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The surface brightness distribution of massive early-type galaxies (ETGs) often deviates from a perfectly elliptical shape. To capture these deviations in their isophotes during an ellipse fitting analysis, Fourier modes of order $m = 3, 4$ are often used. In such analyses the centre of each ellipse is treated as a free parameter, which may result in offsets from the centre of light, particularly for ellipses in the outer regions. This complexity is not currently accounted for in the mass models used in either strong gravitational lensing or galactic dynamical studies. In this work, we adopt a different approach, using the $m=1$ Fourier mode to account for this complexity while keeping the centres of all perturbed ellipses fixed, showing that it fits the data equally well. We applied our method to the distribution of light emission to a sample of ETGs from the MASSIVE survey and found that the majority have low $m_1$ amplitudes, below 2 percent. Five out of the 30 galaxies we analysed have high $m_1$ amplitudes, ranging from 2 to 10 percent in the outer parts ($R \gtrsim 3$ kpc), all of which have a physically associated companion. Based on our findings, we advocate the use of the $m=1$ multipole in the mass models used in strong lensing and dynamical studies, particularly for galaxies with recent or ongoing interactions., Comment: 7 pages, 6 figures. Submitted to MNRAS

Satellite galaxies can be used to indicate the dynamical mass of galaxy groups and clusters. In this study, we apply the axis-symmetric Jeans Anisotropic Multi-Gaussian Expansion JAM modeling to satellite galaxies in 28 galaxy clusters selected from the TNG300-1 simulation with halo mass of $\log_{10}M_{200}/M_\odot>14.3$. If using true bound satellites as tracers, the best constrained total mass within the half-mass radius of satellites, $M(

Observations with the James Webb Space Telescope have revealed a high abundance of bright galaxies at redshift, $z\gtrsim 12$, which has been widely interpreted as conflicting with the $\Lambda$CDM model. In Cowley et al. (2018) predictions were made - prior to the JWST observations - for the expected abundance of these galaxies using the Durham semi-analytic galaxy formation model, GALFORM, which is known to produce a realistic population of galaxies at lower redshifts including the present day. Key to this model is the assumption of a "top-heavy" initial mass function of stars formed in bursts (required to explain the number counts and redshift distribution of sub-millimetre galaxies). Here, we compare the rest-frame ultraviolet luminosity functions derived from JWST observations with those predicted by the Cowley et al. model up to $z=14$ and make further predictions for $z=16$. We find that below $z\sim 10$, the Cowley et al. predictions agree very well with observations, while agreement at $z\gtrsim12$ requires extending the model to take into account the timescale for the growth of obscuring dust grains and its dependence on gas metallicity. We trace the evolution of these galaxies from $z=14$ to $z=0$ and find that their descendants typically reside in halos with a median mass of $10^{13.6}\,h^{-1}\,\mathrm{M_{\odot}}$. The stellar masses of the descendants range from $10^{7}\,h^{-1}\,\mathrm{M_{\odot}}$ to $10^{11.5}\,h^{-1}\,\mathrm{M_{\odot}}$. Although these galaxies were all central galaxies at $z=14$, nearly half of their descendants end up as satellites in massive halos., Comment: 14 pages, 8 figures, submitted to MNRAS on 4 June, 2024

We use the forward modeling pipeline, Obiwan, to study the imaging systematics of the Luminous Red Galaxies (LRGs) targeted by the Dark Energy Spectroscopic Instrument (DESI). We update the Obiwan pipeline, which had previously been developed to simulate the optical images used to target DESI data, to further simulate WISE images in the infrared. This addition makes it possible to simulate the DESI LRGs sample, which utilizes WISE data in the target selection. Deep DESI imaging data combined with a method to account for biases in their shapes is used to define a truth sample of potential LRG targets. We simulate a total of 15 million galaxies to obtain a simulated LRG sample (Obiwan LRGs) that predicts the variations in target density due to imaging properties. We find that the simulations predict the trends with depth observed in the data, including how they depend on the intrinsic brightness of the galaxies. We observe that faint LRGs are the main contributing power of the imaging systematics trend induced by depth. We also find significant trends in the data against Galactic extinction that are not predicted by Obiwan. These trends depend strongly on the particular map of Galactic extinction chosen to test against, implying Large-Scale Structure systematic contamination (e.g. Cosmic-Infrared Background) in the Galactic extinction maps is a likely root cause. We additionally observe that the DESI LRGs sample exhibits a complex dependency on a combination of seeing, depth, and intrinsic galaxy brightness, which is not replicated by Obiwan, suggesting discrepancies between the current simulation settings and the actual observations. The detailed findings we present should be used to guide any observational systematics mitigation treatment for the clustering of the DESI LRG sample., Comment: 46 pages, 26 figures

We examine the morphological and kinematical properties of SPT-2147, a strongly lensed, massive, dusty, star-forming galaxy at $z = 3.762$. Combining data from JWST, HST, and ALMA, we study the galaxy's stellar emission, dust continuum and gas properties. The imaging reveals a central bar structure in the stars and gas embedded within an extended disc with a spiral arm-like feature. The kinematics confirm the presence of the bar and of the regularly rotating disc. Dynamical modeling yields a dynamical mass, ${M}_{\rm dyn} = (9.7 \pm 2.0) \times 10^{10}$ ${\rm M}_{\odot}$, and a maximum rotational velocity to velocity dispersion ratio, $V / \sigma = 9.8 \pm 1.2$. From multi-band imaging we infer, via SED fitting, a stellar mass, ${M}_{\star} = (6.3 \pm 0.9) \times 10^{10}$ $\rm{M}_{\odot}$, and a star formation rate, ${\rm SFR} = 781 \pm 99$ ${\rm M_{\odot} yr^{-1}}$, after correcting for magnification. Combining these measurements with the molecular gas mass, we derive a baryonic-to-total mass ratio of ${M}_{\rm bar} / {M}_{\rm dyn} = 0.9 \pm 0.2$ within 4.0 kpc. This finding suggests that the formation of bars in galaxies begins earlier in the history of the Universe than previously thought and can also occur in galaxies with elevated gas fractions., Comment: 19 pages, 11 figures. Submitted to MNRAS

