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4,482 results on '"Amara P"'

1. Detection of neoplastic-immune hybrid cells with metastatic properties in uveal melanoma

Author

Ashley N. Anderson, Patrick Conley, Christopher D. Klocke, Sidharth K. Sengupta, Amara Pang, Hannah C. Farley, Abigail R. Gillingham, Aubrey D. Dawson, Yichen Fan, Jocelyn A. Jones, Summer L. Gibbs, Alison H. Skalet, Guanming Wu, and Melissa H. Wong

Subjects
Uveal melanoma, Metastasis, Disseminated tumor cells, Multiplexed cyclic immunofluorescence, Single-cell RNA sequencing, Therapeutics. Pharmacology, RM1-950
Abstract

Abstract Background Uveal melanoma is the most common non-cutaneous melanoma and is an intraocular malignancy affecting nearly 7,000 individuals per year worldwide. Of these, approximately 50% will progress to metastatic disease for which there are currently no effective curative therapies. Despite advances in molecular profiling and metastatic stratification of uveal melanoma tumors, little is known regarding their underlying biology of metastasis. Our group has identified a disseminated neoplastic cell population characterized by co-expression of immune and melanoma proteins, circulating hybrid cells (hybrids), in patients with uveal melanoma. Compared to circulating tumor cells, which lack expression of immune proteins, hybrids are detected at an increased prevalence in peripheral blood and can be used as a non-invasive biomarker to predict metastatic progression. Methods To ascertain mechanisms underlying enhanced hybrid cell dissemination we identified hybrid cells within primary uveal melanoma tumors using single cell RNA sequencing (n = 8) and evaluated their gene expression and predicted ligand-receptor interactions in relation to other melanoma and immune cells within the primary tumor. We then verified expression of upregulated hybrid pathways within patient-matched tumor and peripheral blood hybrids (n = 4) using cyclic immunofluorescence and quantified their protein expression relative to other non-hybrid tumor and disseminated tumor cells. Results Among the top upregulated genes and pathways in hybrid cells were those involved in enhanced cell motility and cytoskeletal rearrangement, immune evasion, and altered cellular metabolism. In patient-matched tumor and peripheral blood, we verified gene expression by examining concordant protein expression for each pathway category: TMSB10 (cell motility), CD74 (immune evasion) and GPX1 (metabolism). Both TMSB10 and GPX1 were expressed on significantly higher numbers of disseminated hybrid cells compared to circulating tumor cells, and CD74 and GPX1 were expressed on more disseminated hybrids than tumor-resident hybrids. Lastly, we identified that hybrid cells express ligand-receptor signaling pathways implicated in promoting metastasis including GAS6-AXL, CXCL12-CXCR4, LGALS9-P4HB and IGF1-IGFR1. Conclusion These findings highlight the importance of TMSB10, GPX1 and CD74 for successful hybrid cell dissemination and survival in circulation. Our results contribute to the understanding of uveal melanoma tumor progression and interactions between tumor cells and immune cells in the tumor microenvironment that may promote metastasis.

Published
2024
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2. Epidermal growth factor augments the self-renewal capacity of aged hematopoietic stem cells

Author

Vivian Y. Chang, Yuwei He, Samantha Grohe, Morgan R. Brady, Aldi Chan, Rucha S. Kadam, Tiancheng Fang, Amara Pang, Katherine Pohl, Evelyn Tran, Michelle Li, Jenny Kan, Yurun Zhang, Josie J. Lu, Joshua P. Sasine, Heather A. Himburg, Peibin Yue, and John P. Chute

Subjects
Human physiology, cellular physiology, molecular medicine, stem cells research, functional aspects of cell biology, Science
Abstract

Summary: Hematopoietic aging is associated with decreased hematopoietic stem cell (HSC) self-renewal capacity and myeloid skewing. We report that culture of bone marrow (BM) HSCs from aged mice with epidermal growth factor (EGF) suppressed myeloid skewing, increased multipotent colony formation, and increased HSC repopulation in primary and secondary transplantation assays. Mice transplanted with aged, EGF-treated HSCs displayed increased donor cell engraftment within BM HSCs and systemic administration of EGF to aged mice increased HSC self-renewal capacity in primary and secondary transplantation assays. Expression of a dominant negative EGFR in Scl/Tal1+ hematopoietic cells caused increased myeloid skewing and depletion of long term-HSCs in 15-month-old mice. EGF treatment decreased DNA damage in aged HSCs and shifted the transcriptome of aged HSCs from genes regulating cell death to genes involved in HSC self-renewal and DNA repair but had no effect on HSC senescence. These data suggest that EGFR signaling regulates the repopulating capacity of aged HSCs.

Published
2024
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3. Neuropilin 1 regulates bone marrow vascular regeneration and hematopoietic reconstitution

Author

Christina M. Termini, Amara Pang, Tiancheng Fang, Martina Roos, Vivian Y. Chang, Yurun Zhang, Nicollette J. Setiawan, Lia Signaevskaia, Michelle Li, Mindy M. Kim, Orel Tabibi, Paulina K. Lin, Joshua P. Sasine, Avradip Chatterjee, Ramachandran Murali, Heather A. Himburg, and John P. Chute

Subjects
Science
Abstract

Ionizing radiation and chemotherapy deplete haematopoietic stem cells and damage the vascular niche. Here the authors show that irradiation induces SEMA3A secretion from bone marrow endothelial cells (ECs), inducing EC apoptosis via NRP1 and that NRP1 inhibition promotes vascular regeneration and R spondin 2 dependent hematopoietic regeneration.

