arXiv subjects

Andrina Nicola

Publications and source records attributed to Andrina Nicola.

At least 37 records · Page 2Linked to original sources

arXiv2023

Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Two-point Correlation Functions

We perform a blinded cosmology analysis with cosmic shear two-point correlation functions (2PCFs) measured from more than 25 million galaxies in the Hyper Suprime-Cam three-year shear catalog in four tomographic redshift bins ranging from 0.3 to 1.5. After conservative masking and galaxy selection, the survey covers 416 deg$^2$ of the northern sky with an effective galaxy number density of 15 arcmin$^{-2}$ over the four redshift bins. The 2PCFs adopted for cosmology analysis are measured in the angular range: $7.1 < θ/{\rm arcmin} < 56.6$ for $ξ_+$ and $31.2 <θ/{\rm arcmin} < 248$ for $ξ_-$, with a total signal-to-noise ratio of 26.6. We apply a conservative, wide, flat prior on the photometric redshift errors on the last two tomographic bins, and the relative magnitudes of the cosmic shear amplitude across four redshift bins allow us to calibrate the photometric redshift errors. With this flat prior on redshift errors, we find $Ω_{\rm m}=0.256_{-0.044}^{+0.056}$ and $S_8\equiv σ_8 \sqrt{Ω_{\rm m}/0.3}=0.769_{-0.034}^{+0.031}$ (both 68\% CI) for a flat $Λ$ cold dark matter cosmology. We find, after unblinding, that our constraint on $S_8$ is consistent with the Fourier space cosmic shear and the 3$\times$2pt analyses on the same HSC dataset. We carefully study the potential systematics from astrophysical and systematic model uncertainties in our fiducial analysis using synthetic data, and report no biases (including projection bias in the posterior space) greater than $0.5σ$ in the estimation of $S_8$. Our analysis hints that the mean redshifts of the two highest tomographic bins are higher than initially estimated. In addition, a number of consistency tests are conducted to assess the robustness of our analysis. Comparing our result with Planck-2018 cosmic microwave background observations, we find a ~$2σ$ tension for the $Λ$CDM model.

astro-ph.CO↗

arXiv2023

A General Framework for Removing Point Spread Function Additive Systematics in Cosmological Weak Lensing Analysis

Cosmological weak lensing measurements rely on a precise measurement of the shear two-point correlation function (2PCF) along with a deep understanding of systematics that affect it. In this work, we demonstrate a general framework for detecting and modeling the impact of PSF systematics on the cosmic shear 2PCF, and mitigating its impact on cosmological analysis. Our framework can describe leakage and modeling error from all spin-2 quantities contributed by the PSF second and higher moments, rather than just the second moments, using the cross-correlations between galaxy shapes and PSF moments. We interpret null tests using the HSC Year 3 (Y3) catalogs with this formalism, and find that leakage from the spin-2 combination of PSF fourth moments is the leading contributor to additive shear systematics, with total contamination that is an order of magnitude higher than that contributed by PSF second moments alone. We conducted a mock cosmic shear analysis for HSC Y3, and find that, if uncorrected, PSF systematics can bias the cosmological parameters $Ω_m$ and $S_8$ by $\sim$0.3$σ$. The traditional second moment-based model can only correct for a 0.1$σ$ bias, leaving the contamination largely uncorrected. We conclude it is necessary to model both PSF second and fourth moment contamination for HSC Y3 cosmic shear analysis. We also reanalyze the HSC Y1 cosmic shear analysis with our updated systematics model, and identify a 0.07$σ$ bias on $Ω_m$ when using the more restricted second moment model from the original analysis. We demonstrate how to self-consistently use the method in both real space and Fourier space, assess shear systematics in tomographic bins, and test for PSF model overfitting.

