Publications

Overview

128 refereed publications
Publications on ADS: 8164 citations, h-index 45
ORCID: https://orcid.org/0000-0002-4900-805X

First-author and top-tier publications

  1. Bocquet, Grandis, Krause et al. (2024), Multiprobe Cosmology from the Abundance of SPT Clusters and DES Galaxy Clustering and Weak Lensing, arXiv, DOI:10.48550/arXiv.2412.07765 (ADS abstract)
  2. Mazoun, Bocquet, Mohr et al. (2024), Interacting Dark Sector (ETHOS $n=0$): Cosmological Constraints from SPT Cluster Abundance with DES and HST Weak Lensing Data, arXiv, DOI:10.48550/arXiv.2411.19911 (ADS abstract)
  3. Bocquet, Grandis, Bleem et al. (2024), SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos, PhRvD, 110, 083510, DOI:10.1103/PhysRevD.110.083510 (ADS abstract)
  4. Bocquet, Grandis, Bleem et al. (2024), SPT clusters with DES and HST weak lensing. I. Cluster lensing and Bayesian population modeling of multiwavelength cluster datasets, PhRvD, 110, 083509, DOI:10.1103/PhysRevD.110.083509 (ADS abstract)
  5. Vogt, Bocquet, Davies et al. (2024), Constraints on $f(R)$ gravity from tSZE-selected SPT galaxy clusters and weak lensing mass calibration from DES and HST, arXiv, DOI:10.48550/arXiv.2409.13556 (ADS abstract)
  6. Klein, Mohr, Bocquet et al. (2024), SPT-SZ MCMF: an extension of the SPT-SZ catalogue over the DES region, MNRAS, 531, 3973, DOI:10.1093/mnras/stae1359 (ADS abstract)
  7. Grandis, Ghirardini, Bocquet et al. (2024), The SRG/eROSITA All-Sky Survey. Dark Energy Survey year 3 weak gravitational lensing by eRASS1 selected galaxy clusters, A&A, 687, A178, DOI:10.1051/0004-6361/202348615 (ADS abstract)
  8. Vogt, Bocquet, Davies et al. (2024), Constraining f (R ) gravity using future galaxy cluster abundance and weak-lensing mass calibration datasets, PhRvD, 109, 123503, DOI:10.1103/PhysRevD.109.123503 (ADS abstract)
  9. Mazoun, Bocquet, Garny et al. (2024), Probing interacting dark sector models with future weak lensing-informed galaxy cluster abundance constraints from SPT-3G and CMB-S4, PhRvD, 109, 063536, DOI:10.1103/PhysRevD.109.063536 (ADS abstract)
  10. Zohren, Schrabback, Bocquet et al. (2022), Extending empirical constraints on the SZ-mass scaling relation to higher redshifts via HST weak lensing measurements of nine clusters from the SPT-SZ survey at z ≳ 1, A&A, 668, A18, DOI:10.1051/0004-6361/202142991 (ADS abstract)
  11. Salvati, Saro, Bocquet et al. (2022), Combining Planck and SPT Cluster Catalogs: Cosmological Analysis and Impact on the Planck Scaling Relation Calibration, ApJ, 934, 129, DOI:10.3847/1538-4357/ac7ab4 (ADS abstract)
  12. Grandis, Bocquet, Mohr et al. (2021), Calibration of bias and scatter involved in cluster mass measurements using optical weak gravitational lensing, MNRAS, 507, 5671, DOI:10.1093/mnras/stab2414 (ADS abstract)
  13. Schrabback, Bocquet, Sommer et al. (2021), Mass calibration of distant SPT galaxy clusters through expanded weak-lensing follow-up observations with HST, VLT, & Gemini-South, MNRAS, 505, 3923, DOI:10.1093/mnras/stab1386 (ADS abstract)
  14. Grandis, Mohr, Costanzi, Saro, Bocquet et al. (2021), Exploring the contamination of the DES-Y1 cluster sample with SPT-SZ selected clusters, MNRAS, 504, 1253, DOI:10.1093/mnras/stab869 (ADS abstract)
  15. Costanzi, Saro, Bocquet et al. (2021), Cosmological constraints from DES Y1 cluster abundances and SPT multiwavelength data, PhRvD, 103, 043522, DOI:10.1103/PhysRevD.103.043522 (ADS abstract)
  16. Grandis, Klein, Mohr, Bocquet et al. (2020), Validation of selection function, sample contamination and mass calibration in galaxy cluster samples, MNRAS, 498, 771, DOI:10.1093/mnras/staa2333 (ADS abstract)
