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We present a catalogue of photometric redshifts for galaxies from Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys, which includes |$\sim 0.18$| billion sources covering 14 000 |$\deg ^2$|⁠. The photometric redshifts, along with their uncertainties, are estimated through galaxy images in three optical bands (g, r , and z) from DESI and two near-infrared bands (⁠|$W1$| and |$W2$|⁠) from Wide-field Infrared Survey Explorer (WISE) using a Bayesian neural network (BNN). The training of BNN is performed by above images and their corresponding spectroscopic redshifts given in DESI Early Data Release (EDR). Our results show that categorizing galaxies into individual groups based on their inherent characteristics and estimating their photo- z s within their group separately can effectively improve the performance. Specifically, the galaxies are categorized into four distinct groups based on DESI's target selection criteria: bright galaxy sample (BGS), luminous red galaxies (LRG), emission-line galaxies (ELG), and a group comprising the remaining sources, referred to as NON. As measured by outliers of |$|\Delta z| \gt 0.15 (1 + z_{\rm true})$|⁠ , accuracy |$\sigma _{\rm NMAD}$|⁠ , and mean uncertainty |$\overline{E}$| for BNN, we achieve low outlier percentage, high accuracy, and low uncertainty: 0.14  per cent, 0.018, and 0.0212 for BGS and 0.45  per cent, 0.026, and 0.0293 for LRG respectively, surpassing results without categorization. However, the photo- z s for ELG cannot be reliably estimated, showing result of |$\gt 15~\,\mathrm{ per\,cent}$|⁠ , |$\sim 0.1$|⁠ , and |$\sim 0.1$| irrespective of training strategy. On the other hand, NON sources can reach 1.9  per cent, 0.039, and 0.0445 when a magnitude cut of |$z\lt 21.3$| is applied. Our findings demonstrate that estimating photo- z s directly from galaxy images is significantly potential, and to achieve high-quality photo- z measurement for ongoing and future large-scale imaging survey, it is sensible to implement categorization of sources based on their characteristics. [ABSTRACT FROM AUTHOR]

Void number count (VNC) indicates the number of low-density regions in the large-scale structure (LSS) of the Universe, and we propose to use it as an effective cosmological probe. By generating the galaxy mock catalogue based on Jiutian simulations and considering the spectroscopic survey strategy and instrumental design of the China Space Station Telescope (CSST), which can reach a magnitude limit |$\sim$| 23 AB mag and spectral resolution |$R\gtrsim 200$| with a sky coverage of 17 500 deg2, we identify voids using the watershed algorithm without any assumption of void shape and obtain the mock void catalogue and data of the VNC in six redshift bins from |$z=0.3$| to 1.3. We use the Markov chain Monte Carlo method to constrain the cosmological and VNC parameters. The void linear underdensity threshold |$\delta _{\rm v}$| in the theoretical model is set to be a free parameter at a given redshift to fit the VNC data and explore their redshift evolution. We find that the VNC can correctly derive the cosmological information, and the constraint strength on the cosmological parameters is comparable to that from the void size function method, which can reach a few per cent level in the CSST full spectroscopic survey. This is because, since the VNC is not sensitive to void shape, the modified theoretical model can match the data better by integrating over void features, and more voids could be included in the VNC analysis by applying simpler selection criteria, which will improve the statistical significance. It indicates that the VNC can be an effective cosmological probe for exploring the LSS. [ABSTRACT FROM AUTHOR]

Void size function (VSF) contains the information of the cosmic large-scale structure (LSS), and can be used to derive the properties of dark energy and dark matter. We predict the VSFs measured from the spectroscopic galaxy survey operated by China's Space Survey Telescope (CSST), and study the strength of cosmological constraint. We employ a high-resolution Jiutian simulation to get CSST galaxy mock samples based on an improved semi-analytical model. We identify voids from this galaxy catalogue using the watershed algorithm without assuming a spherical shape, and estimate the VSFs at different redshift bins from |$z=0.5$| to 1.1. We propose a void selection method based on the ellipticity, and assume the void linear underdensity threshold |$\delta _{\rm v}$| in the theoretical model is redshift-dependent and set it as a free parameter in each redshift bin. The Markov Chain Monte Carlo method is adopted to implement the constraints on the cosmological and void parameters. We find that the CSST VSF measurement can constrain the cosmological parameters to a few per cent level. The best-fitting values of |$\delta _{\rm v}$| are ranging from |$\sim -0.4$| to |$-0.1$| as the redshift increases from 0.5 to 1.1, which has a distinct difference from the theoretical calculation with |$\delta _{\rm v}\simeq -2.7$| assuming the spherical evolution and using particles as tracer. Our method can provide a good reference for the void identification and selection in the VSF analysis of the spectroscopic galaxy surveys. [ABSTRACT FROM AUTHOR]