We present a Neural Network based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3$\sigma$ accuracy better than 0.5\% up to $k_{\mathrm{max}}$=0.25$h^{-1}Mpc$ within $\Lambda$CDM and around 0.5\% $w_0$-$w_a$CDM. Our surrogate model only emulates the galaxy bias-invariant terms of 1-loop perturbation theory predictions, these terms are then combined analytically with galaxy bias terms, counter-terms and stochastic terms in order to obtain the non-linear redshift space galaxy power spectrum. This allows us to avoid any galaxy bias prescription in the training of the emulator, which makes it more flexible. Moreover, we include the redshift $z \in [0,1.4]$ in the training which further avoids the need for re-training the emulator. We showcase the performance of the emulator in recovering the cosmological parameters of $\Lambda$CDM by analysing the suite of 25 AbacusSummit simulations that mimic the DESI Luminous Red Galaxies at $z=0.5$ and $z=0.8$, together as the Emission Line Galaxies at $z=0.8$. We obtain similar performance in all cases, demonstrating the reliability of the emulator for any galaxy sample at any redshift in $0 < z < 1.4$

In a strong gravitational lensing system, the distorted light from a source is analysed to infer the properties of the lens. However, light emitted by the lens itself can contaminate the image of the source, introducing systematic errors in the analysis. We present a simple and efficient lens light model based on the well-tested multi-Gaussian expansion (MGE) method for representing galaxy surface brightness profiles, which we combine with a semi-linear inversion scheme for pixelized source modelling. Testing it against realistic mock lensing images, we show that our scheme can fit the lensed images to the noise level, with relative differences between the true input and best-fit lens light model remaining below 5%. We apply the MGE lens light model to 38 lenses from the HST SLACS sample. We find that the new scheme provides a good fit for the majority of the sample with only 3 exceptions -- these show clear asymmetric residuals in the lens light. We examine the radial dependence of the ellipticity and position angles and confirm that it is common for a typical lens galaxy to exhibit twisting, non-elliptical isophotes and boxy / disky isophotes. Our MGE lens light model will be a valuable tool for understanding the hidden complexity of the lens mass distribution., Comment: 22 pages, 15 figures. Submitted to MNRAS. Comments Welcome

In this investigation, we leverage the combination of Dark Energy Spectroscopic Instrument Legacy imaging Surveys Data Release 9 (DESI LS DR9), Survey Validation 3 (SV3), and Year 1 (Y1) data sets to estimate the conditional luminosity and stellar mass functions (CLFs & CSMFs) of galaxies across various halo mass bins and redshift ranges. To support our analysis, we utilize a realistic DESI Mock Galaxy Redshift Survey (MGRS) generated from a high-resolution Jiutian simulation. An extended halo-based group finder is applied to both MGRS catalogs and DESI observation. By comparing the r and z-band luminosity functions (LFs) and stellar mass functions (SMFs) derived using both photometric and spectroscopic data, we quantified the impact of photometric redshift (photo-z) errors on the galaxy LFs and SMFs, especially in the low redshift bin at low luminosity/mass end. By conducting prior evaluations of the group finder using MGRS, we successfully obtain a set of CLF and CSMF measurements from observational data. We find that at low redshift the faint end slopes of CLFs and CSMFs below $10^{9}h^{-2}L_{\odot}$ (or $h^{-2}M_{\odot}$) evince a compelling concordance with the subhalo mass functions. After correcting the cosmic variance effect of our local Universe following arXiv:1809.00523, the faint end slopes of the LFs/SMFs turn out to be also in good agreement with the slope of the halo mass function., Comment: 28 pages, 13 figures, Accepted for publication in ApJ

Emission line galaxies (ELGs) are now the preeminent tracers of large-scale structure at z>0.8 due to their high density and strong emission lines, which enable accurate redshift measurements. However, relatively little is known about ELG evolution and the ELG-halo connection, exposing us to potential modeling systematics in cosmology inference using these sources. In this paper, we propose a physical picture of ELGs and improve ELG-halo connection modeling using a variety of observations and simulated galaxy models. We investigate DESI-selected ELGs in COSMOS data, and infer that ELGs are rapidly star-forming galaxies with a large fraction exhibiting disturbed morphology, implying that many of them are likely to be merger-driven starbursts. We further postulate that the tidal interactions from mergers lead to correlated star formation in central-satellite ELG pairs, a phenomenon dubbed "conformity." We argue for the need to include conformity in the ELG-halo connection using galaxy models such as IllustrisTNG, and by combining observations such as the DESI ELG auto-correlation, ELG cross-correlation with Luminous Red Galaxies (LRGs), and ELG-cluster cross-correlation. We also explore the origin of conformity using the UniverseMachine model and elucidate the difference between conformity and the well-known galaxy assembly bias effect., Comment: 25 pages, 20 figures, submitted to MNRAS, comments welcome

We explore the galaxy-halo connection information that is available in low-redshift samples from the early data release of the Dark Energy Spectroscopic Instrument (DESI). We model the halo occupation distribution (HOD) from z=0.1-0.3 using Survey Validation 3 (SV3; a.k.a., the One-Percent Survey) data of the DESI Bright Galaxy Survey (BGS). In addition to more commonly used metrics, we incorporate counts-in-cylinders (CiC) measurements, which drastically tighten HOD constraints. Our analysis is aided by the Python package, galtab, which enables the rapid, precise prediction of CiC for any HOD model available in halotools. This methodology allows our Markov chains to converge with much fewer trial points, and enables even more drastic speedups due to its GPU portability. Our HOD fits constrain characteristic halo masses tightly and provide statistical evidence for assembly bias, especially at lower luminosity thresholds: the HOD of central galaxies in $z\sim0.15$ samples with limiting absolute magnitude $M_r < -20.0$ and $M_r < -20.5$ samples is positively correlated with halo concentration with a significance of 99.9% and 99.5%, respectively. Our models also favor positive central assembly bias for the brighter $M_r < -21.0$ sample at $z\sim0.25$ (94.8% significance), but there is no significant evidence for assembly bias with the same luminosity threshold at $z\sim0.15$. We provide our constraints for each threshold sample's characteristic halo masses, assembly bias, and other HOD parameters. These constraints are expected to be significantly tightened with future DESI data, which will span an area 100 times larger than that of SV3.

