Data-driven Expectations for Electromagnetic Counterpart Searches Based on LIGO/Virgo Public Alerts

Searches for electromagnetic counterparts of gravitational-wave signals have redoubled since the first detection in 2017 of a binary neutron star merger with a gamma-ray burst, optical/infrared kilonova, and panchromatic afterglow. Yet, one LIGO/Virgo observing run later, there has not yet been a second, secure identification of an electromagnetic counterpart. This is not surprising given that the localization uncertainties of events in LIGO and Virgo’s third observing run, O3, were much larger than predicted. We explain this by showing that improvements in data analysis that now allow LIGO/Virgo to detect weaker and hence more poorly localized events have increased the overall number of detections, of which well-localized, gold-plated events make up a smaller proportion overall. We present simulations of the next two LIGO/Virgo/KAGRA observing runs, O4 and O5, that are grounded in the statistics of O3 public alerts. To illustrate the significant impact that the updated predictions can have, we study the follow-up strategy for the Zwicky Transient Facility. Realistic and timely forecasting of gravitational-wave localization accuracy is paramount given the large commitments of telescope time and the need to prioritize which events are followed up. We include a data release of our simulated localizations as a public proposal planning resource for astronomers.

CH3-Terminated Carbon Chains in the GOTHAM Survey of TMC-1: Evidence of Interstellar CH3C7N

We report a systematic study of all known methyl carbon chains toward TMC-1 using the second data release of the GOTHAM survey, as well as a search for larger species. Using Markov Chain Monte Carlo simulations and spectral line stacking of over 30 rotational transitions, we report statistically significant emission from methylcyanotriacetylene (CH3C7N) at a confidence level of 4.6σ, and use it to derive a column density of ∼1011 cm−2. We also searched for the related species, methyltetraacetylene (CH3C8H), and place upper limits on the column density of this molecule. By carrying out the above statistical analyses for all other previously detected methyl-terminated carbon chains that have emission lines in our survey, we assess the abundances, excitation conditions, and formation chemistry of methylpolyynes (CH3C2n H) and methylcyanopolyynes (CH3C2n-1N) in TMC-1, and compare those with predictions from a chemical model. Based on our observed trends in column density and relative populations of the A and E nuclear spin isomers, we find that the methylpolyyne and methylcyanopolyyne families exhibit stark differences from one another, pointing to separate interstellar formation pathways, which is confirmed through gas–grain chemical modeling with nautilus.

A Census of the Stellar Populations in the Sco-Cen Complex*

I have used high-precision photometry and astrometry from the early installment of the third data release of Gaia (EDR3) to perform a survey for members of the stellar populations within the Sco-Cen complex, which consist of Upper Sco, UCL/LCC, the V1062 Sco group, Ophiuchus, and Lupus. Among Gaia sources with σ π < 1 mas, I have identified 10,509 candidate members of those populations. I have compiled previous measurements of spectral types, Li equivalent widths, and radial velocities for the candidates, which are available for 3169, 1420, and 1740 objects, respectively. In a subset of candidates selected to minimize field star contamination, I estimate that the contamination is ≲1% and the completeness is ∼90% at spectral types of ≲M6–M7 for the populations with low extinction (Upper Sco, V1062 Sco, UCL/LCC). I have used that cleaner sample to characterize the stellar populations in Sco-Cen in terms of their initial mass functions, ages, and space velocities. For instance, all of the populations in Sco-Cen have histograms of spectral types that peak near M4–M5, which indicates that they share similar characteristic masses for their initial mass functions (∼0.15–0.2 M ⊙). After accounting for incompleteness, I estimate that the Sco-Cen complex contains nearly 10,000 members with masses above ∼0.01 M ⊙. Finally, I also present new estimates for the intrinsic colors of young stars and brown dwarfs (≲20 Myr) in bands from Gaia EDR3, the Two Micron All Sky Survey, the Wide-field Infrared Survey Explorer, and the Spitzer Space Telescope.