Published
2021
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4. Angioinvasive aspergillosis mimicking giant cell arteritis in an 81-year-old man with jaw pain and vision loss

Author

Laure Brulhart, Romain Guemara, and Amara Pieren

Subjects
Medicine
Abstract

The present case report focuses on an immunocompromised 81-year-old patient initially diagnosed with Waldenström’s disease. The patient experienced a gradual vision loss and jaw pain with high erythrocyte sedimentation rate. We first suspected giant cell arteritis, despite inconclusive assessment, including a negative temporal artery biopsy. We rapidly started a corticosteroid pulse therapy followed by high-dose corticosteroid therapy that was followed even after discharge from the hospital. The patient was readmitted 20 days later with severe left retro-orbital pain and progressive left vision loss. Clinical examination revealed complete left eyelid ptosis and unilateral blindness with fixed mydriasis and no eye movement. MRI showed signs of ischaemic optic neuropathy with lysis of the left ethmoid sinus wall; thus, indicating ischaemic optic neuropathy related to lymphoplasmacytic infiltration of Waldenström’s disease (Bing-Neel syndrome). Oncological treatment of ibrutinib, a tyrosine kinase inhibitor, was then administered. Despite a favourable prognosis, no improvement was seen. An infectious aetiology was finally confirmed. The left sphenoid sinus biopsy highlighted an angioinvasive aspergillosis with rhino-orbital infiltration observed as ischaemic optic neuropathy. Oncologic treatment was discontinued and antifungal therapy with voriconazole was introduced, leading to a favourable radiological development and analgesic control, without ophtalmological improvement.

Published
2022
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5. Why context matters in VQA and Reasoning: Semantic interventions for VLM input modalities

Author

Amara, Kenza, Klein, Lukas, Lüth, Carsten, Jäger, Paul, Strobelt, Hendrik, and El-Assady, Mennatallah

Subjects
Computer Science - Artificial Intelligence
Abstract

The various limitations of Generative AI, such as hallucinations and model failures, have made it crucial to understand the role of different modalities in Visual Language Model (VLM) predictions. Our work investigates how the integration of information from image and text modalities influences the performance and behavior of VLMs in visual question answering (VQA) and reasoning tasks. We measure this effect through answer accuracy, reasoning quality, model uncertainty, and modality relevance. We study the interplay between text and image modalities in different configurations where visual content is essential for solving the VQA task. Our contributions include (1) the Semantic Interventions (SI)-VQA dataset, (2) a benchmark study of various VLM architectures under different modality configurations, and (3) the Interactive Semantic Interventions (ISI) tool. The SI-VQA dataset serves as the foundation for the benchmark, while the ISI tool provides an interface to test and apply semantic interventions in image and text inputs, enabling more fine-grained analysis. Our results show that complementary information between modalities improves answer and reasoning quality, while contradictory information harms model performance and confidence. Image text annotations have minimal impact on accuracy and uncertainty, slightly increasing image relevance. Attention analysis confirms the dominant role of image inputs over text in VQA tasks. In this study, we evaluate state-of-the-art VLMs that allow us to extract attention coefficients for each modality. A key finding is PaliGemma's harmful overconfidence, which poses a higher risk of silent failures compared to the LLaVA models. This work sets the foundation for rigorous analysis of modality integration, supported by datasets specifically designed for this purpose.

Published
2024

6. Euclid preparation. The impact of relativistic redshift-space distortions on two-point clustering statistics from the Euclid wide spectroscopic survey

Author

Euclid Collaboration, Elkhashab, M. Y., Bertacca, D., Porciani, C., Salvalaggio, J., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, R., Casas, S., Castellano, M., Castignani, G., 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., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Schirmer, M., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Sefusatti, E., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Surace, C., Tallada-Crespí, P., Taylor, A. N., 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., Biviano, A., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Finelli, F., Gracia-Carpio, J., Mauri, N., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Balaguera-Antolinez, A., Ballardini, M., Blanchard, A., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Cooray, A. R., Davini, S., De Caro, B., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Lacasa, F., Graet, J. Le, Legrand, L., Loureiro, A., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Moretti, C., Morgante, G., Nadathur, S., Walton, Nicholas A., Patrizii, L., Popa, V., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Schneider, A., Sereno, M., Sikkema, G., Silvestri, A., Simon, P., Mancini, A. Spurio, Tanidis, K., Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Vernizzi, F., Verza, G., Vielzeuf, P., and Hernández-Monteagudo, C.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Measurements of galaxy clustering are affected by RSD. Peculiar velocities, gravitational lensing, and other light-cone projection effects modify the observed redshifts, fluxes, and sky positions of distant light sources. We determine which of these effects leave a detectable imprint on several 2-point clustering statistics extracted from the EWSS on large scales. We generate 140 mock galaxy catalogues with the survey geometry and selection function of the EWSS and make use of the LIGER method to account for a variable number of relativistic RSD to linear order in the cosmological perturbations. We estimate different 2-point clustering statistics from the mocks and use the likelihood-ratio test to calculate the statistical significance with which the EWSS could reject the null hypothesis that certain relativistic projection effects can be neglected in the theoretical models. We find that the combined effects of lensing magnification and convergence imprint characteristic signatures on several clustering observables. Their S/N ranges between 2.5 and 6 (depending on the adopted summary statistic) for the highest-redshift galaxies in the EWSS. The corresponding feature due to the peculiar velocity of the Sun is measured with a S/N of order one or two. The $P_{\ell}(k)$ from the catalogues that include all relativistic effects reject the null hypothesis that RSD are only generated by the variation of the peculiar velocity along the line of sight with a significance of 2.9 standard deviations. As a byproduct of our study, we demonstrate that the mixing-matrix formalism to model finite-volume effects in the $P_{\ell}(k)$ can be robustly applied to surveys made of several disconnected patches. Our results indicate that relativistic RSD, the contribution from weak gravitational lensing in particular, cannot be disregarded when modelling 2-point clustering statistics extracted from the EWSS., Comment: 23 pages, 14 figures

