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5 results on '"Bojaddaini, Imad El"'

1. Observational constraints on the growth index parameters in $f(Q)$ gravity

Author

Mhamdi, Dalale, Dahmani, Safae, Bouali, Amine, Bojaddaini, Imad El, and Ouali, Taoufik

Subjects
General Relativity and Quantum Cosmology, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

In this study, we analyse constraints on the growth index of matter perturbations, $\gamma$, within the framework of $f(Q)$ gravity, using recent cosmological observations, at the background and the perturbation levels, including Pantheon$^{+}$, Cosmic Chronometer (CC), and Redshift Space Distortion (RSD) datasets. Our analysis focuses on quantifying the distortion parameter, which measures the deviation of the $f(Q)$ gravity model from the concordance $\Lambda$CDM cosmology at the background level. Specifically, we investigate two cases of the growth index parameter: a constant $\gamma$ and a time-varying $\gamma(z)$. We investigate various parametrizations of the growth index $\gamma$, expressed as $\gamma = \gamma_{0} +\gamma_{1} y(z)$, where the function $y(z)$ assumes different forms, including constant ($\Gamma_{0}$), Taylor expansion around $z = 0$ ($\Gamma_{1}$), Taylor expansion around the scale factor ($\Gamma_{2}$), and an exponential form ($\Gamma_{3}$). By employing the Akaike Information Criterion and Bayesian Information Criterion, we find that the combined Pantheon$^{+}$+ CC+ RSD datasets impose stringent constraints on the value of the growth index. For the $\Gamma_{0}$ model, our results indicate that within the concordance $\Lambda$CDM model, $\gamma$ is constrained to $0.545 \pm 0.096$, showing strong agreement with the theoretical expectation of $\gamma_{\Lambda} = \frac{6}{11}$. However, within the framework of $f(Q)$ gravity, we observe $\gamma = 0.571^{+0.095}_{-0.110}$, slightly exceeding the $\Lambda$CDM value by 4.66 $\%$. Furthermore, when considering a time-varying growth index, our analysis reveals that the range of $\gamma_{0}$ spans from $0.596$ to $0.62$ across the $\Gamma_{1-3}$ models., Comment: 15 pages, 6 figures

Published
2024

2. Constraining dark energy equations of state in $F(R,T)$ gravity

Author

Errahmani, Ahmed, Bouali, Amine, Dahmani, Safae, Bojaddaini, Imad El, and Ouali, Taoufik

Subjects
General Relativity and Quantum Cosmology, High Energy Physics - Phenomenology
Abstract

In this paper, we examine the acceleration of the Universe's expansion in $F(R,T)$ gravity, where $R$ denotes the Ricci scalar and $T$ the trace of energy-momentum tensor. Indeed, the unknown nature of the source controlling this acceleration in general relativity leads scientists to investigate its properties by means of some alternative theories to general relativity. Our study is restricted to the particular case where $F(R,T)=R+2\kappa^2 \lambda T$ , with $\lambda$ being a constant. We use a Bayesian analysis of current observational datasets, including the type Ia supernovae constitution compilation and $H(z)$ measurements, to constrain free parameters of the model. To parametrize dark energy, we consider two well known equations of state. We find the best fit values for each model by running a Markov chain Monte Carlo technic. The best fit parameters are used to compare both models to $\Lambda$CDM by means of the Akaike information criterion and the Bayesian information criterion. We show that the Universe underwent recently a transition from a deceleration to an acceleration for both models. Furthermore, the data shows a phantom nature of the equation of state for both models., Comment: 13 pages, 5 figures

Published
2024

3. Smoothing the $H_0$ tension with a dynamical dark energy model

Author

Dahmani, Safae, Bouali, Amine, Bojaddaini, Imad El, Errahmani, Ahmed, and Ouali, Taoufik

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The discrepancy between Planck data and direct measurements of the current expansion rate $H_0$ and the matter fluctuation amplitude $S_8$ has become one of the most intriguing puzzles in cosmology nowadays. The $H_0$ tension has reached $4.2\sigma$ in the context of standard cosmology i.e $\Lambda$CDM. Therefore, explanations to this issue are mandatory to unveil its secrets. Despite its success, $\Lambda$CDM is unable to give a satisfying explanation to the tension problem. Unless some systematic errors might be hidden in the observable measurements, physics beyond the standard model of cosmology must be advocated. In this perspective, we study a phantom dynamical dark energy model as an alternative to $\Lambda$CDM in order to explain the aforementioned issues. This phantom model is characterised by one extra parameter, $\Omega_{pdde}$, compared to $\Lambda$CDM. We obtain a strong positive correlation between $H_0$ and $\Omega_{pdde}$, for all data combinations. Using Planck measurements together with BAO and Pantheon, we find that the $H_0$ and the $S_8$ tensions are $3\sigma$ and $2.6\sigma$, respectively. By introducing a prior on the absolute magnitude, $M_B$, of the SN Ia, the $H_0$ tension decreases to $2.27\sigma$ with $H_0 = 69.76_{-0.82}^{+0.75}$ km s$^{-1}$ Mpc$^{-1}$ and the $S_8$ tension reaches the value $2.37\sigma$ with $S_8 =0.8269_{-0.012}^{+0.011}$., Comment: 11 pages, 4 figures

