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The ephemerides of minor planets are computed on the basis of astrometric observations. The asteroid orbit determination process requires these observations to be properly weighted to take into account the expected accuracy of the data. If not directly provided by the observers, the weights are, in general, computed after a station-specific statistical analysis on the observation residuals, where the influence of external factors such as epoch of observation, magnitude, and employed catalogue has been proven. In this paper, we perform a statistical analysis on observation residuals of the major surveys taking into account a new factor, i.e. the dynamical classification of asteroids, to understand whether the observation quality may have a dependence on the different type of observed object. If an influence is actually found, then it will be possible to develop a new weighting system based on these results. The weights will be easily applicable once one knows the asteroid orbit. In particular, four stations have been found having different qualities depending on whether they are observing near-Earth asteroids or main-belt asteroids. Moreover, the cross-correlation between the dynamic classification and epoch, magnitude, and catalogue is investigated, as well as the influence of these factors on observations’ quality.

We reanalyse observations of type Ia supernovae (SNe) and Cepheids used in the local determination of the Hubble constant and find strong evidence that SN standardisation in the calibration sample (galaxies with observed Cepheids) require a steeper slope of the colour correction than in the cosmological sample (galaxies in the Hubble flow). The colour correction in the calibration sample is consistent with being entirely due to an extinction correction due to dust with properties similar to that of the Milky Way (R_B~4.6+/-0.4) and there is no evidence for intrinsic scatter in the SN peak magnitudes. An immediate consequence of this finding is that the local measurement of the Hubble constant becomes dependent on the choice of SN reference colour, i.e., the colour of an unreddened SN. Specifically, the Hubble constant inferred from the same observations decreases gradually with the reference colour assumed in the SN standardisation. We recover the Hubble constant measured by SH0ES for the standard choice of reference colour (SALT2 colour parameter c=0) while for a reference colour which coincides with the blue end of the observed SN colour distribution (c~-0.13), the Hubble constant from Planck observations of the CMB (assuming a flat LCDM cosmological model) is recovered. These results are intriguing in that they may provide an avenue for resolving the Hubble tension. However, since there is no obvious physical basis for the differences in colour corrections in the two SN samples, the origin of these require further investigations., Comment: 12 pages, 4 figures, 3 tables; accepted for publication in MNRAS

The increase in data volume is challenging the suitability of non-distributed and non-scalable algorithms, despite advancements in hardware. An example of this challenge is clustering. Considering that optimal clustering algorithms scale poorly with increased data volume or are intrinsically non-distributed, accurate clustering of large datasets is increasingly resource-heavy, relying on substantial and expensive compute nodes. This scenario forces users to choose between accuracy and scalability. In this work, we introduce HiErArchical Data Splitting and Stitching (HEADSS), a Python package designed to facilitate clustering at scale. By automating the splitting and stitching, it allows repeatable handling, and removal, of edge effects. We implement HEADSS in conjunction with HDBSCAN, where we achieve orders of magnitude reduction in single node memory requirements for both non-distributed and distributed implementations, with the latter offering similar order of magnitude reductions in total run times while recovering analogous accuracy. Furthermore, our method establishes a hierarchy of features by using a subset of clustering features to split the data.

Residual foreground contamination by thermal Sunyaev–Zeldovich (tSZ) effect from galaxy clusters in cosmic microwave background (CMB) maps propagates into the reconstructed CMB lensing field, and thus biases the intrinsic cross-correlation between CMB lensing and large-scale structure (LSS). Through stacking analysis, we show that residual tSZ contamination causes an increment of lensing convergence in the central part of the clusters and a decrement of lensing convergence in the cluster outskirts. We quantify the impact of residual tSZ contamination on cross-correlations between the Planck 2018 CMB lensing convergence maps and the Sloan Digital Sky Survey-IV galaxy density data through cross-power spectrum computation. In contrast with the Planck 2018 tSZ-deprojected SMICA lensing map, our analysis using the tSZ-contaminated SMICA lensing map measures an ∼2.5 percent negative bias at multipoles ℓ ≲ 500 and transits to an ∼9 percent positive bias at ℓ ≳ 1500, which validates earlier theoretical predictions of the overall shape of such tSZ-induced spurious cross-correlation. The tSZ-induced lensing convergence field in Planck CMB data is detected with more than 1σ significance at ℓ ≲ 500 and more than 14σ significance at ℓ ≳ 1500, yielding an overall 14.8σ detection. We also show that masking galaxy clusters in CMB data is not sufficient to eliminate the spurious lensing signal, still detecting a non-negligible bias with 5.5σ significance on cross-correlations with galaxy density fields. Our results emphasize how essential it is to deproject the tSZ effect from CMB maps at the component separation stage and adopt tSZ-free CMB lensing maps for cross-correlations with LSS data., MR acknowledges support by the ERC Consolidator Grant CMBSPEC (No. 725456) as part of the European Union’s Horizon 2020 research and innovation program.

In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies. We test if SSL with fewer labels can achieve test accuracies comparable to the supervised state-of-the-art and whether this holds when incorporating previously unseen data. We find that for the radio galaxy classification problem considered, SSL provides additional regularisation and outperforms the baseline test accuracy. However, in contrast to model performance metrics reported on computer science benchmarking data-sets, we find that improvement is limited to a narrow range of label volumes, with performance falling off rapidly at low label volumes. Additionally, we show that SSL does not improve model calibration, regardless of whether classification is improved. Moreover, we find that when different underlying catalogues drawn from the same radio survey are used to provide the labelled and unlabelled data-sets required for SSL, a significant drop in classification performance is observered, highlighting the difficulty of applying SSL techniques under dataset shift. We show that a class-imbalanced unlabelled data pool negatively affects performance through prior probability shift, which we suggest may explain this performance drop, and that using the Frechet Distance between labelled and unlabelled data-sets as a measure of data-set shift can provide a prediction of model performance, but that for typical radio galaxy data-sets with labelled sample volumes of O(1000), the sample variance associated with this technique is high and the technique is in general not sufficiently robust to replace a train-test cycle., Comment: Accepted to MNRAS. 14 pages

We apply Gaussian smoothing to obtain mean magnetic field, density, velocity, and magnetic and kinetic energy densities from our numerical model of the interstellar medium, based on three-dimensional magnetohydrodynamic equations in a shearing box $1 \times 1 \times 2 \, {\rm kpc}$ in size. The interstellar medium is highly compressible, as the turbulence is transonic or supersonic; it is thus an excellent context in which to explore the use of smoothing to represent physical variables in a compressible medium in terms of their mean and fluctuating parts. Unlike alternative averaging procedures, such as horizontal averaging, Gaussian smoothing retains the three-dimensional structure of the mean fields. Although Gaussian smoothing does not obey the Reynolds rules of averaging, physically meaningful and mathematically sound central statistical moments are defined as suggested by Germano (1992). We discuss methods to identify an optimal smoothing scale $\ell$ and the effects of this choice on the results. From spectral analysis of the magnetic, density and velocity fields, we find a suitable smoothing length for all three fields, of $\ell \approx 75 \, {\rm pc}$. Such a smoothing scale is likely to be sensitive to the choice of simulation parameters; this may be considered in future work, but here we just explore the methodology. We discuss the properties of third-order statistical moments in fluctuations of kinetic energy density in compressible flows, and suggest their physical interpretation. The mean magnetic field, amplified by a mean-field dynamo, significantly alters the distribution of kinetic energy in space and between scales, reducing the magnitude of kinetic energy at intermediate scales. This intermediate-scale kinetic energy is a useful diagnostic of the importance of SN-driven outflows., 23 pages, 10 figures. Revised, submitted to Geophysical and Astrophysical Fluid Dynamics