Published
2024

7. Euclid preparation: 6x2 pt analysis of Euclid's spectroscopic and photometric data sets

Author

Euclid Collaboration, Paganin, L., Bonici, M., Carbone, C., Camera, S., Tutusaus, I., Davini, S., Bel, J., Tosi, S., Sciotti, D., Di Domizio, S., Risso, I., Testera, G., Sapone, D., Sakr, Z., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Battaglia, P., Bender, R., Bernardeau, F., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Capobianco, V., Cardone, V. F., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Costille, A., Courbin, F., Courtois, H. M., Crocce, M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Di Giorgio, A. M., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., George, K., Gillard, W., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Ilić, S., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., McCracken, H. J., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sartoris, B., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Steinwagner, J., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Fabbian, G., Farinelli, R., Graciá-Carpio, J., Mauri, N., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Blanchard, A., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castro, T., Cañas-Herrera, G., Chambers, K. C., Contarini, S., Cooray, A. R., Coupon, J., Desprez, G., Dole, H., Díaz-Sánchez, A., Vigo, J. A. Escartin, Escoffier, S., Ferreira, P. G., Ferrero, I., Finelli, F., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Gregorio, A., Hall, A., Hildebrandt, H., Hjorth, J., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Pezzotta, A., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Rocci, P. -F., Sahlén, M., Schneider, A., Schultheis, M., Sereno, M., Tao, C., Tessore, N., Teyssier, R., Toft, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., and Vielzeuf, P.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present cosmological parameter forecasts for the Euclid 6x2pt statistics, which include the galaxy clustering and weak lensing main probes together with previously neglected cross-covariance and cross-correlation signals between imaging/photometric and spectroscopic data. The aim is understanding the impact of such terms on the Euclid performance. We produce 6x2pt cosmological forecasts, considering two different techniques: the so-called harmonic and hybrid approaches, respectively. In the first, we treat all the different Euclid probes in the same way, i.e. we consider only angular 2pt-statistics for spectroscopic and photometric clustering, as well as for weak lensing, analysing all their possible cross-covariances and cross-correlations in the spherical harmonic domain. In the second, we do not account for negligible cross-covariances between the 3D and 2D data, but consider the combination of their cross-correlation with the auto-correlation signals. We find that both cross-covariances and cross-correlation signals, have a negligible impact on the cosmological parameter constraints and, therefore, on the Euclid performance. In the case of the hybrid approach, we attribute this result to the effect of the cross-correlation between weak lensing and photometric data, which is dominant with respect to other cross-correlation signals. In the case of the 2D harmonic approach, we attribute this result to two main theoretical limitations of the 2D projected statistics implemented in this work according to the analysis of official Euclid forecasts: the high shot noise and the limited redshift range of the spectroscopic sample, together with the loss of radial information from subleading terms such as redshift-space distortions and lensing magnification. Our analysis suggests that 2D and 3D Euclid data can be safely treated as independent, with a great saving in computational resources., Comment: 32 pages, 20 figures. Comments are welcome

Published
2024

8. Enhancing Explainability in Multimodal Large Language Models Using Ontological Context

Author

Amara, Jihen, König-Ries, Birgitta, and Samuel, Sheeba

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recently, there has been a growing interest in Multimodal Large Language Models (MLLMs) due to their remarkable potential in various tasks integrating different modalities, such as image and text, as well as applications such as image captioning and visual question answering. However, such models still face challenges in accurately captioning and interpreting specific visual concepts and classes, particularly in domain-specific applications. We argue that integrating domain knowledge in the form of an ontology can significantly address these issues. In this work, as a proof of concept, we propose a new framework that combines ontology with MLLMs to classify images of plant diseases. Our method uses concepts about plant diseases from an existing disease ontology to query MLLMs and extract relevant visual concepts from images. Then, we use the reasoning capabilities of the ontology to classify the disease according to the identified concepts. Ensuring that the model accurately uses the concepts describing the disease is crucial in domain-specific applications. By employing an ontology, we can assist in verifying this alignment. Additionally, using the ontology's inference capabilities increases transparency, explainability, and trust in the decision-making process while serving as a judge by checking if the annotations of the concepts by MLLMs are aligned with those in the ontology and displaying the rationales behind their errors. Our framework offers a new direction for synergizing ontologies and MLLMs, supported by an empirical study using different well-known MLLMs.

Published
2024

9. Machine learning analysis of structural data to predict electronic properties in near-surface InAs quantum wells

Author

Strohbeen, Patrick J., Abbaspour, Abtin, Keita, Amara, Nabih, Tarek, Lejuste, Aliona, Danilenko, Alisa, Levy, Ido, Issokson, Jacob, Cowan, Tyler, Strickland, William M., Hatefipour, Mehdi, Argueta, Ashley, Baker, Lukas, Mikalsen, Melissa, and Shabani, Javad

Subjects
Condensed Matter - Mesoscale and Nanoscale Physics, Condensed Matter - Materials Science
Abstract

Semiconductor crosshatch patterns in thin film heterostructures form as a result of strain relaxation processes and dislocation pile-ups during growth of lattice mismatched materials. Due to their connection with the internal misfit dislocation network, these crosshatch patterns are a complex fingerprint of internal strain relaxation and growth anisotropy. Therefore, this mesoscopic fingerprint not only describes the residual strain state of a near-surface quantum well, but also could provide an indicator of the quality of electron transport through the material. Here, we present a method utilizing computer vision and machine learning to analyze AFM crosshatch patterns that exhibits this correlation. Our analysis reveals optimized electron transport for moderate values of $\lambda$ (crosshatch wavelength) and $\epsilon$ (crosshatch height), roughly 1 $\mu$m and 4 nm, respectively, that define the average waveform of the pattern. Simulated 2D AFM crosshatch patterns are used to train a machine learning model to correlate the crosshatch patterns to dislocation density. Furthermore, this model is used to evaluate the experimental AFM images and predict a dislocation density based on the crosshatch waveform. Predicted dislocation density, experimental AFM crosshatch data, and experimental transport characterization are used to train a final model to predict 2D electron gas mean free path. This model shows electron scattering is strongly correlated with elastic effects (e.g. dislocation scattering) below 200 nm $\lambda_{MFP}$.