Published
2023
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4. Event reconstruction for KM3NeT/ORCA using convolutional neural networks

Author

Aiello, Sebastiano, Albert, Arnauld, Garre, Sergio Alves, Aly, Zineb, Ameli, Fabrizio, Andre, Michel, Androulakis, Giorgos, Anghinolfi, Marco, Anguita, Mancia, Anton, Gisela, Ardid, Miquel, Aublin, Julien, Bagatelas, Christos, Barbarino, Giancarlo, Baret, Bruny, Pree, Suzan Basegmez du, Bendahman, Meriem, Berbee, Edward, Bertin, Vincent, Biagi, Simone, Biagioni, Andrea, Bissinger, Matthias, Boettcher, Markus, Boumaaza, Jihad, Bouta, Mohammed, Bouwhuis, Mieke, Bozza, Cristiano, Branzas, Horea, Bruijn, Ronald, Brunner, Jürgen, Buis, Ernst-Jan, Buompane, Raffaele, Busto, Jose, Caiffi, Barbara, Calvo, David, Capone, Antonio, Carretero, Víctor, Castaldi, Paolo, Celli, Silvia, Chabab, Mohamed, Chau, Nhan, Chen, Andrew, Cherubini, Silvio, Chiarella, Vitaliano, Chiarusi, Tommaso, Circella, Marco, Cocimano, Rosanna, Coelho, Joao, Coleiro, Alexis, Molla, Marta Colomer, Coniglione, Rosa, Coyle, Paschal, Creusot, Alexandre, Cuttone, Giacomo, D'Onofrio, Antonio, Dallier, Richard, de Jong, Maarten, de Jong, Paul, De Palma, Mauro, de Wasseige, Gwenhaël, de Wolf, Els, Di Palma, Irene, Diaz, Antonio, Diego-Tortosa, Dídac, Distefano, Carla, Domi, Alba, Donà, Roberto, Donzaud, Corinne, Dornic, Damien, Dörr, Manuel, Drouhin, Doriane, Eberl, Thomas, Bojaddaini, Imad El, Elsaesser, Dominik, Enzenhöfer, Alexander, Fermani, Paolo, Ferrara, Giovanna, Filipovic, Miroslav, Filippini, Francesco, Fusco, Luigi Antonio, Gabella, Omar, Gal, Tamas, Soto, Alfonso Andres Garcia, Garufi, Fabio, Gatelet, Yoann, Geißelbrecht, Nicole, Gialanella, Lucio, Giorgio, Emidio, Gozzini, Sara Rebecca, Gracia, Rodrigo, Graf, Kay, Grasso, Dario, Grella, Giuseppe, Guidi, Carlo, Hallmann, Steffen, Hamdaoui, Hassane, Heijboer, Aart, Hekalo, Amar, Hernandez-Rey, Juan-Jose, Hofestädt, Jannik, Huang, Feifei, Ibnsalih, Walid Idrissi, Illuminati, Giulia, James, Clancy, Jung, Bouke Jisse, Kadler, Matthias, Kalaczyński, Piotr, Kalekin, Oleg, Katz, Uli, Chowdhury, Nafis Rezwan Khan, Kistauri, Giorgi, Koffeman, Els, Kooijman, Paul, Kouchner, Antoine, Kreter, Michael, Kulikovskiy, Vladimir, Lahmann, Robert, Larosa, Giuseppina, Breton, Remy Le, Leonardi, Ornella, Leone, Francesco, Leonora, Emanuele, Levi, Giuseppe, Lincetto, Massimiliano, Clark, Miles Lindsey, Lipreau, Thomas, Lonardo, Alessandro, Longhitano, Fabio, Coto, Daniel Lopez, Maderer, Lukas, Mańczak, Jerzy, Mannheim, Karl, Margiotta, Annarita, Marinelli, Antonio, Markou, Christos, Martin, Lilian, Martínez-Mora, Juan Antonio, Martini, Agnese, Marzaioli, Fabio, Mastroianni, Stefano, Mazzou, Safaa, Melis, Karel, Miele, Gennaro, Migliozzi, Pasquale, Migneco, Emilio, Mijakowski, Piotr, Palacios, Luis Salvador Miranda, Mollo, Carlos Maximiliano, Morganti, Mauro, Moser, Michael, Moussa, Abdelilah, Muller, Rasa, Musumeci, Mario, Nauta, Lodewijk, Navas, Sergio, Nicolau, Carlo Alessandro, Fearraigh, Brían Ó, Organokov, Mukharbek, Orlando, Angelo, Papalashvili, Gogita, Papaleo, Riccardo, Pastore, Cosimo, Paun, Alice, Pavalas, Gabriela Emilia, Pellegrino, Carmelo, Perrin-Terrin, Mathieu, Piattelli, Paolo, Pieterse, Camiel, Pikounis, Konstantinos, Pisanti, Ofelia, Poirè, Chiara, Popa, Vlad, Post, Maarten, Pradier, Thierry, Pühlhofer, Gerd, Pulvirenti, Sara, Rabyang, Omphile, Raffaelli, Fabrizio, Randazzo, Nunzio, Rapicavoli, Antonio, Razzaque, Soebur, Real, Diego, Reck, Stefan, Riccobene, Giorgio, Richer, Marc, Rivoire, Stephane, Rovelli, Alberto, Greus, Francisco Salesa, Samtleben, Dorothea Franziska Elisabeth, Losa, Agustín Sánchez, Sanguineti, Matteo, Santangelo, Andrea, Santonocito, Domenico, Sapienza, Piera, Schnabel, Jutta, Seneca, Jordan, Sgura, Irene, Shanidze, Rezo, Sharma, Ankur, Simeone, Francesco, Sinopoulou, Anna, Spisso, Bernardino, Spurio, Maurizio, Stavropoulos, Dimitris, Steijger, Jos, Stellacci, Simona Maria, Taiuti, Mauro, Tayalati, Yahya, Tenllado, Enrique, Thakore, Tarak, Tingay, Steven, Tzamariudaki, Ekaterini, Tzanetatos, Dimitrios, Berg, Ad van den, van der Knaap, Frits, van Eijk, Daan, Van Elewyck, Véronique, van Haren, Hans, Vannoye, Godefroy, Vasileiadis, George, Versari, Federico, Viola, Salvatore, Vivolo, Daniele, Wilms, Joern, Wojaczyński, Rafał, Zaborov, Dmitry, Zavatarelli, Sandra, Zegarelli, Angela, Zito, Daniele, Zornoza, Juan-de-Dios, Zúñiga, Juan, and Zywucka, Natalia

