Publications

First Author

2025

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

   Maria Acevedo, Nora R. Sherman, Dillon Brout, and 12 more authors

   arXiv, Aug 2025

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

   Maria Acevedo, Daniel Scolnic, Bastien Carreres, and 6 more authors

   arXiv, Oct 2025

   arXiv

   

Significant Contribution

2025

1. The Dark Energy Bedrock All-Sky Supernova Program: Motivation, Design, Implementation, and Preliminary Data Release

   Nora F. Sherman, Maria Acevedo, Dillon Brout, and 20 more authors

   arXiv e-prints, Aug 2025

   arXiv
2. Responsive Placemaking in an Urban STEM Higher Education Ecosystem During a Pandemic: Considerations for Strengthening Resilience in STEM Education

   Demetri L. Morgan, Maria Acevedo, Kendrick B. Davis, and 1 more author

   Urban Education, Aug 2025

   Publisher arXiv

2023

1. The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects

   J. Lee, M. Acevedo, M. Sako, and 64 more authors

   The Astronomical Journal, May 2023

   Publisher

Selected Co-Author

2025

1. The Hubble Tension in Our Own Backyard: DESI and the Nearness of the Coma Cluster

   Daniel Scolnic, Adam G. Riess, Yukei S. Murakami, and 8 more authors

   The Astrophysical Journal Letters, Jan 2025

   Publisher arXiv

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

   Bastien Carreres, Rebecca C. Chen, Erik R. Peterson, and 9 more authors

   May 2025

   arXiv

   
3. Measuring the Distances to Asteroids from One Observatory in One Night with Upcoming All-Sky Telescopes

   Maryann Benny Fernandes, Daniel Scolnic, Erik Peterson, and 4 more authors

   Qeios, Feb 2025

   Publisher

2024

1. The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release

   B. O. Sánchez, D. Brout, M. Vincenzi, and 86 more authors

   The Astrophysical Journal, Oct 2024

   Publisher Abs

   We present griz photometric light curves for the full 5 yr of the Dark Energy Survey Supernova (DES-SN) program, obtained with both forced point-spread function photometry on difference images (DiffImg) performed during survey operations, and scene modelling photometry (SMP) on search images processed after the survey. This release contains 31,636 DiffImg and 19,706 high-quality SMP light curves, the latter of which contain 1635 photometrically classified SNe that pass cosmology quality cuts. This sample spans the largest redshift (z) range ever covered by a single SN survey (0.1 < z < 1.13) and is the largest single sample from a single instrument of SNe ever used for cosmological constraints. We describe in detail the improvements made to obtain the final DES-SN photometry and provide a comparison to what was used in the 3 yr DES-SN spectroscopically confirmed Type Ia SN sample. We also include a comparative analysis of the performance of the SMP photometry with respect to the real-time DiffImg forced photometry and find that SMP photometry is more precise, more accurate, and less sensitive to the host-galaxy surface brightness anomaly. The public release of the light curves and ancillary data can be found at github.com/des-science/DES-SN5YR and doi:10.5281/zenodo.12720777.

2. The Dark Energy Survey: Cosmology Results with ∼1500 New High-redshift Type Ia Supernovae Using the Full 5 yr Data Set

   DES Collaboration: T. M. C. Abbott, M. Acevedo, M. Aguena, and 155 more authors

   The Astrophysical Journal Letters, Oct 2024

   Publisher Abs

   We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find ΩM = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration (q 0 < 0 in ΛCDM) with over 5σ confidence. We find in flat wCDM. For flat w 0 w a CDM, we find , consistent with a constant equation of state to within ∼2σ. Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (ΩM, w) = (0.321 ± 0.007, −0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2σ. Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.

3. The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

   M. Vincenzi, D. Brout, P. Armstrong, and 96 more authors

   The Astrophysical Journal, Oct 2024

   Publisher Abs

   We present the full Hubble diagram of photometrically classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7000 host galaxies. Based on the light-curve quality, we select 1635 photometrically identified SNe Ia with spectroscopic redshift 0.10 < z < 1.13, which is the largest sample of supernovae from any single survey and increases the number of known z > 0.5 supernovae by a factor of 5. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are 0.017 in a flat ΛCDM model, and = (0.082, 0.152) in a flat wCDM model. Combining the DES SN data with the highly complementary cosmic microwave background measurements by Planck Collaboration reduces by a factor of 4 uncertainties on cosmological parameters. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time.

2021

1. A Potential Canary in the Coal Mine: A Critical Policy Analysis of the Illinois LSAMP During the COVID-19 Pandemic

   Demetri L. Morgan, Victoria E. Callais, Maria Acevedo, and 1 more author

   Frontiers in Education, Oct 2021

   Publisher Abs

   The COVID-19 pandemic has continued to impact every industry and test problem-solving capabilities and innovation across the board; education is no exception. As institutions continue to adapt to the impacts of the current public health crisis, colleges and universities are also navigating federal policy prompted by the pandemic. Literature has shown the positive influence of organizations, such as the Louis Stokes Alliances for Minority Participation (LSAMP), and, we argue that they can take a more active intermediary approach, that of an Organizational Buffer, to best support their students during times of uncertainty. Current research highlights the disconnect between STEM education and policy, as well as how the pandemic is disproportionately impacting communities of color. The pervasiveness of whiteness within educational policy and the negative impacts of unequal distribution of resources on students of color in STEM highlight the need to center race in a theoretical framework and policy. The purpose of this study was to understand the policy and communication responses to the pandemic as they pertained to supporting student success in STEM. Using the Theory of Racialized Organizations, which is a qualitative case study approach that leverages diffractive readings, was implemented to understand whether educational policy and communication responses during this time have or perpetuated inequitable systems. Guided by the research question, in what ways do pandemic policies and communications bolster the success of underrepresented minoritized students (URM) majoring in STEM, our study found four versions of policymaking (i.e., Performative, Picking Winners and Losers, Stay in your Lane, and Time Burden) that emerged and did not support URM STEM students equitably and consistently. Based on these findings, we present implications for institutional responses, LSAMP-alliance support, and future research.