Galactic conformity is the phenomenon in which a galaxy of a certain physical property is correlated with its neighbors of the same property, implying a possible causal relationship. The observed auto correlations of emission line galaxies (ELGs) from the highly complete DESI One-Percent survey exhibit a strong clustering signal on small scales, providing clear evidence for the conformity effect of ELGs. Building upon the original subhalo abundance matching (SHAM) method developed by Gao et al. (2022, 2023), we propose a concise conformity model to improve the ELG-halo connection. In this model, the number of satellite ELGs is boosted by a factor of $\sim 5$ in the halos whose central galaxies are ELGs. We show that the mean ELG satellite number in such central halos is still smaller than 1, and the model does not significantly increase the overall satellite fraction. With this model, we can well recover the ELG auto correlations to the smallest scales explored with the current data (i.e. $r_{\mathrm{p}} > 0.03$ $\mathrm{Mpc}\,h^{-1}$ in real space and at $s > 0.3$ $\mathrm{Mpc}\,h^{-1}$ in redshift space), while the cross correlations between luminous red galaxies (LRGs) and ELGs are nearly unchanged. Although our SHAM model has only 8 parameters, we further verify that it can accurately describe the ELG clustering in the entire redshift range from $z = 0.8$ to $1.6$. We therefore expect that this method can be used to generate high-quality ELG lightcone mocks for DESI., Comment: 18 pages, 10 figures, accepted by ApJ

Together with larger spectroscopic surveys such as the Dark Energy Spectroscopic Instrument (DESI), the precision of large scale structure studies and thus the constraints on the cosmological parameters are rapidly improving. Therefore, one must build realistic simulations and robust covariance matrices. We build galaxy catalogues by applying a Halo Occupation Distribution (HOD) model upon the \textsc{FastPM} simulations, such that the resulting galaxy clustering reproduces high resolution $N$-body simulations. While the resolution and halo finder are different from the reference simulations, we reproduce the reference galaxy two-point clustering measurements -- monopole and quadrupole -- to a precision required by the DESI Year 1 Emission Line Galaxy sample down to non-linear scales, i.e. $k<0.5\,h\mathrm{Mpc}$ or $s>10\,\mathrm{Mpc}/h$. Furthermore, we compute covariance matrices based on the resulting \textsc{FastPM} galaxy clustering -- monopole and quadrupole. We study for the first time the effect of fitting on Fourier conjugate [e.g. power spectrum] on the covariance matrix of the Fourier counterpart [e.g. correlation function]. We estimate the uncertainties of the two parameters of a simple clustering model and observe a maximum variation of 20 per cent for the different covariance matrices. Nevertheless, for most studied scales the scatter is between two to ten per cent Consequently, using the current pipeline we can precisely reproduce the clustering of $N$-body simulations and the resulting covariance matrices provide robust uncertainty estimations against HOD fitting scenarios. We expect our methodology will be useful for the coming DESI data analyses and their extension for other studies., Comment: 21 pages, 18 figures, submitted to MNRAS, Zenodo link https://doi.org/10.5281/zenodo.8185822

We use angular clustering of luminous red galaxies from the Dark Energy Spectroscopic Instrument (DESI) imaging surveys to constrain the local primordial non-Gaussianity parameter $\fnl$. Our sample comprises over 12 million targets, covering 14,000 square degrees of the sky, with redshifts in the range $0.2< z < 1.35$. We identify Galactic extinction, survey depth, and astronomical seeing as the primary sources of systematic error, and employ linear regression and artificial neural networks to alleviate non-cosmological excess clustering on large scales. Our methods are tested against simulations with and without $\fnl$ and systematics, showing superior performance of the neural network treatment. The neural network with a set of nine imaging property maps passes our systematic null test criteria, and is chosen as the fiducial treatment. Assuming the universality relation, we find $\fnl = 34^{+24(+50)}_{-44(-73)}$ at 68\%(95\%) confidence. We apply a series of robustness tests (e.g., cuts on imaging, declination, or scales used) that show consistency in the obtained constraints. We study how the regression method biases the measured angular power-spectrum and degrades the $\fnl$ constraining power. The use of the nine maps more than doubles the uncertainty compared to using only the three primary maps in the regression. Our results thus motivate the development of more efficient methods that avoid over-correction, protect large-scale clustering information, and preserve constraining power. Additionally, our results encourage further studies of $\fnl$ with DESI spectroscopic samples, where the inclusion of 3D clustering modes should help separate imaging systematics and lessen the degradation in the $\fnl$ uncertainty., Comment: 21 pages, 17 figures, 7 tables (Appendix excluded). Published in MNRAS

We present an approach for accurate estimation of the covariance of 2-point correlation functions that requires fewer mocks than the standard mock-based covariance. This can be achieved by dividing a set of mocks into jackknife regions and fitting the correction term first introduced in Mohammad & Percival (2022), such that the mean of the jackknife covariances corresponds to the one from the mocks. This extends the model beyond the shot-noise limited regime, allowing it to be used for denser samples of galaxies. We test the performance of our fitted jackknife approach, both in terms of accuracy and precision, using lognormal mocks with varying densities and approximate EZmocks mimicking the DESI LRG and ELG samples in the redshift range of z = [0.8, 1.2]. We find that the Mohammad-Percival correction produces a bias in the 2-point correlation function covariance matrix that grows with number density and that our fitted jackknife approach does not. We also study the effect of the covariance on the uncertainty of cosmological parameters by performing a full-shape analysis. We find that our fitted jackknife approach based on 25 mocks is able to recover unbiased and as precise cosmological parameters as the ones obtained from a covariance matrix based on 1000 or 1500 mocks, while the Mohammad-Percival correction produces uncertainties that are twice as large. The number of mocks required to obtain an accurate estimation of the covariance for 2-point correlation function is therefore reduced by a factor of 40-60., Comment: 13 pages, 14 figures, accepted to MNRAS

We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of Luminous Red Galaxies (LRG) data collected during the initial two months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). We run the pipeline on multiple effective Zel'dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and compare the results with the mock sample covariance to assess the accuracy and its fluctuations. We propose an extension of the previously developed formalism for catalogs processed with standard reconstruction algorithms. We consider methods for comparing covariance matrices in detail, highlighting their interpretation and statistical properties caused by sample variance, in particular, nontrivial expectation values of certain metrics even when the external covariance estimate is perfect. With improved mocks and validation techniques, we confirm a good agreement between our predictions and sample covariance. This allows one to generate covariance matrices for comparable datasets without the need to create numerous mock galaxy catalogs with matching clustering, only requiring 2PCF measurements from the data itself. The code used in this paper is publicly available at https://github.com/oliverphilcox/RascalC., Comment: 19 pages, 1 figure. Published in MNRAS. Code available at https://github.com/oliverphilcox/RascalC, table and figure data available at https://dx.doi.org/10.5281/zenodo.7750637