Published
2024

10. Euclid preparation. Deep learning true galaxy morphologies for weak lensing shear bias calibration

Author

Euclid Collaboration, Csizi, B., Schrabback, T., Grandis, S., Hoekstra, H., Jansen, H., Linke, L., Congedo, G., Taylor, A. N., Amara, A., Andreon, S., Baccigalupi, C., Baldi, M., Bardelli, S., Battaglia, P., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Faustini, F., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Ilić, S., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marcin, S., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sartoris, B., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Gracia-Carpio, J., Matthew, S., Mauri, N., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Ballardini, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cañas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gutierrez, C. M., Hall, A., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Brun, A. M. C. Le, Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martín-Fleitas, J., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Walton, Nicholas A., Pagano, L., Patrizii, L., Popa, V., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Sarpa, E., Schneider, A., Sereno, M., Simon, P., Mancini, A. Spurio, Stadel, J., Tanidis, K., Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., and Vielzeuf, P.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies
Abstract

To date, galaxy image simulations for weak lensing surveys usually approximate the light profiles of all galaxies as a single or double S\'ersic profile, neglecting the influence of galaxy substructures and morphologies deviating from such a simplified parametric characterization. While this approximation may be sufficient for previous data sets, the stringent cosmic shear calibration requirements and the high quality of the data in the upcoming Euclid survey demand a consideration of the effects that realistic galaxy substructures have on shear measurement biases. Here we present a novel deep learning-based method to create such simulated galaxies directly from HST data. We first build and validate a convolutional neural network based on the wavelet scattering transform to learn noise-free representations independent of the point-spread function of HST galaxy images that can be injected into simulations of images from Euclid's optical instrument VIS without introducing noise correlations during PSF convolution or shearing. Then, we demonstrate the generation of new galaxy images by sampling from the model randomly and conditionally. Next, we quantify the cosmic shear bias from complex galaxy shapes in Euclid-like simulations by comparing the shear measurement biases between a sample of model objects and their best-fit double-S\'ersic counterparts. Using the KSB shape measurement algorithm, we find a multiplicative bias difference between these branches with realistic morphologies and parametric profiles on the order of $6.9\times 10^{-3}$ for a realistic magnitude-S\'ersic index distribution. Moreover, we find clear detection bias differences between full image scenes simulated with parametric and realistic galaxies, leading to a bias difference of $4.0\times 10^{-3}$ independent of the shape measurement method. This makes it relevant for stage IV weak lensing surveys such as Euclid., Comment: Submitted to A&A. 29 pages, 20 figures, 2 tables

Published
2024

11. Euclid preparation. Simulations and nonlinearities beyond $\Lambda$CDM. 4. Constraints on $f(R)$ models from the photometric primary probes

Author

Euclid Collaboration, Koyama, K., Pamuk, S., Casas, S., Bose, B., Carrilho, P., Sáez-Casares, I., Atayde, L., Cataneo, M., Fiorini, B., Giocoli, C., Brun, A. M. C. Le, Pace, F., Pourtsidou, A., Rasera, Y., Sakr, Z., Winther, H. -A., Altamura, E., Adamek, J., Baldi, M., Breton, M. -A., Rácz, G., Vernizzi, F., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Bardelli, S., Bernardeau, F., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Castellano, M., Castignani, G., 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., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Hailey, M., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Ilić, S., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kubik, B., Kunz, M., Kurki-Suonio, H., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Neissner, C., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Salvignol, J. -C., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Taylor, A. N., 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., Biviano, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Farinelli, R., Finelli, F., Gracia-Carpio, J., Matthew, S., Mauri, N., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Ballardini, M., Bertacca, D., Blanchard, A., Blot, L., Böhringer, H., Bruton, S., Cabanac, R., Calabro, A., Quevedo, B. Camacho, Cañas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ezziati, M., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gutierrez, C. M., Hall, A., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Liu, S. J., Loureiro, A., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martín-Fleitas, J., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Murray, C., Nadathur, S., Walton, Nicholas A., Pagano, L., Patrizii, L., Popa, V., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Sarpa, E., Schneider, A., Sereno, M., Silvestri, A., Mancini, A. Spurio, Stadel, J., Tanidis, K., Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., Vergani, D., Verza, G., and Vielzeuf, P.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We study the constraint on $f(R)$ gravity that can be obtained by photometric primary probes of the Euclid mission. Our focus is the dependence of the constraint on the theoretical modelling of the nonlinear matter power spectrum. In the Hu-Sawicki $f(R)$ gravity model, we consider four different predictions for the ratio between the power spectrum in $f(R)$ and that in $\Lambda$CDM: a fitting formula, the halo model reaction approach, ReACT and two emulators based on dark matter only $N$-body simulations, FORGE and e-Mantis. These predictions are added to the MontePython implementation to predict the angular power spectra for weak lensing (WL), photometric galaxy clustering and their cross-correlation. By running Markov Chain Monte Carlo, we compare constraints on parameters and investigate the bias of the recovered $f(R)$ parameter if the data are created by a different model. For the pessimistic setting of WL, one dimensional bias for the $f(R)$ parameter, $\log_{10}|f_{R0}|$, is found to be $0.5 \sigma$ when FORGE is used to create the synthetic data with $\log_{10}|f_{R0}| =-5.301$ and fitted by e-Mantis. The impact of baryonic physics on WL is studied by using a baryonification emulator BCemu. For the optimistic setting, the $f(R)$ parameter and two main baryon parameters are well constrained despite the degeneracies among these parameters. However, the difference in the nonlinear dark matter prediction can be compensated by the adjustment of baryon parameters, and the one-dimensional marginalised constraint on $\log_{10}|f_{R0}|$ is biased. This bias can be avoided in the pessimistic setting at the expense of weaker constraints. For the pessimistic setting, using the $\Lambda$CDM synthetic data for WL, we obtain the prior-independent upper limit of $\log_{10}|f_{R0}|< -5.6$. Finally, we implement a method to include theoretical errors to avoid the bias., Comment: 24 pages, 16 figures, submitted on behalf of the Euclid Collaboration

