arXiv subjects

Maciej Bilicki

Publications and source records attributed to Maciej Bilicki.

At least 37 records · Page 2Linked to original sources

arXiv2025

The 4MOST-Cosmology Redshift Survey: Target Selection of Bright Galaxies and Luminous Red Galaxies

The Cosmology Redshift Survey of the 4-metre Multi-Object Spectroscopic Telescope (4MOST-CRS) will provide redshift measurements of galaxies and quasars over 5700{\degsq} in the southern hemisphere. As targets for the 4MOST-CRS, we present a selection of an $r<19.25$ magnitude limited sample of Bright Galaxies (BG) and a colour selected sample of Luminous Red Galaxies (LRG) based on DESI Legacy Survey DR10.1 photometric data, in the redshift ranges $0.1<z<0.5$ and $0.4<z<1$, respectively. These samples are selected using the $g$, $r$, $z$, and $W1$ (from unWISE) photometric bands. For BGs, the star--galaxy separation is performed based on Gaia and Tycho-2 star catalogues. Following 4MOST requirements, the target densities of BGs and LRGs are 250{\mdegsq} and 400{\mdegsq}, respectively. We quantified the stellar contamination to be $<2-4\%$ for both galaxy samples using data from the first DESI data release. Using angular clustering, we show that both samples are robust against imaging systematics, confirming a low stellar contamination. Finally, we provide forecasts of the baryonic acoustic oscillation (BAO) and growth of structure ($σ_8$) measurements from the 4MOST-CRS alone and in combination with DESI. From the 4MOST-CRS, we expect 3\% and 25\% precision on BAO and $σ_8$ measurements, respectively. Combining 4MOST-CRS with DESI improves the constraints from DESI alone by 12$\%$ in the 0.4 < z < 0.8 redshift range, leading to the most stringent constraints on BAO measurements from spectroscopic galaxy clustering.

astro-ph.CO↗

arXiv2025

Hybrid-z: Enhancing Kilo-Degree Survey bright galaxy sample photometric redshifts with deep learning

We employ deep learning (DL) to improve photometric redshifts (photo-$z$s) in the Kilo-Degree Survey Data Release 4 Bright galaxy sample (KiDS-Bright DR4). This dataset, used as a foreground for KiDS lensing and clustering studies, is flux-limited to $r<20$ mag with mean $z=0.23$ and covers 1000 deg$^2$. Its photo-$z$s were previously derived with artificial neural networks from the ANNz2 package, trained on the Galaxy And Mass Assembly (GAMA) spectroscopy. Here we considerably improve over these previous redshift estimations by building a DL model, Hybrid-z, which combines four-band KiDS images with nine-band magnitudes from KiDS+VIKING. The Hybrid-z framework provides photo-$z$s for KiDS-Bright, with negligible mean residuals of O($10^{-4}$) and scatter at the level of $0.014(1+z)$ -- reduction by 20% over the previous nine-band derivations with ANNz2. We check our photo-$z$ model performance on test data drawn from GAMA, as well as from other KiDS-overlapping wide-angle spectroscopic surveys, namely SDSS, 2dFLenS, and 2dFGRS. We find stable behavior and consistent improvement over ANNz2 throughout. We finally apply Hybrid-z trained on GAMA to the entire KiDS-Bright DR4 sample of 1.2 million galaxies. For these final predictions, we design a method of smoothing the input redshift distribution of the training set, to avoid propagation of features present in GAMA, related to its small sky area and large-scale structure imprint in its fields. Our work paves the way towards the best-possible photo-$z$s achievable with machine learning for any galaxy type both for the final KiDS-Bright DR5 data and for future deeper imaging, such as from the Legacy Survey of Space and Time.

astro-ph.CO↗

arXiv2025

Intrinsic galaxy alignments in the KiDS-1000 bright sample: dependence on colour, luminosity, morphology, and galaxy scale