Type Ia supernova (SN Ia) as a standard candle is an ideal tool to measure cosmic distance and expansion history of the Universe. Here, we investigate the SN Ia photometric measurement in the China Space Station Telescope Ultra Deep Field (CSST-UDF) survey, and study the constraint power on the cosmological parameters, such as the equation of state of dark energy. The CSST-UDF survey is expected to cover a 9 deg2 sky area in 2 yr with 250 s × 60 exposures for each band. The magnitude limit can reach i ≃ 26 AB mag for 5σ point source detection with a single exposure. We generate light-curve mock data for SNe Ia and different types of core-collapse SNe (CCSNe). sncosmo is chosen as the framework by utilizing the salt3 model to simulate SN Ia data. After selecting high-quality data and fitting the light curves, we derive the light-curve parameters and identify CCSNe as contamination, resulting in ∼2200 SNe with an |$\sim\!\! 7{{\ \rm per\, cent}}$| CCSN contamination rate. We adopt a calibration method similar to Chauvenet's criterion, and apply it to the distance modulus data to further reduce the contamination. We find that this method is effective and can suppress the contamination fraction to |$\sim\!\! 3.5{{\ \rm per\, cent}}$| with 2012 SNe Ia and 73 CCSNe. In the cosmological fitting stage, we did not distinguish between SNe Ia and CCSNe. We find that the constraint accuracies on ΩM, ΩΛ, and w are about two times better than the current SN surveys, and they could be further improved by a factor of ∼1.4 if including the baryon acoustic oscillation data from the CSST spectroscopic wide-field galaxy survey. [ABSTRACT FROM AUTHOR]

At the Royal Society meeting in 2023, we have mainly presented our lunar orbit array concept called DSL, and also briefly introduced a concept of a lunar surface array, LARAF. As the DSL concept had been presented before, in this article, we introduce the LARAF. We propose to build an array in the far side of the Moon, with a master station which handles the data collection and processing, and 20 stations with maximum baseline of 10 km. Each station consists of 12 membrane antenna units, and the stations are connected to the master station by power line and optical fibre. The array will make interferometric observation in the 0.1–50 MHz band during the lunar night, powered by regenerated fuel cells. The whole array can be carried to the lunar surface with a heavy rocket mission, and deployed with a rover in eight months. Such an array would be an important step in the long-term development of lunar-based ultralong wavelength radio astronomy. It has a sufficiently high sensitivity to observe many radio sources in the sky, though still short of the dark age fluctuations. We discuss the possible options in the power supply, data communication, deployment etc. This article is part of a discussion meeting issue 'Astronomy from the Moon: the next decades (part 2)'. [ABSTRACT FROM AUTHOR]

Ultralight axion (ULA) can be one of the potential candidates for dark matter. The extremely low mass of the ULA can lead to a de Broglie wavelength the size of galaxies which results in a suppression of the growth of structure on small scales. In this work, we forecast the constraint on the ULA particle mass m a and relative fraction to dark matter f a = Ωa/Ωd for the forthcoming Stage IV space-based optical survey equipment CSST (China Space Station Telescope). We focus on the CSST cosmic shear and galaxy clustering photometric surveys, and forecast the measurements of shear, galaxy, and galaxy–galaxy lensing power spectra (i.e. 3 × 2 pt). The effects of neutrino, baryonic feedback, and uncertainties of intrinsic alignment, shear calibration, galaxy bias, and photometric redshift are also included in the analysis. After performing a joint constraint on all the cosmological and systematical parameters based on the simulated data from the theoretical prediction, we obtain a lower limit of the ULA particle mass log10 (m a/eV) ≥ −22.5 and an upper limit of the ULA fraction f a ≤ 0.83 at 95 per cent confidence level, and log10 (m a/eV) ≥ −21.9 with f a ≤ 0.77 when ignoring the baryonic feedback. We find that the CSST photometric surveys can improve the constraint on the ULA mass by about one order of magnitude, compared to the current constraints using the same kind of observational data. [ABSTRACT FROM AUTHOR]

