paper

paper/paper.bib

@@ -180,4 +180,44 @@ @software{sbidirmeier
   url = {https://github.com/dirmeier/sbijax},
   version = {0.3.0},
   year = {2024},
+}
+
+@misc{Euclid,
+      title={Euclid Definition Study Report}, 
+      author={R. Laureijs and J. Amiaux and S. Arduini and J. -L. Auguères and J. Brinchmann and R. Cole and M. Cropper and C. Dabin and L. Duvet and A. Ealet and B. Garilli and P. Gondoin and L. Guzzo and J. Hoar and H. Hoekstra and R. Holmes and T. Kitching and T. Maciaszek and Y. Mellier and F. Pasian and W. Percival and J. Rhodes and G. Saavedra Criado and M. Sauvage and R. Scaramella and L. Valenziano and S. Warren and R. Bender and F. Castander and A. Cimatti and O. Le Fèvre and H. Kurki-Suonio and M. Levi and P. Lilje and G. Meylan and R. Nichol and K. Pedersen and V. Popa and R. Rebolo Lopez and H. -W. Rix and H. Rottgering and W. Zeilinger and F. Grupp and P. Hudelot and R. Massey and M. Meneghetti and L. Miller and S. Paltani and S. Paulin-Henriksson and S. Pires and C. Saxton and T. Schrabback and G. Seidel and J. Walsh and N. Aghanim and L. Amendola and J. Bartlett and C. Baccigalupi and J. -P. Beaulieu and K. Benabed and J. -G. Cuby and D. Elbaz and P. Fosalba and G. Gavazzi and A. Helmi and I. Hook and M. Irwin and J. -P. Kneib and M. Kunz and F. Mannucci and L. Moscardini and C. Tao and R. Teyssier and J. Weller and G. Zamorani and M. R. Zapatero Osorio and O. Boulade and J. J. Foumond and A. Di Giorgio and P. Guttridge and A. James and M. Kemp and J. Martignac and A. Spencer and D. Walton and T. Blümchen and C. Bonoli and F. Bortoletto and C. Cerna and L. Corcione and C. Fabron and K. Jahnke and S. Ligori and F. Madrid and L. Martin and G. Morgante and T. Pamplona and E. Prieto and M. Riva and R. Toledo and M. Trifoglio and F. Zerbi and F. Abdalla and M. Douspis and C. Grenet and S. Borgani and R. Bouwens and F. Courbin and J. -M. Delouis and P. Dubath and A. Fontana and M. Frailis and A. Grazian and J. Koppenhöfer and O. Mansutti and M. Melchior and M. Mignoli and J. Mohr and C. Neissner and K. Noddle and M. Poncet and M. Scodeggio and S. Serrano and N. Shane and J. -L. Starck and C. Surace and A. Taylor and G. Verdoes-Kleijn and C. Vuerli and O. R. Williams and A. Zacchei and B. Altieri and I. Escudero Sanz and R. Kohley and T. Oosterbroek and P. Astier and D. Bacon and S. Bardelli and C. Baugh and F. Bellagamba and C. Benoist and D. Bianchi and A. Biviano and E. Branchini and C. Carbone and V. Cardone and D. Clements and S. Colombi and C. Conselice and G. Cresci and N. Deacon and J. Dunlop and C. Fedeli and F. Fontanot and P. Franzetti and C. Giocoli and J. Garcia-Bellido and J. Gow and A. Heavens and P. Hewett and C. Heymans and A. Holland and Z. Huang and O. Ilbert and B. Joachimi and E. Jennins and E. Kerins and A. Kiessling and D. Kirk and R. Kotak and O. Krause and O. Lahav and F. van Leeuwen and J. Lesgourgues and M. Lombardi and M. Magliocchetti and K. Maguire and E. Majerotto and R. Maoli and F. Marulli and S. Maurogordato and H. McCracken and R. McLure and A. Melchiorri and A. Merson and M. Moresco and M. Nonino and P. Norberg and J. Peacock and R. Pello and M. Penny and V. Pettorino and C. Di Porto and L. Pozzetti and C. Quercellini and M. Radovich and A. Rassat and N. Roche and S. Ronayette and E. Rossetti and B. Sartoris and P. Schneider and E. Semboloni and S. Serjeant and F. Simpson and C. Skordis and G. Smadja and S. Smartt and P. Spano and S. Spiro and M. Sullivan and A. Tilquin and R. Trotta and L. Verde and Y. Wang and G. Williger and G. Zhao and J. Zoubian and E. Zucca},
+      year={2011},
+      eprint={1110.3193},
+      archivePrefix={arXiv},
+      primaryClass={astro-ph.CO},
+      url={https://arxiv.org/abs/1110.3193}, 