In this work we present the results of the study of the cosmic microwave background TT power spectrum through auto-encoders in which the latent variables are the cosmological parameters. This method was trained and calibrated using a data-set composed by 80000 power spectra from random cosmologies computed numerically with the CAMB code. Due to the specific architecture of the auto-encoder, the encoder part is a model that estimates the maximum-likelihood parameters from a given power spectrum. On the other hand, the decoder part is a model that computes the power spectrum from the cosmological parameters and can be used as a forward model in a fully Bayesian analysis. We show that the encoder is able to estimate the true cosmological parameters with a precision varying from $\approx 0.004\% $ to $\approx 0.2\% $ (depending on the cosmological parameter), while the decoder computes the power spectra with a mean percentage error of $\approx 0.0018\% $ for all the multipole range. We also demonstrate that the decoder recovers the expected trends when varying the cosmological parameters one by one, and that it does not introduce any significant bias on the estimation of cosmological parameters through a Bayesian analysis. These studies gave place to the Cosmic Kite python software that is publicly available and can be downloaded and installed from https://github.com/Martindelosrios/cosmic-kite. Although this algorithm does not improve the precision of the measurements compared with the traditional methods, it reduces significantly the computation time and represents the first attempt towards forcing the latent variables to have a physical interpretation., Comment: Accepted for its publication in MNRAS

Blind cleaning methods are currently the preferred strategy for handling foreground contamination in single-dish HI intensity mapping surveys. Despite the increasing sophistication of blind techniques, some signal loss will be inevitable across all scales. Constructing a corrective transfer function using mock signal injection into the contaminated data has been a practice relied on for HI intensity mapping experiments. However, assessing whether this approach is viable for future intensity mapping surveys where precision cosmology is the aim, remains unexplored. In this work, using simulations, we validate for the first time the use of a foreground transfer function to reconstruct power spectra of foreground-cleaned low-redshift intensity maps and look to expose any limitations. We reveal that even when aggressive foreground cleaning is required, which causes ${>}\,50\%$ negative bias on the largest scales, the power spectrum can be reconstructed using a transfer function to within sub-percent accuracy. We specifically outline the recipe for constructing an unbiased transfer function, highlighting the pitfalls if one deviates from this recipe, and also correctly identify how a transfer function should be applied in an auto-correlation power spectrum. We validate a method that utilises the transfer function variance for error estimation in foreground-cleaned power spectra. Finally, we demonstrate how incorrect fiducial parameter assumptions (up to ${\pm}100\%$ bias) in the generation of mocks, used in the construction of the transfer function, do not significantly bias signal reconstruction or parameter inference (inducing ${, 25 pages, 20 figures. See Figure 4 for the main demonstration of the transfer function's performance for reconstructing signal loss from foreground cleaning. Accepted for publication in MNRAS

Recent works have shown that weak lensing magnification must be included in upcoming large-scale structure analyses, such as for the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), to avoid biasing the cosmological results. In this work we investigate whether including magnification has a positive impact on the precision of the cosmological constraints, as well as being necessary to avoid bias. We forecast this using an LSST mock catalog and a halo model to calculate the galaxy power spectra. We find that including magnification has little effect on the precision of the cosmological parameter constraints for an LSST galaxy clustering analysis, where the halo model parameters are additionally constrained by the galaxy luminosity function. In particular, we find that for the LSST gold sample ($i < 25.3$) including weak lensing magnification only improves the galaxy clustering constraint on $Ω_{\rm{m}}$ by a factor of 1.03, and when using a very deep LSST mock sample ($i, v2: Version accepted in MNRAS. Minor clarifications, conclusions unchanged

The detailed modelling of stellar oscillations is a powerful approach to characterising stars. However, poor treatment of systematics in theoretical models leads to misinterpretations of stars. Here we propose a more principled statistical treatment for the systematics to be applied to fitting individual mode frequencies with a typical stellar model grid. We introduce a correlated noise model based on a Gaussian Process (GP) kernel to describe the systematics given that mode frequency systematics are expected to be highly correlated. We show that tuning the GP kernel can reproduce general features of frequency variations for changing model input physics and fundamental parameters. Fits with the correlated noise model better recover stellar parameters than traditional methods which either ignore the systematics or treat them as uncorrelated noise., Comment: 11 pages

The local Cosmic Ray (CR) energy spectrum exhibits a spectral softening at energies around 3~PeV. Sources which are capable of accelerating hadrons to such energies are called hadronic PeVatrons. However, hadronic PeVatrons have not yet been firmly identified within the Galaxy. Several source classes, including Galactic Supernova Remnants (SNRs), have been proposed as PeVatron candidates. The potential to search for hadronic PeVatrons with the Cherenkov Telescope Array (CTA) is assessed. The focus is on the usage of very high energy $\gamma$-ray spectral signatures for the identification of PeVatrons. Assuming that SNRs can accelerate CRs up to knee energies, the number of Galactic SNRs which can be identified as PeVatrons with CTA is estimated within a model for the evolution of SNRs. Additionally, the potential of a follow-up observation strategy under moonlight conditions for PeVatron searches is investigated. Statistical methods for the identification of PeVatrons are introduced, and realistic Monte--Carlo simulations of the response of the CTA observatory to the emission spectra from hadronic PeVatrons are performed. Based on simulations of a simplified model for the evolution for SNRs, the detection of a $\gamma$-ray signal from in average 9 Galactic PeVatron SNRs is expected to result from the scan of the Galactic plane with CTA after 10 hours of exposure. CTA is also shown to have excellent potential to confirm these sources as PeVatrons in deep observations with $\mathcal{O}(100)$ hours of exposure per source., Comment: 34 pages, 16 figures, Accepted for publication in Astroparticle Physics

We present a machine learning model to classify active galactic nuclei (AGNs) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test tree-based algorithms, using training samples built from the X-ray Multi-Mirror Mission–Newton (XMM–Newton) catalogue and the Sloan Digital Sky Survey (SDSS), with labels derived from the SDSS survey. The performance was tested making use of simulations and of cross-validation techniques. With a set of features including spectroscopic redshifts and X-ray parameters connected to source properties (e.g. fluxes and extension), as well as features related to X-ray instrumental conditions, the precision and recall for AGN identification are 94 and 93 per cent, while the type 1/2 classifier has a precision of 74 per cent and a recall of 80 per cent for type 2 AGNs. The performance obtained with photometric redshifts is very similar to that achieved with spectroscopic redshifts in both test cases, while there is a decrease in performance when excluding redshifts. Our machine learning model trained on X-ray features can accurately identify AGN in extragalactic surveys. The type 1/2 classifier has a valuable performance for type 2 AGNs, but its ability to generalize without redshifts is hampered by the limited census of absorbed AGN at high redshift., FJC acknowledges financial support from the Spanish Ministry MCIU under project RTI2018-096686-B-C21 (MCIU/AEI/FEDER/UE), cofunded by FEDER funds and from the Agencia Estatal de Investigación, Unidad de Excelencia María de Maeztu, ref. MDM-2017- 0765. Funding for the Sloan Digital Sky Survey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Department of Energy Office of Science, and the Participating Institutions.

We study the demographics of z similar to 6 broad-line quasars in the black hole (BH) mass-luminosity plane using a sample of more than 100 quasars at 5.7 < z < 6.5. These quasars have well-quantified selection functions and nearly one-third of them also have virial BH masses estimated from near-IR spectroscopy. We use forward modelling of parametrized intrinsic distributions of BH masses and Eddington ratios, and account for the sample flux limits and measurement uncertainties of the BH masses and luminosities. We find significant differences between the intrinsic and observed distributions of the quantities due to measurement uncertainties and sample flux limits. There is also marginal evidence that the virial BH masses are susceptible to a positive luminosity-dependent bias (BH mass is overestimated when luminosity is above the average), and that the mean Eddington ratio increases with BH mass. Our models provide reliable constraints on the z similar to 6 BH mass function at M-BH > 10(8.5) M-circle dot, with a median 1 sigma uncertainty of similar to 0.5 dex in abundance. The intrinsic Eddington ratio distribution of M-BH > 10(8.5) M-circle dot quasars can be approximated by a mass-dependent Schechter model, with a broad peak around log (L-bol/L-Edd)similar to -0.9. We also find that, at 4.5 less than or similar to z less than or similar to 6, the number densities of more massive BHs tend to decline more rapidly with increasing redshift, contrary to the trend at 2.5 less than or similar to z less than or similar to 4.5 reported previously.