astro-ph.CO↗

arXiv2023

Galaxy bias in the era of LSST: perturbative bias expansions

Upcoming imaging surveys will allow for high signal-to-noise measurements of galaxy clustering at small scales. In this work, we present the results of the LSST bias challenge, the goal of which is to compare the performance of different nonlinear galaxy bias models in the context of LSST Y10 data. Specifically, we compare two perturbative approaches, Lagrangian perturbation theory (LPT) and Eulerian PT (EPT) to two variants of Hybrid Effective Field Theory (HEFT), with our fiducial implementation of these models including terms up to second order in the bias expansion as well as nonlocal bias and deviations from Poissonian stochasticity. We consider different simulated galaxy samples and test the performance of the bias models in a tomographic joint analysis of LSST-Y10-like galaxy clustering, galaxy-galaxy-lensing and cosmic shear. We find both HEFT methods as well as LPT and EPT combined with non-perturbative predictions for the matter power spectrum to yield unbiased constraints on cosmological parameters up to at least a maximal scale of $k_{\mathrm{max}}=0.4 \; \mathrm{Mpc}^{-1}$ for all samples considered, even in the presence of assembly bias. While we find that we can reduce the complexity of the bias model for HEFT without compromising fit accuracy, this is not generally the case for the perturbative models. We find significant detections of non-Poissonian stochasticity in all cases considered, and our analysis shows evidence that small-scale galaxy clustering predominantly improves constraints on galaxy bias rather than cosmological parameters. These results therefore suggest that the systematic uncertainties associated with current nonlinear bias models are likely to be subdominant compared to other sources of error for tomographic analyses of upcoming photometric surveys, which bodes well for future galaxy clustering analyses using these high signal-to-noise data. [abridged]

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arXiv2023

Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Power Spectra

We measure weak lensing cosmic shear power spectra from the three-year galaxy shear catalog of the Hyper Suprime-Cam (HSC) Subaru Strategic Program imaging survey. The shear catalog covers $416 \ \mathrm{deg}^2$ of the northern sky, with a mean $i$-band seeing of 0.59 arcsec and an effective galaxy number density of 15 $\mathrm{arcmin}^{-2}$ within our adopted redshift range. With an $i$-band magnitude limit of 24.5 mag, and four tomographic redshift bins spanning $0.3 \leq z_{\mathrm{ph}} \leq 1.5$ based on photometric redshifts, we obtain a high-significance measurement of the cosmic shear power spectra, with a signal-to-noise ratio of approximately 26.4 in the multipole range $300<\ell<1800$. The accuracy of our power spectrum measurement is tested against realistic mock shear catalogs, and we use these catalogs to get a reliable measurement of the covariance of the power spectrum measurements. We use a robust blinding procedure to avoid confirmation bias, and model various uncertainties and sources of bias in our analysis, including point spread function systematics, redshift distribution uncertainties, the intrinsic alignment of galaxies and the modeling of the matter power spectrum. For a flat $Λ$CDM model, we find $S_8 \equiv σ_8 (Ω_m/0.3)^{0.5} =0.776^{+0.032}_{-0.033}$, which is in excellent agreement with the constraints from the other HSC Year 3 cosmology analyses, as well as those from a number of other cosmic shear experiments. This result implies a $\sim$$2σ$-level tension with the Planck 2018 cosmology. We study the effect that various systematic errors and modeling choices could have on this value, and find that they can shift the best-fit value of $S_8$ by no more than $\sim$$0.5σ$, indicating that our result is robust to such systematics.

astro-ph.CO↗

arXiv2022

Combining cosmic shear data with correlated photo-$z$ uncertainties: constraints from DESY1 and HSC-DR1