The intrinsic alignment (IA) of galaxies is a major astrophysical contaminant to weak gravitational lensing measurements, and the study of its dependence on galaxy properties helps provide meaningful physical priors that aid cosmological analyses. This work studied for the first time the dependence of IA on galaxy structural parameters. We measured the IA of bright galaxies, selected on apparent r-band magnitude r<20, in the Kilo-Degree Survey (KiDS). Machine-learning-based photometric redshift estimates are available for this galaxy sample that helped us obtain a clean measurement of its IA signal. We supplemented this sample with a catalogue of structural parameters from Sersic profile fits to the surface-brightness profiles of the galaxies. We split the sample on galaxy intrinsic colour, luminosity, and Sersic index, and we fitted the non-linear linear alignment model to galaxy position-shape projected correlation function measurements on large scales. We observe a power-law luminosity dependence of the large-scale IA amplitude, $A_{IA}$, for both the red and high-Sersic-index ($n_s>2.5$) samples, and find no significant difference between the two. We measure an $\sim1.5σ$ lower $A_{IA}$ for red galaxies that also have a Sersic index of $n_s<4$ compared to the expected amplitude predicted using the sample's luminosity. We also probe the IA of red galaxies as a function of galaxy scale by varying the radial weight employed in the shape measurement. On large scales (above 6 Mpc/$h$), we do not detect a significant difference in the alignment. On smaller scales, we observe that IA increase with galaxy scale, with outer galaxy regions showing stronger alignments than inner regions. Finally, for intrinsically blue galaxies, we find $A_{IA}=-0.67\pm1.00$, which is consistent with previous works, and we find IA to be consistent with zero for the low-Sersic-index ($n_s<2.5$) sample.

astro-ph.CO↗

arXiv2025

Caught in the cosmic web: environmental effects on subhalo abundance and internal density profiles

Using the high-resolution $N$-body cosmological simulation COLOR, we explore the cosmic web (CW) environmental effects on subhalo populations and their internal properties. We use CaCTus, which incorporates an implementation of the state-of-the-art segmentation method NEXUS+, to delineate the simulation volume into nodes, filaments, walls, and voids. We group host haloes by virial mass and segment each mass bin into consecutive CW elements. This reveals that subhalo populations in hosts within specific environments differ on average from the cosmic mean. The subhalo mass function is affected strongly, where hosts in filaments typically contain more subhaloes ($5$ to $20\%$), while hosts in voids are subhalo-poor, with $25\%$ fewer subhaloes. We find that the abundance of the most massive subhaloes, with reduced masses of $μ\equiv M_\mathrm{sub}/M_{200}$ is most sensitive to the CW environment. A corresponding picture emerges when looking at subhalo mass fractions, $f_\mathrm{sub}$, where the filament hosts are significantly more `granular' (having higher $f_\mathrm{sub}$) than the cosmic mean, while the void hosts have much smoother density distributions (with $f_\mathrm{sub}$ lower by $2$ - $20\%$ than the mean). Finally, when we look at the subhalo internal kinematic $V_{\rm max}$-$R_{\rm max}$ relations, we find that subhaloes located in the void and wall hosts exhibit density profiles with lower concentrations than the mean, while the filament hosts demonstrate much more concentrated mass profiles. Across all our samples, the effect of the CW environment generally strengthens with decreasing host halo virial mass. Our results show that host location in the large-scale CW introduces significant systematic effects on internal subhalo properties and population statistics.

astro-ph.CO↗

arXiv2025

The fifth data release of the Kilo Degree Survey: Multi-epoch optical/NIR imaging covering wide and legacy-calibration fields