The origin of Petaelectronvolt (PeV) astrophysical neutrinos is fundamental to our understanding of the high-energy Universe. Apart from the technical challenges of operating detectors deep below ice, oceans, and lakes, the phenomenological challenges are even greater than those of gravitational waves; the sources are unknown, hard to predict, and we lack clear signatures. Neutrino astronomy therefore represents the greatest challenge faced by the astronomy and physics communities thus far. The possible neutrino sources range from accretion disks and tidal disruption events, to relativistic jets and galaxy clusters with blazar TXS~0506+056 the most compelling association thus far. Since that association, immense effort has been put into proving or disproving that jets are indeed neutrino emitters, but to no avail. By generating simulated neutrino counterpart samples, we explore the potential of detecting a significant correlation of neutrinos with jets from active galactic nuclei. We find that, given the existing challenges, even our best experiments could not have produced a $>3\sigma$ result. Larger programs over the next few years will be able to detect a significant correlation only if the brightest radio sources, rather than all jetted active galactic nuclei, are neutrino emitters. We discuss the necessary strategies required to steer future efforts into successful experiments., Comment: 8 pages, 1 figure, 1 table, accepted for publication in A&A

While Gaia has observed the phase space coordinates of over a billion stars in the Galaxy, in the overwhelming majority of cases it has only obtained five of the six coordinates, the missing dimension being the radial (line-of-sight) velocity. Using a realistic mock dataset, we show that Bayesian neural networks are highly capable of 'learning' these radial velocities as a function of the other five coordinates, and thus filling in the gaps. For a given star, the network outputs are not merely point predictions, but full posterior distributions encompassing the intrinsic scatter of the stellar phase space distribution, the observational uncertainties on the network inputs, and any 'epistemic' uncertainty stemming from our ignorance about the stellar phase space distribution. Applying this technique to the real Gaia data, we generate and publish a catalogue of posteriors (median width: 25 km/s) for the radial velocities of 16 million Gaia DR2/EDR3 stars in the magnitude range 6, Comment: Updated to match version accepted by MNRAS. Radial velocity prediction catalogues available at https://dx.doi.org/10.17639/nott.7216

Estimating the distances to asymptotic giant branch (AGB) stars using optical measurements of their parallaxes is not straightforward because of the large uncertainties introduced by their dusty envelopes, their large angular sizes, and their surface brightness variability. We compared the parallaxes from Gaia DR3 with parallaxes measured with maser observations with very long baseline interferometry (VLBI) to determine a statistical correction factor for the DR3 parallaxes using a sub-sample of 33 maser-emitting oxygen-rich nearby AGB stars. We found that the nominal errors on the Gaia DR3 parallaxes are underestimated by a factor of 5.44 for the brightest sources ($G, 15 pages, 12 figures, 3 appendices, accepted for publication in A&A, corrected typos, distance catalogue available on the CDS (and any updates that may occur)

Gamma-ray bursts (GRBs) come in two types, short and long. The distribution of logarithmic durations of long GRBs is asymmetric rather than Gaussian. Such an asymmetry, when modelled with a mixture of Gaussian distributions, requires an introduction of an additional component, often associated with another class of GRBs. However, when modelled with inherently asymmetric distributions, there is no need for such a component. The cosmological dilation was already ruled out as a source of the asymmetry, hence its origin resides in the progenitors. GRB light curves (LCs) are usually well described by a series of fast-rise-exponential-decay pulses. A statistical analysis of ensembles of simulated LCs shows that the asymmetry is a natural consequence of the pulse shape and the multi-pulse character of the LCs., Comment: 8 pages, 3+2 figures; accepted in MNRAS

Precision calibration poses challenges to experiments probing the redshifted 21-cm signal of neutral hydrogen from the Cosmic Dawn and Epoch of Reionization (z~30-6). In both interferometric and global signal experiments, systematic calibration is the leading source of error. Though many aspects of calibration have been studied, the overlap between the two types of instruments has received less attention. We investigate the sky based calibration of total power measurements with a HERA dish and an EDGES style antenna to understand the role of auto-correlations in the calibration of an interferometer and the role of sky in calibrating a total power instrument. Using simulations we study various scenarios such as time variable gain, incomplete sky calibration model, and primary beam model. We find that temporal gain drifts, sky model incompleteness, and beam inaccuracies cause biases in the receiver gain amplitude and the receiver temperature estimates. In some cases, these biases mix spectral structure between beam and sky resulting in spectrally variable gain errors. Applying the calibration method to the HERA and EDGES data, we find good agreement with calibration via the more standard methods. Although instrumental gains are consistent with beam and sky errors similar in scale to those simulated, the receiver temperatures show significant deviations from expected values. While we show that it is possible to partially mitigate biases due to model inaccuracies by incorporating a time-dependent gain model in calibration, the resulting errors on calibration products are larger and more correlated. Completely addressing these biases will require more accurate sky and primary beam models., Comment: 16 pages, 13 figures, 1 table; accepted for publication in MNRAS main journal

With the ever-increasing sensitivity and timing baselines of modern radio telescopes, a growing number of pulsars are being shown to exhibit transitions in their rotational and radio emission properties. In many of these cases, the two are correlated with pulsars assuming a unique spin-down rate ($\dot{\nu}$) for each of their specific emission states. In this work we revisit 17 radio pulsars previously shown to exhibit spin-down rate variations. Using a Gaussian process regression (GPR) method to model the timing residuals and the evolution of the profile shape, we confirm the transitions already observed and reveal new transitions in 8 years of extended monitoring with greater time resolution and enhanced observing bandwidth. We confirm that 7 of these sources show emission-correlated $\dot{\nu}$ transitions ($\Delta \dot{\nu}$) and we characterise this correlation for one additional pulsar, PSR B1642$-$03. We demonstrate that GPR is able to reveal extremely subtle profile variations given sufficient data quality. We also corroborate the dependence of $\Delta \dot{\nu}$ amplitude on $\dot{\nu}$ and pulsar characteristic age. Linking $\Delta \dot{\nu}$ to changes in the global magnetospheric charge density $\Delta \rho$, we speculate that $\dot{\nu}$ transitions associated with large $\Delta \rho$ values may be exhibiting detectable profile changes with improved data quality, in cases where they have not previously been observed., Comment: 20 pages, 16 figures, 3 tables

While most purpose-built 21cm intensity mapping experiments are close-packed interferometer arrays, general-purpose dish arrays should also be capable of measuring the cosmological 21cm signal. This can be achieved most efficiently if the array is used as a collection of scanning autocorrelation dishes rather than as an interferometer. As a first step towards demonstrating the feasibility of this observing strategy, we show that we are able to successfully calibrate dual-polarisation autocorrelation data from 64 MeerKAT dishes in the L-band (856-1712 MHz, 4096 channels), with 10.5 hours of data retained from six nights of observing. We describe our calibration pipeline, which is based on multi-level RFI flagging, periodic noise diode injection to stabilise gain drifts and an absolute calibration based on a multi-component sky model. We show that it is sufficiently accurate to recover maps of diffuse celestial emission and point sources over a 10 deg x 30 deg patch of the sky overlapping with the WiggleZ 11hr field. The reconstructed maps have a good level of consistency between per-dish maps and external datasets, with the estimated thermal noise limited to 1.4 x the theoretical noise level (~ 2 mK). The residual maps have rms amplitudes below 0.1 K, corresponding to, 24 pages, 34 figures, accepted for publication in MNRAS

Aims: We investigate the thermal and chemical structure in the near-core region of stars with a convective core by means of gravito-inertial modes. We do so by determining the probing power of different asteroseismic observables and fitting methodologies. We focus on the case of the B-type star KIC$\,$7760680, rotating at a quarter of its critical rotation velocity. Methods: We compute grids of 1D stellar structure and evolution models for two different prescriptions of the temperature gradient and mixing profile in the near-core region. We determine which of these prescriptions is preferred according to the prograde dipole modes detected in 4-yr $\textit{Kepler}$ photometry of KIC$\,$7760680. We consider different sets of asteroseismic observables and compare the outcomes of the regression problem for a $\chi^2$ and Mahalanobis Distance merit function, where the latter takes into account realistic uncertainties for the theoretical predictions and the former does not. Results: Period spacings of modes with consecutive radial order offer a better diagnostic than mode periods or mode frequencies for asteroseismic modelling of stars revealing only high-order gravito-inertial modes. We find KIC$\,$7760680 to reveal a radiative temperature gradient in models with convective boundary mixing, but less complex models without such mixing are statistically preferred for this rotating star, revealing extremely low vertical envelope mixing. Conclusions: Our results strongly suggest the use of measured individual period spacing values for modes of consecutive radial order as an asteroseismic diagnostic for stellar modelling of B-type pulsators with gravito-inertial modes., Comment: Accepted for publication in A&A, 20 pages, 30 figures, 11 tables

An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo-Cℓ estimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N-body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10–20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component.