Published
2024

12. Euclid preparation. Simulations and nonlinearities beyond $\Lambda$CDM. 1. Numerical methods and validation

Author

Euclid Collaboration, Adamek, J., Fiorini, B., Baldi, M., Brando, G., Breton, M. -A., Hassani, F., Koyama, K., Brun, A. M. C. Le, Rácz, G., Winther, H. -A., Casalino, A., Hernández-Aguayo, C., Li, B., Potter, D., Altamura, E., Carbone, C., Giocoli, C., Mota, D. F., Pourtsidou, A., Sakr, Z., Vernizzi, F., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Bardelli, S., Battaglia, P., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Capobianco, V., Cardone, V. F., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Castignani, G., 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., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Ilić, S., Jahnke, K., Jhabvala, M., Joachimi, B., 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., Mainetti, G., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Neissner, C., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Finelli, F., Gracia-Carpio, J., Matthew, S., Mauri, N., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Balaguera-Antolinez, A., Ballardini, M., Blanchard, A., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Quevedo, B. Camacho, Cañas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gutierrez, C. M., Hall, A., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Kruk, S., Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Migliaccio, M., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Nadathur, S., Patrizii, L., Popa, V., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Sarpa, E., Schneider, A., Sereno, M., Silvestri, A., Mancini, A. Spurio, Tanidis, K., Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., Vielzeuf, P., and Walton, N. A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

To constrain models beyond $\Lambda$CDM, the development of the Euclid analysis pipeline requires simulations that capture the nonlinear phenomenology of such models. We present an overview of numerical methods and $N$-body simulation codes developed to study the nonlinear regime of structure formation in alternative dark energy and modified gravity theories. We review a variety of numerical techniques and approximations employed in cosmological $N$-body simulations to model the complex phenomenology of scenarios beyond $\Lambda$CDM. This includes discussions on solving nonlinear field equations, accounting for fifth forces, and implementing screening mechanisms. Furthermore, we conduct a code comparison exercise to assess the reliability and convergence of different simulation codes across a range of models. Our analysis demonstrates a high degree of agreement among the outputs of different simulation codes, providing confidence in current numerical methods for modelling cosmic structure formation beyond $\Lambda$CDM. We highlight recent advances made in simulating the nonlinear scales of structure formation, which are essential for leveraging the full scientific potential of the forthcoming observational data from the Euclid mission., Comment: 20 pages, 7 figures, 1 appendix; submitted on behalf of the Euclid Collaboration

Published
2024

13. Spin Multipartitions

Author

Amara-Omari, Ola and Schaps, Mary

Subjects
Mathematics - Combinatorics, Mathematics - Representation Theory, 17B10, 20C08
Abstract

We conjecture an algorithm to construct spin multipartitions, prove that the reduced signature is well-defined and give evidence to support our choice of the combinatorics of the spin multipartitions.

Published
2024

14. Euclid preparation: Determining the weak lensing mass accuracy and precision for galaxy clusters

Author

Euclid Collaboration, Ingoglia, L., Sereno, M., Farrens, S., Giocoli, C., Baumont, L., Lesci, G. F., Moscardini, L., Murray, C., Vannier, M., Biviano, A., Carbone, C., Covone, G., Despali, G., Maturi, M., Maurogordato, S., Meneghetti, M., Radovich, M., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bellagamba, F., Bender, R., Bernardeau, F., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Faustini, F., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillard, W., Gillis, B., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Ilić, S., Jahnke, K., Jhabvala, M., Joachimi, B., 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., Mainetti, G., Maiorano, E., Mansutti, O., Marcin, S., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Munari, E., Niemi, S. -M., Padilla, C., Paech, K., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., 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., Salvignol, J. -C., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., 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., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., Finelli, F., Gracia-Carpio, J., Matthew, S., Pezzotta, A., Pöntinen, M., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Ballardini, M., Bertacca, D., Bethermin, M., Blanchard, A., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cañas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Costanzi, M., Cucciati, O., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ezziati, M., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gutierrez, C. M., Hall, A., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Brun, A. M. C. Le, Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martín-Fleitas, J., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Miluzio, M., Monaco, P., Montoro, A., Mora, A., Moretti, C., Morgante, G., Nadathur, S., Walton, Nicholas A., Pagano, L., Patrizii, L., Popa, V., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Sarpa, E., Schneider, A., Schultheis, M., Simon, P., Mancini, A. Spurio, Stadel, J., Stanford, S. A., Tanidis, K., Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., and Vielzeuf, P.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We investigate the level of accuracy and precision of cluster weak-lensing (WL) masses measured with the \Euclid data processing pipeline. We use the DEMNUni-Cov $N$-body simulations to assess how well the WL mass probes the true halo mass, and, then, how well WL masses can be recovered in the presence of measurement uncertainties. We consider different halo mass density models, priors, and mass point estimates. WL mass differs from true mass due to, e.g., the intrinsic ellipticity of sources, correlated or uncorrelated matter and large-scale structure, halo triaxiality and orientation, and merging or irregular morphology. In an ideal scenario without observational or measurement errors, the maximum likelihood estimator is the most accurate, with WL masses biased low by $\langle b_M \rangle = -14.6 \pm 1.7 \, \%$ on average over the full range $M_\text{200c} > 5 \times 10^{13} \, M_\odot$ and $z < 1$. Due to the stabilising effect of the prior, the biweight, mean, and median estimates are more precise. The scatter decreases with increasing mass and informative priors significantly reduce the scatter. Halo mass density profiles with a truncation provide better fits to the lensing signal, while the accuracy and precision are not significantly affected. We further investigate the impact of additional sources of systematic uncertainty on the WL mass, namely the impact of photometric redshift uncertainties and source selection, the expected performance of \Euclid cluster detection algorithms, and the presence of masks. Taken in isolation, we find that the largest effect is induced by non-conservative source selection. This effect can be mostly removed with a robust selection. As a final \Euclid-like test, we combine systematic effects in a realistic observational setting and find results similar to the ideal case, $\langle b_M \rangle = - 15.5 \pm 2.4 \, \%$, under a robust selection.