  17. Bocquet, Heitmann, Habib et al. (2020), The Mira-Titan Universe. III. Emulation of the Halo Mass Function, ApJ, 901, 5, DOI:10.3847/1538-4357/abac5c (ADS abstract)
  18. Bleem, Bocquet, Stalder et al. (2020), The SPTpol Extended Cluster Survey, ApJS, 247, 25, DOI:10.3847/1538-4365/ab6993 (ADS abstract)
  19. Grandis, Mohr, Dietrich, Bocquet et al. (2019), Impact of weak lensing mass calibration on eROSITA galaxy cluster cosmological studies - a forecast, MNRAS, 488, 2041, DOI:10.1093/mnras/stz1778 (ADS abstract)
  20. Bocquet, Dietrich, Schrabback et al. (2019), Cluster Cosmology Constraints from the 2500 deg2 SPT-SZ Survey: Inclusion of Weak Gravitational Lensing Data from Magellan and the Hubble Space Telescope, ApJ, 878, 55, DOI:10.3847/1538-4357/ab1f10 (ADS abstract)
  21. Stern, Dietrich, Bocquet et al. (2019), Weak-lensing analysis of SPT-selected galaxy clusters using Dark Energy Survey Science Verification data, MNRAS, 485, 69, DOI:10.1093/mnras/stz234 (ADS abstract)
  22. Dietrich, Bocquet, Schrabback et al. (2019), Sunyaev-Zel'dovich effect and X-ray scaling relations from weak lensing mass calibration of 32 South Pole Telescope selected galaxy clusters, MNRAS, 483, 2871, DOI:10.1093/mnras/sty3088 (ADS abstract)
  23. Chiu, Mohr, McDonald, Bocquet et al. (2018), Baryon content in a sample of 91 galaxy clusters selected by the South Pole Telescope at 0.2 <z < 1.25, MNRAS, 478, 3072, DOI:10.1093/mnras/sty1284 (ADS abstract)
  24. Schrabback, Applegate, Dietrich, Hoekstra, Bocquet et al. (2018), Cluster mass calibration at high redshift: HST weak lensing analysis of 13 distant galaxy clusters from the South Pole Telescope Sunyaev-Zel'dovich Survey, MNRAS, 474, 2635, DOI:10.1093/mnras/stx2666 (ADS abstract)
  25. Saro, Bocquet, Mohr et al. (2017), Optical-SZE scaling relations for DES optically selected clusters within the SPT-SZ Survey, MNRAS, 468, 3347, DOI:10.1093/mnras/stx594 (ADS abstract)
  26. Bocquet, Saro, Dolag et al. (2016), Halo mass function: baryon impact, fitting formulae, and implications for cluster cosmology, MNRAS, 456, 2361, DOI:10.1093/mnras/stv2657 (ADS abstract)
  27. Chiu, Mohr, McDonald, Bocquet et al. (2016), Baryon content of massive galaxy clusters at 0.57 < z < 1.33, MNRAS, 455, 258, DOI:10.1093/mnras/stv2303 (ADS abstract)
  28. Bocquet & Carter (2016), pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms), JOSS, 1, 46, DOI:10.21105/joss.00046 (ADS abstract)
  29. Saro, Bocquet, Rozo et al. (2015), Constraints on the richness-mass relation and the optical-SZE positional offset distribution for SZE-selected clusters, MNRAS, 454, 2305, DOI:10.1093/mnras/stv2141 (ADS abstract)
  30. Bocquet, Saro, Mohr et al. (2015), Mass Calibration and Cosmological Analysis of the SPT-SZ Galaxy Cluster Sample Using Velocity Dispersion σ v and X-Ray Y X Measurements, ApJ, 799, 214, DOI:10.1088/0004-637X/799/2/214 (ADS abstract)

Co-authored publications

  1. Zhou et al. (2024), Forecasting the constraints on optical selection bias and projection effects of galaxy cluster lensing with multiwavelength data, PhRvD, 110, 103508, DOI:10.1103/PhysRevD.110.103508 (ADS abstract)
  2. Euclid Collaboration et al. (2024), Euclid preparation: L. Calibration of the halo linear bias in Λ(v)CDM cosmologies, A&A, 691, A62, DOI:10.1051/0004-6361/202451230 (ADS abstract)
  3. Kelly et al. (2024), Dark energy survey year 3 results: miscentring calibration and X-ray-richness scaling relations in redMaPPer clusters, MNRAS, 533, 572, DOI:10.1093/mnras/stae1786 (ADS abstract)
  4. Singh et al. (2024), Galaxy cluster matter profiles: I. Self-similarity and mass calibration, arXiv, DOI:10.48550/arXiv.2407.10961 (ADS abstract)
  5. Ansarinejad et al. (2024), Mass calibration of DES Year-3 clusters via SPT-3G CMB cluster lensing, JCAP, 2024, 024, DOI:10.1088/1475-7516/2024/07/024 (ADS abstract)