Upham, R.E., et al. (Euclid Consortium), An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo-Cestimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N-body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10-20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component., This work would not have been possible without the contributions to the community from the developers of NaMaster (Alonso et al. 2019; García-García et al. 2019), CosmoLike (Krause & Eifler 2017; Fang et al. 2020) and the simulations of Takahashi et al. (2017).

An accurate covariance matrix is essential for obtaining reliable cosmological results when using a Gaussian likelihood. In this paper we study the covariance of pseudo-Cestimates of tomographic cosmic shear power spectra. Using two existing publicly available codes in combination, we calculate the full covariance matrix, including mode-coupling contributions arising from both partial sky coverage and non-linear structure growth. For three different sky masks, we compare the theoretical covariance matrix to that estimated from publicly available N-body weak lensing simulations, finding good agreement. We find that as a more extreme sky cut is applied, a corresponding increase in both Gaussian off-diagonal covariance and non-Gaussian super-sample covariance is observed in both theory and simulations, in accordance with expectations. Studying the different contributions to the covariance in detail, we find that the Gaussian covariance dominates along the main diagonal and the closest off-diagonals, but farther away from the main diagonal the super-sample covariance is dominant. Forming mock constraints in parameters that describe matter clustering and dark energy, we find that neglecting non-Gaussian contributions to the covariance can lead to underestimating the true size of confidence regions by up to 70 per cent. The dominant non-Gaussian covariance component is the super-sample covariance, but neglecting the smaller connected non-Gaussian covariance can still lead to the underestimation of uncertainties by 10-20 per cent. A real cosmological analysis will require marginalisation over many nuisance parameters, which will decrease the relative importance of all cosmological contributions to the covariance, so these values should be taken as upper limits on the importance of each component.

Euclid Consortium: M. S. Cagliari et al., [Context] The ESA Euclid mission will produce photometric galaxy samples over 15 000 square degrees of the sky that will be rich for clustering and weak lensing statistics. The accuracy of the cosmological constraints derived from these measurements will depend on the knowledge of the underlying redshift distributions based on photometric redshift calibrations., [Aims] A new approach is proposed to use the stacked spectra from Euclid slitless spectroscopy to augment broad-band photometric information to constrain the redshift distribution with spectral energy distribution fitting. The high spectral resolution available in the stacked spectra complements the photometry and helps to break the colour-redshift degeneracy and constrain the redshift distribution of galaxy samples., [Methods] We modelled the stacked spectra as a linear mixture of spectral templates. The mixture may be inverted to infer the underlying redshift distribution using constrained regression algorithms. We demonstrate the method on simulated Vera C. Rubin Observatory and Euclid mock survey data sets based on the Euclid Flagship mock galaxy catalogue. We assess the accuracy of the reconstruction by considering the inference of the baryon acoustic scale from angular two-point correlation function measurements., [Results] We selected mock photometric galaxy samples at redshift z > 1 using the self-organising map algorithm. Considering the idealised case without dust attenuation, we find that the redshift distributions of these samples can be recovered with 0.5% accuracy on the baryon acoustic scale. The estimates are not significantly degraded by the spectroscopic measurement noise due to the large sample size. However, the error degrades to 2% when the dust attenuation model is left free. We find that the colour degeneracies introduced by attenuation limit the accuracy considering the wavelength coverage of Euclid near-infrared spectroscopy., The Euclid Consortium acknowledges the European Space Agency and a number of agencies and institutes that have supported the development of Euclid, in particular the Academy of Finland, the Agenzia Spaziale Italiana, the Belgian Science Policy, the Canadian Euclid Consortium, the Centre National d’Etudes Spatiales, the Deutsches Zentrum für Luft-und Raumfahrt, the Danish Space Research Institute, the Fundação para a Ciência e a Tecnologia, the Ministerio de Economia y Competitividad, the National Aeronautics and Space Administration, the National Astronomical Observatory of Japan, the Netherlandse Onderzoekschool Voor Astronomie, the Norwegian Space Agency, the Romanian Space Agency, the State Secretariat for Education, Research and Innovation (SERI) at the Swiss Space Office (SSO), and the United Kingdom Space Agency. A complete and detailed list is available on the Euclid web site (http://www.euclid-ec.org). This work has made use of CosmoHub. CosmoHub has been developed by the Port d’Informació Científica (PIC), maintained through a collaboration of the Institut de Física d’Altes Energies (IFAE) and the Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT) and the Institute of Space Sciences (CSIC & IEEC), and was partially funded by the “Plan Estatal de Investigación Científica y Técnica y de Innovación” programme of the Spanish government.

El Gordo (ACT-CL J0102-4915) is an extremely massive galaxy cluster ($M_{200} \approx 3 \times 10^{15}$ $M_{\odot}$) at redshift $z = 0.87 $ composed of two subclusters with mass ratio 3.6 merging at speed $V_{infall} \approx 2500$ km/s. Such a fast collision between individually rare massive clusters is unexpected in Lambda cold dark matter ($\Lambda$CDM) cosmology at such high $z$. However, this is required for non-cosmological hydrodynamical simulations of the merger to match its observed properties (Zhang et al. 2015). Here, we determine the probability of finding a similar object in a $\Lambda$CDM context using the Jubilee simulation box with side length $6 \, h^{-1}$ Gpc. We search for galaxy cluster pairs that have turned around from the cosmic expansion with properties similar to El Gordo in terms of total mass, mass ratio, redshift, and collision velocity relative to virial velocity. We fit the distribution of pair total mass quite accurately, with the fits used in two methods to infer the probability of observing El Gordo in the surveyed region. The more conservative (and detailed) method involves considering the expected distribution of pairwise mass and redshift for analogue pairs with similar dimensionless parameters to El Gordo in the past lightcone of a $z = 0$ observer. Detecting one pair with its mass and redshift rules out $\Lambda$CDM cosmology at $6.16\sigma$. We also use the results of Kraljic & Sarkar (2015) to show that the Bullet Cluster is in $2.78\sigma$ tension once the sky coverage of its discovery survey is accounted for. Using a $\chi^2$ approach, the combined tension can be estimated as $6.43 \sigma$. Both collisions arise naturally in a Milgromian dynamics (MOND) cosmology with light sterile neutrinos., Comment: 21 pages, 9 figures, 4 tables, 4 MB. Accepted for publication in Monthly Notices of the Royal Astronomical Society in this form

We present an algorithm for inferring the dynamical mass of galaxy clusters directly from their respective phase-space distributions, i.e. the observed line-of-sight velocities and projected distances of galaxies from the cluster centre. Our method employs normalizing flows, a deep neural network capable of learning arbitrary high-dimensional probability distributions, and inherently accounts, to an adequate extent, for the presence of interloper galaxies which are not bounded to a given cluster, the primary contaminant of dynamical mass measurements. We validate and showcase the performance of our neural flow approach to robustly infer the dynamical mass of clusters from a realistic mock cluster catalogue. A key aspect of our novel algorithm is that it yields the probability density function of the mass of a particular cluster, thereby providing a principled way of quantifying uncertainties, in contrast to conventional machine learning approaches. The neural network mass predictions, when applied to a contaminated catalogue with interlopers, have a mean overall logarithmic residual scatter of 0.028 dex, with a log-normal scatter of 0.126 dex, which goes down to 0.089 dex for clusters in the intermediate to high mass range. This is an improvement by nearly a factor of four relative to the classical cluster mass scaling relation with the velocity dispersion, and outperforms recently proposed machine learning approaches. We also apply our neural flow mass estimator to a compilation of galaxy observations of some well-studied clusters with robust dynamical mass estimates, further substantiating the efficacy of our algorithm., 14 pages, 9 figures, 1 table. Improved approach, saliency maps added