Published
2024

15. iToT: An Interactive System for Customized Tree-of-Thought Generation

Author

Boyle, Alan, Gupta, Isha, Hönig, Sebastian, Mautner, Lukas, Amara, Kenza, Cheng, Furui, and El-Assady, Mennatallah

Subjects
Computer Science - Human-Computer Interaction
Abstract

As language models have become increasingly successful at a wide array of tasks, different prompt engineering methods have been developed alongside them in order to adapt these models to new tasks. One of them is Tree-of-Thoughts (ToT), a prompting strategy and framework for language model inference and problem-solving. It allows the model to explore multiple solution paths and select the best course of action, producing a tree-like structure of intermediate steps (i.e., thoughts). This method was shown to be effective for several problem types. However, the official implementation has a high barrier to usage as it requires setup overhead and incorporates task-specific problem templates which are difficult to generalize to new problem types. It also does not allow user interaction to improve or suggest new thoughts. We introduce iToT (interactive Tree-of-Thoughts), a generalized and interactive Tree of Thought prompting system. iToT allows users to explore each step of the model's problem-solving process as well as to correct and extend the model's thoughts. iToT revolves around a visual interface that facilitates simple and generic ToT usage and transparentizes the problem-solving process to users. This facilitates a better understanding of which thoughts and considerations lead to the model's final decision. Through three case studies, we demonstrate the usefulness of iToT in different human-LLM co-writing tasks., Comment: 6 pages excl. figures and comments; 8 figures. Will appear in IEEE 2024 NLVIZ Workshop

Published
2024

16. Euclid preparation. XLIX. Selecting active galactic nuclei using observed colours

Author

Euclid Collaboration, Bisigello, L., Massimo, M., Tortora, C., Fotopoulou, S., Allevato, V., Bolzonella, M., Gruppioni, C., Pozzetti, L., Rodighiero, G., Serjeant, S., Cunha, P. A. C., Gabarra, L., Feltre, A., Humphrey, A., La Franca, F., Landt, H., Mannucci, F., Prandoni, I., Radovich, M., Ricci, F., Salvato, M., Shankar, F., Stern, D., Spinoglio, L., Vergani, D., Vignali, C., Zamorani, G., Yung, L. Y. A., Charlot, S., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Battaglia, P., Bender, R., Bonino, D., Branchini, E., Brau-Nogue, S., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Franzetti, P., Fumana, M., Galeotta, S., Garilli, B., Gillis, B., Giocoli, C., Granett, B. R., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Surace, C., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Zoubian, J., Zucca, E., Biviano, A., Bozzo, E., Colodro-Conde, C., Di Ferdinando, D., Fabbian, G., Graciá-Carpio, J., Marcin, S., Mauri, N., Sakr, Z., Scottez, V., Tenti, M., Akrami, Y., Baccigalupi, C., Ballardini, M., Bethermin, M., Blanchard, A., Borgani, S., Borla, A. S., Bruton, S., Burigana, C., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Coupon, A. R. Cooray J., Cucciati, O., Davini, S., De Lucia, G., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Vigo, J. A. Escartin, Escoffier, S., Ferrero, I., Finelli, F., Ganga, K., García-Bellido, J., Giacomini, F., Gozaliasl, G., Gregorio, A., Hildebrandt, H., Muñoz, A. Jiminez, Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mainetti, G., Maoli, R., Martinelli, M., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Morgante, G., Nadathur, S., Patrizii, L., Popa, V., Porciani, C., Potter, D., Pöntinen, M., Rocci, P. -F., Sánchez, A. G., Schneider, A., Sereno, M., Simon, P., Stadel, J., Stanford, S. A., Steinwagner, J., Testera, G., Tewes, M., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valiviita, J., Viel, M., and Zinchenko, I. A.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Euclid will cover over 14000 $deg^{2}$ with two optical and near-infrared spectro-photometric instruments, and is expected to detect around ten million active galactic nuclei (AGN). This unique data set will make a considerable impact on our understanding of galaxy evolution and AGN. In this work we identify the best colour selection criteria for AGN, based only on Euclid photometry or including ancillary photometric observations, such as the data that will be available with the Rubin legacy survey of space and time (LSST) and observations already available from Spitzer/IRAC. The analysis is performed for unobscured AGN, obscured AGN, and composite (AGN and star-forming) objects. We make use of the spectro-photometric realisations of infrared-selected targets at all-z (SPRITZ) to create mock catalogues mimicking both the Euclid Wide Survey (EWS) and the Euclid Deep Survey (EDS). Using these catalogues we estimate the best colour selection, maximising the harmonic mean (F1) of completeness and purity. The selection of unobscured AGN in both Euclid surveys is possible with Euclid photometry alone with F1=0.22-0.23, which can increase to F1=0.43-0.38 if we limit at z>0.7. Such selection is improved once the Rubin/LSST filters (a combination of the u, g, r, or z filters) are considered, reaching F1=0.84 and 0.86 for the EDS and EWS, respectively. The combination of a Euclid colour with the [3.6]-[4.5] colour, which is possible only in the EDS, results in an F1-score of 0.59, improving the results using only Euclid filters, but worse than the selection combining Euclid and LSST. The selection of composite ($f_{{\rm AGN}}$=0.05-0.65 at 8-40 $\mu m$) and obscured AGN is challenging, with F1<0.3 even when including ancillary data. This is driven by the similarities between the broad-band spectral energy distribution of these AGN and star-forming galaxies in the wavelength range 0.3-5 $\mu m$., Comment: 25 pages, 28 figures, accepted for publication on A&A