  6. Cross et al. (2024), Examining the self-interaction of dark matter through central cluster galaxy offsets, MNRAS, 529, 52, DOI:10.1093/mnras/stae442 (ADS abstract)
  7. Bleem et al. (2024), Galaxy Clusters Discovered via the Thermal Sunyaev-Zel'dovich Effect in the 500-square-degree SPTpol Survey, OJAp, 7, 13, DOI:10.21105/astro.2311.07512 (ADS abstract)
  8. Anbajagane et al. (2024), Cosmological shocks around galaxy clusters: a coherent investigation with DES, SPT, and ACT, MNRAS, 527, 9378, DOI:10.1093/mnras/stad3726 (ADS abstract)
  9. Klein et al. (2023), RASS-MCMF: a full-sky X-ray selected galaxy cluster catalogue, MNRAS, 526, 3757, DOI:10.1093/mnras/stad2729 (ADS abstract)
  10. Ragagnin et al. (2023), Dependency of high-mass satellite galaxy abundance on cosmology in Magneticum simulations, A&A, 675, A77, DOI:10.1051/0004-6361/202142392 (ADS abstract)
  11. Chiu et al. (2023), Cosmological constraints from galaxy clusters and groups in the eROSITA final equatorial depth survey, MNRAS, 522, 1601, DOI:10.1093/mnras/stad957 (ADS abstract)
  12. Euclid Collaboration et al. (2023), Euclid preparation. XXIV. Calibration of the halo mass function in Λ(ν)CDM cosmologies, A&A, 671, A100, DOI:10.1051/0004-6361/202244674 (ADS abstract)
  13. Amon et al. (2023), Consistent lensing and clustering in a low-S8 Universe with BOSS, DES Year 3, HSC Year 1, and KiDS-1000, MNRAS, 518, 477, DOI:10.1093/mnras/stac2938 (ADS abstract)
  14. Wu et al. (2022), Optical selection bias and projection effects in stacked galaxy cluster weak lensing, MNRAS, 515, 4471, DOI:10.1093/mnras/stac2048 (ADS abstract)
  15. Anbajagane et al. (2022), Shocks in the stacked Sunyaev-Zel'dovich profiles of clusters II: Measurements from SPT-SZ + Planck Compton-y map, MNRAS, 514, 1645, DOI:10.1093/mnras/stac1376 (ADS abstract)
  16. Chaubal et al. (2022), Improving Cosmological Constraints from Galaxy Cluster Number Counts with CMB-cluster-lensing Data: Results from the SPT-SZ Survey and Forecasts for the Future, ApJ, 931, 139, DOI:10.3847/1538-4357/ac6a55 (ADS abstract)
  17. Chiu et al. (2022), The eROSITA Final Equatorial-Depth Survey (eFEDS). X-ray observable-to-mass-and-redshift relations of galaxy clusters and groups with weak-lensing mass calibration from the Hyper Suprime-Cam Subaru Strategic Program survey, A&A, 661, A11, DOI:10.1051/0004-6361/202141755 (ADS abstract)
  18. Prat et al. (2022), Dark energy survey year 3 results: High-precision measurement and modeling of galaxy-galaxy lensing, PhRvD, 105, 083528, DOI:10.1103/PhysRevD.105.083528 (ADS abstract)
  19. Bleem et al. (2022), CMB/kSZ and Compton-y Maps from 2500 deg2 of SPT-SZ and Planck Survey Data, ApJS, 258, 36, DOI:10.3847/1538-4365/ac35e9 (ADS abstract)
  20. Abazajian et al. (2022), CMB-S4: Forecasting Constraints on Primordial Gravitational Waves, ApJ, 926, 54, DOI:10.3847/1538-4357/ac1596 (ADS abstract)
  21. Adhikari et al. (2021), Probing Galaxy Evolution in Massive Clusters Using ACT and DES: Splashback as a Cosmic Clock, ApJ, 923, 37, DOI:10.3847/1538-4357/ac0bbc (ADS abstract)
  22. Shin et al. (2021), The mass and galaxy distribution around SZ-selected clusters, MNRAS, 507, 5758, DOI:10.1093/mnras/stab2505 (ADS abstract)
  23. To et al. (2021), Dark Energy Survey Year 1 Results: Cosmological Constraints from Cluster Abundances, Weak Lensing, and Galaxy Correlations, PhRvL, 126, 141301, DOI:10.1103/PhysRevLett.126.141301 (ADS abstract)
  24. Ghirardini et al. (2021), Evolution of the Thermodynamic Properties of Clusters of Galaxies out to Redshift of 1.8, ApJ, 910, 14, DOI:10.3847/1538-4357/abc68d (ADS abstract)