We present the final data release of the Kilo-Degree Survey (KiDS-DR5), a public European Southern Observatory (ESO) wide-field imaging survey optimised for weak gravitational lensing studies. We combined matched-depth multi-wavelength observations from the VLT Survey Telescope and the VISTA Kilo-degree INfrared Galaxy (VIKING) survey to create a nine-band optical-to-near-infrared survey spanning $1347$ deg$^2$. The median $r$-band $5σ$ limiting magnitude is 24.8 with median seeing $0.7^{\prime\prime}$. The main survey footprint includes $4$ deg$^2$ of overlap with existing deep spectroscopic surveys. We complemented these data in DR5 with a targeted campaign to secure an additional $23$ deg$^2$ of KiDS- and VIKING-like imaging over a range of additional deep spectroscopic survey fields. From these fields, we extracted a catalogue of $126\,085$ sources with both spectroscopic and photometric redshift information, which enables the robust calibration of photometric redshifts across the full survey footprint. In comparison to previous releases, DR5 represents a $34\%$ areal extension and includes an $i$-band re-observation of the full footprint, thereby increasing the effective $i$-band depth by $0.4$ magnitudes and enabling multi-epoch science. Our processed nine-band imaging, single- and multi-band catalogues with masks, and homogenised photometry and photometric redshifts can be accessed through the ESO Archive Science Portal.

astro-ph.GA↗

arXiv2025

Target Selection for the Redshift-Limited WAVES-Wide with Machine Learning

The forthcoming Wide Area Vista Extragalactic Survey (WAVES) on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) has a key science goal of probing the halo mass function to lower limits than possible with previous surveys. For that purpose, in its Wide component, galaxies targetted by WAVES will be flux-limited to $Z<21.1$ mag and will cover the redshift range of $z<0.2$, at a spectroscopic success rate of $\sim95\%$. Meeting this completeness requirement, when the redshift is unknown a priori, is a challenge. We solve this problem with supervised machine learning to predict the probability of a galaxy falling within the WAVES-Wide redshift limit, rather than estimate each object's redshift. This is done by training an XGBoost tree-based classifier to decide if a galaxy should be a target or not. Our photometric data come from 9-band VST+VISTA observations, including KiDS+VIKING surveys. The redshift labels for calibration are derived from an extensive spectroscopic sample overlapping with KiDS and ancillary fields. Our current results indicate that with our approach, we should be able to achieve the completeness of $\sim95\%$, which is the WAVES success criterion.

astro-ph.CO↗

arXiv2025

Dynamics of pairwise motions in the fully non-linear regime in LCDM and Modified Gravity cosmologies

In contrast to our understanding of density field tracers, the modelling of direct statistics pertaining to the cosmic velocity field remains open to significant opportunities for improvement. The lack of accurate modelling for the non-linear domain of pairwise velocities restricts our capacity to fully exploit the information encoded in this observable. We present a robust approach for modelling the mean infall velocities, $v_{12}(r,a)$, with broad applicability spanning sub-megaparsec scales and cosmologies extending beyond the standard LCDM paradigm. Our approach involves solving the full pair-conservation equation using accurate non-linear power spectrum descriptions. To assess the robustness of our model, we extend it to cosmologies beyond the standard LCDM, in particular, the Hu-Sawicki $f(R)$-gravity and Dvali-Gabadadze-Porrati (DGP) modified gravity models. Remarkably, our predictions for pairwise velocities of dark matter particles at kilo-parsec scales exhibit excellent agreement with N-body simulations throughout the entire dynamical range ($0.1 \lesssim ξ\lesssim 1000$, or $r\geq0.4$Mpc/h). Furthermore, we show that different gravity models leave distinct signatures in the shape and dynamics of the mean pairwise velocities, providing a potent test of cosmological gravity laws.