An accurate calibration of the source redshift distribution $p(z)$ is a key aspect in the analysis of cosmic shear data. This, one way or another, requires the use of spectroscopic or high-quality photometric samples. However, the difficulty to obtain colour-complete spectroscopic samples matching the depth of weak lensing catalogs means that the analyses of different cosmic shear datasets often use the same samples for redshift calibration. This introduces a source of statistical and systematic uncertainty that is highly correlated across different weak lensing datasets, and which must be accurately characterised and propagated in order to obtain robust cosmological constraints from their combination. In this paper we introduce a method to quantify and propagate the uncertainties on the source redshift distribution in two different surveys sharing the same calibrating sample. The method is based on an approximate analytical marginalisation of the $p(z)$ statistical uncertainties and the correlated marginalisation of residual systematics. We apply this method to the combined analysis of cosmic shear data from the DESY1 data release and the HSC-DR1 data, using the COSMOS 30-band catalog as a common redshift calibration sample. We find that, although there is significant correlation in the uncertainties on the redshift distributions of both samples, this does not change the final constraints on cosmological parameters significantly. The same is true also for the impact of residual systematic uncertainties from the errors in the COSMOS 30-band photometric redshifts. Additionally, we show that these effects will still be negligible in Stage-IV datasets. Finally, the combination of DESY1 and HSC-DR1 allows us to constrain the ``clumpiness'' parameter to $S_8 = 0.768^{+0.021}_{-0.017}$. This corresponds to a $\sim\sqrt{2}$ improvement in uncertainties with respect to either DES or HSC alone.

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arXiv2022

A Unified Catalog-level Reanalysis of Stage-III Cosmic Shear Surveys

Cosmological parameter constraints from recent galaxy imaging surveys are reaching $2-3\%$-level accuracy. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will produce sub-percent level measurements of cosmological parameters, providing a milestone test of the $Λ$CDM model. To supply guidance to the upcoming LSST analysis, it is important to understand thoroughly the results from different recent galaxy imaging surveys and assess their consistencies. In this work we perform a unified catalog-level reanalysis of three cosmic shear datasets: the first year data from the Dark Energy Survey (DES-Y1), the 1,000 deg$^{2}$ dataset from the Kilo-Degree Survey (KiDS-1000), and the first year data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-Y1). We utilize a pipeline developed and rigorously tested by the LSST Dark Energy Science Collaboration to perform the reanalysis and assess the robustness of the results to analysis choices. We find the $S_{8}$ constraint to be robust to two different small-scale modeling approaches, and varying choices of cosmological priors. Our unified analysis allows the consistency of the surveys to be rigorously tested and we find the three surveys to be statistically consistent. Due to the partially overlapping footprint, we model the cross-covariance between KiDS-1000 and HSC-Y1 approximately when combining all three datasets, resulting in a $1.6-1.9\%$ constraint on $S_8$ given different assumptions on the cross-covariance.

astro-ph.CO↗

arXiv2022

Breaking baryon-cosmology degeneracy with the electron density power spectrum

Uncertain feedback processes in galaxies affect the distribution of matter, currently limiting the power of weak lensing surveys. If we can identify cosmological statistics that are robust against these uncertainties, or constrain these effects by other means, then we can enhance the power of current and upcoming observations from weak lensing surveys such as DES, Euclid, the Rubin Observatory, and the Roman Space Telescope. In this work, we investigate the potential of the electron density auto-power spectrum as a robust probe of cosmology and baryonic feedback. We use a suite of (magneto-)hydrodynamic simulations from the CAMELS project and perform an idealized analysis to forecast statistical uncertainties on a limited set of cosmological and physically-motivated astrophysical parameters. We find that the electron number density auto-correlation, measurable through either kinematic Sunyaev-Zel'dovich observations or through Fast Radio Burst dispersion measures, provides tight constraints on $Ω_{m}$ and the mean baryon fraction in intermediate-mass halos, $\bar{f}_{\mathrm{bar}}$. By obtaining an empirical measure for the associated systematic uncertainties, we find these constraints to be largely robust to differences in baryonic feedback models implemented in hydrodynamic simulations. We further discuss the main caveats associated with our analysis, and point out possible directions for future work.