  25. Abbott et al. (2020), Dark Energy Survey Year 1 Results: Cosmological constraints from cluster abundances and weak lensing, PhRvD, 102, 023509, DOI:10.1103/PhysRevD.102.023509 (ADS abstract)
  26. Huang et al. (2020), Galaxy Clusters Selected via the Sunyaev-Zel'dovich Effect in the SPTpol 100-square-degree Survey, AJ, 159, 110, DOI:10.3847/1538-3881/ab6a96 (ADS abstract)
  27. Avva et al. (2019), Particle Physics with the Cosmic Microwave Background with SPT-3G, arXiv, DOI:10.48550/arXiv.1911.08047 (ADS abstract)
  28. Raghunathan et al. (2019), Detection of CMB-Cluster Lensing using Polarization Data from SPTpol, PhRvL, 123, 181301, DOI:10.1103/PhysRevLett.123.181301 (ADS abstract)
  29. Carlstrom et al. (2019), CMB-S4, BAAS, 51, 209, DOI:10.48550/arXiv.1908.01062 (ADS abstract)
  30. Shin et al. (2019), Measurement of the splashback feature around SZ-selected Galaxy clusters with DES, SPT, and ACT, MNRAS, 487, 2900, DOI:10.1093/mnras/stz1434 (ADS abstract)
  31. Abazajian et al. (2019), CMB-S4 Science Case, Reference Design, and Project Plan, arXiv, DOI:10.48550/arXiv.1907.04473 (ADS abstract)
  32. Strazzullo et al. (2019), Galaxy populations in the most distant SPT-SZ clusters. I. Environmental quenching in massive clusters at 1.4 ≲ z ≲ 1.7, A&A, 622, A117, DOI:10.1051/0004-6361/201833944 (ADS abstract)
  33. Capasso et al. (2019), Galaxy kinematics and mass calibration in massive SZE-selected galaxy clusters to z = 1.3, MNRAS, 482, 1043, DOI:10.1093/mnras/sty2645 (ADS abstract)
  34. Bulbul et al. (2019), X-Ray Properties of SPT-selected Galaxy Clusters at 0.2 < z < 1.5 Observed with XMM-Newton, ApJ, 871, 50, DOI:10.3847/1538-4357/aaf230 (ADS abstract)
  35. Khullar et al. (2019), Spectroscopic Confirmation of Five Galaxy Clusters at z > 1.25 in the 2500 deg2 SPT-SZ Survey, ApJ, 870, 7, DOI:10.3847/1538-4357/aaeed0 (ADS abstract)
  36. Abbott et al. (2018), The Dark Energy Survey: Data Release 1, ApJS, 239, 18, DOI:10.3847/1538-4365/aae9f0 (ADS abstract)
  37. Bender et al. (2018), Year two instrument status of the SPT-3G cosmic microwave background receiver, SPIE, 10708, 1070803, DOI:10.1117/12.2312426 (ADS abstract)
  38. Diehl et al. (2018), Dark energy survey operations: years 4 and 5, SPIE, 10704, 107040D, DOI:10.1117/12.2312113 (ADS abstract)
  39. Hennig et al. (2017), Galaxy populations in massive galaxy clusters to z = 1.1: colour distribution, concentration, halo occupation number and red sequence fraction, MNRAS, 467, 4015, DOI:10.1093/mnras/stx175 (ADS abstract)
  40. Gupta et al. (2017), High-frequency cluster radio galaxies: luminosity functions and implications for SZE-selected cluster samples, MNRAS, 467, 3737, DOI:10.1093/mnras/stx095 (ADS abstract)
  41. Nurgaliev et al. (2017), Testing for X-Ray-SZ Differences and Redshift Evolution in the X-Ray Morphology of Galaxy Clusters, ApJ, 841, 5, DOI:10.3847/1538-4357/aa6db4 (ADS abstract)
  42. Bayliss et al. (2017), Velocity Segregation and Systematic Biases In Velocity Dispersion Estimates with the SPT-GMOS Spectroscopic Survey, ApJ, 837, 88, DOI:10.3847/1538-4357/aa607c (ADS abstract)
  43. Bayliss et al. (2016), SPT-GMOS: A Gemini/GMOS-South Spectroscopic Survey of Galaxy Clusters in the SPT-SZ Survey, ApJS, 227, 3, DOI:10.3847/0067-0049/227/1/3 (ADS abstract)
  44. de Haan et al. (2016), Cosmological Constraints from Galaxy Clusters in the 2500 Square-degree SPT-SZ Survey, ApJ, 832, 95, DOI:10.3847/0004-637X/832/1/95 (ADS abstract)
  45. Zenteno et al. (2016), Galaxy populations in the 26 most massive galaxy clusters in the South Pole Telescope SPT-SZ survey, MNRAS, 462, 830, DOI:10.1093/mnras/stw1649 (ADS abstract)
  46. Chiu et al. (2016), Stellar mass to halo mass scaling relation for X-ray-selected low-mass galaxy clusters and groups out to redshift z ≈ 1, MNRAS, 458, 379, DOI:10.1093/mnras/stw292 (ADS abstract)