In a previous paper, we studied two statistical methods used to analyse the variability of active galactic nuclei (AGNs): the C and F statistics. Applying them to observed differential light-curves of 39 AGNs, we found that, even though the C criterion cannot be considered as an actual statistical test, it could still be a useful parameter to detect variability, whereas F is a good detector of non-variability. In order to test these results under controlled input conditions, so that the different error sources could be individually evaluated, we generated a series of synthetic differential light curves simulating astronomical images with different atmospheric conditions, such as cloud cover, seeing or sky brightness, as well as several types of intrinsic variability of the AGN, all with a specific instrumental configuration. Having obtained light curves for each case, we applied both statistics to them in order to test their reliability. We found that a weight factor should always be used with these indices. The F test has a tendency to classify noisy non-variable curves as variable (i.e. false positives), although it is reliable and robust to correctly classify non-variable curves. On the contrary, although the C index tends to give false negatives, we found that whenever the C index indicates a source to be variable, it effectively is. Finally, light curves with low amplitude variabilities are more likely to be affected by changes in atmospheric conditions., Facultad de Ciencias Astronómicas y Geofísicas

We present Atacama Large Millimeter/sub-millimeter Array (ALMA) observations towards 27 low-redshift ($0.02< z, 16 pages, 10 figures to be published in MNRAS

We report on the discovery and validation of TOI 813b (TIC 55525572 b), a transiting exoplanet identified by citizen scientists in data from NASA's Transiting Exoplanet Survey Satellite (TESS) and the first planet discovered by the Planet Hunters TESS project. The host star is a bright (V = 10.3 mag) subgiant ($R_\star=1.94\,R_\odot$, $M_\star=1.32\,M_\odot$). It was observed almost continuously by TESS during its first year of operations, during which time four individual transit events were detected. The candidate passed all the standard light curve-based vetting checks, and ground-based follow-up spectroscopy and speckle imaging enabled us to place an upper limit of $2 M_{Jup}$ (99 % confidence) on the mass of the companion, and to statistically validate its planetary nature. Detailed modelling of the transits yields a period of $83.8911_{ - 0.0031 } ^ { + 0.0027 }$ days, a planet radius of $6.71 \pm 0.38$ $R_{\oplus}$, and a semi major axis of $0.423_{ - 0.037 } ^ { + 0.031 }$ AU. The planet's orbital period combined with the evolved nature of the host star places this object in a relatively under-explored region of parameter space. We estimate that TOI-813b induces a reflex motion in its host star with a semi-amplitude of $\sim6$ ms$^{-1}$, making this system a promising target to measure the mass of a relatively long-period transiting planet., Accepted for publication in MNRAS (16 pages, 10 figures, 3 tables)

The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission. Such separation relies on the different spectral properties of the two components, although, in real life, the foreground intrinsic spectrum is often corrupted by the instrumental response, inducing systematic effects that can further jeopardize the measurement of the 21-cm signal. In this paper, we use Gaussian Process Regression to model both foreground emission and instrumental systematics in $\sim 2$ hours of data from the Hydrogen Epoch of Reionization Array. We find that a simple co-variance model with three components matches the data well, giving a residual power spectrum with white noise properties. These consist of an "intrinsic" and instrumentally corrupted component with a coherence-scale of 20 MHz and 2.4 MHz respectively (dominating the line of sight power spectrum over scales $k_{\parallel} \le 0.2$ h cMpc$^{-1}$) and a baseline dependent periodic signal with a period of $\sim 1$ MHz (dominating over $k_{\parallel} \sim 0.4 - 0.8$h cMpc$^{-1}$) which should be distinguishable from the 21-cm EoR signal whose typical coherence-scales is $\sim 0.8$ MHz., 15 pages, 15 figures, 1 table, Accepted to MNRAS

We present a study of 9242 spectroscopically-confirmed quasars with multi-epoch ugriz photometry from the SDSS Southern Survey. By fitting a separable linear model to each quasar's spectral variations, we decompose their five-band spectral energy distributions into variable (disc) and non-variable (host galaxy) components. In modelling the disc spectra, we include attenuation by dust on the line of sight through the host galaxy to its nucleus. We consider five commonly used attenuation laws, and find that the best description is by dust similar to that of the Small Magellanic Cloud, inferring a lack of carbonaceous grains from the relatively weak 2175AA absorption feature. We go on to construct a composite spectrum for the quasar variations spanning 700 to 8000AA. By varying the assumed power-law $L_{\nu}\propto\nu^\alpha$ spectral slope, we find a best-fit value $\alpha=0.71\pm0.02$, excluding at high confidence the canonical $L_{\nu}\propto\nu^{1/3}$ prediction for a steady-state accretion disc with a $T\propto r^{-3/4}$ temperature profile. The bluer spectral index of the observed quasar variations instead supports the model of Mummery & Balbus in which a steeper temperature profile, $T\propto r^{-7/8}$, develops as a result of finite magnetically-induced stress at the innermost stable circular orbit extracting energy and angular momentum from the black hole spin., Comment: 18 pages, 13 figures, accepted for publication in MNRAS

We introduce the public version of the BAyesian STellar Algorithm (BASTA), an open-source code written in PYTHON to determine stellar properties based on a set of astrophysical observables. BASTA has been specifically designed to robustly combine large data sets that include asteroseismology, spectroscopy, photometry, and astrometry. We describe the large number of asteroseismic observations that can be fit by the code and how these can be combined with atmospheric properties (as well as parallaxes and apparent magnitudes), making it the most complete analysis pipeline available for oscillating main-sequence, subgiant, and red giant stars. BASTA relies on a set of pre-built stellar isochrones or a custom-designed library of stellar tracks, which can be further refined using our interpolation method (both along and across stellar tracks or isochrones). We perform recovery tests with simulated data that reveal levels of accuracy at the few percent level for radii, masses, and ages when individual oscillation frequencies are considered, and show that asteroseismic ages with statistical uncertainties below 10 per cent are within reach if our stellar models are reliable representations of stars. BASTAis extensively documented and includes a suite of examples to support easy adoption and further development by new users., Funding for the Stellar Astrophysics Centre is provided by The Danish National Research Foundation (Grant agreement No. DNRF106). VAB-K acknowledges support from VILLUM FONDEN (research grant 10118), the Independent Research Fund Denmark (Research grant 7027-00096B), and the Carlsberg foundation (grant agreement CF19-0649). SC acknowledges support from Premiale INAF MITiC (PI: B. Garilli), from Istituto Nazionale di Fisica Nucleare (INFN) (Iniziativa specifica TAsP), from Progetto Mainstream INAF (PI: S. Cassisi) and grant AYA2013- 42781P from the Ministry of Economy and Competitiveness of Spain. AMS is partially supported by grant PID2019-108709GB-I00 of the MICINN of Spain. AS acknowledge support from the European Research Council Consolidator Grant funding scheme (project ASTEROCHRONOMETRY, G.A. n. 772293, http://www.asterochronometry.eu).

Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify stars in simulated galaxy clusters after subtracting the member galaxies. These dynamically different components are interpreted as the individual properties of the stars in the Brightest Cluster Galaxy (BCG) and IntraCluster Light (ICL). We employ matched stellar catalogues (built from the different dynamical properties of BCG and ICL) of 29 simulated clusters from the DIANOGA set to train and test the classifier. The input features are cluster mass, normalized particle cluster-centric distance, and rest-frame velocity. The model is found to correctly identify most of the stars, while the larger errors are exhibited at the BCG outskirt, where the differences between the physical properties of the two components are less obvious. We investigate the robustness of the classifier to numerical resolution, redshift dependence (up to $z=1$), and included astrophysical models. We claim that our classifier provides consistent results in simulations for $z 0.1$ \r200) is significantly affected by uncertainties in the classification process. In conclusion, this work suggests the importance of employing Machine Learning to speed up a computationally expensive classification in simulations., 19 pages, accepted to MNRAS

The combination and cross-correlation of the upcoming $Euclid$ data with cosmic microwave background (CMB) measurements is a source of great expectation since it will provide the largest lever arm of epochs, ranging from recombination to structure formation across the entire past light cone. In this work, we present forecasts for the joint analysis of $Euclid$ and CMB data on the cosmological parameters of the standard cosmological model and some of its extensions. This work expands and complements the recently published forecasts based on $Euclid$-specific probes, namely galaxy clustering, weak lensing, and their cross-correlation. With some assumptions on the specifications of current and future CMB experiments, the predicted constraints are obtained from both a standard Fisher formalism and a posterior-fitting approach based on actual CMB data. Compared to a $Euclid$-only analysis, the addition of CMB data leads to a substantial impact on constraints for all cosmological parameters of the standard $\Lambda$-cold-dark-matter model, with improvements reaching up to a factor of ten. For the parameters of extended models, which include a redshift-dependent dark energy equation of state, non-zero curvature, and a phenomenological modification of gravity, improvements can be of the order of two to three, reaching higher than ten in some cases. The results highlight the crucial importance for cosmological constraints of the combination and cross-correlation of $Euclid$ probes with CMB data., Comment: 20 pages, 8 figures, 5 tables, 1 appendix; updated to match version accepted by journal

Context. Determining properties of dust formed in and around supernovae from observations remains challenging. This may be due to either incomplete coverage of data in wavelength or time but also due to often inconspicuous signatures of dust in the observed data. Aims. Here we address this challenge using modern machine learning methods to determine the amount, composition and temperature of dust from a large set of simulated data. We aim to determine whether such methods are suitable to infer these properties from future observations of supernovae. Methods. We calculate spectral energy distributions (SEDs) of dusty shells around supernovae. We develop a neural network consisting of eight fully connected layers and an output layer with specified activation functions that allow us to predict the dust mass, temperature and composition and their respective uncertainties from each SED. We conduct a feature importance analysis via SHapley Additive exPlanations (SHAP) to find the minimum set of JWST filters required to accurately predict these properties. Results. We find that our neural network predicts dust masses and temperatures with a root-mean-square error (RMSE) of $\sim$ 0.12 dex and $\sim$ 38 K, respectively. Moreover, our neural network can well distinguish between the different dust species included in our work, reaching a classification accuracy of up to 95\% for carbon and 99\% for silicate dust. Conclusions. Our analysis shows that the JWST filters NIRCam F070W, F140M, F356W, F480M and MIRI F560W, F770W, F1000W, F1130W, F1500W, F1800W are likely the most important needed to determine the properties of dust formed in and around supernovae from future observations. We tested this on selected optical to infrared data of SN 1987A at 615 days past explosion and find good agreement with dust masses and temperatures inferred with standard fitting methods in the literature., Comment: 24 pages,19 figures, 7 tables, submitted to A&A 10/01/2022

We provide perturbation theory predictions for the HI intensity mapping power spectrum multipoles using the Effective Field Theory of Large Scale Structure (EFTofLSS), which should allow us to constrain cosmological parameters exploiting mildly nonlinear scales. Assuming survey specifications typical of proposed interferometric HI intensity mapping experiments like CHORD and PUMA, and realistic ranges of validity for the perturbation theory modelling, we run mock full shape MCMC analyses at a redshift bin centred at $z=0.5$, and compare with Stage-IV optical galaxy surveys. We include the impact of 21cm foreground removal using simulations-based prescriptions, and quantify the effects on the precision and accuracy of the parameter estimation. We vary 11 parameters in total: 3 cosmological parameters, 7 bias and counterterms parameters, and the HI brightness temperature. Amongst them, the 4 parameters of interest are: the cold dark matter density, $\omega_{\rm c}$, the Hubble parameter, $h$, the primordial amplitude of the power spectrum, $A_{\rm s}$, and the linear HI bias, $b_1$. For the best case scenario, we obtain unbiased constraints on all parameters with $, Comment: 11 pages, 1 table, 10 figures; matches version accepted for publication in MNRAS

Headline constraints on cosmological parameters from current weak lensing surveys are derived from two-point statistics that are known to be statistically sub-optimal, even in the case of Gaussian fields. We study the performance of a new fast implementation of the Quadratic Maximum Likelihood (QML) estimator, optimal for Gaussian fields, to test the performance of Pseudo-Cl estimators for upcoming weak lensing surveys and quantify the gain from a more optimal method. Through the use of realistic survey geometries, noise levels, and power spectra, we find that there is a decrease in the errors in the statistics of the recovered E-mode spectra to the level of ~20% when using the optimal QML estimator over the Pseudo-Cl estimator on the largest angular scales, while we find significant decreases in the errors associated with the B-modes for the QML estimator. This raises the prospects of being able to constrain new physics through the enhanced sensitivity of B-modes for forthcoming surveys that our implementation of the QML estimator provides. We test the QML method with a new implementation that uses conjugate-gradient and finite-differences differentiation methods resulting in the most efficient implementation of the full-sky QML estimator yet, allowing us to process maps at resolutions that are prohibitively expensive using existing codes. In addition, we investigate the effects of apodisation, B-mode purification, and the use of non-Gaussian maps on the statistical properties of the estimators. Our QML implementation is publicly available and can be accessed from GitHub., Comment: 16 pages, 19 figures, submitted to MNRAS

Recent cosmological analyses rely on the ability to accurately sample from high-dimensional posterior distributions. A variety of algorithms have been applied in the field, but justification of the particular sampler choice and settings is often lacking. Here we investigate three such samplers to motivate and validate the algorithm and settings used for the Dark Energy Survey (DES) analyses of the first 3 years (Y3) of data from combined measurements of weak lensing and galaxy clustering. We employ the full DES Year 1 likelihood alongside a much faster approximate likelihood, which enables us to assess the outcomes from each sampler choice and demonstrate the robustness of our full results. We find that the ellipsoidal nested sampling algorithm $\texttt{MultiNest}$ reports inconsistent estimates of the Bayesian evidence and somewhat narrower parameter credible intervals than the sliced nested sampling implemented in $\texttt{PolyChord}$. We compare the findings from $\texttt{MultiNest}$ and $\texttt{PolyChord}$ with parameter inference from the Metropolis-Hastings algorithm, finding good agreement. We determine that $\texttt{PolyChord}$ provides a good balance of speed and robustness, and recommend different settings for testing purposes and final chains for analyses with DES Y3 data. Our methodology can readily be reproduced to obtain suitable sampler settings for future surveys., 16 pages, 11 figures

Compact groups (CGs) of galaxies are defined as isolated and dense galaxy systems that appear to be a unique site of multiple galaxy interactions. Semi-analytical models of galaxy formation (SAMs) are a prime tool to understand CGs. We investigate how the frequency and the three-dimensional nature of CGs depends on the SAM and its underlying cosmological parameters. Extracting 9 lightcones of galaxies from 5 different SAMs and selecting CGs as in observed samples, we find that the frequency and nature of CGs depends strongly on the cosmological parameters. Moving from the WMAP1 to the WMAP7 and Planck cosmologies (increasing density of the Universe and decreasing normalisation of the power spectrum), the space density of CGs is decreased by a factor 2.5, while the fraction of CGs that are physically dense falls from 50 to 35 percent. The lower $\sigma_8$ leads to fewer dense groups, while the higher $\Omega_{\rm m}$ causes more chance alignments. However, with increased mass and spatial resolution, the fraction of CGs that are physically dense is pushed back up to 50 percent. The intrinsic differences in the SAM recipes also lead to differences in the frequency and nature of CGs, particularly those related to how SAMs treat orphan galaxies. We find no dependence of CG properties on the flux limit of the mock catalogues nor on the waveband in which galaxies are selected. One should thus be cautious when interpreting a particular SAM for the frequency and nature of CGs., Comment: 17 pages, 10 figures, 4 tables, accepted for publication in MNRAS