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches.

Published
2020
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5. gSeaGen: the KM3NeT GENIE-based code for neutrino telescopes

Author

Aiello, Sebastiano, Albert, Arnauld, Garre, Sergio Alves, Aly, Zineb, Ameli, Fabrizio, Andre, Michel, Androulakis, Giorgos, Anghinolfi, Marco, Anguita, Mancia, Anton, Gisela, Ardid, Miquel, Aublin, Julien, Bagatelas, Christos, Barbarino, Giancarlo, Baret, Bruny, Pree, Suzan Basegmez du, Bendahman, Meriem, Berbee, Edward, Bertin, Vincent, Biagi, Simone, Biagioni, Andrea, Bissinger, Matthias, Boettcher, Markus, Boumaaza, Jihad, Bourret, Simon, Bouta, Mohammed, Bouwhuis, Mieke, Bozza, Cristiano, Branzas, Horea, Bruchner, Marc, Bruijn, Ronald, Brunner, Jürgen, Buis, Ernst-Jan, Buompane, Raffaele, Busto, Jose, Calvo, David, Capone, Antonio, Carretero, Víctor, Castaldi, Paolo, Celli, Silvia, Chabab, Mohamed, Chau, Nhan, Chen, Andrew, Cherubini, Silvio, Chiarella, Vitaliano, Chiarusi, Tommaso, Circella, Marco, Cocimano, Rosanna, Coelho, Joao, Coleiro, Alexis, Molla, Marta Colomer, Coniglione, Rosa, Corredoira, Imanol, Coyle, Paschal, Creusot, Alexandre, Cuttone, Giacomo, D'Onofrio, Antonio, Dallier, Richard, de Jong, Maarten, de Jong, Paul, De Palma, Mauro, de Wasseige, Gwenhaël, de Wolf, Els, Di Palma, Irene, Diaz, Antonio, Diego-Tortosa, Dídac, Distefano, Carla, Domi, Alba, Donà, Roberto, Donzaud, Corinne, Dornic, Damien, Dörr, Manuel, Drouhin, Doriane, Durocher, Mora, Eberl, Thomas, Bojaddaini, Imad El, Elsaesser, Dominik, Enzenhöfer, Alexander, Fermani, Paolo, Ferrara, Giovanna, Filipovic, Miroslav, Filippini, Francesco, Franco, Antonio, Fusco, Luigi Antonio, Gabella, Omar, Gal, Tamas, Soto, Alfonso Andres Garcia, Garufi, Fabio, Gatelet, Yoann, Geißelbrecht, Nicole, Gialanella, Lucio, Giorgio, Emidio, Gozzini, Sara Rebecca, Gracia, Rodrigo, Graf, Kay, Grasso, Dario, Grégoire, Timothée, Grella, Giuseppe, Guidi, Carlo, Hallmann, Steffen, Hamdaoui, Hassane, Heijboer, Aart, Hekalo, Amar, Hernandez-Rey, Juan-Jose, Hofestädt, Jannik, Ibnsalih, Walid Idrissi, Illuminati, Giulia, James, Clancy, Jung, Bouke Jisse, Kadler, Matthias, Kalaczyński, Piotr, Kalekin, Oleg, Katz, Uli, Chowdhury, Nafis Rezwan Khan, Koffeman, Els, Kooijman, Paul, Kouchner, Antoine, Kreter, Michael, Kulikovskiy, Vladimir, Lahmann, Robert, Larosa, Giuseppina, Breton, Remy Le, Leonardi, Ornella, Leone, Francesco, Leonora, Emanuele, Levi, Giuseppe, Lincetto, Massimiliano, Clark, Miles Lindsey, Lipreau, Thomas, Lonardo, Alessandro, Longhitano, Fabio, Coto, Daniel Lopez, Mańczak, Jerzy, Mannheim, Karl, Margiotta, Annarita, Marinelli, Antonio, Markou, Christos, Martin, Lilian, Martínez-Mora, Juan