Opportunities to search for extraterrestrial intelligence with the FAST

The discovery of ubiquitous habitable extrasolar planets,combined with revolutionary advances in instrumentation and observational capabilities,has ushered in a renaissance in the search for extraterrestrial intelligence(SETI).Large scale SETI activities are now underway at numerous international facilities.The Five-hundred-meter Aperture Spherical radio Telescope(FAST)is the largest single-aperture radio telescope in the world,and is well positioned to conduct sensitive searches for radio emission indicative of exo-intelligence.SETI is one of the five key science goals specified in the original FAST project plan.A collaboration with the Breakthrough Listen Initiative was initiated in 2016 with a joint statement signed both by Dr.Jun Yan,the then director of National Astronomical Observatories,Chinese Academy of Sciences(NAOC),and Dr.Peter Worden,Chairman of the Breakthrough Prize Foundation.In this paper,we highlight some of the unique features of FAST that will allow for novel SETI observations.We identify and describe three different signal types indicative of a technological source,namely,narrow band,wide-band artificially dispersed and modulated signals.Here,we propose observations with FAST to achieve sensitivities never before explored.For nearby exoplanets,such as TESS targets,FAST will be sensitive to an EIRP of 1.9×1011 W,well within the reach of current human technology.For the Andromeda Galaxy,FAST will be able to detect any Kardashev type II or more advanced civilization there.

Genome size evolution of the extant lycophytes and ferns

Ferns and lycophytes have remarkably large genomes.However,little is known about how their genome size evolved in fern lineages.To explore the origins and evolution of chromosome numbers and genome size in ferns,we used flow cytometry to measure the genomes of 240 species(255 samples)of extant ferns and lycophytes comprising 27 families and 72 genera,of which 228 species(242 samples)represent new reports.We analyzed correlations among genome size,spore size,chromosomal features,phylogeny,and habitat type preference within a phylogenetic framework.We also applied ANOVA and multinomial logistic regression analysis to preference of habitat type and genome size.Using the phylogeny,we conducted ancestral character reconstruction for habitat types and tested whether genome size changes simultaneously with shifts in habitat preference.We found that 2 C values had weak phylogenetic signal,whereas the base number of chromosomes(x)had a strong phylogenetic signal.Furthermore,our analyses revealed a positive correlation between genome size and chromosome traits,indicating that the base number of chromosomes(x),chromosome size,and polyploidization may be primary contributors to genome expansion in ferns and lycophytes.Genome sizes in different habitat types varied significantly and were significantly correlated with habitat types;specifically,multinomial logistic regression indicated that species with larger 2 C values were more likely to be epiphytes.Terrestrial habitat is inferred to be ancestral for both extant ferns and lycophytes,whereas transitions to other habitat types occurred as the major clades emerged.Shifts in habitat types appear be followed by periods of genomic stability.Based on these results,we inferred that habitat type changes and multiple whole-genome duplications have contributed to the formation of large genomes of ferns and their allies during their evolutionary history.

Model comparison of dark energy models using deep network

This work uses a combination of a variational auto-encoder and generative adversarial network to compare different dark energy models in light of observations, e.g., the distance modulus from type Ia supernovae. The network finds an analytical variational approximation to the true posterior of the latent parameters in the models, yielding consistent model comparison results with those derived by the standard Bayesian method, which suffers from a computationally expensive integral over the parameters in the product of the likelihood and the prior. The parallel computational nature of the network together with the stochastic gradient descent optimization technique leads to an efficient way to compare the physical models given a set of observations. The converged network also provides interpolation for a dataset, which is useful for data reconstruction.

Forecasting supernova observations with the CSST:I.Photometric samples

The China Space Station Telescope(CSST,also known as Xuntian)is a serviceable two-meter-aperture wide-field telescope operating in the same orbit as the China Space Station.The CSST plans to survey a sky area of 17,500 deg^(2)of the medium-to-high Galactic latitude to a depth of 25-26 AB mag in at least 6 photometric bands over 255-1,000 nm.Within such a large sky area,slitless spectra will also be taken over the same wavelength range as the imaging survey.Even though the CSST survey is not dedicated to time-domain studies,it would still detect a large number of transients,such as supernovae(SNe).In this paper,we simulate photometric SN observations based on a strawman survey plan using the Sncosmo package.During its 10-year survey,the CSST is expected to observe about 5 million SNe of various types.With quality cuts,we obtain a“gold”sample that comprises roughly 7,400 SNe Ia,2,200 SNe Ibc,and 6,500 SNeⅡcandidates with correctly classified percentages reaching 91%,63%,and 93%(formally defined as classification precision),respectively.The same survey can also trigger alerts for the detection of about 15,500 SNe Ia(precision 61%)and 2,100 SNeⅡ(precision 49%)candidates at least two days before the light maxima.Moreover,the near-ultraviolet observations of the CSST will be able to catch hundreds of shock-cooling events serendipitously every year.These results demonstrate that the CSST can make a potentially significant contribution to SN studies.

Forecast of cosmological constraints with type Ia supernovae from the Chinese Space Station Telescope

The 2-m aperture Chinese Space Station Telescope(CSST),which observes at wavelengths ranging from 255 to 1000 nm,is expected to start science operations in 2024.An ultra-deep field observation program covering approximately 10 deg2is proposed with supernovae(SNe) and other transients as one of its primary science drivers.This paper presents the simulated detection results of type Ⅰa supernovae(SNe Ⅰa) and explores the impact of new datasets on the determinations of cosmological parameters.The simulated observations are conducted with an exposure time of 150 s and cadences of 10,20,and 30 d.The survey mode covering a total of 80 observations but with a random cadence in the range of 4 to 14 d is also explored.Our simulation results indicate that the CSST can detect up to ~1800 SNe la at z <1.3.The simulated SNe la are then used to constrain the cosmological parameters.The constraint on can be improved by 37.5% using the 10-d cadence sample in comparison with the Pantheon sample.A deeper measurement simulation with a 300 s exposure time together with the Pantheon sample improves the current constraints on Ωmby 58.3% and ω by 47.7%.Taking future lager sets of nearby SN Ⅰa sample form ground-based surveys(i.e.,N~3400) into consideration,the constraints on ω can be improved by 59.1%.The CSST ultra-deep field observation program is expected to discover large amounts of SNe la over a broad redshift span and enhance our understanding of the nature of dark energy.