astro-ph.CO↗

arXiv2022

The CAMELS project: public data release

The Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project was developed to combine cosmology with astrophysics through thousands of cosmological hydrodynamic simulations and machine learning. CAMELS contains 4,233 cosmological simulations, 2,049 N-body and 2,184 state-of-the-art hydrodynamic simulations that sample a vast volume in parameter space. In this paper we present the CAMELS public data release, describing the characteristics of the CAMELS simulations and a variety of data products generated from them, including halo, subhalo, galaxy, and void catalogues, power spectra, bispectra, Lyman-$α$ spectra, probability distribution functions, halo radial profiles, and X-rays photon lists. We also release over one thousand catalogues that contain billions of galaxies from CAMELS-SAM: a large collection of N-body simulations that have been combined with the Santa Cruz Semi-Analytic Model. We release all the data, comprising more than 350 terabytes and containing 143,922 snapshots, millions of halos, galaxies and summary statistics. We provide further technical details on how to access, download, read, and process the data at \url{https://camels.readthedocs.io}.

astro-ph.CO↗

arXiv2021

The LSST-DESC 3x2pt Tomography Optimization Challenge

This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included.

astro-ph.IM↗

arXiv2021

The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence

We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield Dataset, CMD, a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span $\sim$100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project. Designed to train machine learning models, CMD is the largest dataset of its kind containing more than 70 Terabytes of data. In this paper we describe CMD in detail and outline a few of its applications. We focus our attention on one such task, parameter inference, formulating the problems we face as a challenge to the community. We release all data and provide further technical details at https://camels-multifield-dataset.readthedocs.io.

cs.LG↗

arXiv2021

Robust marginalization of baryonic effects for cosmological inference at the field level

We train neural networks to perform likelihood-free inference from $(25\,h^{-1}{\rm Mpc})^2$ 2D maps containing the total mass surface density from thousands of hydrodynamic simulations of the CAMELS project. We show that the networks can extract information beyond one-point functions and power spectra from all resolved scales ($\gtrsim 100\,h^{-1}{\rm kpc}$) while performing a robust marginalization over baryonic physics at the field level: the model can infer the value of $Ω_{\rm m} (\pm 4\%)$ and $σ_8 (\pm 2.5\%)$ from simulations completely different to the ones used to train it.

astro-ph.CO↗

arXiv2021

Multifield Cosmology with Artificial Intelligence

Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical obstacles to extract information from cosmological surveys. We use 2,000 state-of-the-art hydrodynamic simulations from the CAMELS project spanning a wide variety of cosmological and astrophysical models and generate hundreds of thousands of 2-dimensional maps for 13 different fields: from dark matter to gas and stellar properties. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information while marginalizing over astrophysical effects at the field level. Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $Ω_{\rm m}$ and $σ_8$ with a few percent level precision for most of the fields. We find that the marginalization performed by the network retains a wealth of cosmological information compared to a model trained on maps from gravity-only N-body simulations that are not contaminated by astrophysical effects. Finally, we train our networks on multifields -- 2D maps that contain several fields as different colors or channels -- and find that not only they can infer the value of all parameters with higher accuracy than networks trained on individual fields, but they can constrain the value of $Ω_{\rm m}$ with higher accuracy than the maps from the N-body simulations.

astro-ph.CO↗

arXiv2021

The growth of density perturbations in the last $\sim$10 billion years from tomographic large-scale structure data

In order to investigate the origin of the ongoing tension between the amplitude of matter fluctuations measured by weak lensing experiments at low redshifts and the value inferred from the cosmic microwave background anisotropies, we reconstruct the evolution of this amplitude from $z\sim2$ using existing large-scale structure data. To do so, we decouple the linear growth of density inhomogeneities from the background expansion, and constrain its redshift dependence making use of a combination of 6 different data sets, including cosmic shear, galaxy clustering and CMB lensing. We analyze these data under a consistent harmonic-space angular power spectrum-based pipeline. We show that current data constrain the amplitude of fluctuations mostly in the range $0.2<z<0.7$, where it is lower than predicted by Planck. This difference is mostly driven by current cosmic shear data, although the growth histories reconstructed from different data combinations are consistent with each other, and we find no evidence of systematic deviations in any particular experiment. In spite of the tension with Planck, the data are well-described by the $Λ$CDM model, albeit with a lower value of $S_8\equivσ_8(Ω_m/0.3)^{0.5}$. As part of our analysis, we find constraints on this parameter of $S_8=0.7781\pm0.0094$ (68\% confidence level), reaching almost percent-level errors comparable with CMB measurements, and 3.4$σ$ away from the value found by Planck.