Binary Fractions of G and K Dwarf Stars Based on Gaia EDR3 and LAMOST DR5: Impacts of the Chemical Abundances

Based on the large volume Gaia Early Data Release 3 and LAMOST Data Release 5 data, we estimate the bias-corrected binary fractions of the field late G and early K dwarfs. A stellar locus outlier method is used in this work, which works well for binaries of various periods and inclination angles with single-epoch data. With a well-selected, distance-limited sample of about 90,000 GK dwarfs covering wide stellar chemical abundances, it enables us to explore the binary fraction variations with different stellar populations. The average binary fraction is 0.42 ± 0.01 for the whole sample. Thin-disk stars are found to have a binary fraction of 0.39 ± 0.02, thick-disk stars have a higher one of 0.49 ± 0.02, while inner halo stars possibly have the highest binary fraction. For both the thin- and thick-disk stars, the binary fractions decrease toward higher [Fe/H], [α/H], and [M/H] abundances. However, the suppressing impacts of [Fe/H], [α/H], and [M/H] are more significant for the thin-disk stars than those for the thick-disk stars. For a given [Fe/H], a positive correlation between [α/Fe] and the binary fraction is found for the thin-disk stars. However, this tendency disappears for the thick-disk stars. We suspect that it is likely related to the different formation histories of the thin and thick disks. Our results provide new clues for theoretical works on binary formation.

BICEP/Keck and cosmological attractors

We discuss implications of the latest BICEP/Keck data release for inflationary models, with special emphasis on the cosmological attractors which can describe all presently available inflation-related observational data. These models are compatible with any value of the tensor to scalar ratio r, all the way down to r = 0. Some of the string theory motivated models of this class predict 10-3 ≤ r ≤ 10-2. The upper part of this range can be explored by the ongoing BICEP/Keck observations.

Discovery of 2716 hot emission-line stars from LAMOST DR5

We present a catalog of 3339 hot emission-line stars (ELSs) identified from 451 695 O, B and A type spectra, provided by LAMOST Data Release 5 (DR5). We developed an automated Python routine that identified 5437 spectra having a peak between 6561 and 6568 Å. False detections and bad spectra were removed, leaving 4138 good emission-line spectra of 3339 unique ELSs. We re-estimated the spectral types of 3307 spectra as the LAMOST Stellar Parameter Pipeline (LASP) did not provide accurate spectral types for these emission-line spectra. As Herbig Ae/Be stars exhibit higher excess in near-infrared and mid-infrared wavelengths than classical Ae/Be stars, we relied on 2MASS and WISE photometry to distinguish them. Finally, we report 1089 classical Be, 233 classical Ae and 56 Herbig Ae/Be stars identified from LAMOST DR5. In addition, 928 B[em]/A[em] stars and 240 CAe/CBe potential candidates are identified. From our sample of 3339 hot ELSs, 2716 ELSs identified in this work do not have any record in the SIMBAD database and they can be considered as new detections. Identification of such a large homogeneous set of emission-line spectra will help the community study the emission phenomenon in detail without worrying about the inherent biases when compiling from various sources.

A Catalog of 323 Cataclysmic Variables from LAMOST DR6

In this work, we present a catalog of cataclysmic variables (CVs) identified from the sixth data release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 Hα emission spectra, and obtain a classification accuracy of the algorithm of over 99.6% from the cross-validation set. We then apply the template-matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which help us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all of the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further classify the new CV candidates in subtypes based on their spectral features, including five DN subtypes during outbursts, five NL subtypes, and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry and confirm their previous classification with the LAMOST spectra.

Estimation of Photometric Redshifts. I. Machine-learning Inference for Pan-STARRS1 Galaxies Using Neural Networks

We present a new machine-learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle the difficulty for inferring photometric redshifts. Moreover, to reduce bias induced by the new model's ability to deal with estimation difficulty, it exploits the power of ensemble learning. We extensively examine the mapping between input features and target redshift spaces to which the model is validly applicable to discover the strength and weaknesses of the trained model. Because our trained model is well calibrated, our model produces reliable confidence information about objects with non-catastrophic estimation. While our model is highly accurate for most test examples residing in the input space, where training samples are densely populated, its accuracy quickly diminishes for sparse samples and unobserved objects (i.e., unseen samples) in training. We report that out-of-distribution (OOD) samples for our model contain both physically OOD objects (i.e., stars and quasars) and galaxies with observed properties not represented by training data. The code for our model is available at https://github.com/GooLee0123/MBRNN for other uses of the model and retraining the model with different data.