astro-ph.CO↗

arXiv2025

KiDS-1000: Weak lensing and intrinsic alignment around luminous red galaxies

We study the properties of luminous red galaxies (LRGs) selected from the fourth data release of the Kilo Degree Survey (KiDS-1000) via galaxy-galaxy lensing of the background galaxies from KiDS-1000. We used a halo model formalism to interpret our measurements and obtain estimates of the halo masses as well as the satellite fractions of the LRGs, resulting in halo masses of $2.7 \times 10^{12} h^{-1} {\rm M}_{\odot}<M_{\rm h}< 2.6 \times 10^{13} h^{-1} {\rm M}_{\odot}$. We studied the strength of intrinsic alignments (IA) using the position-shape correlations as a function of LRG luminosity, where we used a double power law to describe the relation between luminosity and halo mass to allow for a comparison with previous works. Here, we directly linked the observed IA of the (central) galaxy to the mass of the hosting halo, which is expected to be a fundamental quantity in establishing the alignment. We find that the dependence of the IA amplitude on halo mass is described well by a single power law, with an amplitude of $A = 5.74\pm{0.32}$ and slope of $β_M = 0.44\pm{0.04}$, in the range of $1.9 \times 10^{12}h^{-1} {\rm M}_{\odot}<M_{\rm h}<3.7 \times 10^{14} h^{-1} {\rm M}_{\odot}$. We also find that both red and blue galaxies from the source sample associated with the LRGs are randomly oriented, with respect to the LRGs, although our detection significance is limited by the uncertainty in our photometric redshifts.

astro-ph.CO↗

arXiv2025

KiDS-Legacy: Angular galaxy clustering from deep surveys with complex selection effects

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an $r$-band $5σ$ magnitude limit of $24.8$ and survey footprint of $1347$ deg$^2$, it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an organised random (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOMs) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this SOM+HC method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the $27σ$ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal set-up suggested by the mock tests.

astro-ph.CO↗

arXiv2024

Enhancing Photometric Redshift Catalogs Through Color-Space Analysis: Application to KiDS-Bright Galaxies

We present a method to refine photometric redshift galaxy catalogs by comparing their color-space matching with overlapping spectroscopic calibration data. We focus on cases where photometric redshifts (photo-$z$) are estimated empirically. Identifying galaxies that are poorly represented in spectroscopic data is crucial, as their photo-$z$ may be unreliable due to extrapolation beyond the training sample. Our approach uses a self-organizing map (SOM) to project a multi-dimensional parameter space of magnitudes and colors onto a 2-D manifold, allowing us to analyze the resulting patterns as a function of various galaxy properties. Using SOM, we compare the Kilo-Degree Survey bright galaxy sample (KiDS-Bright), limited to $r<20$ mag, with various spectroscopic samples, including the Galaxy And Mass Assembly (GAMA). Our analysis reveals that GAMA under-represents KiDS-Bright at its faintest ($r\gtrsim19.5$) and highest-redshift ($z\gtrsim0.4$) ranges, however no strong trends in color or stellar mass. By incorporating additional spectroscopic data from the SDSS, 2dF, and early DESI, we identify SOM cells where photo-$z$ are estimated suboptimally. We derive a set of SOM-based criteria to refine the photometric sample and improve photo-$z$ statistics. For the KiDS-Bright sample, this improvement is modest: exclusion of the least represented 20% of the sample reduces photo-$z$ scatter by less than 10%. We conclude that GAMA, used for KiDS-Bright photo-$z$ training, is sufficiently representative for reliable redshift estimation across most of the color space. Future spectroscopic data from surveys such as DESI should be better suited for exploiting the full improvement potential of our method.

astro-ph.GA↗

arXiv2024

Wide Area VISTA Extra-galactic Survey (WAVES): Unsupervised star-galaxy separation on the WAVES-Wide photometric input catalogue using UMAP and ${\rm{\scriptsize HDBSCAN}}$