  47. Chiu et al. (2016), Detection of enhancement in number densities of background galaxies due to magnification by massive galaxy clusters, MNRAS, 457, 3050, DOI:10.1093/mnras/stw190 (ADS abstract)
  48. Baxter et al. (2015), A Measurement of Gravitational Lensing of the Cosmic Microwave Background by Galaxy Clusters Using Data from the South Pole Telescope, ApJ, 806, 247, DOI:10.1088/0004-637X/806/2/247 (ADS abstract)
  49. Hlavacek-Larrondo et al. (2015), X-Ray Cavities in a Sample of 83 SPT-selected Clusters of Galaxies: Tracing the Evolution of AGN Feedback in Clusters of Galaxies out to z=1.2, ApJ, 805, 35, DOI:10.1088/0004-637X/805/1/35 (ADS abstract)
  50. Liu et al. (2015), Analysis of Sunyaev-Zel'dovich effect mass-observable relations using South Pole Telescope observations of an X-ray selected sample of low-mass galaxy clusters and groups, MNRAS, 448, 2085, DOI:10.1093/mnras/stv080 (ADS abstract)
  51. Bleem et al. (2015), Galaxy Clusters Discovered via the Sunyaev-Zel'dovich Effect in the 2500-Square-Degree SPT-SZ Survey, ApJS, 216, 27, DOI:10.1088/0067-0049/216/2/27 (ADS abstract)
  52. Saliwanchik et al. (2015), Measurement of Galaxy Cluster Integrated Comptonization and Mass Scaling Relations with the South Pole Telescope, ApJ, 799, 137, DOI:10.1088/0004-637X/799/2/137 (ADS abstract)
  53. McDonald et al. (2014), The Redshift Evolution of the Mean Temperature, Pressure, and Entropy Profiles in 80 SPT-Selected Galaxy Clusters, ApJ, 794, 67, DOI:10.1088/0004-637X/794/1/67 (ADS abstract)
  54. Bayliss et al. (2014), SPT-CL J2040-4451: An SZ-selected Galaxy Cluster at z = 1.478 with Significant Ongoing Star Formation, ApJ, 794, 12, DOI:10.1088/0004-637X/794/1/12 (ADS abstract)
  55. Ruel et al. (2014), Optical Spectroscopy and Velocity Dispersions of Galaxy Clusters from the SPT-SZ Survey, ApJ, 792, 45, DOI:10.1088/0004-637X/792/1/45 (ADS abstract)
  56. Saro et al. (2014), Constraints on the CMB temperature evolution using multiband measurements of the Sunyaev-Zel'dovich effect with the South Pole Telescope, MNRAS, 440, 2610, DOI:10.1093/mnras/stu575 (ADS abstract)
  57. McDonald et al. (2013), The Growth of Cool Cores and Evolution of Cooling Properties in a Sample of 83 Galaxy Clusters at 0.3 < z < 1.2 Selected from the SPT-SZ Survey, ApJ, 774, 23, DOI:10.1088/0004-637X/774/1/23 (ADS abstract)
  58. Semler et al. (2012), High-redshift Cool-core Galaxy Clusters Detected via the Sunyaev-Zel'dovich Effect in the South Pole Telescope Survey, ApJ, 761, 183, DOI:10.1088/0004-637X/761/2/183 (ADS abstract)

Publications as DES builder

  1. Shah et al. (2025), Constraints on compact objects from the Dark Energy Survey 5-yr supernova sample, MNRAS, 536, 946, DOI:10.1093/mnras/stae2614 (ADS abstract)
  2. Esteves et al. (2025), Copacabana: a probabilistic membership assignment method for galaxy clusters, MNRAS, 536, 931, DOI:10.1093/mnras/stae2593 (ADS abstract)
  3. Jeffrey et al. (2024), Dark Energy Survey Year 3 results: likelihood-free, simulation-based wCDM inference with neural compression of weak-lensing map statistics, MNRAS, DOI:10.1093/mnras/stae2629 (ADS abstract)
  4. Demirbozan et al. (2024), The gravitational lensing imprints of DES Y3 superstructures on the CMB: a matched filtering approach, MNRAS, 534, 2328, DOI:10.1093/mnras/stae2206 (ADS abstract)
  5. McCullough et al. (2024), Dark Energy Survey Year 3: Blue Shear, arXiv, DOI:10.48550/arXiv.2410.22272 (ADS abstract)
  6. Kwiecien et al. (2024), Improving Galaxy Cluster Selection with the Outskirt Stellar Mass of Galaxies, arXiv, DOI:10.48550/arXiv.2410.20205 (ADS abstract)
  7. Bigwood et al. (2024), Weak lensing combined with the kinetic Sunyaev-Zel'dovich effect: a study of baryonic feedback, MNRAS, 534, 655, DOI:10.1093/mnras/stae2100 (ADS abstract)