Antonio, Martini, Agnese, Marzaioli, Fabio, Mastroianni, Stefano, Mazzou, Safaa, Melis, Karel, Miele, Gennaro, Migliozzi, Pasquale, Migneco, Emilio, Mijakowski, Piotr, Palacios, Luis Salvador Miranda, Modebadze, Zurab, Mollo, Carlos Maximiliano, Morganti, Mauro, Moser, Michael, Moussa, Abdelilah, Muller, Rasa, Musumeci, Mario, Nauta, Lodewijk, Navas, Sergio, Nicolau, Carlo Alessandro, Fearraigh, Brían Ó, Organokov, Mukharbek, Orlando, Angelo, Papalashvili, Gogita, Papaleo, Riccardo, Pastore, Cosimo, Paun, Alice, Pavalas, Gabriela Emilia, Pellegrino, Carmelo, Perrin-Terrin, Mathieu, Piattelli, Paolo, Pieterse, Camiel, Pikounis, Konstantinos, Pisanti, Ofelia, Poirè, Chiara, Popa, Vlad, Post, Maarten, Pradier, Thierry, Pühlhofer, Gerd, Pulvirenti, Sara, Quinn, Liam, Rabyang, Omphile, Raffaelli, Fabrizio, Randazzo, Nunzio, Rapicavoli, Antonio, Razzaque, Soebur, Real, Diego, Reck, Stefan, Reubelt, Jonas, Riccobene, Giorgio, Richer, Marc, Rivoire, Stephane, Rovelli, Alberto, Greus, Francisco Salesa, Samtleben, Dorothea Franziska Elisabeth, Losa, Agustín Sánchez, Sanguineti, Matteo, Santangelo, Andrea, Santonocito, Domenico, Sapienza, Piera, Schnabel, Jutta, Sciacca, Virginia, Seneca, Jordan, Sgura, Irene, Shanidze, Rezo, Sharma, Ankur, Simeone, Francesco, Sinopoulou, Anna, Spisso, Bernardino, Spurio, Maurizio, Stavropoulos, Dimitris, Steijger, Jos, Stellacci, Simona Maria, Taiuti, Mauro, Tayalati, Yahya, Tenllado, Enrique, Thakore, Tarak, Tingay, Steven, Tzamariudaki, Ekaterini, Tzanetatos, Dimitrios, Berg, Ad van den, van der Knaap, Frits, van Eijk, Daan, Van Elewyck, Véronique, Vannoye, Godefroy, Vasileiadis, George, Versari, Federico, Viola, Salvatore, Vivolo, Daniele, Wilms, Joern, Wojaczyński, Rafał, Zaborov, Dmitry, Zavatarelli, Sandra, Zegarelli, Angela, Zornoza, Juan-de-Dios, Zúñiga, Juan, and Zywucka, Natalia

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The gSeaGen code is a GENIE-based application developed to efficiently generate high statistics samples of events, induced by neutrino interactions, detectable in a neutrino telescope. The gSeaGen code is able to generate events induced by all neutrino flavours, considering topological differences between track-type and shower-like events. Neutrino interactions are simulated taking into account the density and the composition of the media surrounding the detector. The main features of gSeaGen are presented together with some examples of its application within the KM3NeT project., Comment: 45 pages, 10 figures

Published
2020
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