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Black Hole

Lensing of space time around a black hole. At Oxford we study black holes observationally and theoretically on all size and time scales - it is some of our core work.

Credit: ALAIN RIAZUELO, IAP/UPMC/CNRS. CLICK HERE TO VIEW MORE IMAGES.

Professor Pedro Ferreira

Professor of Astrophysics

Research theme

  • Particle astrophysics & cosmology

Sub department

  • Astrophysics

Research groups

  • Beecroft Institute for Particle Astrophysics and Cosmology
pedro.ferreira@physics.ox.ac.uk
Telephone: 01865 (2)73366
Denys Wilkinson Building, room 757
Personal Webpage
  • About
  • Publications

Optimal Inflationary Potentials

(2023)

Authors:

Tomás Sousa, Deaglan J Bartlett, Harry Desmond, Pedro G Ferreira
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LimberJack.jl: auto-differentiable methods for angular power spectra analyses

(2023)

Authors:

J Ruiz-Zapatero, D Alonso, C García-García, A Nicola, A Mootoovaloo, JM Sullivan, M Bonici, PG Ferreira
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Euclid preparation. XXXIV. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear

(2023)

Authors:

Euclid Collaboration, K Tanidis, VF Cardone, M Martinelli, I Tutusaus, S Camera, N Aghanim, A Amara, S Andreon, N Auricchio, M Baldi, S Bardelli, E Branchini, M Brescia, J Brinchmann, V Capobianco, C Carbone, J Carretero, S Casas, M Castellano, S Cavuoti, A Cimatti, R Cledassou, G Congedo, L Conversi, Y Copin, L Corcione, F Courbin, HM Courtois, A DaSilva, H Degaudenzi, J Dinis, F Dubath, X Dupac, S Dusini, M Farina, S Farrens, S Ferriol, P Fosalba, M Frailis, E Franceschi, M Fumana, S Galeotta, B Garilli, W Gillard, B Gillis, C Giocoli, A Grazian, F Grupp, L Guzzo, SVH Haugan, W Holmes, I Hook, A Hornstrup, K Jahnke, B Joachimi, E Keihanen, S Kermiche, A Kiessling, M Kunz, H Kurki-Suonio, PB Lilje, V Lindholm, I Lloro, E Maiorano, O Mansutti, O Marggraf, K Markovic, N Martinet, F Marulli, R Massey, S Maurogordato, E Medinaceli, S Mei, M Meneghetti, G Meylan, M Moresco, L Moscardini, E Munari, S-M Niemi, C Padilla, S Paltani, F Pasian, K Pedersen, WJ Percival, V Pettorino, S Pires, G Polenta, JE Pollack, M Poncet, LA Popa, F Raison, A Renzi, J Rhodes, G Riccio, E Romelli, M Roncarelli, E Rossetti, R Saglia, D Sapone, B Sartoris, M Schirmer, P Schneider, A Secroun, G Seidel, S Serrano, C Sirignano, G Sirri, L Stanco, P Tallada-Crespí, AN Taylor, I Tereno, R Toledo-Moreo, F Torradeflot, EA Valentijn, L Valenziano, T Vassallo, A Veropalumbo, Y Wang, J Weller, G Zamorani, J Zoubian, E Zucca, A Biviano, A Boucaud, E Bozzo, C Colodro-Conde, D Di Ferdinando, R Farinelli, J Graciá-Carpio, S Marcin, N Mauri, V Scottez, M Tenti, A Tramacere, Y Akrami, V Allevato, C Baccigalupi, A Balaguera-Antolínez, M Ballardini, D Benielli, F Bernardeau, S Borgani, AS Borlaff, C Burigana, R Cabanac, A Cappi, CS Carvalho, G Castignani, T Castro, G Cañas-Herrera, KC Chambers, AR Cooray, J Coupon, A Díaz-Sánchez, S Davini, S delaTorre, G DeLucia, G Desprez, S DiDomizio, H Dole, JA Escartin Vigo, S Escoffier, PG Ferreira, I Ferrero, F Finelli, L Gabarra, J García-Bellido, E Gaztanaga, F Giacomini, G Gozaliasl, H Hildebrandt, S Ilić, JJE Kajava, V Kansal, CC Kirkpatrick, L Legrand, A Loureiro, J Macias-Perez, M Magliocchetti, G Mainetti, R Maoli, CJAP Martins, S Matthew, L Maurin, RB Metcalf, M Migliaccio, P Monaco, G Morgante, S Nadathur, AA Nucita, M Pöntinen, L Patrizii, A Pezzotta, V Popa, D Potter, AG Sánchez, Z Sakr, JA Schewtschenko, A Schneider, M Sereno, P Simon, A Spurio Mancini, J Steinwagner, M Tewes, R Teyssier, S Toft, J Valiviita, M Viel, L Linke
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Priors for symbolic regression

GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation Association for Computing Machinery (2023) 2402-2411

Authors:

Deaglan Bartlett, Harry Desmond, Pedro Ferreira

Abstract:

When choosing between competing symbolic models for a data set, a human will naturally prefer the “simpler” expression or the one which more closely resembles equations previously seen in a similar context. This suggests a non-uniform prior on functions, which is, however, rarely considered within a symbolic regression (SR) framework. In this paper we develop methods to incorporate detailed prior information on both functions and their parameters into SR. Our prior on the structure of a function is based on a ngram language model, which is sensitive to the arrangement of operators relative to one another in addition to the frequency of occurrence of each operator. We also develop a formalism based on the Fractional Bayes Factor to treat numerical parameter priors in such a way that models may be fairly compared though the Bayesian evidence, and explicitly compare Bayesian, Minimum Description Length and heuristic methods for model selection. We demonstrate the performance of our priors relative to literature standards on benchmarks and a real-world dataset from the field of cosmology.
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Exhaustive symbolic regression

IEEE Transactions on Evolutionary Computation IEEE (2023)

Authors:

Deaglan Bartlett, Harry Desmond, Pedro Ferreira

Abstract:

Symbolic Regression (SR) algorithms attempt to learn analytic expressions which fit data accurately and in a highly interpretable manner. Conventional SR suffers from two fundamental issues which we address here. First, these methods search the space stochastically (typically using genetic programming) and hence do not necessarily find the best function. Second, the criteria used to select the equation optimally balancing accuracy with simplicity have been variable and subjective. To address these issues we introduce Exhaustive Symbolic Regression (ESR), which systematically and efficiently considers all possible equations—made with a given basis set of operators and up to a specified maximum complexity— and is therefore guaranteed to find the true optimum (if parameters are perfectly optimised) and a complete function ranking subject to these constraints. We implement the minimum description length principle as a rigorous method for combining these preferences into a single objective. To illustrate the power of ESR we apply it to a catalogue of cosmic chronometers and the Pantheon+ sample of supernovae to learn the Hubble rate as a function of redshift, finding 40 functions (out of 5.2 million trial functions) that fit the data more economically than the Friedmann equation. These low-redshift data therefore do not uniquely prefer the expansion history of the standard model of cosmology. We make our code and full equation sets publicly available.
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