+}
+
+@article{DESI,
+    author = "Levi, Michael E. and others",
+    collaboration = "DESI",
+    title = "{The Dark Energy Spectroscopic Instrument (DESI)}",
+    eprint = "1907.10688",
+    archivePrefix = "arXiv",
+    primaryClass = "astro-ph.IM",
+    reportNumber = "FERMILAB-PUB-19-434-AE",
+    month = "7",
+    year = "2019"
+}
+
+@ARTICLE{DES,
+       author = {{Dark Energy Survey Collaboration} and {Abbott}, T. and {Abdalla}, F.~B. and {Aleksi{\'c}}, J. and {Allam}, S. and {Amara}, A. and {Bacon}, D. and {Balbinot}, E. and {Banerji}, M. and {Bechtol}, K. and {Benoit-L{\'e}vy}, A. and {Bernstein}, G.~M. and {Bertin}, E. and {Blazek}, J. and {Bonnett}, C. and {Bridle}, S. and {Brooks}, D. and {Brunner}, R.~J. and {Buckley-Geer}, E. and {Burke}, D.~L. and {Caminha}, G.~B. and {Capozzi}, D. and {Carlsen}, J. and {Carnero-Rosell}, A. and {Carollo}, M. and {Carrasco-Kind}, M. and {Carretero}, J. and {Castander}, F.~J. and {Clerkin}, L. and {Collett}, T. and {Conselice}, C. and {Crocce}, M. and {Cunha}, C.~E. and {D'Andrea}, C.~B. and {da Costa}, L.~N. and {Davis}, T.~M. and {Desai}, S. and {Diehl}, H.~T. and {Dietrich}, J.~P. and {Dodelson}, S. and {Doel}, P. and {Drlica-Wagner}, A. and {Estrada}, J. and {Etherington}, J. and {Evrard}, A.~E. and {Fabbri}, J. and {Finley}, D.~A. and {Flaugher}, B. and {Foley}, R.~J. and {Fosalba}, P. and {Frieman}, J. and {Garc{\'\i}a-Bellido}, J. and {Gaztanaga}, E. and {Gerdes}, D.~W. and {Giannantonio}, T. and {Goldstein}, D.~A. and {Gruen}, D. and {Gruendl}, R.~A. and {Guarnieri}, P. and {Gutierrez}, G. and {Hartley}, W. and {Honscheid}, K. and {Jain}, B. and {James}, D.~J. and {Jeltema}, T. and {Jouvel}, S. and {Kessler}, R. and {King}, A. and {Kirk}, D. and {Kron}, R. and {Kuehn}, K. and {Kuropatkin}, N. and {Lahav}, O. and {Li}, T.~S. and {Lima}, M. and {Lin}, H. and {Maia}, M.~A.~G. and {Makler}, M. and {Manera}, M. and {Maraston}, C. and {Marshall}, J.~L. and {Martini}, P. and {McMahon}, R.~G. and {Melchior}, P. and {Merson}, A. and {Miller}, C.~J. and {Miquel}, R. and {Mohr}, J.~J. and {Morice-Atkinson}, X. and {Naidoo}, K. and {Neilsen}, E. and {Nichol}, R.~C. and {Nord}, B. and {Ogando}, R. and {Ostrovski}, F. and {Palmese}, A. and {Papadopoulos}, A. and {Peiris}, H.~V. and {Peoples}, J. and {Percival}, W.~J. and {Plazas}, A.~A. and {Reed}, S.~L. and {Refregier}, A. and {Romer}, A.~K. and {Roodman}, A. and {Ross}, A. and {Rozo}, E. and {Rykoff}, E.~S. and {Sadeh}, I. and {Sako}, M. and {S{\'a}nchez}, C. and {Sanchez}, E. and {Santiago}, B. and {Scarpine}, V. and {Schubnell}, M. and {Sevilla-Noarbe}, I. and {Sheldon}, E. and {Smith}, M. and {Smith}, R.~C. and {Soares-Santos}, M. and {Sobreira}, F. and {Soumagnac}, M. and {Suchyta}, E. and {Sullivan}, M. and {Swanson}, M. and {Tarle}, G. and {Thaler}, J. and {Thomas}, D. and {Thomas}, R.~C. and {Tucker}, D. and {Vieira}, J.~D. and {Vikram}, V. and {Walker}, A.~R. and {Wechsler}, R.~H. and {Weller}, J. and {Wester}, W. and {Whiteway}, L. and {Wilcox}, H. and {Yanny}, B. and {Zhang}, Y. and {Zuntz}, J.},
+        title = "{The Dark Energy Survey: more than dark energy - an overview}",
+      journal = {\mnras},
+     keywords = {surveys, minor planets, asteroids: general, supernovae: general, Galaxy: general, galaxies: general, quasars: general, Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies},
+         year = 2016,
+        month = aug,
+       volume = {460},
+       number = {2},
+        pages = {1270-1299},
+          doi = {10.1093/mnras/stw641},
+archivePrefix = {arXiv},
+       eprint = {1601.00329},
+ primaryClass = {astro-ph.CO},
+       adsurl = {https://ui.adsabs.harvard.edu/abs/2016MNRAS.460.1270D},
+      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
 }

