109 refereed publications
Publications on ADS: 6755 citations, h-index 39
Profile on Google Scholar: 7216 citations, h-index 40

First-author and top-tier publications

  1. 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)
  2. 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, arXiv, DOI:10.48550/arXiv.2402.08455 (ADS abstract)
  3. Vogt, Bocquet, Davies et al. (2024), Constraining $f(R)$ gravity using future galaxy cluster abundance and weak-lensing mass calibration datasets, arXiv, DOI:10.48550/arXiv.2401.09959 (ADS abstract)
  4. Bocquet, Grandis, Bleem et al. (2024), SPT Clusters with DES and HST Weak Lensing. II. Cosmological Constraints from the Abundance of Massive Halos, arXiv, DOI:10.48550/arXiv.2401.02075 (ADS abstract)
  5. Bocquet, Grandis, Bleem et al. (2023), SPT Clusters with DES and HST Weak Lensing. I. Cluster Lensing and Bayesian Population Modeling of Multi-Wavelength Cluster Datasets, arXiv, DOI:10.48550/arXiv.2310.12213 (ADS abstract)
  6. Klein, Mohr, Bocquet et al. (2023), SPT-SZ MCMF: An extension of the SPT-SZ catalog over the DES region, arXiv, DOI:10.48550/arXiv.2309.09908 (ADS abstract)
  7. 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)
  8. 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)
  9. 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)
  10. 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)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. Bleem, Bocquet, Stalder et al. (2020), The SPTpol Extended Cluster Survey, ApJS, 247, 25, DOI:10.3847/1538-4365/ab6993 (ADS abstract)
  16. 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)
  17. 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)
  18. 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)
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. 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)
  24. 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)
  25. Bocquet & Carter (2016), pygtc: beautiful parameter covariance plots (aka. Giant Triangle Confusograms), JOSS, 1, 46, DOI:10.21105/joss.00046 (ADS abstract)
  26. 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)
  27. 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. Ansarinejad et al. (2024), Mass calibration of DES Year-3 clusters via SPT-3G CMB cluster lensing, arXiv, DOI:10.48550/arXiv.2404.02153 (ADS abstract)
  2. 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)
  3. 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)
  4. 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)
  5. Zhou et al. (2023), Forecasting the constraints on optical selection bias and projection effects of galaxy cluster lensing with multiwavelength data, arXiv, DOI:10.48550/arXiv.2312.11789 (ADS abstract)
  6. 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)
  7. Kelly et al. (2023), Dark Energy Survey Year 3 Results: Mis-centering calibration and X-ray-richness scaling relations in redMaPPer clusters, arXiv, DOI:10.48550/arXiv.2310.13207 (ADS abstract)
  8. 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)
  9. 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)
  10. 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)
  11. 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)
  12. 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)
  13. 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)
  14. 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)
  15. 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)
  16. 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)
  17. 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)
  18. Abazajian et al. (2022), CMB-S4: Forecasting Constraints on Primordial Gravitational Waves, ApJ, 926, 54, DOI:10.3847/1538-4357/ac1596 (ADS abstract)
  19. 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)
  20. Shin et al. (2021), The mass and galaxy distribution around SZ-selected clusters, MNRAS, 507, 5758, DOI:10.1093/mnras/stab2505 (ADS abstract)
  21. 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)
  22. 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)
  23. 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)
  24. 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)
  25. Avva et al. (2019), Particle Physics with the Cosmic Microwave Background with SPT-3G, arXiv, DOI:10.48550/arXiv.1911.08047 (ADS abstract)
  26. 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)
  27. Carlstrom et al. (2019), CMB-S4, BAAS, 51, 209, DOI:10.48550/arXiv.1908.01062 (ADS abstract)
  28. 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)
  29. Abazajian et al. (2019), CMB-S4 Science Case, Reference Design, and Project Plan, arXiv, DOI:10.48550/arXiv.1907.04473 (ADS abstract)
  30. 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)
  31. 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)
  32. 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)
  33. 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)
  34. Abbott et al. (2018), The Dark Energy Survey: Data Release 1, ApJS, 239, 18, DOI:10.3847/1538-4365/aae9f0 (ADS abstract)
  35. 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)
  36. Diehl et al. (2018), Dark energy survey operations: years 4 and 5, SPIE, 10704, 107040D, DOI:10.1117/12.2312113 (ADS abstract)
  37. 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)
  38. 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)
  39. 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)
  40. 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)
  41. 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)
  42. 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)
  43. 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)
  44. 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)
  45. 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)
  46. 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)
  47. 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)
  48. 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)
  49. 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)
  50. 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)
  51. 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)
  52. 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)
  53. 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)
  54. 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)
  55. 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)
  56. 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. Bigwood et al. (2024), Weak lensing combined with the kinetic Sunyaev Zel'dovich effect: A study of baryonic feedback, arXiv, DOI:10.48550/arXiv.2404.06098 (ADS abstract)
  2. Jeffrey et al. (2024), Dark Energy Survey Year 3 results: likelihood-free, simulation-based $w$CDM inference with neural compression of weak-lensing map statistics, arXiv, DOI:10.48550/arXiv.2403.02314 (ADS abstract)
  3. Mena-Fernández et al. (2024), Dark Energy Survey: Galaxy Sample for the Baryonic Acoustic Oscillation Measurement from the Final Dataset, arXiv, DOI:10.48550/arXiv.2402.10697 (ADS abstract)
  4. DES Collaboration et al. (2024), Dark Energy Survey: A 2.1% measurement of the angular Baryonic Acoustic Oscillation scale at redshift $z_{\rm eff}$=0.85 from the final dataset, arXiv, DOI:10.48550/arXiv.2402.10696 (ADS abstract)
  5. Esteves et al. (2024), Copacabana: A Probabilistic Membership Assignment Method for Galaxy Clusters, arXiv, DOI:10.48550/arXiv.2401.12049 (ADS abstract)
  6. DES Collaboration et al. (2024), The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset, arXiv, DOI:10.48550/arXiv.2401.02929 (ADS abstract)
  7. 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)
  8. 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)
  9. 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)
  10. 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)
  11. 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)
  12. 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)
  13. Zhang et al. (2023), Dark Energy Survey Year 6 Results: Intra-Cluster Light from Redshift 0.2 to 0.5, arXiv, DOI:10.48550/arXiv.2309.00671 (ADS abstract)
  14. 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)
  15. 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)
  16. 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)
  17. 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)
  18. 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)
  19. 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)
  20. 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)
  21. 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)
  22. 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)
  23. 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)
  24. 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)
  25. 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)
  26. 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)
  27. 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)
  28. 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)
  29. 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)
  30. 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)
  31. 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)
  32. 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)
  33. 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)
  34. 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)
  35. 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)
  36. 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)
  37. 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)
  38. 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)
  39. 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)
  40. 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)
  41. Wiseman et al. (2022), A galaxy-driven model of type Ia supernova luminosity variations, MNRAS, 515, 4587, DOI:10.1093/mnras/stac1984 (ADS abstract)
  42. 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)
  43. 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)
  44. 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)
  45. 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)
  46. Stone et al. (2022), Optical variability of quasars with 20-yr photometric light curves, MNRAS, 514, 164, DOI:10.1093/mnras/stac1259 (ADS abstract)
  47. 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)
  48. 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)
  49. 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)