The One-Percent survey of the Dark Energy Spectroscopic Instrument collected ~ 270k emission line galaxies (ELGs) at 0.8 < z < 1.6. The high completeness of the sample allowed the clustering to be measured down to scales never probed before, 0.04 Mpc/h in rp for the projected 2-point correlation function (2PCF) and 0.17 Mpc/h in galaxy pair separation s for the 2PCF monopole and quadrupole. The most striking feature of the measurements is a strong signal at the smallest scales, below 0.2 Mpc/h in rp and 1 Mpc/h in s. We analyze these data in the halo occupation distribution framework. We consider different distributions for central galaxies, a standard power law for satellites with no condition on the presence of a central galaxy and explore several extensions of these models. For all considered models, the mean halo mass of the sample is found to be log10 ~ 11.9. We obtain a satellite mean occupation function which agrees with physically motivated ELG models only if we introduce central-satellite conformity, meaning that the satellite occupation is conditioned by the presence of central galaxies of the same type. To achieve in addition a good modeling of the clustering between 0.1 and 1 Mpc/h in rp, we allow for ELG positioning outside of the halo virial radius and find 0.5% of ELGs residing in the outskirts of halos. Furthermore, the satellite velocity dispersion inside halos is found to be ~ 30% larger than that of the halo dark matter particles. These are the main findings of our work. We investigate assembly bias as a function of halo concentration, local density or local density anisotropies and observe no significant change in our results. We split the data sample in two redshift bins and report no significant evolution with redshift. Lastly, changing the cosmology in the modeling impacts only slightly our results.

We present the probabilistic stellar mass function (pSMF) of galaxies in the DESI Bright Galaxy Survey (BGS), observed during the One-Percent Survey. The One-Percent Survey was one of DESI's survey validation programs conducted from April to May 2021, before the start of the main survey. It used the same target selection and similar observing strategy as the main survey and successfully observed the spectra and redshifts of 143,017 galaxies in the $r < 19.5$ magnitude-limited BGS Bright sample and 95,499 galaxies in the fainter surface brightness and color selected BGS Faint sample over $z < 0.6$. We derive pSMFs from posteriors of stellar mass, $M_*$, inferred from DESI photometry and spectroscopy using the Hahn et al. (2022a; arXiv:2202.01809) PRObabilistic Value-Added BGS (PROVABGS) Bayesian SED modeling framework. We use a hierarchical population inference framework that statistically and rigorously propagates the $M_*$ uncertainties. Furthermore, we include correction weights that account for the selection effects and incompleteness of the BGS observations. We present the redshift evolution of the pSMF in BGS as well as the pSMFs of star-forming and quiescent galaxies classified using average specific star formation rates from PROVABGS. Overall, the pSMFs show good agreement with previous stellar mass function measurements in the literature. Our pSMFs showcase the potential and statistical power of BGS, which in its main survey will observe >100$\times$ more galaxies. Moreover, we present the statistical framework for subsequent population statistics measurements using BGS, which will characterize the global galaxy population and scaling relations at low redshifts with unprecedented precision., Comment: 25 pages, 12 figures; data used to generate figures is available at https://doi.org/10.5281/zenodo.8018936; submitted to ApJ

The distribution of mass in galaxy-scale strong gravitational lenses is often modelled as an elliptical power law plus 'external shear', which notionally accounts for neighbouring galaxies and cosmic shear. We show that it does not. Except in a handful of rare systems, the best-fit values of external shear do not correlate with independent measurements of shear: from weak lensing in 45 Hubble Space Telescope images, or in 50 mock images of lenses with complex distributions of mass. Instead, the best-fit shear is aligned with the major or minor axis of 88% of lens galaxies; and the amplitude of the external shear increases if that galaxy is disky. We conclude that 'external shear' attached to a power law model is not physically meaningful, but a fudge to compensate for lack of model complexity. Since it biases other model parameters that are interpreted as physically meaningful in several science analyses (e.g. measuring galaxy evolution, dark matter physics or cosmological parameters), we recommend that future studies of galaxy-scale strong lensing should employ more flexible mass models., Comment: 13 pages, 11 figures, submitted to MNRAS and Review Response Submitted

The cold dark matter (DM) model predicts that every galaxy contains thousands of DM subhalos; almost all other DM models include a physical process that smooths away the subhalos. The subhalos are invisible, but could be detected via strong gravitational lensing, if they lie on the line of sight to a multiply-imaged background source, and perturb its apparent shape. We present a predominantly automated strong lens analysis framework, and scan for DM subhalos in Hubble Space Telescope imaging of 54 strong lenses. We identify five DM subhalo candidates, including two especially compelling candidates (one previously known in SLACS0946+1006) where a subhalo is favoured after all of our tests for systematics. We find that the detectability of subhalos depends upon the assumed parametric form for the lens galaxy's mass distribution, especially its degree of azimuthal freedom. Using separate components for dark matter and stellar mass reveals two DM subhalo candidates and removes four false-positives compared to the single power-law mass model that is common in the literature. We identify 45 lenses without substructures, the number of which is key to statistical tests able to rule out models of e.g. warm or self-interacting DM. Our full analysis results are available at https://github.com/Jammy2211/autolens_subhalo., Comment: 25 Pages, 15 Figures

Over the next five years, the Dark Energy Spectroscopic Instrument (DESI) will use 10 spectrographs with 5000 fibers on the 4m Mayall Telescope at Kitt Peak National Observatory to conduct the first Stage-IV dark energy galaxy survey. At $z < 0.6$, the DESI Bright Galaxy Survey (BGS) will produce the most detailed map of the Universe during the dark energy dominated epoch with redshifts of >10 million galaxies over 14,000 deg$^2$. In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target a $r < 19.5$ magnitude-limited sample (BGS Bright); a fainter $19.5 < r < 20.175$ sample, color-selected to have high redshift efficiency (BGS Faint); and a smaller low-z quasar sample. BGS will observe these targets using exposure times, scaled to achieve uniform completeness, and visit each point on the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with realistic simulations to show that BGS can complete its strategy and make optimal use of `bright' time. We demonstrate that BGS targets have stellar contamination <1% and that their densities do not depend strongly on imaging properties. We also confirm that BGS Bright will achieve >80% fiber assignment efficiency. Finally, we show that BGS Bright and Faint will achieve >95% redshift success rates with no significant dependence on observing conditions. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise Baryon Acoustic Oscillations and Redshift-Space Distortions measurements at $z < 0.4$. It also presents opportunities to exploit new methods that require highly complete and dense galaxy samples (e.g. N-point statistics, multi-tracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter., Comment: AJ, submitted, 34 pages, 22 figures, one of a suite of 8 papers detailing targeting for DESI

