Dense ReLU Neural Networks for Temporal-spatial Model

Autor: Zhang, Zhi, Padilla, Carlos Misael Madrid, Luo, Xiaokai, Padilla, Oscar Hernan Madrid, Wang, Daren

In this paper, we focus on fully connected deep neural networks utilizing the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. We derive non-asymptotic bounds that lead to convergence rates, addressing both temporal and

Externí odkaz: http://arxiv.org/abs/2411.09961

Bayesian Transfer Learning for Enhanced Estimation and Inference

Autor: Lai, Daoyuan, Padilla, Oscar Hernan Madrid, Gu, Tian

Transfer learning enhances model performance in a target population with limited samples by leveraging knowledge from related studies. While many works focus on improving predictive performance, challenges of statistical inference persist. Bayesian a

Externí odkaz: http://arxiv.org/abs/2412.02986

Euclid preparation: TBD. The impact of line-of-sight projections on the covariance between galaxy cluster multi-wavelength observable properties -- insights from hydrodynamic simulations

Autor: Euclid Collaboration, Ragagnin, A., Saro, A., Andreon, S., Biviano, A., Dolag, K., Ettori, S., Giocoli, C., Brun, A. M. C. Le, Mamon, G. A., Maughan, B. J., Meneghetti, M., Moscardini, L., Pacaud, F., Pratt, G. W., Sereno, M., Borgani, S., Calura, F., Castignani, G., De Petris, M., Eckert, D., Lesci, G. F., Macias-Perez, J., Maturi, M., Amara, A., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Dubath, F., Dupac, X., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., George, K., Gillis, B., Grazian, A., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meylan, G., Moresco, M., Munari, E., Neissner, C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Scaramella, R., Schneider, P., Schrabback, T., Secroun, A., Sefusatti, E., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Gracia-Carpio, J., Mauri, N., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Bergamini, P., Blanchard, A., Blot, L., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Costanzi, M., De Caro, B., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finelli, F., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Hall, A., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Moretti, C., Morgante, G., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Schneider, A., Schultheis, M., Simon, P., Mancini, A. Spurio, Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G.

Cluster cosmology can benefit from combining multi-wavelength studies, which can benefit from characterising the correlation coefficients between different mass-observable relations. In this work, we aim to provide information on the scatter, the ske

Externí odkaz: http://arxiv.org/abs/2412.00191

Quenching of Galaxies at Cosmic Noon: Understanding the Effect of Environment

Autor: Singh, Akriti, Guaita, Lucia, Hibon, Pascale, Häussler, Boris, Lee, Kyoung-Soo, Ramakrishnan, Vandana, Kumar, Ankit, Padilla, Nelson, Firestone, Nicole M., Song, Hyunmi, Artale, Maria Celeste, Hwang, Ho Seong, Iribarren, Paulina Troncoso, Gronwall, Caryl, Gawiser, Eric, Nantais, Julie, Valdes, Francisco, Park, Changbom, Yang, Yujin

The aim of this study is to identify quiescent galaxies in the 2-deg$^2$ COSMOS field at $z \sim 3.1$ and analyze their environment. Using data from the ODIN survey and COSMOS2020 catalog, we identify 24 massive quiescent galaxies (MQGs) with stellar

Externí odkaz: http://arxiv.org/abs/2411.12722

Do we live on the End of the World?

Autor: Muntz, Benjamin, Padilla, Antonio, Saffin, Paul M.

We propose a scenario of a de Sitter universe living on an End-of-the-World brane. Motivated by the Swampland programme and in particular the Cobordism Conjecture, we consider a compact region of AdS$_5$ nucleating from nothing, with a dS$_4$ living

Externí odkaz: http://arxiv.org/abs/2411.05912