astro-ph.CO↗

arXiv2021

The Quijote simulations

The Quijote simulations are a set of 44,100 full N-body simulations spanning more than 7,000 cosmological models in the $\{Ω_{\rm m}, Ω_{\rm b}, h, n_s, σ_8, M_ν, w \}$ hyperplane. At a single redshift the simulations contain more than 8.5 trillions of particles over a combined volume of 44,100 $(h^{-1}{\rm Gpc})^3$; each simulation follow the evolution of $256^3$, $512^3$ or $1024^3$ particles in a box of $1~h^{-1}{\rm Gpc}$ length. Billions of dark matter halos and cosmic voids have been identified in the simulations, whose runs required more than 35 million core hours. The Quijote simulations have been designed for two main purposes: 1) to quantify the information content on cosmological observables, and 2) to provide enough data to train machine learning algorithms. In this paper we describe the simulations and show a few of their applications. We also release the Petabyte of data generated, comprising hundreds of thousands of simulation snapshots at multiple redshifts, halo and void catalogs, together with millions of summary statistics such as power spectra, bispectra, correlation functions, marked power spectra, and estimated probability density functions.

astro-ph.CO↗

arXiv2021

Fast Lightcones for Combined Cosmological Probes

The combination of different cosmological probes offers stringent tests of the $Λ$CDM model and enhanced control of systematics. For this purpose, we present an extension of the lightcone generator UFalcon first introduced in Sgier et al. 2019 (arXiv:1801.05745), enabling the simulation of a self-consistent set of maps for different cosmological probes. Each realization is generated from the same underlying simulated density field, and contains full-sky maps of different probes, namely weak lensing shear, galaxy overdensity including RSD, CMB lensing, and CMB temperature anisotropies from the ISW effect. The lightcone generation performed by UFalcon is parallelized and based on the replication of a large periodic volume simulated with the GPU-accelerated $N$-Body code PkdGrav3. The post-processing to construct the lightcones requires only a runtime of about 1 walltime-hour corresponding to about 100 CPU-hours. We use a randomization procedure to increase the number of quasi-independent full-sky UFalcon map-realizations, which enables us to compute an accurate multi-probe covariance matrix. Using this framework, we forecast cosmological parameter constraints by performing a multi-probe likelihood analysis for a combination of simulated future stage-IV-like surveys. We find that the inclusion of the cross-correlations between the probes significantly increases the information gain in the parameter constraints. We also find that the use of a non-Gaussian covariance matrix is increasingly important, as more probes and cross-correlation power spectra are included. A version of the UFalcon package currently including weak gravitational lensing is publicly available.

astro-ph.CO↗

arXiv2021

Hefty enhancement of cosmological constraints from the DES Y1 data using a Hybrid Effective Field Theory approach to galaxy bias

We present a re-analysis of cosmic shear and galaxy clustering from first-year Dark Energy Survey data (DES Y1), making use of a Hybrid Effective Field Theory (HEFT) approach to model the galaxy-matter relation on weakly non-linear scales, initially proposed in Modi et al. (2020) (arXiv:1910.07097). This allows us to explore the enhancement in cosmological constraining power enabled by extending the galaxy clustering scale range typically used in projected large-scale structure analyses. Our analysis is based on a recomputed harmonic-space data vector and covariance matrix, carefully accounting for all sources of mode-coupling, non-Gaussianity and shot noise, which allows us to provide robust goodness-of-fit measures. We use the \textsc{AbacusSummit} suite of simulations to build an emulator for the HEFT model predictions. We find that this model can explain the galaxy clustering and shear data up to wavenumbers $k_{\rm max}\sim 0.6\, {\rm Mpc}^{-1}$. We constrain $(S_8,Ω_m) = (0.786\pm 0.020,0.273^{+0.030}_{-0.036})$ at the fiducial $k_{\rm max}\sim 0.3\, {\rm Mpc}^{-1}$, improving to $(S_8,Ω_m) = (0.786^{+0.015}_{-0.018},0.266^{+0.024}_{-0.027})$ at $k_{\rm max}\sim 0.5\, {\rm Mpc}^{-1}$. This represents a $\sim10\%$ and $\sim35\%$ improvement on the constraints derived respectively on both parameters using a linear bias relation on a reduced scale range ($k_{\rm max}\lesssim0.15\,{\rm Mpc}^{-1}$), in spite of the 15 additional parameters involved in the HEFT model. We investigate whether HEFT can be used to constrain the Hubble parameter and find $H_0= 70.7_{-3.5}^{+3.0}\,{\rm km}\,s^{-1}\,{\rm Mpc}^{-1}$. Our constraints are investigative and subject to certain caveats discussed in the text.

