9 pages, 5 figures
Gravitational atoms produced from the superradiant extraction of rotational energy of spinning black holes can reach energy densities significantly higher than that of dark matter, turning black holes into powerful potential detectors for ultralight bosons. These structures are formed by coherently oscillating bosons, which induce oscillating metric perturbations, deflecting photon geodesics passing through their interior. The deviation of nearby geodesics can be further amplified near critical bound photon orbits. We discuss the prospect of detecting this deflection using photon ring autocorrelations with the Event Horizon Telescope and its next generation upgrade, which can probe a large unexplored region of the cloud mass parameter space when compared with previous constraints.
Photo-nuclear reactions of light nuclei below a mass of $A=60$ are studied experimentally and theoretically by the PANDORA (Photo-Absorption of Nuclei and Decay Observation for Reactions in Astrophysics) project. Two experimental methods, virtual-photon excitation by proton scattering and real-photo absorption by a high-brilliance gamma-ray beam produced by laser Compton scattering, will be applied to measure the photo-absorption cross sections and the decay branching ratio of each decay channel as a function of the photon energy. Several nuclear models, {\em e.g.} anti-symmetrized molecular dynamics, mean-field type models, a large-scale shell model, and {\em ab initio} models, will be employed to predict the photo-nuclear reactions. The uncertainty in the model predictions will be evaluated from the discrepancies between the model predictions and the experimental data. The data and the predictions will be implemented in a general reaction calculation code \talys . The results will be applied to the simulation of the photo-disintegration process of ultra-high-energy cosmic rays in inter-galactic propagation.
10 pages, 6 figures, comments welcome
In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep Reinforcement Learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of Halo Occupancy Distribution (HOD) models which are used to connect galaxies with their dark matter halos. Using a combination of continuous relaxation and gradient parameterization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogs realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte-Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalog generated from the Bolshoi simulation using the Zheng et al. 2007 HOD model and find near identical posteriors as standard Markov Chain Monte Carlo techniques with an increase of ~8x in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.
6 pages, 4 figures, accepted at the Machine Learning and the Physical Sciences workshop, NeurIPS 2022
Planet formation is a multi-scale process in which the coagulation of $\mathrm{\mu m}$-sized dust grains in protoplanetary disks is strongly influenced by the hydrodynamic processes on scales of astronomical units ($\approx 1.5\times 10^8 \,\mathrm{km}$). Studies are therefore dependent on subgrid models to emulate the micro physics of dust coagulation on top of a large scale hydrodynamic simulation. Numerical simulations which include the relevant physical effects are complex and computationally expensive. Here, we present a fast and accurate learned effective model for dust coagulation, trained on data from high resolution numerical coagulation simulations. Our model captures details of the dust coagulation process that were so far not tractable with other dust coagulation prescriptions with similar computational efficiency.
10 pages, 8 figures, accepted for publication in MNRAS
We propose a Multimodal Machine Learning method for estimating the Photometric Redshifts of quasars (PhotoRedshift-MML for short), which has long been the subject of many investigations. Our method includes two main models, i.e. the feature transformation model by multimodal representation learning, and the photometric redshift estimation model by multimodal transfer learning. The prediction accuracy of the photometric redshift was significantly improved owing to the large amount of information offered by the generated spectral features learned from photometric data via the MML. A total of 415,930 quasars from Sloan Digital Sky Survey (SDSS) Data Release 17, with redshifts between 1 and 5, were screened for our experiments. We used |{\Delta}z| = |(z_phot-z_spec)/(1+z_spec)| to evaluate the redshift prediction and demonstrated a 4.04% increase in accuracy. With the help of the generated spectral features, the proportion of data with |{\Delta}z| < 0.1 can reach 84.45% of the total test samples, whereas it reaches 80.41% for single-modal photometric data. Moreover, the Root Mean Square (RMS) of |{\Delta}z| is shown to decreases from 0.1332 to 0.1235. Our method has the potential to be generalized to other astronomical data analyses such as galaxy classification and redshift prediction. The algorithm code can be found at https://github.com/HongShuxin/PhotoRedshift-MML .
29 pages, 20 figures, 10 tables, accepted for publication in MNRAS
A rare class of supernovae (SNe) is characterized by strong interaction between the ejecta and several solar masses of circumstellar matter (CSM) as evidenced by strong Balmer-line emission. Within the first few weeks after the explosion, they may display spectral features similar to overluminous Type Ia SNe, while at later phase their observation properties exhibit remarkable similarities with some extreme case of Type IIn SNe that show strong Balmer lines years after the explosion. We present polarimetric observations of SN2018evt obtained by the ESO Very Large Telescope from 172 to 219 days after the estimated time of peak luminosity to study the geometry of the CSM. The nonzero continuum polarization decreases over time, suggesting that the mass loss of the progenitor star is aspherical. The prominent H$\alpha$ emission can be decomposed into a broad, time-evolving component and an intermediate-width, static component. The former shows polarized signals, and it is likely to arise from a cold dense shell (CDS) within the region between the forward and reverse shocks. The latter is significantly unpolarized, and it is likely to arise from shocked, fragmented gas clouds in the H-rich CSM. We infer that SN2018evt exploded inside a massive and aspherical circumstellar cloud. The symmetry axes of the CSM and the SN appear to be similar. SN\,2018evt shows observational properties common to events that display strong interaction between the ejecta and CSM, implying that they share similar circumstellar configurations. Our preliminary estimate also suggests that the circumstellar environment of SN2018evt has been significantly enriched at a rate of $\sim0.1$ M$_\odot$ yr$^{-1}$ over a period of $>100$ yr.
