18 pages, 12 figures, accepted for publication in The Astrophysical Journal
Binary evolution leads to the formation of important objects crucial to the development of astrophysics, but the statistical properties of binary populations are still poorly understood. The LAMOST-MRS has provided a large sample of stars to study the properties of binary populations, especially for the mass ratio distributions and the binary fractions. We have devised a Peak Amplitude Ratio (PAR) approach to derive the mass ratio of a binary system based on results obtained from its spectrum. By computing a cross-correlation function (CCF), we established a relationship between the derived mass ratio and the PARs of the binary systems. By utilizing spectral observations obtained from LAMSOT DR6 & DR7, we applied the PAR approach to form distributions of the derived mass ratio of the binary systems to the spectral types. We selected the mass ratio within the range of $0.6-1.0$ for investigating the mass-ratio distribution. Through a power-law fitting, we obtained the power index $\gamma$ values of $-0.42\pm0.27$, $0.03\pm0.12$, and $2.12\pm0.19$ for A-, F-, and G-type stars identified in the sample, respectively. The derived $\gamma$-values display an increasing trend toward lower primary star masses, and G-type binaries tend to be more in twins. The close binary fractions (for $P\lesssim 150\,{\rm d}$ and $q\gtrsim 0.6$) in our sample for A, F and G binaries are $7.6\pm 0.5 \%$, $4.9\pm 0.2 \%$ and $3.7 \pm 0.1 \%$, respectively. Note that the PAR approach can be applied to large spectroscopic surveys of stars.
31 pages, 10 figures, Submitted for publication. Comments welcome. Code will be made available upon publication
We present a data-driven method for reconstructing the galactic acceleration field from phase-space measurements of stellar streams. Our approach is based on a flexible and differentiable fit to the stream in phase-space, enabling a direct estimate of the acceleration vector along the stream. Reconstruction of the local acceleration field can be applied independently to each of several streams, allowing us to sample the acceleration field due to the underlying galactic potential across a range of scales. Our approach is methodologically different from previous works, since a model for the gravitational potential does not need to be adopted beforehand. Instead, our flexible neural-network-based model treats the stream as a collection of orbits with a locally similar mixture of energies, rather than assuming that the stream delineates a single stellar orbit. Accordingly, our approach allows for distinct regions of the stream to have different mean energies, as is the case for real stellar streams. Once the acceleration vector is sampled along the stream, standard analytic models for the galactic potential can then be rapidly constrained. We find our method recovers the correct parameters for a ground-truth triaxial logarithmic halo potential when applied to simulated stellar streams. Alternatively, we demonstrate that a flexible potential can be constrained with a neural network, though standard multipole expansions can also be constrained. Our approach is applicable to simple and complicated gravitational potentials alike, and enables potential reconstruction from a fully data-driven standpoint using measurements of slowly phase-mixing tidal debris.
17 pages, 11 figures
We present a method for fast evaluation of the covariance matrix for a two-point galaxy correlation function (2PCF) measured with the Landy-Szalay estimator. The standard way of evaluating the covariance matrix consists in running the estimator on a large number of mock catalogs, and evaluating their sample covariance. With large random catalog sizes (data-to-random objects ratio M>>1) the computational cost of the standard method is dominated by that of counting the data-random and random-random pairs, while the uncertainty of the estimate is dominated by that of data-data pairs. We present a method called Linear Construction (LC), where the covariance is estimated for small random catalogs of size M = 1 and M = 2, and the covariance for arbitrary M is constructed as a linear combination of these. We validate the method with PINOCCHIO simulations in range r = 20-200 Mpc/h, and show that the covariance estimate is unbiased. With M = 50 and with 2 Mpc/h bins, the theoretical speed-up of the method is a factor of 14. We discuss the impact on the precision matrix and parameter estimation, and derive a formula for the covariance of covariance.
35 pages, 9 figures, published in Nuclear Instruments and Methods in Physics Research Section A
The High Altitude Water Cherenkov (HAWC) gamma-ray observatory observes atmospheric showers produced by incident gamma rays and cosmic rays with energy from 300 GeV to more than 100 TeV. A crucial phase in analyzing gamma-ray sources using ground-based gamma-ray detectors like HAWC is to identify the showers produced by gamma rays or hadrons. The HAWC observatory records roughly 25,000 events per second, with hadrons representing the vast majority ($>99.9\%$) of these events. The standard gamma/hadron separation technique in HAWC uses a simple rectangular cut involving only two parameters. This work describes the implementation of more sophisticated gamma/hadron separation techniques, via machine learning methods (boosted decision trees and neural networks), and summarizes the resulting improvements in gamma/hadron separation obtained in HAWC.
19 pages, 7 figures, 4 tables - submitted to A&A
31 pages, 16 figures, 7 tables, in submission to AAS Journals, including some minor updates based on feedback we have received from the community
29 pages, 15 figures, 7 tables, 2 appendices; submitted to MNRAS. This is a companion paper to Pop et al. 2022: "Unifying Sunyaev-Zel'dovich and X-ray predictions from clusters to galaxy groups: the impact of X-ray mass estimates on the Y-M scaling relation"
30 pages, 14 figures
25 Pages, 20 Figures, MNRAS Accepted
Published in the 'The Astrophysical Journal'
12 pages, 5 figures; submitted to MNRAS. This is a companion paper to Pop et al. 2022: "Sunyaev-Zel'dovich effect and X-ray scaling relations of galaxies, groups and clusters in the IllustrisTNG simulations"
24 pages, 12 figures, 2 tables
24 pages, 12 figures, accepted for publication on ApJS
11 pages, 4 figures, 1 table
20 pages, 21 figures, 3 tables. Accepted for publication in AJ
16 pages, 11 figures, 1 table. Accepted for publication in MNRAS main journal
7 pages, 9 figures
11 pages, 4 figures, accepted for publication in Astrophysics and Space Science
20 pages, 10 figures, accepted for publication in MNRAS
33 pages, 11 figures, 8 tables (incl. Supplementary Information). Accepted version; published as Cotton et al. (2022), Nature Astro., Vol 6, pp 154-164. Data is available through VizieR as: J/other/NatAs/6.154. Two explainer articles are available: Cotton & Buzasi (2022), Nature Astro., Vol 6, pp 24-25; Baade (2022), Nature Astro., Vol 6, pp 20-21
12 pages, 5 figures, accepted by ApJ
16 pages, 8 figures
14 pages, 6 figures, accepted for publication in ApJ
Invited review article for AAPPS-bulletin
22 pages, 2 figures, 1 table. Accepted for publication in Solar Physics journal
9 pages, 10 figures, Accepted for publication in MNRAS
12 pages and 12 figures. Submitted to PRD
17 pages (including bibliography), no figures
22 pages, 18 figures, 3 tables (plus appendix). Submitted to A&A
9 pages, 10 figures, accepted for publication in MNRAS
Accepted for publication in PASJ (May 23, 2022)
5 pages, 2 figures, submitted to AAS Journals
12 pages, 12 figures
Will be submitted in two days to allow for comments
11 pages, 5 figures, 3 tables; Accepted for publication in MNRAS
18 pages, 9 figures, and 3 tables. Acepted to be published in the Astrophysical Journal
25 pages, 17 figures, submitted to ApJ
Accepted for publication in MNRAS. 9 pages, 10 figures, 3 tables
17 pages, 11 figures. Submitted to AAS Journals
20 pages
in press in Phys. Rev E (Letter). For Supplemental Material, see this http URL
20 pages, 5 figures
23 pages, 6 figures, 1 table
NPB Accepted
20 pages, 4 figures
36 pages, 10 figures
7 pages, 3 figures