Scattering transforms have been successfully used to describe dust polarization for flat-sky images. This paper expands this framework to noisy observations on the sphere with the aim of obtaining denoised Stokes Q and U all-sky maps at 353GHz, as well as a non-Gaussian model of dust polarization, from the Planck data. To achieve this goal, we extend the computation of scattering coefficients to the Healpix pixelation and introduce cross-statistics that allow us to make use of half-mission maps as well as the correlation between dust temperature and polarization. Introducing a general framework, we develop an algorithm that uses the scattering statistics to separate dust polarization from data noise. The separation is validated on mock data, before being applied to the SRoll2 Planck maps at N_side = 256. The validation shows that the statistics of the dust emission, including its non-Gaussian properties, are recovered until l < 700, where, at high Galactic latitudes, the dust power is smaller than that of the dust by two orders of magnitude. On scales where the dust power is lower than one tenth of that of the noise, structures in the output maps have comparable statistics but are not spatially coincident with those of the input maps. Our results on \Planck\ data are significant milestones opening new perspectives for statistical studies of dust polarization and for the simulation of Galactic polarized foregrounds. The Planck denoised maps is available (see http://sroll20.ias.u-psud.fr/sroll40_353_data.html) together with results from our validation on mock data, which may be used to quantify uncertainties., Published A&A : 12/19/2022

[Context] The distribution of member stars in the surroundings of an open cluster (OC) can shed light on the process of its formation, evolution, and dissolution. The analysis of structural parameters of OCs as a function of their age and position in the Galaxy constrains theoretical models of cluster evolution. The Gaia catalog is very appropriate for finding members of OCs at large distance from their centers. [Aims] We revisit the membership lists of OCs from the solar vicinity, in particular, by extending these membership lists to the peripheral areas through Gaia EDR3. We then take advantage of these new member lists to study the morphological properties and the mass segregation levels of the clusters. [Methods] We used the clustering algorithm HDBSCAN on Gaia parallaxes and proper motions to systematically search for members up to 50 pc from the cluster centers. We fit a King's function on the radial density profile of these clusters and a Gaussian mixture model (GMM) on their two-dimensional member distribution to study their shape. We also evaluated the degree of mass segregation of the clusters and the correlations of these parameters with the age and Galactic position of the clusters. [Results]. Our method performs well on 389 clusters out of the 467 clusters we selected, including several recently discovered clusters that were poorly studied until now. We report the detection of vast coronae around almost all the clusters and report the detection of 71 OCs with tidal tails. This multiplies the number of these structures that are identified by more than four. The size of the cores is smaller for old clusters than for young ones on average. Moreover, the overall size of the clusters seems to increase slightly with age, but the fraction of stars in the halo seems to decrease. As expected, the mass segregation is more pronounced in the oldest clusters, but no clear trend with age is evident. [Conclusions] OCs are more extended than previously expected, regardless of their age. The decrease in the proportion of stars populating the clusters halos highlights the different cluster evaporation processes and the short timescales they need to affect the clusters. Reported parameters such as cluster sizes or mass segregation levels all depend on cluster ages, but cannot be described as single functions of time., Y.T., C.S., and L.C. acknowledge support from “programme national de physique stellaire” (PNPS) and from the “programme national cosmologie et galaxies” (PNCG) of CNRS/INSU. L.C. acknowledges the support of the postdoc fellowship from French Centre National d’Etudes Spatiales (CNES). This work was (partially) supported by the Spanish Ministry of Science, Innovation and University (MICIU/FEDER, UE) through grant RTI2018-095076-B-C21, and the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia ‘María de Maeztu’) through grant CEX2019-000918-M. J.O. acknowledges financial support from: (i) from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 682903, P.I. H. Bouy), (ii) the French State in the framework of the “Investments for the future” Program, IdEx Bordeaux, reference ANR-10-IDEX-03-02, and iii) the Agencia Estatal de Investigación of the Ministerio de Ciencia, Innovación y Universidades through project PID2019-109522GB-C53.

We present a new algorithm for efficiently computing the N-point correlation functions (NPCFs) of a 3D density field for arbitrary N. This can be applied both to a discrete spectroscopic galaxy survey and a continuous field. By expanding the statistics in a separable basis of isotropic functions built from spherical haonics, the NPCFs can be estimated by counting pairs of particles in space, leading to an algorithm with complexity $ {O}(N_math{g}2)$ for Ng particles, or $ {O}(N_math{FFT}log N_math{FFT})$ when using a Fast Fourier Transfo with NFFT grid-points. In practice, the rate-limiting step for N > 3 will often be the summation of the histogrammed spherical haonic coefficients, particularly if the number of radial and angular bins is large. In this case, the algorithm scales linearly with Ng. The approach is implemented in the encore code, which can compute the 3PCF, 4PCF, 5PCF, and 6PCF of a BOSS-like galaxy survey in $100$ CPU-hours, including the corrections necessary for non-unifo survey geometries. We discuss the implementation in depth, along with its GPU acceleration, and provide practical demonstration on realistic galaxy catalogues. Our approach can be straightforwardly applied to current and future data sets to unlock the potential of constraining cosmology from the higher point functions.

We present the results of the most complete ever scan of the parameter space for cosmic ray (CR) injection and propagation. We perform a Bayesian search of the main GALPROP parameters, using the MultiNest nested sampling algorithm, augmented by the BAMBI neural network machine learning package. This is the first such study to separate out low-mass isotopes ($p$, $\bar p$ and He) from the usual light elements (Be, B, C, N, O). We find that the propagation parameters that best fit $p$, $\bar p$, He data are significantly different from those that fit light elements, including the B/C and $^{10}$Be/$^9$Be secondary-to-primary ratios normally used to calibrate propagation parameters. This suggests each set of species is probing a very different interstellar medium, and that the standard approach of calibrating propagation parameters using B/C can lead to incorrect results. We present posterior distributions and best fit parameters for propagation of both sets of nuclei, as well as for the injection abundances of elements from H to Si. The input GALDEF files with these new parameters will be included in an upcoming public GALPROP update., Comment: 20 pages, 10 figures, 5 tables. v2 accepted for publication in ApJ

