8 pages, no figures
15 pages, 11 figures
17 pages, with 5 appendices. 12 figures. Accepted for publication in MNRAS
10 pages, 11 figures, submitted to MNRAS
7 pages, 2 figures, uses RevTex
10 pages, 8 figures, 2 tables. Accepted for publication in NIMA
13 pages, 5 figures
4 pages, 1 figure, submitted to Open Journal of Astrophysics
Accepted for publication in MNRAS
Astrophysical Journal (to be published). 14 pages with 8 figures
3 pages, 1 figure. Proceedings of the "Machine Learning: a giant leap towards space discovery in the era of peta and exabyte scale surveys" Symposia of the 2022 Annual meeting of the European Astronomical Society. To be published in Memorie della SAIt
23 pages, 5 figures
9 pages, 3 figures, accepted to Astrophysical Journal Letters
14 pages, 13 figures, submitted to MNRAS. Comments welcome
18 pages, 9 figures
Accepted in MNRAS 2023 February 28. Received 2023 February 28; in original form 2022 July 09
ApJS submitted
Accepted for publication in MNRAS. 15 pages, 12 figures, 8 tables (plus 15 pages of appendices, with 31 figures)
100 pages, 23 figures, Horizon 2061 is a science-driven, foresight exercise, for future scientific investigations
Accepted for Space Science Review
12 pages, 9 figures, 5 tables
5 pages, 3 figures
14 pages, 6 figures
10 pages, 4 figures, 1 table
Accepted for publication on EpJC
18 pages, one figure
53 pages, 26 figures, 2 tables, comments are welcome
14 pages, 7 figures, 2 tables
29 pages, 8 figures