  8. Lokken et al. (2024), Superclustering with the Atacama Cosmology Telescope and Dark Energy Survey: II. Anisotropic large-scale coherence in hot gas, galaxies, and dark matter, arXiv, DOI:10.48550/arXiv.2409.04535 (ADS abstract)
  9. Abbott et al. (2024), Dark Energy Survey: A 2.1% measurement of the angular baryonic acoustic oscillation scale at redshift zeff=0.85 from the final dataset, PhRvD, 110, 063515, DOI:10.1103/PhysRevD.110.063515 (ADS abstract)
  10. Mena-Fernández et al. (2024), Dark Energy Survey: Galaxy sample for the baryonic acoustic oscillation measurement from the final dataset, PhRvD, 110, 063514, DOI:10.1103/PhysRevD.110.063514 (ADS abstract)
  11. White et al. (2024), The Dark Energy Survey Supernova Program: slow supernovae show cosmological time dilation out to z 1., MNRAS, 533, 3365, DOI:10.1093/mnras/stae2008 (ADS abstract)
  12. Camilleri et al. (2024), The dark energy survey supernova program: investigating beyond-ΛCDM, MNRAS, 533, 2615, DOI:10.1093/mnras/stae1988 (ADS abstract)
  13. Dixon et al. (2024), Calibrating the Absolute Magnitude of Type Ia Supernovae in Nearby Galaxies using [OII] and Implications for $H_{0}$, arXiv, DOI:10.48550/arXiv.2408.01001 (ADS abstract)
  14. Campos et al. (2024), Enhancing weak lensing redshift distribution characterization by optimizing the Dark Energy Survey Self-Organizing Map Photo-z method, arXiv, DOI:10.48550/arXiv.2408.00922 (ADS abstract)
  15. Shah et al. (2024), The dark energy survey: detection of weak lensing magnification of supernovae and constraints on dark matter haloes, MNRAS, 532, 932, DOI:10.1093/mnras/stae1515 (ADS abstract)
  16. Faga et al. (2024), Dark Energy Survey Year 3 Results: Cosmology from galaxy clustering and galaxy-galaxy lensing in harmonic space, arXiv, DOI:10.48550/arXiv.2406.12675 (ADS abstract)
  17. Camilleri et al. (2024), The Dark Energy Survey Supernova Program: An updated measurement of the Hubble constant using the Inverse Distance Ladder, arXiv, DOI:10.48550/arXiv.2406.05049 (ADS abstract)
  18. Zhang et al. (2024), Dark Energy Survey Year 6 results: Intra-cluster light from redshift 0.2 to 0.5, MNRAS, 531, 510, DOI:10.1093/mnras/stae1165 (ADS abstract)
  19. Gatti et al. (2024), Dark Energy Survey Year 3 results: simulation-based cosmological inference with wavelet harmonics, scattering transforms, and moments of weak lensing mass maps II. Cosmological results, arXiv, DOI:10.48550/arXiv.2405.10881 (ADS abstract)
  20. Demirbozan et al. (2024), The Gravitational Lensing Imprints of DES Y3 Superstructures on the CMB: A Matched Filtering Approach, arXiv, DOI:10.48550/arXiv.2404.18278 (ADS abstract)
  21. Giannini et al. (2024), Dark Energy Survey Year 3 results: redshift calibration of the MAGLIM lens sample from the combination of SOMPZ and clustering and its impact on cosmology, MNRAS, 527, 2010, DOI:10.1093/mnras/stad2945 (ADS abstract)
  22. Bom et al. (2024), Designing an Optimal Kilonova Search Using DECam for Gravitational-wave Events, ApJ, 960, 122, DOI:10.3847/1538-4357/ad0462 (ADS abstract)
  23. Duarte et al. (2023), A sample of dust attenuation laws for Dark Energy Survey supernova host galaxies, A&A, 680, A56, DOI:10.1051/0004-6361/202346534 (ADS abstract)
  24. Sánchez et al. (2023), The Dark Energy Survey Year 3 high-redshift sample: selection, characterization, and analysis of galaxy clustering, MNRAS, 525, 3896, DOI:10.1093/mnras/stad2402 (ADS abstract)
  25. Dark Energy Survey and Kilo-Degree Survey Collaboration et al. (2023), DES Y3 + KiDS-1000: Consistent cosmology combining cosmic shear surveys, OJAp, 6, 36, DOI:10.21105/astro.2305.17173 (ADS abstract)