[Aims] We estimated the spectral evolution of white dwarfs with effective temperature using the Javalambre Photometric Local Universe Survey (J-PLUS) second data release (DR2), which provides 12 photometric optical passbands over 2176 deg2. [Methods] We analyzed 5926 white dwarfs with r 19:5 mag in common between a white dwarf catalog defined from Gaia EDR3 and J-PLUS DR2. We performed a Bayesian analysis by comparing the observed J-PLUS photometry with theoretical models of hydrogen- and helium-dominated atmospheres. We estimated the probability distribution functions for effiective temperature (Teffi), surface gravity, parallax, and composition; and the probability of having a H-dominated atmosphere (pH) for each source. We applied a prior in parallax, using Gaia EDR3 measurements as a reference, and derived a self-consistent prior for the atmospheric composition as a function of Teffi. [Results] We described the fraction of white dwarfs with a He-dominated atmosphere ( fHe) with a linear function of the effiective temperature at 5000 < Teffi < 30 000 K. We find fHe = 0:24 - 0:01 at Teffi = 10 000 K, a change rate along the cooling sequence of 0:14 - 0:02 per 10 kK, and a minimum He-dominated fraction of 0:08 - 0:02 at the high-temperature end. We tested the obtained pH by comparison with spectroscopic classifications, finding that it is reliable. We estimated the mass distribution for the 351 sources with distance d < 100 pc, mass M > 0:45 M, and Teffi > 6000 K. The result for H-dominated white dwarfs agrees with previous studies, with a dominant M = 0:59 M peak and the presence of an excess at M0:8 M. This high-mass excess is absent in the He-dominated distribution, which presents a single peak. [Conclusions]The J-PLUS optical data provide a reliable statistical classification of white dwarfs into H- and He-dominated atmospheres. We find a 21 - 3% increase in the fraction of He-dominated white dwarfs from Teffi = 20 000K to Teffi = 5000 K., Funding for the J-PLUS Project has been provided by the Governments of Spain and Aragón through the Fondo de Inversiones de Teruel; the Aragonese Government through the Reseach Groups E96, E103, and E16_17R; the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI/FEDER, UE) with grants PGC2018-097585-B-C21 and PGC2018-097585-B-C22; the Spanish Ministry of Economy and Competitiveness (MINECO) under AYA2015-66211-C2-1-P, AYA2015-66211-C2-2, AYA2012-30789, and ICTS-2009-14; and European FEDER funding (FCDD10-4E-867, FCDD13-4E-2685). The Brazilian agencies FINEP, FAPESP, and the National Observatory of Brazil have also contributed to this project. P.-E. T. has received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme n. 677706 (white dwarf3D). J. M. C. acknowledges financial support by the Spanish Ministry of Science, Innovation and University (MICIU/FEDER, UE) through grant RTI2018-095076-B-C21, and the Institute of Cosmos Sciences University of Barcelona (ICCUB, Unidad de Excelencia ‘María de Maeztu’) through grant CEX2019-000918-M. J. V. acknowledges the technical members of the UPAD for their invaluable work: Juan Castillo, Javier Hernández, Ángel López, Alberto Moreno, and David Muniesa. B. T. G. was supported by STFC grantST/T000406/1 and by a Leverhulme Research Fellowship. F. J. E. acknowledges financial support from the Spanish MINECO/FEDER through the grant AYA2017-84089 and MDM-2017-0737 at Centro de Astrobiología (CSIC-INTA), Unidad de Excelencia María de Maeztu, and from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement no. 824064 through the ESCAPE - The European Science Cluster of Astronomy & Particle Physics ESFRI Research Infrastructures project. R. A. D. acknowledges support from the CNPq through BP grant 308105/2018-4.

Martinelli, M., et al., In physically realistic, scalar-field-based dynamical dark energy models (including, e.g., quintessence), one naturally expects the scalar field to couple to the rest of the model's degrees of freedom. In particular, a coupling to the electromagnetic sector leads to a time (redshift) dependence in the fine-structure constant and a violation of the weak equivalence principle. Here we extend the previous Euclid forecast constraints on dark energy models to this enlarged (but physically more realistic) parameter space, and forecast how well Euclid, together with high-resolution spectroscopic data and local experiments, can constrain these models. Our analysis combines simulated Euclid data products with astrophysical measurements of the fine-structure constant, α, and local experimental constraints, and it includes both parametric and non-parametric methods. For the astrophysical measurements of α, we consider both the currently available data and a simulated dataset representative of Extremely Large Telescope measurements that are expected to be available in the 2030s. Our parametric analysis shows that in the latter case, the inclusion of astrophysical and local data improves the Euclid dark energy figure of merit by between 8% and 26%, depending on the correct fiducial model, with the improvements being larger in the null case where the fiducial coupling to the electromagnetic sector is vanishing. These improvements would be smaller with the current astrophysical data. Moreover, we illustrate how a genetic algorithms based reconstruction provides a null test for the presence of the coupling. Our results highlight the importance of complementing surveys like Euclid with external data products, in order to accurately test the wider parameter spaces of physically motivated paradigms.

We implement a model for the two-point statistics of biased tracers that combines dark matter dynamics from $N$-body simulations with an analytic Lagrangian bias expansion. Using Aemulus, a suite of $N$-body simulations built for emulation of cosmological observables, we emulate the cosmology dependence of these nonlinear spectra from redshifts $z = 0$ to $z=2$. We quantify the accuracy of our emulation procedure, which is sub-per cent at $k=1\, h {\rm Mpc}^{-1}$ for the redshifts probed by upcoming surveys and improves at higher redshifts. We demonstrate its ability to describe the statistics of complex tracer samples, including those with assembly bias and baryonic effects, reliably fitting the clustering and lensing statistics of such samples at redshift $z\simeq 0.4$ to scales of $k_{\rm max} \approx 0.6\, h\mathrm{Mpc}^{-1}$. We show that the emulator can be used for unbiased cosmological parameter inference in simulated joint clustering and galaxy--galaxy lensing analyses with data drawn from an independent $N$-body simulation. These results indicate that our emulator is a promising tool that can be readily applied to the analysis of current and upcoming datasets from galaxy surveys., Comment: 19 pages, 17 figures. Updated to reflect the journal version. Code available at https://github.com/kokron/anzu

Lemos, P. et al., Quantifying tensions – inconsistencies amongst measurements of cosmological parameters by different experiments – has emerged as a crucial part of modern cosmological data analysis. Statistically significant tensions between two experiments or cosmological probes may indicate new physics extending beyond the standard cosmological model and need to be promptly identified. We apply several tension estimators proposed in the literature to the dark energy survey (DES) large-scale structure measurement and Planck cosmic microwave background data. We first evaluate the responsiveness of these metrics to an input tension artificially introduced between the two, using synthetic DES data. We then apply the metrics to the comparison of Planck and actual DES Year 1 data. We find that the parameter differences, Eigentension, and Suspiciousness metrics all yield similar results on both simulated and real data, while the Bayes ratio is inconsistent with the rest due to its dependence on the prior volume. Using these metrics, we calculate the tension between DES Year 1 3 × 2pt and Planck, finding the surveys to be in ∼2.3σ tension under the ΔCDM paradigm. This suite of metrics provides a toolset for robustly testing tensions in the DES Year 3 data and beyond., The DES data management system is supported by the National Science Foundation under grant numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by Ministerio de Ciencia e Innovación (MICINN) under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the Centres de Recerca de Catalunya (CERCA) program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

Determining photometric redshifts to high accuracy is paramount to measure distances in wide-field cosmological experiments. With only photometric information at hand, photo-zs are prone to systematic uncertainties in the intervening extinction and the unknown underlying spectral-energy distribution of different astrophysical sources. Here, we aim to resolve these model degeneracies and obtain a clear separation between intrinsic physical properties of astrophysical sources and extrinsic systematics. We aim at estimates of the full photo-z probability distributions, and their uncertainties. We perform a probabilistic photo-z determination using Mixture Density Networks (MDN). The training data-set is composed of optical ($griz$) point-spread-function and model magnitudes and extinction measurements from the SDSS-DR15, and WISE midinfrared ($3.4 \mu$m and $4.6 \mu$m) model magnitudes. We use Infinite Gaussian Mixture models to classify the objects in our data-set as stars, galaxies or quasars, and to determine the number of MDN components to achieve optimal performance. The fraction of objects that are correctly split into the main classes is 94%. Our method improves the bias of photometric redshift estimation (i.e. the mean $\Delta z$ = (zp - zs)/(1 + zs)) by one order of magnitude compared to the SDSS photo-z, and decreases the fraction of $3 \sigma$ outliers (i.e. 3rms$(\Delta z) < \Delta z$). The relative, root-mean-square systematic uncertainty in our resulting photo-zs is down to 1.7% for low-redshift galaxies (zs $, Comment: 14 pages, 9 figures, 7 tables, submitted to A&A 14/10/2020

We study the magnetic fields in galaxy clusters through Faraday rotation measurements crossing systems in different dynamical states. We confirm that magnetic fields are present in those systems and analyze the difference between relaxed and unrelaxed samples with respect to the dispersion between their inherent Faraday Rotation measurements. We found an increase of this RM dispersion and a higher RM overlapping frequency for unrelaxed clusters. This fact suggests that a large scale physical process is involved in the nature of unrelaxed systems and possible depolarization effects are present in the relaxed ones. We show that dynamically unrelaxed systems can enhance magnetic fields to large coherence lengths. In contrast, the results for relaxed systems suggests that small-scale dynamo can be a dominant mechanism for sustaining magnetic fields, leading to intrinsic depolarization., Comment: 8 pages, 9 figures, accepted for publication in the MNRAS