Star-galaxy separation is a crucial step in creating target catalogues for extragalactic spectroscopic surveys. A classifier biased towards inclusivity risks including spurious stars, wasting fibre hours, while a more conservative classifier might overlook galaxies, compromising completeness and hence survey objectives. To avoid bias introduced by a training set in supervised methods, we employ an unsupervised machine learning approach. Using photometry from the Wide Area VISTA Extragalactic Survey (WAVES)-Wide catalogue comprising 9-band $u-K_s$ data, we create a feature space with colours, fluxes, and apparent size information extracted by ${\rm P{\scriptsize RO} F{\scriptsize OUND}}$. We apply the non-linear dimensionality reduction method UMAP (Uniform Manifold Approximation and Projection) combined with the classifier ${\rm{\scriptsize HDBSCAN}}$ to classify stars and galaxies. Our method is verified against a baseline colour and morphological method using a truth catalogue from Gaia, SDSS, GAMA, and DESI. We correctly identify 99.72% of galaxies within the AB magnitude limit of $Z = 21.2$, with an F1 score of $0.9971 \pm 0.0018$ across the entire ground truth sample, compared to $0.9879 \pm 0.0088$ from the baseline method. Our method's higher purity ($0.9967 \pm 0.0021$) compared to the baseline ($0.9795 \pm 0.0172$) increases efficiency, identifying 11% fewer galaxy or ambiguous sources, saving approximately 70,000 fibre hours on the 4MOST instrument. We achieve reliable classification statistics for challenging sources including quasars, compact galaxies, and low surface brightness galaxies, retrieving 95.1%, 84.6%, and 99.5% of them respectively. Angular clustering analysis validates our classifications, showing consistency with expected galaxy clustering, regardless of the baseline classification.

astro-ph.GA↗

arXiv2024

Improved analytical modeling of the non-linear power spectrum in modified gravity cosmologies

Reliable analytical modeling of the non-linear power spectrum (PS) of matter perturbations is among the chief pre-requisites for cosmological analyses from the largest sky surveys. This is especially true for the models that extend the standard general-relativity paradigm by adding the fifth force, where numerical simulations can be prohibitively expensive. Here we present a method for building accurate PS models for two modified gravity (MG) variants: namely the Hu-Sawicki $f(R)$, and the normal branch of the Dvali-Gabadadze-Porrati (nDGP) braneworld. We start by modifying the standard halo model (HM) with respect to the baseline Lambda-Cold-Dark-Matter ($Λ$CDM) scenario, by using the HM components with specific MG extensions. We find that our $P(k)_{\text{HM}}$ retains 5% accuracy only up to mildly non-linear scales ($k \lesssim 0.3$ $h/\,\mbox{Mpc}$) when compared to PS from numerical simulations. At the same time, our HM prescription much more accurately captures the ratio $Υ(k) = P(k)_{\text{MG}}/P(k)_{Λ\text{CDM}}$ up to non-linear scales. We show that using HM-derived $Υ(k)$ together with a viable non-linear $Λ$CDM $P(k)$ prescription (such as HALOFIT), we render a much better and more accurate PS predictions in MG. The new approach yields considerably improved performance, with modeled $P(k)_{\text{MG}}$ being now accurate to within 5% all the way to non-linear scales of $k \lesssim 2.5-3$ $h/\,\mbox{Mpc}$. The magnitude of deviations from GR as fostered by these MG models is typically $\mathcal{O}(10\%)$ in these regimes. Therefore reaching 5% PS modeling is enough for forecasting constraints on modern-era cosmological observables.

astro-ph.CO↗

arXiv2024

6x2pt: Forecasting gains from joint weak lensing and galaxy clustering analyses with spectroscopic-photometric galaxy cross-correlations