  26. Aguena et al. (2023), Building an Efficient Cluster Cosmology Software Package for Modeling Cluster Counts and Lensing, arXiv, DOI:10.48550/arXiv.2309.06593 (ADS abstract)
  27. Samuroff et al. (2023), The Dark Energy Survey Year 3 and eBOSS: constraining galaxy intrinsic alignments across luminosity and colour space, MNRAS, 524, 2195, DOI:10.1093/mnras/stad2013 (ADS abstract)
  28. Zaborowski et al. (2023), Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning, ApJ, 954, 68, DOI:10.3847/1538-4357/ace4ba (ADS abstract)
  29. Elvin-Poole et al. (2023), Dark Energy Survey Year 3 results: magnification modelling and impact on cosmological constraints from galaxy clustering and galaxy-galaxy lensing, MNRAS, 523, 3649, DOI:10.1093/mnras/stad1594 (ADS abstract)
  30. Upsdell et al. (2023), The XMM cluster survey: exploring scaling relations and completeness of the dark energy survey year 3 redMaPPer cluster catalogue, MNRAS, 522, 5267, DOI:10.1093/mnras/stad1220 (ADS abstract)
  31. Yu et al. (2023), OzDES Reverberation Mapping Programme: Mg II lags and R-L relation, MNRAS, 522, 4132, DOI:10.1093/mnras/stad1224 (ADS abstract)
  32. Sánchez et al. (2023), Mapping gas around massive galaxies: cross-correlation of DES Y3 galaxies and Compton-y maps from SPT and Planck, MNRAS, 522, 3163, DOI:10.1093/mnras/stad1167 (ADS abstract)
  33. dal Ponte et al. (2023), Ultracool dwarfs candidates based on 6 yr of the Dark Energy Survey data, MNRAS, 522, 1951, DOI:10.1093/mnras/stad955 (ADS abstract)
  34. Prat et al. (2023), Non-local contribution from small scales in galaxy-galaxy lensing: comparison of mitigation schemes, MNRAS, 522, 412, DOI:10.1093/mnras/stad847 (ADS abstract)
  35. Lee et al. (2023), The Dark Energy Survey Supernova Program: Corrections on Photometry Due to Wavelength-dependent Atmospheric Effects, AJ, 165, 222, DOI:10.3847/1538-3881/acca15 (ADS abstract)
  36. Lemos et al. (2023), Robust sampling for weak lensing and clustering analyses with the Dark Energy Survey, MNRAS, 521, 1184, DOI:10.1093/mnras/stac2786 (ADS abstract)
  37. Stone et al. (2023), Correction to: Optical variability of quasars with 20-year photometric light curves, MNRAS, 521, 836, DOI:10.1093/mnras/stad592 (ADS abstract)
  38. Golden-Marx et al. (2023), Characterizing the intracluster light over the redshift range 0.2 < z < 0.8 in the DES-ACT overlap, MNRAS, 521, 478, DOI:10.1093/mnras/stad469 (ADS abstract)
  39. Abbott et al. (2023), Dark Energy Survey Year 3 results: Constraints on extensions to Λ CDM with weak lensing and galaxy clustering, PhRvD, 107, 083504, DOI:10.1103/PhysRevD.107.083504 (ADS abstract)
  40. Malik et al. (2023), OzDES Reverberation Mapping Program: Hβ lags from the 6-yr survey, MNRAS, 520, 2009, DOI:10.1093/mnras/stad145 (ADS abstract)
  41. Grayling et al. (2023), Core-collapse supernovae in the Dark Energy Survey: luminosity functions and host galaxy demographics, MNRAS, 520, 684, DOI:10.1093/mnras/stad056 (ADS abstract)
  42. Schiappucci et al. (2023), Measurement of the mean central optical depth of galaxy clusters via the pairwise kinematic Sunyaev-Zel'dovich effect with SPT-3G and DES, PhRvD, 107, 042004, DOI:10.1103/PhysRevD.107.042004 (ADS abstract)
  43. Kelsey et al. (2023), Concerning colour: The effect of environment on type Ia supernova colour in the dark energy survey, MNRAS, 519, 3046, DOI:10.1093/mnras/stac3711 (ADS abstract)
  44. Chen et al. (2023), Constraining the baryonic feedback with cosmic shear using the DES Year-3 small-scale measurements, MNRAS, 518, 5340, DOI:10.1093/mnras/stac3213 (ADS abstract)
  45. Abbott et al. (2023), Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck. III. Combined cosmological constraints, PhRvD, 107, 023531, DOI:10.1103/PhysRevD.107.023531 (ADS abstract)
