16 pages, 8 figures. Submitted to ApJ on 12/21/21
We present a spectrum of the diffuse Galactic light (DGL) between 3700 and 10,000 A, obtained by correlating optical sky intensity with far-infrared dust emission. We use nearly 250,000 blank-sky spectra from BOSS/SDSS-III together with IRIS-reprocessed maps from the IRAS satellite. The larger sample size compared to SDSS-II results in a factor-of-two increase in signal to noise. We combine these data sets with a model for the optical/far-infrared correlation that accounts for self-absorption by dust. The spectral features of the DGL agree remarkably well with features present in stellar spectra. There is evidence for a difference in the DGL continuum between the regions covered by BOSS in the northern and southern Galactic hemisphere. We interpret the difference at red wavelengths as the result of a difference in stellar populations, with mainly old stars in both regions but a higher fraction of young stars in the south. There is also a broad excess in the southern DGL spectrum over the prediction of a simple radiative transfer model, without a clear counterpart in the north. We interpret this excess, centered at ~6500 A, as evidence for luminescence in the form of extended red emission (ERE). The observed strength of the 4000 A break indicates that at most ~7% of the dust-correlated light at 4000 A can be due to blue luminescence. Our DGL spectrum provides constraints on dust scattering and luminescence independent of measurements of extinction.
18 pages, 3 figures. More than 350 Tb of data from thousands of simulations publicly available at this https URL
The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$\alpha$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.
11+6 pages, 8+3 figures. The code and data associated with this paper will be uploaded upon the acceptance of this paper
Complex systems (stars, supernovae, galaxies, and clusters) often exhibit low scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period, temperature). These scaling relations can illuminate the underlying physics and can provide observational tools for estimating masses and distances. Machine learning can provide a systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models the patterns in a given dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux$-$cluster mass relation ($Y_\mathrm{SZ}-M$), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines $Y_\mathrm{SZ}$ and concentration of ionized gas ($c_\mathrm{gas}$): $M \propto Y_\mathrm{conc}^{3/5} \equiv Y_\mathrm{SZ}^{3/5} (1-A\, c_\mathrm{gas})$. $Y_\mathrm{conc}$ reduces the scatter in the predicted $M$ by $\sim 20-30$% for large clusters ($M\gtrsim 10^{14}\, h^{-1} \, M_\odot$) at both high and low redshifts, as compared to using just $Y_\mathrm{SZ}$. We show that the dependence on $c_\mathrm{gas}$ is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test $Y_\mathrm{conc}$ on clusters from simulations of the CAMELS project and show that $Y_\mathrm{conc}$ is robust against variations in cosmology, astrophysics, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from current and upcoming CMB and X-ray surveys like ACT, SO, SPT, eROSITA and CMB-S4.
13 pages, 12 figures
It has recently been demonstrated experimentally that a turbulent plasma created by the collision of two inhomogeneous, asymmetric, weakly magnetised laser-produced plasma jets can generate strong stochastic magnetic fields via the small-scale turbulent dynamo mechanism, provided the magnetic Reynolds number of the plasma is sufficiently large. In this paper, we compare such a plasma with one arising from two pre-magnetised plasma jets whose creation is identical save for the addition of a strong external magnetic field imposed by a pulsed magnetic field generator (`MIFEDS'). We investigate the differences between the two turbulent systems using a Thomson-scattering diagnostic, X-ray self-emission imaging and proton radiography. The Thomson-scattering spectra and X-ray images suggest that the presence of the external magnetic field has a limited effect on the plasma dynamics in the experiment. While the presence of the external magnetic field induces collimation of the flows in the colliding plasma jets and the initial strengths of the magnetic fields arising from the interaction between the colliding jets are significantly larger as a result of the external field, the energy and morphology of the stochastic magnetic fields post-amplification are indistinguishable. We conclude that, for turbulent laser-plasmas with super-critical magnetic Reynolds numbers, the dynamo-amplified magnetic fields are determined by the turbulent dynamics rather than the seed fields and modest changes in the initial flow dynamics of the plasma, a finding consistent with theoretical expectations and simulations of turbulent dynamos.