We investigate the population of bright satellites ($M_{*} \geq 10^{5} \mathrm{M}_{\odot}$) of haloes of mass comparable to that of the Milky Way in cosmological simulations in which the dark matter (DM) is either cold, warm or self-interacting (CDM, WDM and SIDM respectively). The nature of the DM gives rise to differences in the abundance and structural properties of field halos. In WDM, the main feature is a reduction in the total number of galaxies that form, reflecting a suppression of low-mass DM haloes and lower galaxy formation efficiency compared to CDM. For SIDM, the changes are structural, restricted to the central regions of haloes and dependent on the assumed self-interaction cross-section. We also consider different baryonic subgrid physics models for galaxy formation, in which supernova gas blowouts can or cannot induce the formation of a core in dwarf galaxies. Overall, the inclusion of baryons lessen the differences in the halo properties in the different DM models compared to DM-only simulations. This affects the satellite properties at infall and therefore their subsequent tidal stripping and survival rates. Nonetheless, we find slightly less concentrated satellite radial distributions as the SIDM cross-section increases. Unfortunately, we also find that the satellite populations in simulations with baryon-induced cores in CDM and WDM can mimic the results found in SIDM, making the satellite stellar mass and maximum circular velocity functions heavily degenerate on the assumed nature of the DM and the adopted subgrid modelling. These degeneracies preclude using the brightest satellites of the Milky Way to constrain the nature of DM., Comment: 15 pages, 11 figures. Accepted for publication in MNRAS. Corrected typo

The use of realistic mock galaxy catalogues is essential in the preparation of large galaxy surveys, in order to test and validate theoretical models and to assess systematics. We present an updated version of the mock catalogue constructed from the Millennium-XXL simulation, which uses a halo occupation distribution (HOD) method to assign galaxies r-band magnitudes and g-r colours. We have made several modifications to the mock to improve the agreement with measurements from the SDSS and GAMA surveys. We find that cubic interpolation, which was used to build the original halo lightcone, produces extreme velocities between snapshots. Using linear interpolation improves the correlation function quadrupole measurements on small scales. We also update the g-r colour distributions so that the observed colours better agree with measurements from GAMA data, particularly for faint galaxies. As an example of the science that can be done with the mock, we investigate how the luminosity function depends on environment and colour, and find good agreement with measurements from the GAMA survey. This full-sky mock catalogue is designed for the ongoing Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey (BGS), and is complete to a magnitude limit r=20.2., Comment: 15 pages, 11 figures. Accepted for publication in MNRAS

Observations suggest that the dark matter and stars in early-type galaxies `conspire' to produce a surprisingly simple distribution of total mass, $\rho(r)\propto\rho^{-\gamma}$, with $\gamma\approx2$. We measure the distribution of mass in 48 early-type galaxies that gravitationally lens a resolved background source. By fitting the source light in every pixel of images from the Hubble Space Telescope, we find a mean $\langle\gamma\rangle=2.075_{-0.024}^{+0.023}$ with intrinsic scatter between galaxies of $\sigma_\gamma=0.172^{+0.022}_{-0.032}$ for the overall sample. This is consistent with, and has similar precision to traditional techniques that employ spectroscopic observations to supplement lensing with mass estimates from stellar dynamics. Comparing measurements of $\gamma$ for individual lenses using both techniques, we find a statistically insignificant correlation of $-0.150^{+0.223}_{-0.217}$ between the two, indicating a lack of statistical power or deviations from a power-law density in certain lenses. At fixed surface mass density, we measure a redshift dependence, $\partial\langle\gamma\rangle/\partial z=0.345^{+0.322}_{-0.296}$, that is consistent with traditional techniques for the same sample of SLACS and GALLERY lenses. Interestingly, the consistency breaks down when we measure the dependence of $\gamma$ on the surface mass density of a lens galaxy. We argue that this is tentative evidence for an inflection point in the total-mass density profile at a few times the galaxy effective radius -- breaking the conspiracy., Comment: 14 pages, 11 figures, 5 tables, submitted to MNRAS

Realistic lightcone mocks are important in the clustering analyses of large galaxy surveys. For simulations where only the snapshots are available, it is common to create approximate lightcones by joining together the snapshots in spherical shells. We assess the two-point clustering measurements of central galaxies in approximate lightcones built from the Millennium-XXL simulation, which are constructed using different numbers of snapshots. The monopole and quadrupole of the real-space correlation function is strongly boosted on small scales below 1 Mpc/h, due to some galaxies being duplicated at the boundaries between snapshots in the lightcone. When more snapshots are used, the total number of duplicated galaxies is approximately constant, but they are pushed to smaller separations. The effect of this in redshift space is small, as long as the snapshots are cut into shells in real space. Randomly removing duplicated galaxies is able to reduce the excess clustering signal. Including satellite galaxies will reduce the impact of the duplicates, since many small-scale pairs come from satellites in the same halo. Galaxies that are missing from the lightcone at the boundaries can be added to the lightcone by having a small overlap between each shell. This effect will impact analyses that use very small-scale clustering measurements, and when using mocks to test the impact of fibre collisions., Comment: 10 pages, 7 figures, 1 table. Accepted for publication in MNRAS

We use sparse regression methods (SRM) to build accurate and explainable models that predict the stellar mass of central and satellite galaxies as a function of properties of their host dark matter halos. SRM are machine learning algorithms that provide a framework for modelling the governing equations of a system from data. In contrast with other machine learning algorithms, the solutions of SRM methods are simple and depend on a relatively small set of adjustable parameters. We collect data from 35,459 galaxies from the EAGLE simulation using 19 redshift slices between $z=0$ and $z=4$ to parameterize the mass evolution of the host halos. Using an appropriate formulation of input parameters, our methodology can model satellite and central halos using a single predictive model that achieves the same accuracy as when predicted separately. This allows us to remove the somewhat arbitrary distinction between those two galaxy types and model them based only on their halo growth history. Our models can accurately reproduce the total galaxy stellar mass function and the stellar mass-dependent galaxy correlation functions ($\xi(r)$) of EAGLE. We show that our SRM model predictions of $\xi(r)$ is competitive with those from sub-halo abundance matching and might be comparable to results from extremely randomized trees. We suggest SRM as an encouraging approach for populating the halos of dark matter only simulations with galaxies and for generating mock catalogues that can be used to explore galaxy evolution or analyse forthcoming large-scale structure surveys., Comment: 19 pages, 12 figures