Published
2024

17. Leveraging Graph Neural Networks to Forecast Electricity Consumption

Author

Campagne, Eloi, Amara-Ouali, Yvenn, Goude, Yannig, and Kalogeratos, Argyris

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Accurate electricity demand forecasting is essential for several reasons, especially as the integration of renewable energy sources and the transition to a decentralized network paradigm introduce greater complexity and uncertainty. The proposed methodology leverages graph-based representations to effectively capture the spatial distribution and relational intricacies inherent in this decentralized network structure. This research work offers a novel approach that extends beyond the conventional Generalized Additive Model framework by considering models like Graph Convolutional Networks or Graph SAGE. These graph-based models enable the incorporation of various levels of interconnectedness and information sharing among nodes, where each node corresponds to the combined load (i.e. consumption) of a subset of consumers (e.g. the regions of a country). More specifically, we introduce a range of methods for inferring graphs tailored to consumption forecasting, along with a framework for evaluating the developed models in terms of both performance and explainability. We conduct experiments on electricity forecasting, in both a synthetic and a real framework considering the French mainland regions, and the performance and merits of our approach are discussed., Comment: 17 pages, ECML PKDD 2024 Workshop paper

Published
2024

18. A Compaction Function Analysis of CMB $\mu$ distortion Constraints on Primordial Black Holes

Author

Yang, Junyue, Wang, Xiaoding, Ma, Xiao-Han, Zhang, Dongdong, Yan, Sheng-Feng, Ilyas, Amara, and Cai, Yi-Fu

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Primordial black holes (PBHs) are considered viable candidates for dark matter and the seeds of supermassive black holes (SMBHs), with their fruitful physical influences providing significant insights into the conditions of the early Universe. Cosmic microwave background (CMB) $\mu$ distortion tightly constrain the abundance of PBHs in the mass range of $10^4 \sim 10^{11} M_{\odot}$ recently, limiting their potential to serve as seeds for the SMBHs observed. Given that $\mu$ distortion directly constrain the primordial power spectrum, it is crucial to employ more precise methods in computing PBH abundance to strengthen the reliability of these constraints. By a Press-Schechter (PS) type method utilizing the compaction function, we find that the abundance of PBHs could be higher than previously estimated constraints from $\mu$ distortion observations. Furthermore, our analysis shows that variations in the shape of the power spectrum have a negligible impact on our conclusions within the mass ranges under consideration. This conclusion provides us a perspective for further research on the constrain of PBH by $\mu$ distortion., Comment: 9 pages, 3 figures

Published
2024

19. Euclid preparation. Angular power spectra from discrete observations

Author

Euclid Collaboration, Tessore, N., Joachimi, B., Loureiro, A., Hall, A., Cañas-Herrera, G., Tutusaus, I., Jeffrey, N., Naidoo, K., McEwen, J. D., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bernardeau, F., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Faustini, F., Ferriol, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marggraf, O., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Morin, B., Moscardini, L., Munari, E., Nakajima, R., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., 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., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Seiffert, M., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Finelli, F., Gracia-Carpio, J., Matthew, S., Mauri, N., Pezzotta, A., Pöntinen, M., Scottez, V., Mancini, A. Spurio, Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Balaguera-Antolinez, A., Ballardini, M., Benielli, D., Blanchard, A., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Quevedo, B. Camacho, Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Cooray, A. R., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Gutierrez, C. M., Hartley, W. G., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Kruk, S., Lacasa, F., Lattanzi, M., Brun, A. M. C. Le, Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Macias-Perez, J., Magliocchetti, M., Mannucci, F., Maoli, R., Martín-Fleitas, J., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Miluzio, M., Monaco, P., Montoro, A., Moretti, C., Morgante, G., Murray, C., Nadathur, S., Walton, Nicholas A., Patrizii, L., Popa, V., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Rollins, R. P., Sahlén, M., Sarpa, E., Schneider, A., Sereno, M., Simon, P., Tanidis, K., Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., Vielzeuf, P., Brown, M. L., and Sellentin, E.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the framework for measuring angular power spectra in the Euclid mission. The observables in galaxy surveys, such as galaxy clustering and cosmic shear, are not continuous fields, but discrete sets of data, obtained only at the positions of galaxies. We show how to compute the angular power spectra of such discrete data sets, without treating observations as maps of an underlying continuous field that is overlaid with a noise component. This formalism allows us to compute exact theoretical expectations for our measured spectra, under a number of assumptions that we track explicitly. In particular, we obtain exact expressions for the additive biases ("shot noise") in angular galaxy clustering and cosmic shear. For efficient practical computations, we introduce a spin-weighted spherical convolution with a well-defined convolution theorem, which allows us to apply exact theoretical predictions to finite-resolution maps, including HEALPix. When validating our methodology, we find that our measurements are biased by less than 1% of their statistical uncertainty in simulations of Euclid's first data release., Comment: 27 pages, 12 figures. Submitted to A&A. Code available at https://github.com/heracles-ec/heracles

