arXiv subjects

Damiano Rosselli

Publications and source records attributed to Damiano Rosselli.

5 recordsLinked to original sources

arXiv2026

Field-level inference of growth rate from DESI BGS galaxy and ZTF supernova simulations

Galaxy peculiar velocities are a powerful probe of the growth rate of structure $f$ at low redshifts ($z < 0.2$), measured through the combination $fσ_8$. These constraints are improved by combining peculiar velocities with redshift space distortion measurements from the galaxy density field. We create a dedicated simulation to test growth rate measurement from the combination of Type Ia supernova peculiar velocities from the Zwicky Transient Facility (ZTF) with the Bright Galaxy Survey (BGS) of the Dark Energy Spectroscopic Instrument (DESI), using projections for 6 years of ZTF data and the Data Release 3 (DR3) of DESI. We apply a wide-angle likelihood-based field-level inference to combine ZTF velocity and DESI BGS density fields simultaneously. This procedure is performed while varying the different density and velocity field configurations. Adding DESI galaxy density information to the ZTF supernovae improves the constraint on nuisance parameters and yields up to a 50 \% reduction in the growth-rate uncertainty without introducing bias. If we consider the conservatively best DESI density configuration, the mean error bar achievable by a DESI ZTF measurement is $σ_{fσ_8} = {}^{+0.047}_{-0.048}$ corresponding to a 10 \% error. These measurements will play a critical role for testing modified gravity theories.

astro-ph.CO↗

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.

astro-ph.CO↗

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

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.

astro-ph.CO↗

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↗