J-PAS: A neural network approach to single stellar population characterisation
Por:
Sánchez, HD, Coelho, P, Bruzual, G, Hernán-Caballero, A, Sanjuan, CL, Fernandez-Ontiveros, JA, Díaz-García, LA, Suelves, L, Alvarez-Candal, A, Breda, I, Gurung-López, S, Placco, V, Vega-Ferrero, J, Vílchez, JM, Abramo, R, Alcaniz, J, Benitez, N, Bonoli, S, Carneiro, S, Cenarro, J, Cristóbal-Hornillos, D, Dupke, R, Ederoclite, A, Hernández-Monteagudo, C, Marín-Franch, A, de Oliveira, CM, Moles, M, Sodré, L Jr, Taylor, K, Varela, J, Ramió, HV
Publicada:
20 ene 2026
Resumen:
J-PAS (Javalambre Physics of the Accelerating Universe Astrophysical Survey) will present a groundbreaking photometric survey covering 8500 deg2 of the visible sky from Javalambre, capturing data in 56 narrow-band filters. This survey promises to revolutionise galaxy evolution studies by observing similar to 10(8) galaxies with low spectral resolution. A crucial aspect of this analysis involves predicting stellar population parameters from the observed galaxy photometry. In this study, we combined the exquisite J-PAS photometry with state-of-the-art single stellar population (SSP) libraries to accurately predict stellar age, metallicity, and dust attenuation with a neural network (NN) model. The NN was trained on synthetic J-PAS photometry from different SSP libraries (E-MILES, Charlot & Bruzual, and XSL) to enhance the robustness of our predictions against individual SSP model variations and limitations. To create mock samples with varying observed magnitudes, we added artificial noise in the form of random Gaussian variations within typical observational uncertainties in each band. Our results indicate that the NN was able to accurately estimate stellar parameters for SSP models without any evident degeneracies, surpassing a Bayesian SED-fitting method on the same test set. We obtained the median bias, scatter, and the percentage of outliers: mu= (0.01 dex, 0.00 dex, 0.00 mag), sigma(NMAD)= (0.23 dex, 0.29 dex, 0.04 mag), f(o )= (17%, 24%, 1%) at i similar to 17 mag for the age, metallicity and dust attenuation, respectively. The accuracy of the predictions is highly dependent on the signal-to-noise ratio (S/N) of the photometry, achieving robust predictions up to i similar to 20 mag.
Filiaciones:
Inst Fis Cantabria, Ave Castros, Santander 39005, Cantabria, Spain
Ctr Estudios Fis Cosmos Aragon, Plaza San Juan 1, E-44001 Teruel, Spain
Univ Sao Paulo, Inst Astron Geofis & Ciencias Atmosfer, Rua Matao 1226, BR-05508090 Sao Paulo, SP, Brazil
Univ Nacl Autonoma Mexico, Inst Radioastron & Astrofis, Morelia 58089, Michoacan, Mexico
CSIC IAA, Unidad Asociada CEFCA IAA, Unidad Asociada, CEFCA, Plaza San Juan 1, Teruel 44001, Spain
IFCA, Plaza San Juan 1, Teruel 44001, Spain
CSIC, Inst Astrofis Andalucia, POB 3004, Granada 18080, Spain
Univ Tartu, Tartu Observ, Observatooriumi 1, EE-61602 Toravere, Estonia
Univ Valencia, Observ Astron, Ed Inst Invest,Parc Cient C Catedratico Jose Beltr, Paterna 46980, Valencia, Spain
Univ Valencia, Dept Astron & Astrofis, Burjassot 46100, Spain
NSF NOIRLab, Tucson, AZ 85719 USA
Univ Sao Paulo, Dept Astron, Inst Astron Geofis & Ciencias Atmosfer, Sao Paulo, Brazil
Observ Nacl, Rua Gen Jose Cristino 77, BR-20921400 Rio De Janeiro, RJ, Brazil
Donostia Int Phys Ctr, Manuel Lardizabal Ibilbidea 4, San Sebastian, Spain
Inst Astrofis Canarias, C Via Lactea S-N, E-38205 San Cristobal la Laguna, Tenerife, Spain
Univ La Laguna, Avda Francisco Sanchez, San Cristobal De La Lagun E-38206, Tenerife, Spain
Instruments4, 4121 Pembury Pl, La Canada Flintridge, CA 91011 USA
Green Submitted, gold, All Open Access; Gold Open Access; Green Open Access
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