Strong gravitational lensing offers a compelling test of the cold dark matter paradigm, as it allows for subhaloes with masses of $\sim10^{9}$ M$_\odot$ and below to be detected. We test commonly-used techniques for detecting subhaloes superposed in images of strongly lensed galaxies. For the lens we take a simulated galaxy in a $\sim10^{13}$ M$_\odot$ halo grown in a high-resolution cosmological hydrodynamical simulation, which we view from two different directions. Though the resolution is high, we note the simulated galaxy still has an artificial core which adds additional complexity to the baryon dominated region. To remove particle noise, we represent the projected galaxy mass distribution by a series of Gaussian profiles which precisely capture the features of the projected galaxy. We first model the lens mass as a (broken) power-law density profile and then search for small haloes. Of the two projections, one has a regular elliptical shape, while the other has distinct deviations from an elliptical shape. For the former, the broken power-law model gives no false positives and correctly recovers the mass of the superposed small halo, but for the latter we find false positives and the inferred halo mass is overestimated by $\sim4-5$ times. We then use a more complex model in which the lens mass is decomposed into stellar and dark matter components. In this case, we show that we can capture the simulated galaxy's complex projected structures and correctly infer the input small halo., Comment: Accepted by MNRAS. Comments welcome!

The distribution of dark and luminous matter can be mapped around galaxies that gravitationally lens background objects into arcs or Einstein rings. New surveys will soon observe hundreds of thousands of galaxy lenses, and current, labour-intensive analysis methods will not scale up to this challenge. We instead develop a fully automatic, Bayesian method which we use to fit a sample of 59 lenses imaged by the Hubble Space Telescope in uniform conditions. We set out to \textit{leave no lens behind} and focus on ways in which automated fits fail in a small handful of lenses, describing adjustments to the pipeline that allows us to infer accurate lens models. Our pipeline ultimately fits {\em all} 59 lenses in our sample, with a high success rate key because catastrophic outliers would bias large samples with small statistical errors. Machine Learning techniques might further improve the two most difficult steps: subtracting foreground lens light and initialising a first, approximate lens model. After that, increasing model complexity is straightforward. We find a mean $\sim1\%$ measurement precision on the measurement of the Einstein radius across the lens sample which {\em does not degrade with redshift} up to at least $z=0.7$ -- in stark contrast to other techniques used to study galaxy evolution, like stellar dynamics. Our \texttt{PyAutoLens} software is open source, and is also installed in the Science Data Centres of the ESA Euclid mission., Comment: 23 pages, 20 figures, 5 tables. Submitted to MNRAS

Analysis of large galaxy surveys requires confidence in the robustness of numerical simulation methods. The simulations are used to construct mock galaxy catalogs to validate data analysis pipelines and identify potential systematics. We compare three $N$-body simulation codes, ABACUS, GADGET, and SWIFT, to investigate the regimes in which their results agree. We run $N$-body simulations at three different mass resolutions, $6.25\times10^{8}$, $2.11\times10^{9}$, and $5.00\times10^{9}~h^{-1}$M$_{\odot}$, matching phases to reduce the noise within the comparisons. We find systematic errors in the halo clustering between different codes are smaller than the DESI statistical error for $s > 20\, h^{-1}$Mpc in the correlation function in redshift space. Through the resolution comparison we find that simulations run with a mass resolution of $2.1\times10^{9}~h^{-1}$M$_{\odot}$ are sufficiently converged for systematic effects in the halo clustering to be smaller than the DESI statistical error at scales larger than $20 \, h^{-1}$Mpc. These findings show that the simulations are robust for extracting cosmological information from large scales which is the key goal of the DESI survey. Comparing matter power spectra, we find the codes agree to within 1% for $k \leq 10~h$Mpc$^{-1}$. We also run a comparison of three initial condition generation codes and find good agreement. In addition, we include a quasi-$N$-body code, FastPM, since we plan use it for certain DESI analyses. The impact of the halo definition and galaxy-halo relation will be presented in a follow up study., Comment: 18 pages, 17 figures. Submitted to MNRAS

The elliptical power-law (EPL) model of the mass in a galaxy is widely used in strong gravitational lensing analyses. However, the distribution of mass in real galaxies is more complex. We quantify the biases due to this model mismatch by simulating and then analysing mock {\it Hubble Space Telescope} imaging of lenses with mass distributions inferred from SDSS-MaNGA stellar dynamics data. We find accurate recovery of source galaxy morphology, except for a slight tendency to infer sources to be more compact than their true size. The Einstein radius of the lens is also robustly recovered with 0.1% accuracy, as is the global density slope, with 2.5% relative systematic error, compared to the 3.4% intrinsic dispersion. However, asymmetry in real lenses also leads to a spurious fitted `external shear' with typical strength, $\gamma_{\rm ext}=0.015$. Furthermore, time delays inferred from lens modelling without measurements of stellar dynamics are typically underestimated by $\sim$5%. Using such measurements from a sub-sample of 37 lenses would bias measurements of the Hubble constant $H_0$ by $\sim$9%. Although this work is based on a particular set of MaNGA galaxies, and the specific value of the detected biases may change for another set of strong lenses, our results strongly suggest the next generation cosmography needs to use more complex lens mass models., Comment: Accepted by RAA; Fix a few typos, add some references

We rederive the number density of intervening line-of-sight haloes relative to lens subhaloes in galaxy-galaxy strong lensing observations, where these perturbers can generate detectable image fluctuations. Previous studies have calculated the detection limit of a line-of-sight small-mass dark halo by comparing the lensing deflection angles it would cause, to those caused by a subhalo within the lens. However, this overly simplifies the difference in observational consequences between a subhalo and a line-of-sight halo. Furthermore, it does not take into account degeneracies between an extra subhalo and the uncertain properties of the main lens. More in keeping with analyses of real-world observations, we regard a line-of-sight halo as detectable only if adding it to a smooth model generates a statistically significant improvement in the reconstructed image. We find that the number density of detectable line-of-sight perturbers has been overestimated by as much as a factor of two in the previous literature. For typical lensing geometries and configurations, very deep imaging is sensitive to twice as many line-of-sight perturbers as subhaloes, but moderate depth imaging is sensitive to only slightly more line-of-sight perturbers than subhaloes., Comment: Accepted by MNRAS. Comments welcome