  46. Chang et al. (2023), Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and P l a n c k . II. Cross-correlation measurements and cosmological constraints, PhRvD, 107, 023530, DOI:10.1103/PhysRevD.107.023530 (ADS abstract)
  47. Omori et al. (2023), Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck. I. Construction of CMB lensing maps and modeling choices, PhRvD, 107, 023529, DOI:10.1103/PhysRevD.107.023529 (ADS abstract)
  48. Cheng et al. (2023), Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks, MNRAS, 518, 2794, DOI:10.1093/mnras/stac3228 (ADS abstract)
  49. Meldorf et al. (2023), The Dark Energy Survey Supernova Program results: type Ia supernova brightness correlates with host galaxy dust, MNRAS, 518, 1985, DOI:10.1093/mnras/stac3056 (ADS abstract)
  50. Morgan et al. (2023), DeepZipper. II. Searching for Lensed Supernovae in Dark Energy Survey Data with Deep Learning, ApJ, 943, 19, DOI:10.3847/1538-4357/ac721b (ADS abstract)
  51. Chan et al. (2022), Dark Energy Survey Year 3 results: Measurement of the baryon acoustic oscillations with three-dimensional clustering, PhRvD, 106, 123502, DOI:10.1103/PhysRevD.106.123502 (ADS abstract)
  52. Dixon et al. (2022), Using host galaxy spectroscopy to explore systematics in the standardization of Type Ia supernovae, MNRAS, 517, 4291, DOI:10.1093/mnras/stac2994 (ADS abstract)
  53. Chen et al. (2022), Measuring Cosmological Parameters with Type Ia Supernovae in redMaGiC Galaxies, ApJ, 938, 62, DOI:10.3847/1538-4357/ac8b82 (ADS abstract)
  54. Wiseman et al. (2022), A galaxy-driven model of type Ia supernova luminosity variations, MNRAS, 515, 4587, DOI:10.1093/mnras/stac1984 (ADS abstract)
  55. Kovács et al. (2022), Dark Energy Survey Year 3 results: Imprints of cosmic voids and superclusters in the Planck CMB lensing map, MNRAS, 515, 4417, DOI:10.1093/mnras/stac2011 (ADS abstract)
  56. Doux et al. (2022), Dark energy survey year 3 results: cosmological constraints from the analysis of cosmic shear in harmonic space, MNRAS, 515, 1942, DOI:10.1093/mnras/stac1826 (ADS abstract)
  57. Möller et al. (2022), The dark energy survey 5-yr photometrically identified type Ia supernovae, MNRAS, 514, 5159, DOI:10.1093/mnras/stac1691 (ADS abstract)
  58. Drlica-Wagner et al. (2022), The DECam Local Volume Exploration Survey Data Release 2, ApJS, 261, 38, DOI:10.3847/1538-4365/ac78eb (ADS abstract)
  59. Stone et al. (2022), Optical variability of quasars with 20-yr photometric light curves, MNRAS, 514, 164, DOI:10.1093/mnras/stac1259 (ADS abstract)
  60. Mau et al. (2022), Milky Way Satellite Census. IV. Constraints on Decaying Dark Matter from Observations of Milky Way Satellite Galaxies, ApJ, 932, 128, DOI:10.3847/1538-4357/ac6e65 (ADS abstract)
  61. Secco et al. (2022), Dark Energy Survey Year 3 Results: Three-point shear correlations and mass aperture moments, PhRvD, 105, 103537, DOI:10.1103/PhysRevD.105.103537 (ADS abstract)
  62. Morgan et al. (2022), DeepZipper: A Novel Deep-learning Architecture for Lensed Supernovae Identification, ApJ, 927, 109, DOI:10.3847/1538-4357/ac5178 (ADS abstract)

White papers

  1. Abazajian et al. (2022), Snowmass 2021 CMB-S4 White Paper, arXiv, DOI:10.48550/arXiv.2203.08024 (ADS abstract)
  2. Baxter et al. (2022), Snowmass2021: Opportunities from Cross-survey Analyses of Static Probes, arXiv, DOI:10.48550/arXiv.2203.06795 (ADS abstract)
  3. Mantz et al. (2019), The Future Landscape of High-Redshift Galaxy Cluster Science, BAAS, 51, 279, DOI:10.48550/arXiv.1903.05606 (ADS abstract)

Other publications

  1. Sebastian Bocquet (2015), Galaxy cluster cosmology, PhDT (ADS abstract)
  2. Abbott et al. (2012), First SN Discoveries from the Dark Energy Survey, ATel, 4668, 1 (ADS abstract)