arXiv subjects

Bastien Carreres

Publications and source records attributed to Bastien Carreres.

11 recordsLinked to original sources

arXiv2026

The Peculiar Growth of Structure: Validating $fσ_8$ Measurements from TITAN Type Ia Supernovae and the Uchuu Simulations

The growth rate of cosmic structure, parameterized by $fσ_8$, is a fundamental test of $Λ$CDM and general relativity. Using Type Ia supernova (SN Ia) peculiar velocities in conjunction with galaxy redshift surveys may be one of the most precise pathways to measuring $fσ_8$ in the local Universe, yet existing analyses have not quantified its systematic uncertainties. Here, we present an end-to-end simulated $fσ_8$ measurement tailored to the Type Ia Supernova Trove from ATLAS in the Nearby Universe (TITAN) survey, using ~2000 simulated SNe Ia at $z < 0.067$. We use the Uchuu $N$-body simulations to generate mock galaxy catalogs and SN Ia simulations with realistic, correlated peculiar velocities, and use these catalogs to reconstruct density fields that replicate the 2M++ redshift survey. Using a modified forward likelihood framework across eight mock realizations, we recover $\langle fσ_8 \rangle = 0.429 \pm 0.038$ ($σ_{\rm stat} = 0.030$, $σ_{\rm sys} = 0.023$), consistent with the Uchuu simulation input $fσ_8 = 0.428$ to within 0.1%. The mock-to-mock scatter of 0.031 is consistent with our uncertainties, highlighting the reliability of our error estimates. Our measurement is dominated by the statistical uncertainty, with approximately equal contributions from SN Ia ($σ_{\rm sys}^{\rm SN}=0.017$) and density reconstruction ($σ_{\rm sys}^{\rm recon}=0.016$) systematic uncertainties. The assumed intrinsic scatter model is the largest single systematic contribution, and a different model choice can shift $fσ_8$ to lower values, largely driven by red SNe with a skewed color distribution. Our analysis provides the first systematic uncertainty budget for the "reconstruction-and-scaling" method of measuring $fσ_8$ with SNe Ia, and demonstrates that SNe Ia are a competitive probe of the growth of structure in the local Universe.

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arXiv2026

Calibration-Induced Systematics in SALT3 Training and Their Impact on Dark Energy Constraints from Stage IV Supernova Surveys

In the coming years, the Vera Rubin Observatory's Legacy Survey of Space and Time (Rubin-LSST) and the Nancy Grace Roman Space Telescope's (Roman) High Latitude Time Domain Survey (HLTDS) are expected to discover more than a million Type Ia supernovae (SNe Ia), several orders of magnitude more than current samples and with a tighter control on systematic uncertainties. One of the largest systematic uncertainties in cosmological analyses with SNe Ia is the accuracy of the spectro-photometric model for SNe Ia time series data, which depends on the photometric calibration of the surveys. To quantify the impact of this uncertainty, we analyze simulated Rubin-LSST and HLTDS data, perturb the photometric zero-points and filter mean wavelengths, and propagate these systematics to spectral model recovery, estimated distances, and dark energy figure of merit (FoM) based on the $w_0 w_a$CDM model. Zero-point shifts of 5 mmag and filter mean wavelength shifts of 5 angstrom lead to a $\sim 50\%$ decrease in the FoM relative to a statistical-only case when calibration uncertainties are propagated only through light-curve fitting. The same calibration shifts applied only during model training produce a smaller $\sim 13\%$ degradation. Contrary to previous analyses, calibration uncertainties in light-curve fitting dominate over those from model training. Their effect during light-curve fitting varies smoothly with redshift and is nearly degenerate with cosmology, preventing mitigation through self-calibration. Finally, we show that the FoM dependence on the size of the calibration uncertainties (in the range of expected sizes) is roughly linear.