12 pages, 11 figures
The equation of state (EoS) of the strongly interacting cold and ultra-dense matter remains a major challenge in the field of nuclear physics. With the advancements in measurements of neutron star masses, radii, and tidal deformabilities from electromagnetic and gravitational wave observations, neutron stars play an important role in constraining the ultra-dense matter EoS. In this work, we present a novel method that exploits deep learning techniques to reconstruct the neutron star EoS from mass-radius (M-R) observations. We employ neural networks (NNs) to represent the EoS in a model-independent way, within the range of 1-7.4 times the nuclear saturation density. In an unsupervised manner, we implement the Automatic Differentiation (AD) framework to optimize the EoS, as to yield through TOV equations an M-R curve that best fits the observations. We demonstrate it in rebuilding the EoS on mock data, i.e., mass-radius pairs derived from a generated set of polytropic EoSs. The results show that it is possible to reconstruct the EoS with reasonable accuracy, using just 11 mock M-R pairs observations, which is close to the current number of observations.
15 pages, 1 table, 8 figures, submitted to Physical Review D
5 pages. Visualizations available at this https URL
Submitted to AAS Journals on December 15, 2021. Comments welcome
Accepted to ApJ, 16 pages, 9 figures
Accepted for publication in ApJ
14 pages, 6 figures, 5 tables. Nature, accepted
25 pages, 29 figures, submitted to MNRAS
8 pages, 9 figures, 2 tables. Accepted for publication in A&A
9 pages, 2 figures. Accepted for publication in A&A. Results table at this https URL
17 pages, 6 figures, accepted to ApJ
18 pages, 15 figures; Submitted to ApJ
11 pages, 8 figures, Published in conjunction with two other manuscripts, Planetary Science Journal, in press
6 pages, 2 figures, Accepted to the Planetary Science Journal
Planetary Science Journal, in press
60 pages, 22 figures, 9 tables, accepted for publication in The Astrophysical Journal
Submitted to ApJ, comments welcome
16 pages, 4 figures, 7 tables, accepted for publication in MNRAS
12 pages, 11 figures, accepted for publication in ApJ
Accepted for publication in Astronomy & Astrophysics
18 pages, 13 figures, accepted for publication in A&A
18 pages, 10 figures, invited paper conference
20 pages, 18 figures, 7 tables, to be published in Astronomy & Astrophysics
Accepted for publication in Astronomy and Astrophysics. Part of the CASCADES series of papers
Accepted to MNRAS. 19 pages, 11 figures
Accepted for publication in ApJS. 54 pages, 17 figures, 4 tables. See this http URL for figures with full resolution. Code avaliable at this https URL
16 pages, 20 figures
8 pages, 6 figures, submitted to A&A. arXiv admin note: text overlap with arXiv:2007.02952
10+5 pages, 7 figures
13 pages, 11 figures
10 pages, 11 figures. Accepted for publication in A&A
9 pages, 6 figures, 4 tables. Submitted to MNRAS
6 pages, 6 figures, Proceedings of the 40th meeting of the Polish Astronomical Society, submitted
14 pages, 16 figures. To be published in MNRAS
4 pages , 2 figures , ADASS XXX1 Conference Proceedings held on 24-28 October 2021
20 pages, 24 figures. Accepted to be published in MNRAS
26 pages, 16 figures, accepted to MNRAS
17 pages, 12 figures, 5 tables. Accepted for publication in MNRAS
8 pages, 4 figures, Proceedings of the Mustansiriyah International Conference on Applied Physics (MICAP-2021)
3 pages, submitted to Phys. Rev. Lett
8 pages, 8 figures
14 pages, 7 figures