astro-ph.CO↗

arXiv2021

The Novel Probes Project -- Tests of Gravity on Astrophysical Scales

We introduce The Novel Probes Project, an initiative to advance the field of astrophysical tests of the dark sector by creating a forum that connects observers and theorists. This review focuses on tests of gravity and is intended to be of use primarily to observers, but also to theorists with interest in the development of experimental tests. It is twinned with a separate review on tests of dark matter self-interactions (Adhikari et al., in prep.). Our focus is on astrophysical probes of gravity in the weak-field regime, ranging from stars to quasilinear cosmological scales. These are complementary to both strong-field tests and background and linear probes in cosmology. In particular, the nonlinear screening mechanisms that are an integral part of viable modified gravity models lead to characteristic signals specifically on astrophysical scales. The constraining power of these signals is not limited by cosmic variance, but comes with the challenge of building robust theoretical models of the nonlinear dynamics of stars, galaxies, clusters and large scale structure. In this review we lay the groundwork for a thorough exploration of the astrophysical regime with an eye to using the current and next generation of observations for tests of gravity. We begin by setting the scene for how theories beyond General Relativity are expected to behave, focusing primarily on screened fifth forces. We describe the analytic and numerical techniques for exploring the pertinent astrophysical systems, as well as the signatures of modified gravity. With these in hand we present a range of observational tests, and discuss prospects for future measurements and theoretical developments.

astro-ph.CO↗

arXiv2020

The Atacama Cosmology Telescope: DR4 Maps and Cosmological Parameters

We present new arcminute-resolution maps of the Cosmic Microwave Background temperature and polarization anisotropy from the Atacama Cosmology Telescope, using data taken from 2013-2016 at 98 and 150 GHz. The maps cover more than 17,000 deg$^2$, the deepest 600 deg$^2$ with noise levels below 10 $μ$K-arcmin. We use the power spectrum derived from almost 6,000 deg$^2$ of these maps to constrain cosmology. The ACT data enable a measurement of the angular scale of features in both the divergence-like polarization and the temperature anisotropy, tracing both the velocity and density at last-scattering. From these one can derive the distance to the last-scattering surface and thus infer the local expansion rate, $H_0$. By combining ACT data with large-scale information from WMAP we measure $H_0 = 67.6 \pm 1.1$ km/s/Mpc, at 68% confidence, in excellent agreement with the independently-measured Planck satellite estimate (from ACT alone we find $H_0 = 67.9 \pm 1.5$ km/s/Mpc). The $Λ$CDM model provides a good fit to the ACT data, and we find no evidence for deviations: both the spatial curvature, and the departure from the standard lensing signal in the spectrum, are zero to within 1$σ$; the number of relativistic species, the primordial Helium fraction, and the running of the spectral index are consistent with $Λ$CDM predictions to within $1.5 - 2.2σ$. We compare ACT, WMAP, and Planck at the parameter level and find good consistency; we investigate how the constraints on the correlated spectral index and baryon density parameters readjust when adding CMB large-scale information that ACT does not measure. The DR4 products presented here will be publicly released on the NASA Legacy Archive for Microwave Background Data Analysis.

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