A large number of binary neutron star (BNS) mergers are expected to be detected by gravitational wave (GW) detectors and the electromagnetic (EM) counterparts (e.g. kilonovae) of a fraction of these mergers may be detected in multibands by large area survey telescopes. For a given number of BNS mergers detected by their GW signals, the expected numbers of their EM counterparts that can be detected by a survey with given selection criteria depend on the kilonova properties, including the anisotropy. In this paper, we investigate whether the anisotropy of kilonova radiation and the kilonova model can be constrained statistically by the counting method, i.e. using the numbers of BNS mergers detected via GW and multiband EM signals. Adopting simple models for the BNS mergers, afterglows, and a simple two (blue and red)-component model for kilonovae, we generate mock samples for GW detected BNS mergers, their associated kilonovae, and afterglows detected in multibands. By assuming some criteria for searching the EM counterparts, we simulate the observations of these EM counterparts and obtain the EM observed samples in different bands. With the numbers of BNS mergers detected by GW detectors and EM survey telescopes in different bands, we show that the anisotropy of kilonova radiation and the kilonova model can be well constrained by using the Bayesian analysis. Our results suggest that the anisotropy of kilonova radiation may be demographically and globally constrained by simply using the detection numbers of BNS mergers by GW detectors and EM survey telescopes in multibands. [ABSTRACT FROM AUTHOR]

Line intensity mapping (LIM) is a promising probe to study star formation, the large-scale structure of the Universe, and the epoch of reionization (EoR). Since carbon monoxide (CO) is the second most abundant molecule in the Universe except for molecular hydrogen H2, it is suitable as a tracer for LIM surveys. However, just like other LIM surveys, CO intensity mapping also suffers strong foreground contamination that needs to be eliminated for extracting valuable astrophysical and cosmological information. In this work, we take 12CO(⁠|$\it J$|  = 1-0) emission line as an example to investigate whether deep learning method can effectively recover the signal by removing the foregrounds. The CO(1-0) intensity maps are generated by N-body simulations considering CO luminosity and halo mass relation, and we discuss two cases with median and low CO signals by comparing different relations. We add foregrounds generated from real observations, including thermal dust, spinning dust, free–free, synchrotron emission, and cosmic microwave background anisotropy. The beam with sidelobe effect is also considered. Our deep learning model is built upon ResUNet, which combines image generation algorithm UNet with the state-of-the-art architecture of deep learning, ResNet. The principal component analysis (PCA) method is employed to preprocess data before feeding it to the ResUNet. We find that, in the case of low instrumental noise, our UNet can efficiently reconstruct the CO signal map with correct line power spectrum by removing the foregrounds and recovering PCA signal loss and beam effects. Our method also can be applied to other intensity mappings like neutral hydrogen 21-cm surveys. [ABSTRACT FROM AUTHOR]

The cross-correlation of optical galaxies with the neutral hydrogen (H  i) radiation intensity can enhance the signal-to-noise ratio (SNR) of the H  i intensity measurement. In this paper, we investigate the cross-correlation of the galaxy samples obtained by the spectroscopic survey of the China Space Station Telescope (CSST) with the H  i Intensity mapping (IM) survey of the Five-hundred-meter Aperture Spherical Telescope (FAST). Using the IllusitrisTNG simulation result at redshift 0.2 ∼ 0.3, we generate mock data of the CSST survey and a FAST L -band drift scan survey. The CSST spectroscopic survey can yield a sample of galaxies with a high comoving number density of |$10^{-2}\, ({\ \rm Mpc}\,h^{ -1})^{-3}$| at z ∼ 0.3. We cross-correlate the foreground-removed radio intensity with the CSST galaxies, including both the whole sample, and red and blue galaxy subsamples separately. We find that in all cases the H  i and optical galaxies are well correlated. The total H  i abundance can be measured with a high precision from this correlation. A relative error of |$\sim 0.6{{\ \rm per\ cent}}$| for |$\Omega _{\rm H\, \small {I}}$| could be achieved at z ∼ 0.3 for an overlapping survey area of |$10\,000\, {\ \rm deg}^2$|⁠. [ABSTRACT FROM AUTHOR]