paper/paper.md

@@ -53,7 +53,7 @@ As shown in [@homersbi], SBI can successfully obtain the correct posterior width
 
 Simulation-based inference (SBI) covers a broad class of statistical techniques such as Approximate Bayesian Computation (ABC), Neural Ratio Estimation (NRE), Neural Likelihood Estimation (NLE) and Neural Posterior Estimation (NPE). These techniques can derive posterior distributions conditioned of noisy data vectors in a rigorous and efficient manner. In particular, density-estimation methods have emerged as a promising method, given their efficiency, using generative models to fit likelihoods or posteriors directly using simulations.
 
-In the field of cosmology, SBI is of particular interest due to complexity and non-linearity of models for the expectations of non-standard summary statistics of the large-scale structure, as well as the non-Gaussian noise distributions for these statistics. The assumptions required for the complex analytic modelling of these statistics as well as the increasing dimensionality of data returned by spectroscopic and photometric galaxy surveys limits the amount of information that can be obtained on fundamental physical parameters. Therefore, the study and research into current and future statistical methods for Bayesian inference is of paramount importance for the field of cosmology.
+In the field of cosmology, SBI is of particular interest due to complexity and non-linearity of models for the expectations of non-standard summary statistics of the large-scale structure, as well as the non-Gaussian noise distributions for these statistics. The assumptions required for the complex analytic modelling of these statistics as well as the increasing dimensionality of data returned by spectroscopic and photometric galaxy surveys limits the amount of information that can be obtained on fundamental physical parameters. Therefore, the study and research into current and future statistical methods for Bayesian inference is of paramount importance for the cosmology, especially in light of current and next-generation survey missions such as DES [@Euclid], DESI [@DESI] and Euclid [@Euclid].
 
 The software we present, `sbiax`, is designed to be used by machine learning and physics researchers for running Bayesian inferences using density-estimation SBI techniques. These models can be fit easily with multi-accelerator training and inference within the code. This software - written in `jax` [@jax] - allows for seemless integration of cutting edge generative models to SBI, including continuous normalising flows [@ffjord], matched flows [@flowmatching], masked autoregressive flows [@mafs; @flowjax] and Gaussian mixture models - all of which are implemented in the code. The code features integration with the `optuna` [@optuna] hyperparameter optimisation framework which would be used to ensure consistent analyses, `blackjax` [@blackjax] for fast MCMC sampling and `equinox` [@equinox] for neural network methods. The design of `sbiax` allows for new density estimation algorithms to be trained and sampled from.