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arXiv2025

Type Ia supernova growth-rate measurement with LSST simulations: intrinsic scatter systematics

Measurement of the growth rate of structures ($\fsig$) with Type Ia supernovae (\sns) will improve our understanding of the nature of dark energy and enable tests of general relativity. In this paper, we generate simulations of the 10 year \sn\ dataset of the Rubin-LSST survey, including a correlated velocity field from a N-body simulation and realistic models of \sns\ properties and their correlations with host-galaxy properties. We find, similar to SN~Ia analyses that constrain the dark energy equation-of-state parameters $w_0w_a$, that constraints on $\fsig$ can be biased depending on the intrinsic scatter of \sns. While for the majority of intrinsic scatter models we recover $\fsig$ with a precision of $\sim13 - 14\%$, for the most realistic dust-based model, we find that the presence of non-Gaussianities in Hubble diagram residuals leads to a bias on $\fsig$ of about $\sim-20\%$. When trying to correct for the dust-based intrinsic scatter, we find that the propagation of the uncertainty on the model parameters does not significantly increase the error on $\fsig$. We also find that while the main component of the error budget of $\fsig$ is the statistical uncertainty ($>75\%$ of the total error budget), the systematic error budget is dominated by the uncertainty on the damping parameter, $σ_u$, that gives an empirical description of the effect of redshift space distortions on the velocity power spectrum. Our results motivate a search for new methods to correct for the non-Gaussian distribution of the Hubble diagram residuals, as well as an improved modeling of the damping parameter.

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arXiv2025

Generalized framework for likelihood-based field-level inference of growth rate from velocity and density fields

Measuring the growth rate of large-scale structures (f) as a function of redshift has the potential to break degeneracies between modified gravity and dark energy models, when combined with expansion-rate probes. Direct estimates of peculiar velocities of galaxies have attracted interest as a means of estimating $fσ_8$. In particular, field-level methods can be used to fit the field nuisance parameter along with cosmological parameters simultaneously. This article aims to provide the community with a unified framework for the theoretical modeling of the likelihood-based field-level inference by performing fast field covariance calculations for velocity and density fields. Our purpose is to lay the foundations for a nonlinear extension of the likelihood-based method at the field level. We have developed a generalized framework, implemented in the dedicated software flip to perform a likelihood-based inference of $fσ_8$. We derived a new field covariance model, which includes wide-angle corrections. We also included the models previously described in the literature inside our framework. We compared their performance against ours, and we validated our model by comparing it with the two-point statistics of a recent N-body simulation. The tests we performed have allowed us to validate our software and determine the appropriate wavenumber range to integrate our covariance model and its validity in terms of separation. Our framework allows for a wider wavenumber coverage to be used in our calculations than in previous works. Finally, our generalized framework allows us to efficiently perform a survey geometry-dependent Fisher forecast of the $fσ_8$ parameter. We show that the Fisher forecast method we developed gives an error bar that is 30 % closer to a full likelihood-based estimation than a standard volume Fisher forecast.

astro-ph.CO↗

arXiv2025

The Weighing Halos Accurately, Locally, and Efficiently with Supernovae (WHALES) Survey Overview and Initial Data Release

We present an overview of the Weighing Halos Accurately, Locally, and Efficiently with Supernovae (WHALES) survey, the first to discover and measure Type Ia supernovae (SNe Ia) in and around galaxy superclusters. By building a sample of SNe~Ia around these massive environments, we aim to provide new constraints on bulk-flow models while laying the groundwork for improved estimates of supercluster masses. Here, we present data from the first two seasons targeting the Shapley Supercluster (0.02<z<0.06), which is responsible for a large but unknown fraction of our local group's motion. Until now, no supernovae had been analyzed in the direction of Shapley. Through the WHALES survey, we have identified 12 likely SNe Ia in this region using SkyMapper, including 8 with spectroscopic confirmation. We present the first light curves of these SNe and combine our observations with data from ATLAS. We demonstrate that the low number of discovered SNe Ia per season is consistent with various rate calculations, highlighting the need for future surveys to monitor superclusters over a multi-year timespan. Finally, we present simulations of SN Ia observations in the environments of massive galaxy clusters, demonstrating how the inferred peculiar velocities can constrain cluster masses, and highlighting the added complexity within superclusters. We find that a sample of 100 SNe Ia would enable a 25% precision measurement of the total mass of the Shapley Supercluster.

astro-ph.CO↗

arXiv2025

The Dark Energy Bedrock All-Sky Supernova Program: Cross Calibration, Simulations, and Cosmology Forecasts