A defining prediction of the cold dark matter (CDM) cosmological model is the existence of a very large population of low-mass haloes. This population is absent in models in which the dark matter particle is warm (WDM). These alternatives can, in principle, be distinguished observationally because halos along the line-of-sight can perturb galaxy-galaxy strong gravitational lenses. Furthermore, the WDM particle mass could be deduced because the cut-off in their halo mass function depends on the mass of the particle. We systematically explore the detectability of low-mass haloes in WDM models by simulating and fitting mock lensed images. Contrary to previous studies, we find that halos are harder to detect when they are either behind or in front of the lens. Furthermore, we find that the perturbing effect of haloes increases with their concentration: detectable haloes are systematically high-concentration haloes, and accounting for the scatter in the mass-concentration relation boosts the expected number of detections by as much as an order of magnitude. Haloes have lower concentration for lower particle masses and this further suppresses the number of detectable haloes beyond the reduction arising from the lower halo abundances alone. Taking these effects into account can make lensing constraints on the value of the mass function cut-off at least an order of magnitude more stringent than previously appreciated., Comment: MNRAS submitted. Comments welcome

We select a sample of Milky Way (MW) mass haloes from a high-resolution version of the EAGLE simulation to study their inner dark matter (DM) content and how baryons alter it. As in previous studies, we find that all haloes are more massive at the centre compared to their DM-only (DMO) counterparts at the present day as a result of the dissipational collapse of baryons during the assembly of the galaxy. However, we identify two processes that can reduce the central halo mass during the evolution of the galaxy. Firstly, gas blowouts induced by AGN feedback can lead to a substantial decrease of the central DM mass. Secondly, the formation of a stellar bar and its interaction with the DM can induce a secular expansion of the halo; the rate at which DM is evacuated from the central region by this process is related to the average bar strength and the timescale on which it acts determines how much the halo has decontracted. Although the inner regions of the haloes we have investigated are still more massive than their DMO counterparts at $z = 0$, they are significantly less massive than in the past and less massive than expected from the classic adiabatic contraction model. Since the MW has both a central supermassive black hole and a bar, the extent to which its halo has contracted is uncertain. This may affect estimates of the mass of the MW halo and of the expected signals in direct and indirect DM detection experiments., Comment: 13 pages, 9 figures. Accepted for publication in MNRAS

We characterise the selection cuts and clustering properties of a magnitude-limited sample of bright galaxies that is part of the Bright Galaxy Survey (BGS) of the Dark Energy Spectroscopic Instrument (DESI) using the ninth data release of the Legacy Imaging Surveys (DR9). We describe changes in the DR9 selection compared to the DR8 one as explored in Ruiz-Macias et al. (2021). We also compare the DR9 selection in three distinct regions: BASS/MzLS in the north Galactic Cap (NGC), DECaLS in the NGC, and DECaLS in the south Galactic Cap (SGC). We investigate the systematics associated with the selection and assess its completeness by matching the BGS targets with the Galaxy and Mass Assembly (GAMA) survey. We measure the angular clustering for the overall bright sample (r $\leq$ 19.5) and as function of apparent magnitude and colour. This enables to determine the clustering strength and slope by fitting a power-law model that can be used to generate accurate mock catalogues for this tracer. We use a counts-in-cells technique to explore higher-order statistics and cross-correlations with external spectroscopic data sets in order to check the evolution of the clustering with redshift and the redshift distribution of the BGS targets using clustering-redshifts. While this work validates the properties of the BGS bright targets, the final target selection pipeline and clustering properties of the entire DESI BGS will be fully characterised and validated with the spectroscopic data of Survey Validation., Comment: 16 pages, 17 figures

Strong gravitational lensing, which can make a background source galaxy appears multiple times due to its light rays being deflected by the mass of one or more foreground lens galaxies, provides astronomers with a powerful tool to study dark matter, cosmology and the most distant Universe. PyAutoLens is an open-source Python 3.6+ package for strong gravitational lensing, with core features including fully automated strong lens modeling of galaxies and galaxy clusters, support for direct imaging and interferometer datasets and comprehensive tools for simulating samples of strong lenses. The API allows users to perform ray-tracing by using analytic light and mass profiles to build strong lens systems. Accompanying PyAutoLens is the autolens workspace (see https://github.com/Jammy2211/autolens_workspace), which includes example scripts, lens datasets and the HowToLens lectures in Jupyter notebook format which introduce non experts to strong lensing using PyAutoLens. Readers can try PyAutoLens right now by going to the introduction Jupyter notebook on Binder (see https://mybinder.org/v2/gh/Jammy2211/autolens_workspace/master) or checkout the readthedocs (see https://pyautolens.readthedocs.io/en/latest/) for a complete overview of PyAutoLens's features., Comment: 2 pages, 1 figure

Sparse regression algorithms have been proposed as the appropriate framework to model the governing equations of a system from data, without needing prior knowledge of the underlying physics. In this work, we use sparse regression to build an accurate and explainable model of the stellar mass of central galaxies given properties of their host dark matter (DM) halo. Our data set comprises 9,521 central galaxies from the EAGLE hydrodynamic simulation. By matching the host halos to a DM-only simulation, we collect the halo mass and specific angular momentum at present time and for their main progenitors in 10 redshift bins from $z=0$ to $z=4$. The principal component of our governing equation is a third-order polynomial of the host halo mass, which models the stellar-mass halo-mass relation. The scatter about this relation is driven by the halo mass evolution and is captured by second and third-order correlations of the halo mass evolution with the present halo mass. An advantage of sparse regression approaches is that unnecessary terms are removed. Although we include information on halo specific angular momentum, these parameters are discarded by our methodology. This suggests that halo angular momentum has little connection to galaxy formation efficiency. Our model has a root mean square error (RMSE) of $0.167 \log_{10}(M^*/M_\odot)$, and accurately reproduces both the stellar mass function and central galaxy correlation function of EAGLE. The methodology appears to be an encouraging approach for populating the halos of DM-only simulations with galaxies, and we discuss the next steps that are required., Comment: 20 pages, 15 figures