34 pages, 18 figures, accepted for publication in Journal of Astronomy and Astrophysics
16 pages, 13 figures, arxiv version contains both the letter and the supplemental material \c{opyright} 2022 American Physical Society
60 pages, 269 references, 29 figures. Review submitted to Royal Society Open Science. Comments and feedback welcome
Accepted for publication at MNRAS, 21 pages, 20 figures, 6 tables
18 pages, 13 figures, 4 tables, submitting to MNRAS
12 pages, 5 figures, 2 tables, submitted to ApJL
5+6 pages, 3 figures, Accepted (poster + contributed talk) for the Machine Learning and the Physical Sciences Workshop at the 36th conference on Neural Information Processing Systems (NeurIPS 2022)
Talk given by Tsvi Piran at the IWARA 2022 and RAGTIME24 conferences
Accepted for publication in MNRAS, 14 pages, 6 figures
Accepted in A&A
6 pages, 3 figures, accepted in A&A
15 pages, 7 figures, 5 tables
47 pages, 23 figures, accepted for publication in PASJ
Accepted for publication at A&A
15 pages, 15 figures, accepted by MNRAS. Movies of calculations available at this this https URL
Accepted for publication in ApJ; 29 pages, 18 figures, 8 tables. arXiv admin note: text overlap with arXiv:1807.05434
41 pages, 14 figures, 3 tables; To be published in Comets III (2023), K. J. Meech and M. Combi (Eds.), University of Arizona Press, Tucson
Accepted for publication in the Publications of the Astronomical Society of the Pacific
14 pages, 11 figures, submitted to ApJ
23 pages, 8 figures, 7 tables, submitted to ApJ
Submitted for Galaxies Special Issue "From Vision to Instrument: Creating a Next-Generation Event Horizon Telescope for a New Era of Black Hole Science"
Submitted for Galaxies Special Issue "From Vision to Instrument: Creating a Next-Generation Event Horizon Telescope for a New Era of Black Hole Science"
Accepted for publication in the MNRAS; the paper consists of 20 pages, incuding 13 figures
16 pages, 9 figures, accepted for publication in MNRAS
29 pages, no figure
10 pages, 3 figures; accepted to MNRAS
22 pages, 8 figures, submitted to ApJL. Please let us know if we missed any optical and/or radio observations of the FRB sample
Accepted for publication in ApJ
Accepted to PSJ
7 pages, 2 figures
11 pages, 4 figures, 1 table, accepted for publication in ApJ Letters
23 pages, 10 figures
13 pages, 1 table, 5 figures, accepted to Astronomy Letters
8 pages, 7 figures. Proceeding for IDM22. To be published on scipost
18 pages, 13 figures, abstract shortened, accepted for publication in A&A
13 pages, 15 figures, accepted by A&A
17 pages, 15 figures, paper accepted for publication by MNRAS
Accepted for publication in A&A
83 pages, 39 figures
Accepted for the NeurIPS 2022 workshop Machine Learning and the Physical Sciences; 8 pages, 3 figures
10 pages, 13 figures, Manuscript presented at the 73rd International Astronautical Congress, IAC 2022, Paris, France, 18 - 22 September 2022
24 Pages, 7 Figures, Review Article to appear in Special Issue of Journal of Astrophysics and Astronomy on "Indian Participation in the SKA'', comments are welcome
18 pages, 24 figures
MNRAS in press; 8 pages, 9 figures
Accepted for publication in ApJ Letters
Will be submitted in a week to allow for comments
15 pages, 6 figures, data available in Zenodo and GitHub
Accepted for the NeurIPS 2022 workshop Machine Learning and the Physical Sciences; 9 pages, 4 figures
12 pages, 4 figures, 5 tables
3 pages, 1 figure
3 pages, 1 figure
Primary file: 16 pages, 5 figures. Supplemental File (attached): 12 pages, 3 figures, 1 table
Accepted for publication in MNRAS. Contains 15 pages, 11 figures, 4 tables
3 pages, 3 figures, Draft accepted for publication at 73rd International Astronautical Congress, Paris 2022
Submitted to ApJ
6 pages, 3 figures
6+8 pages, 7 figures
22 pages, 15 figures, code repository: www.bitbucket.org/weast/superrad
14 pages, 3 figures, 1 table; to appear in Frontiers in Astronomy and Space Sciences
6 pages, 1 figure
40 pages, 3 figures, 4 tables, ancillary data available at this url: this https URL