Type Ia supernovae (SNe Ia) have been essential for probing the nature of dark energy; however, most SN analyses rely on the same low-redshift sample, which may lead to shared systematics. In a companion paper (arXiv:2508.10878), we introduce the Dark Energy Bedrock All-Sky Supernova (DEBASS) program, which has already collected more than 500 low-redshift SNe Ia on the Dark Energy Camera (DECam), and present an initial release of 77 SNe Ia within the Dark Energy Survey (DES) footprint observed between 2021 and 2024. Here, we examine the systematics, including photometric calibration and selection effects. We find agreement at the 10 millimagnitude level among the tertiary standard stars of DEBASS, DES, and Pan-STARRS1. Our simulations reproduce the observed distributions of DEBASS SN light-curve properties, and we measure a bias-corrected Hubble residual scatter of $0.08$ mag, which, while small, is found in 10% of our simulations. We compare the DEBASS SN distances to the Foundation sample and find consistency with a median residual offset of $0.016 \pm 0.019$ mag. Selection effects have negligible impacts on distances, but a different photometric calibration solution shifts the median residual $-0.015 \pm 0.019$ mag, highlighting calibration sensitivity. Using conservative simulations, we forecast that replacing historical low-redshift samples with the full DEBASS sample (>400 SNe Ia) will improve the statistical uncertainties on dark energy parameters $w_0$ and $w_a$ by 30% and 24% respectively, enhance the dark energy Figure of Merit by up to 60%, and enable a measurement of $fσ_8$ at the 25% level.

astro-ph.CO↗

arXiv2025

Forecast for growth-rate measurement using peculiar velocities from LSST supernovae

In this work, we investigate the feasibility of measuring the cosmic growth-rate parameter, $fσ_8$, using peculiar velocities (PVs) derived from Type Ia supernovae (SNe Ia) in the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). We produce simulations of different SN types using a realistic LSST observing strategy, incorporating noise, photometric detection from the Difference Image Analysis (DIA) pipeline, and a PV field modeled from the Uchuu UniverseMachine simulations. We test three observational scenarios, ranging from ideal conditions with spectroscopic host-galaxy redshifts and spectroscopic SN classification, to more realistic settings involving photometric classification and contamination from non-Ia supernovae. Using a maximum-likelihood technique, we show that LSST can measure $fσ_8$ with a precision of $10\%$ in the redshift range $ 0.02 < z < 0.14 $ in the most realistic case. Using three tomographic bins, LSST can constrain the growth-rate parameter with errors below $18\%$ up to $z = 0.14$. We also test the impact of contamination on the maximum likelihood method and find that for contamination fractions below $\sim 2\%$, the measurement remains unbiased. These results highlight the potential of the LSST SN Ia sample to complement redshift-space distortion measurements at high redshift, providing a novel avenue for testing general relativity and dark energy models.

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arXiv2025

Improving the Determination of Supernova Cosmological Redshifts by Using Galaxy Groups

At the low-redshift end ($z<0.05$) of the Hubble diagram with Type Ia Supernovae (SNe Ia), the contribution to Hubble residual scatter from peculiar velocities is of similar size to that due to the limitations of the standardization of the SN Ia light curves. A way to improve the redshift measurement of the SN host galaxy is to utilize the average redshift of the galaxy group, effectively averaging over small-scale/intracluster peculiar velocities. One limiting factor is the fraction of SN host galaxies in galaxy groups, previously found to be 30% using (relatively incomplete) magnitude-limited galaxy catalogs. Here, we do the first analysis of N-body simulations to predict this fraction, finding $\sim$73% should have associated groups and group averaging should improve redshift precision by $\sim$135 km s$^{-1}$ ($\sim$0.04 mag at $z=0.025$). Furthermore, using spectroscopic data from the Anglo-Australian Telescope, we present results from the first pilot program to evaluate whether or not 23 previously unassociated SN Ia hosts belong in groups. We find that 91% of these candidates can be associated with groups, consistent with predictions from simulations given the sample size. Combining with previously assigned SN host galaxies in Pantheon+, we demonstrate improvement in Hubble residual scatter equivalent to 145 km s$^{-1}$, also consistent with simulations. For new and upcoming low-$z$ samples from, for example, ZTF and LSST, a separate follow-up program identifying galaxy groups of SN hosts is a highly cost-effective way to enhance their constraining power.