A fundamental prediction of the cold dark matter (CDM) model of structure formation is the existence of a vast population of dark matter haloes extending to subsolar masses. By contrast, other dark matter models, such as a warm thermal relic (WDM), predict a cutoff in the mass function at a mass which, for popular models, lies approximately between $10^7$ and $10^{10}~{\rm M}_\odot$. We use mock observations to demonstrate the viability of a forward modelling approach to extract information about low-mass dark haloes lying along the line-of-sight to galaxy-galaxy strong lenses. This can be used to constrain the mass of a thermal relic dark matter particle, $m_\mathrm{DM}$. With 50 strong lenses at Hubble Space Telescope resolution and a maximum pixel signal-to-noise ratio of $\sim50$, the expected median 2$\sigma$ constraint for a CDM-like model (with a halo mass cutoff at $10^{7}~{\rm M}_\odot$) is $m_\mathrm{DM} > 4.10 \, \mathrm{keV}$ (50% chance of constraining $m_{\rm DM}$ to be better than 4.10 keV). If, however, the dark matter is a warm particle of $m_\mathrm{DM}=2.2 \, \mathrm{keV}$, our 'Approximate Bayesian Computation' method would result in a median estimate of $m_\mathrm{DM}$ between 1.43 and 3.21 keV. Our method can be extended to the large samples of strong lenses that will be observed by future telescopes, and could potentially rule out the standard CDM model of cosmogony. To aid future survey design, we quantify how these constraints will depend on data quality (spatial resolution and integration time) as well as on the lensing geometry (source and lens redshifts)., Comment: Accepted by MNRAS. Comments welcome

The Dark Energy Spectroscopic Instrument (DESI) will execute a nearly magnitude-limited survey of low redshift galaxies ($0.05 \leq z \leq 0.4$, median $z \approx 0.2$). Clustering analyses of this Bright Galaxy Survey (BGS) will yield the most precise measurements to date of baryon acoustic oscillations and redshift-space distortions at low redshift. DESI BGS will comprise two target classes: (i) BRIGHT ($r<19.5$~mag), and (ii) FAINT ($19.5

We search for the Baryon Acoustic Oscillations in the projected cross-correlation function binned into transverse comoving radius between the SDSS-IV DR16 eBOSS quasars and a dense photometric sample of galaxies selected from the DESI Legacy Imaging Surveys. We estimate the density of the photometric sample of galaxies in this redshift range to be about 2900 deg$^{-2}$, which is deeper than the official DESI ELG selection, and the density of the spectroscopic sample is about 20 deg$^{-2}$. In order to mitigate the systematics related to the use of different imaging surveys close to the detection limit, we use a neural network approach that accounts for complex dependencies between the imaging attributes and the observed galaxy density. We find that we are limited by the depth of the imaging surveys which affects the density and purity of the photometric sample and its overlap in redshift with the quasar sample, which thus affects the performance of the method. When cross-correlating the photometric galaxies with quasars in $0.6 \leq z \leq 1.2$, the cross-correlation function can provide better constraints on the comoving angular distance, $D_{\rm M}$ (6\% precision) compared to the constraint on the spherically-averaged distance $D_{\rm V}$ (9\% precision) obtained from the auto-correlation. Although not yet competitive, this technique will benefit from the arrival of deeper photometric data from upcoming surveys which will enable it to go beyond the current limitations we have identified in this work., Comment: 21 pages, 25 figures

The scientific exploitation of the Dark Energy Spectroscopic Instrument Bright Galaxy Survey (DESI BGS) data requires the construction of mocks with galaxy population properties closely mimicking those of the actual DESI BGS targets. We create a high fidelity mock galaxy catalogue, including information about galaxies and their host dark matter subhaloes. The mock catalogue uses subhalo abundance matching (SHAM) with scatter to populate the P-Millennium N-body simulation with galaxies at the median BGS redshift of ~ 0.2, using formation redshift information to assign (g-r) rest-frame colours. The mock provides information about r-band absolute magnitudes, (g-r) rest-frame colours, 3D positions and velocities of a complete sample of DESI BGS galaxies in a volume of (542 Mpc/h)^3, as well as the masses of host dark matter haloes. This P-Millennium DESI BGS mock catalogue is ideally suited for the tuning of approximate mocks unable to resolve subhaloes that DESI BGS galaxies reside in, to test for systematics in analysis pipelines and to interpret (non-cosmological focused) DESI BGS analysis., Comment: 14 pages, 14 figures. Accepted at MNRAS in April 2021

We present the steps taken to produce a reliable and complete input galaxy catalogue for the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey (BGS) using the photometric Legacy Survey DR8 DECam. We analyse some of the main issues faced in the selection of targets for the DESI BGS, such as star-galaxy separation, contamination by fragmented stars and bright galaxies. Our pipeline utilizes a new way to select BGS galaxies using Gaia photometry and we implement geometrical and photometric masks that reduce the number of spurious objects. The resulting catalogue is cross-matched with the Galaxy And Mass Assembly (GAMA) survey to assess the completeness of the galaxy catalogue and the performance of the target selection. We also validate the clustering of the sources in our BGS catalogue by comparing with mock catalogues and the Sloan Digital Sky Survey (SDSS) data. Finally, the robustness of the BGS selection criteria is assessed by quantifying the dependence of the target galaxy density on imaging and other properties. The largest systematic correlation we find is a 7 per cent suppression of the target density in regions of high stellar density., Comment: 23 pages, 18 Figures

We test a non-parametric higher-order Jeans analysis method, GravSphere, on 32 simulated dwarf galaxies comparable to classical Local Group dwarfs like Fornax. The galaxies are selected from the APOSTLE suite of cosmological hydrodynamics simulations with Cold Dark Matter (CDM) and Self-Interacting Dark Matter (SIDM) models, allowing us to investigate cusps and cores in density distributions. We find that, for CDM dwarfs, the recovered enclosed mass profiles have a bias of no more than 10 per cent, with a 50 per cent scatter in the inner regions and a 20 per cent scatter near the half-light radius, consistent with standard mass estimators. The density profiles are also recovered with a bias of no more than 10 per cent and a scatter of 30 per cent in the inner regions. For SIDM dwarfs, the mass and density profiles are recovered within our 95 per cent confidence intervals, but are biased towards cuspy dark matter distributions. This is mainly due to a lack of sufficient constraints from the data. We explore the sources of scatter in the accuracy of the recovered profiles and suggest a $\chi^2$ statistic to separate successful models from biased ones. Finally, we show that the uncertainties on the mass profiles obtained with GravSphere are smaller than those for comparable Jeans methods, and that they can be further improved if stronger priors, motivated by cosmological simulations, are placed on the velocity anisotropy. We conclude that GravSphere is a promising Jeans-based approach for modelling dark matter distributions in dwarf galaxies., Comment: 22 pages, 13 figures. Accepted in MNRAS