We explore the enhanced self-calibration of photometric galaxy redshift distributions, $n(z)$, through the combination of up to six two-point functions. Our $\rm 3\times2pt$ configuration is comprised of photometric shear, spectroscopic galaxy clustering, and spectroscopic-photometric galaxy-galaxy lensing (GGL). We further include spectroscopic-photometric cross-clustering; photometric GGL; and photometric auto-clustering, using the photometric shear sample as density tracer. We perform simulated likelihood forecasts of the cosmological and nuisance parameter constraints for Stage-III- and Stage-IV-like surveys. For the Stage-III-like case, we employ realistic but perturbed redshift distributions, and distinguish between "coherent" shifting in one direction, versus more internal scattering and full-shape errors. For perfectly known $n(z)$, a $\rm 6\times2pt$ analysis gains $\sim40\%$ in Figure of Merit (FoM) in the $S_8\equivσ_8\sqrt{Ω_{\rm m}/0.3}$ and $Ω_{\rm m}$ plane relative to the $\rm 3\times2pt$ analysis. If untreated, coherent and incoherent redshift errors lead to inaccurate inferences of $S_8$ and $Ω_{\rm m}$, respectively. Employing bin-wise scalar shifts $δ{z}_i$ in the tomographic mean redshifts reduces cosmological parameter biases, with a $\rm 6x2pt$ analysis constraining the shift parameters with $2-4$ times the precision of a photometric $\rm 3^{ph}\times2pt$ analysis. For the Stage-IV-like survey, a $\rm 6\times2pt$ analysis doubles the FoM($σ_8{-}Ω_{\rm m}$) compared to any $\rm 3\times2pt$ or $\rm 3^{ph}\times2pt$ analysis, and is only $8\%$ less constraining than if the $n(z)$ were perfectly known. A Gaussian mixture model for the $n(z)$ reduces mean-redshift errors and preserves the $n(z)$ shape. It also yields the most accurate and precise cosmological constraints for any $N\rm\times2pt$ configuration given $n(z)$ biases.

astro-ph.CO↗

arXiv2024

KiDS-1000: Combined halo-model cosmology constraints from galaxy abundance, galaxy clustering and galaxy-galaxy lensing

We present constraints on the flat $Λ$CDM cosmological model through a joint analysis of galaxy abundance, galaxy clustering and galaxy-galaxy lensing observables with the Kilo-Degree Survey. Our theoretical model combines a flexible conditional stellar mass function, to describe the galaxy-halo connection, with a cosmological N-body simulation-calibrated halo model to describe the non-linear matter field. Our magnitude-limited bright galaxy sample combines 9-band optical-to-near-infrared photometry with an extensive and complete spectroscopic training sample to provide accurate redshift and stellar mass estimates. Our faint galaxy sample provides a background of accurately calibrated lensing measurements. We constrain the structure growth parameter $S_8=σ_8\sqrt{Ω_{\mathrm{m}}/0.3}=0.773^{+0.028}_{-0.030}$, and the matter density parameter $Ω_{\mathrm{m}}=0.290^{+0.021}_{-0.017}$. The galaxy-halo connection model adopted in the work is shown to be in agreement with previous studies. Our constraints on cosmological parameters are comparable to, and consistent with, joint $3\times2{\mathrm{pt}}$ clustering-lensing analyses that additionally include a cosmic shear observable. This analysis therefore brings attention to the significant constraining power in the often-excluded non-linear scales for galaxy clustering and galaxy-galaxy lensing observables. By adopting a theoretical model that accounts for non-linear halo bias, halo exclusion, scale-dependent galaxy bias and the impact of baryon feedback, this work demonstrates the potential and a way forward to include non-linear scales in cosmological analyses. Varying the width of the satellite galaxy distribution with an additional parameter yields a strong preference for sub-Poissonian variance, improving the goodness of fit by 0.18 in reduced $χ^{2}$ value compared to a fixed Poisson distribution.

astro-ph.CO↗

arXiv2024

Constraints on dark matter and astrophysics from tomographic $γ$-ray cross-correlations

We study the cross-correlation between maps of the unresolved $γ$-ray background constructed from the 12-year data release of the Fermi Large-Area Telescope, and the overdensity of galaxies in the redshift range $z\lesssim0.4$ as measured by the 2MASS Photometric Redshift survey and the WISE-SuperCOSMOS photometric survey. A signal is detected at the $8-10σ$ level, which we interpret in terms of both astrophysical $γ$-ray sources, and WIMP dark matter decay and annihilation. The sensitivity achieved allows us to characterise the energy and redshift dependence of the signal, and we show that the latter is incompatible with a pure dark matter origin. We thus use our measurement to place an upper bound on the WIMP decay rate and the annihilation cross-section, finding constraints that are competitive with those found in other analyses. Our analysis is based on the extraction of clean model-independent observables that can then be used to constrain arbitrary astrophysical and particle physics models. In this sense we produce measurements of the $γ$-ray emissivity as a function of redshift and rest-frame energy $ε$, and of a quantity $F(ε)$ encapsulating all WIMP parameters relevant for dark matter decay or annihilation. We make these measurements, together with a full account of their statistical uncertainties, publicly available.