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arXiv2024

ZTF SN Ia DR2: Overview

We present the first homogeneous release of several thousand Type Ia supernovae (SNe Ia), all having spectroscopic classification, and spectroscopic redshifts for half the sample. This release, named the "DR2", contains 3628 nearby (z < 0.3) SNe Ia discovered, followed and classified by the Zwicky Transient Facility survey between March 2018 and December 2020. Of these, 3000 have good-to-excellent sampling and 2667 pass standard cosmology light-curve quality cuts. This release is thus the largest SN Ia release to date, increasing by an order of magnitude the number of well characterized low-redshift objects. With the "DR2", we also provide a volume-limited (z < 0.06) sample of nearly a thousand SNe Ia. With such a large, homogeneous and well controlled dataset, we are studying key current questions on SN cosmology, such as the linearity SNe Ia standardization, the SN and host dependencies, the diversity of the SN Ia population, and the accuracy of the current light-curve modeling. These, and more, are studied in detail in a series of articles associated with this release. Alongside the SN Ia parameters, we publish our force-photometry gri-band light curves, 5138 spectra, local and global host properties, observing logs, and a python tool to ease use and access of these data. The photometric accuracy of the "DR2" is not yet suited for cosmological parameter inference, which will follow as "DR2.5" release. We nonetheless demonstrate that the multi-thousand SN Ia Hubble Diagram has a typical 0.15 mag scatter.

astro-ph.CO↗

arXiv2024

The Hubble Tension in our own Backyard: DESI and the Nearness of the Coma Cluster

The Dark Energy Spectroscopic Instrument (DESI) collaboration measured a tight relation between the Hubble constant ($H_0$) and the distance to the Coma cluster using the fundamental plane (FP) relation of the deepest, most homogeneous sample of early-type galaxies. To determine $H_0$, we measure the distance to Coma by several independent routes each with its own geometric reference. We measure the most precise distance to Coma from 12 Type Ia Supernovae (SNe Ia) in the cluster with mean standardized brightness of $m_B^0=15.712\pm0.041$ mag. Calibrating the absolute magnitude of SNe Ia with the HST distance ladder yields $D_{\textrm Coma}=98.5\pm2.2$ Mpc, consistent with its canonical value of 95--100 Mpc. This distance results in $H_0=76.5 \pm 2.2$ km/s/Mpc from the DESI FP relation. Inverting the DESI relation by calibrating it instead to the Planck+$Λ$CDM value of $H_0=67.4$ km/s/Mpc implies a much greater distance to Coma, $D_{\textrm Coma}=111.8\pm1.8$ Mpc, $4.6σ$ beyond a joint, direct measure. Independent of SNe Ia, the HST Key Project FP relation as calibrated by Cepheids, Tip of the Red Giant Branch from JWST, or HST NIR surface brightness fluctuations all yield $D_{\textrm Coma}<$ 100 Mpc, in joint tension themselves with the Planck-calibrated route at $>3σ$. From a broad array of distance estimates compiled back to 1990, it is hard to see how Coma could be located as far as the Planck+$Λ$CDM expectation of $>$110 Mpc. By extending the Hubble diagram to Coma, a well-studied location in our own backyard whose distance was in good accord well before the Hubble Tension, DESI indicates a more pervasive conflict between our knowledge of local distances and cosmological expectations. We expect future programs to refine the distance to Coma and nearer clusters to help illuminate this new, local window on the Hubble Tension.

astro-ph.CO↗

arXiv2023

Growth-rate measurement with type-Ia supernovae using ZTF survey simulations

Measurements of the growth rate of structures at $z < 0.1$ with peculiar velocity surveys have the potential of testing the validity of general relativity on cosmic scales. In this work, we present growth-rate measurements from realistic simulated sets of type-Ia supernovae (SNe Ia) from the Zwicky Transient Facility (ZTF). We describe our simulation methodology, the light-curve fitting and peculiar velocity estimation. Using the maximum likelihood method, we derive constraints on $fσ_8$ using only ZTF SN Ia peculiar velocities. We carefully tested the method and we quantified biases due to selection effects (photometric detection, spectroscopic follow-up for typing) on several independent realizations. We simulated the equivalent of 6 years of ZTF data, and considering an unbiased spectroscopically typed sample at $z < 0.06$, we obtained unbiased estimates of $fσ_8$ with an average uncertainty of 19% precision. We also investigated the information gain in applying bias correction methods. Our results validate our framework which can be used on real ZTF data.

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