astro-ph.CO↗

arXiv2024

Dark matter solution to the $H_{0}$ and $S_{8}$ tensions, and the integrated Sachs-Wolfe void anomaly

We consider a phenomenological model of dark matter with an equation-of-state $w$ that is negative and changing at late times. We show this couples the $H_{0}$ and $S_{8}$ tensions, alleviating them both simultaneously, reducing the $H_{0}$ tension from $\sim5σ$ to $\sim3σ$ and the $S_{8}$ tension from $\sim3σ$ to $\sim1σ$. Furthermore, the model provides an explanation for the anomalously large integrated Sachs-Wolfe (ISW) effect from cosmic voids, a unique consequence of the changing and negative equation-of-state. Observations of high ISW from cosmic voids may therefore be evidence that dark matter plays a significant role in both the $H_{0}$ and $S_{8}$ tensions. We predict the ISW from cosmic voids to be a factor of up to $\sim2$ greater in this model than what is expected from the standard model $Λ$CDM. These results extend to other degenerate models of dark matter, such as unified or interacting dark matter and dark energy models.

astro-ph.CO↗

arXiv2024

EMU/GAMA: A Technique for Detecting Active Galactic Nuclei in Low Mass Systems

We propose a new method for identifying active galactic nuclei (AGN) in low mass ($\rm M_*\leq10^{10}M_\odot$) galaxies. This method relies on spectral energy distribution (SED) fitting to identify galaxies whose radio flux density has an excess over that expected from star formation alone. Combining data in the Galaxy and Mass Assembly (GAMA) G23 region from GAMA, Evolutionary Map of the Universe (EMU) early science observations, and Wide-field Infrared Survey Explorer (WISE), we compare this technique with a selection of different AGN diagnostics to explore the similarities and differences in AGN classification. We find that diagnostics based on optical and near-infrared criteria (the standard BPT diagram, the WISE colour criterion, and the mass-excitation, or MEx diagram) tend to favour detection of AGN in high mass, high luminosity systems, while the ``ProSpect'' SED fitting tool can identify AGN efficiently in low mass systems. We investigate an explanation for this result in the context of proportionally lower mass black holes in lower mass galaxies compared to higher mass galaxies and differing proportions of emission from AGN and star formation dominating the light at optical and infrared wavelengths as a function of galaxy stellar mass. We conclude that SED-derived AGN classification is an efficient approach to identify low mass hosts with low radio luminosity AGN.

astro-ph.GA↗

arXiv2023

Deep learning based photometric redshifts for the Kilo-Degree Survey Bright Galaxy Sample

In cosmological analyses, precise redshift determination remains pivotal for understanding cosmic evolution. However, with only a fraction of galaxies having spectroscopic redshifts (spec-$z$s), the challenge lies in estimating redshifts for a larger number. To address this, photometry-based redshift (photo-$z$) estimation, employing machine learning algorithms, is a viable solution. Identifying the limitations of previous methods, this study focuses on implementing deep learning (DL) techniques within the Kilo-Degree Survey (KiDS) Bright Galaxy Sample for more accurate photo-$z$ estimations. Comparing our new DL-based model against prior `shallow' neural networks, we showcase improvements in redshift accuracy. Our model gives mean photo-$z$ bias $\langle Δz\rangle= 10^{-3}$ and scatter $\mathrm{SMAD}(Δz)=0.016$, where $Δz = (z_\mathrm{phot}-z_\mathrm{spec})/(1+z_\mathrm{spec})$. This research highlights the promising role of DL in revolutionizing photo-$z$ estimation.

astro-ph.CO↗