Published
2024

20. Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks

Author

Diaf, Alaeddine, Korba, Abdelaziz Amara, Karabadji, Nour Elislem, and Ghamri-Doudane, Yacine

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intrusion detection systems remain mostly reactive, responding to specific patterns or observed anomalies. This work proposes a proactive approach to anticipate and mitigate malicious activities before they cause damage. This paper proposes a novel network intrusion prediction framework that combines Large Language Models (LLMs) with Long Short Term Memory (LSTM) networks. The framework incorporates two LLMs in a feedback loop: a fine-tuned Generative Pre-trained Transformer (GPT) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) for evaluating the predicted traffic. The LSTM classifier model then identifies malicious packets among these predictions. Our framework, evaluated on the CICIoT2023 IoT attack dataset, demonstrates a significant improvement in predictive capabilities, achieving an overall accuracy of 98%, offering a robust solution to IoT cybersecurity challenges.

Published
2024

21. Optical and microstructural studies of femtosecond laser treated amorphous germanium thin coatings

Author

Kotsedi, L., Abdelmalek, A., Bharadwaj, V., Mtshali, C. B., Nuru, Z. Y., Sotillo, B., Coccia, G., Eaton, S. M., Ramponi, R., Amara, El-H., and Maaza, M.

Subjects
Physics - Optics, Condensed Matter - Materials Science
Abstract

The study of the relaxation mechanism of amorphous germanium after femtosecond laser irradiation is presented in this work. In particular, a thin germanium coating was deposited onto a glass substrate through the electron beam vacuum coating method. The substrate was kept at room temperature during the coating process, which resulted in a deposited layer characterized by an amorphous microstructure, as observed from the X-ray diffraction. The germanium layer was then irradiated with a femtosecond laser at 1030 nm wavelength, while varying the net fluence from 15 J cm-2 to 90 J cm-2. Moreover, an extended two temperature model was used to extract the electronic and lattice temperature of the laser heated germanium coating, showing a 32% contribution from heating due to the thermal accumulation effect. The microstructural and morphological studies of the irradiated samples were carried out using X-ray diffraction and high-resolution scanning electron microscopy. From the X-ray diffraction results, it was observed that at higher laser fluence there is an emergence of crystallinity on the germanium layer, with no evidence of oxidation. On the surface, the morphology was observed to evolve to granular sphere, attributed to melting of the material. Finally, an increase in absorbance with laser fluence was observed and the optical band gap of the coating was calculated., Comment: 21 pages, 9 figures

Published
2024

22. Understanding the Local Geometry of Generative Model Manifolds

Author

Humayun, Ahmed Imtiaz, Amara, Ibtihel, Schumann, Candice, Farnadi, Golnoosh, Rostamzadeh, Negar, and Havaei, Mohammad

Subjects
Computer Science - Machine Learning, Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep generative models learn continuous representations of complex data manifolds using a finite number of samples during training. For a pre-trained generative model, the common way to evaluate the quality of the manifold representation learned, is by computing global metrics like Fr\'echet Inception Distance using a large number of generated and real samples. However, generative model performance is not uniform across the learned manifold, e.g., for \textit{foundation models} like Stable Diffusion generation performance can vary significantly based on the conditioning or initial noise vector being denoised. In this paper we study the relationship between the \textit{local geometry of the learned manifold} and downstream generation. Based on the theory of continuous piecewise-linear (CPWL) generators, we use three geometric descriptors - scaling ($\psi$), rank ($\nu$), and complexity ($\delta$) - to characterize a pre-trained generative model manifold locally. We provide quantitative and qualitative evidence showing that for a given latent, the local descriptors are correlated with generation aesthetics, artifacts, uncertainty, and even memorization. Finally we demonstrate that training a \textit{reward model} on the local geometry can allow controlling the likelihood of a generated sample under the learned distribution., Comment: Pre-print. 11 pages main, 8 pages app., 28 figures

Published
2024

23. Euclid: The Early Release Observations Lens Search Experiment

Author

Barroso, J. A. Acevedo, O'Riordan, C. M., Clément, B., Tortora, C., Collett, T. E., Courbin, F., Gavazzi, R., Metcalf, R. B., Busillo, V., Andika, I. T., Cabanac, R., Courtois, H. M., Crook-Mansour, J., Delchambre, L., Despali, G., Ecker, L. R., Franco, A., Holloway, P., Jackson, N., Jahnke, K., Mahler, G., Marchetti, L., Matavulj, P., Melo, A., Meneghetti, M., Moustakas, L. A., Müller, O., Nucita, A. A., Paulino-Afonso, A., Pearson, J., Rojas, K., Scarlata, C., Schuldt, S., Serjeant, S., Sluse, D., Suyu, S. H., Vaccari, M., Verma, A., Vernardos, G., Walmsley, M., Bouy, H., Walth, G. L., Powell, D. M., Bolzonella, M., Cuillandre, J. -C., Kluge, M., Saifollahi, T., Schirmer, M., Stone, C., Acebron, A., Bazzanini, L., Díaz-Sánchez, A., Hogg, N. B., Koopmans, L. V. E., Kruk, S., Leuzzi, L., Manjón-García, A., Mannucci, F., Nagam, B. C., Pearce-Casey, R., Scharré, L., Wilde, J., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Balestra, A., Bardelli, S., Basset, A., Battaglia, P., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Caillat, A., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Castignani, G., Cavuoti, S., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Cropper, M., Da Silva, A., Degaudenzi, H., De Lucia, G., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., Garilli, B., George, K., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kunz, M., Kurki-Suonio, H., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marcin, S., Marggraf, O., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Melchior, M., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Nichol, R. C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Steinwagner, J., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Wang, Y., Weller, J., Zucca, E., Burigana, C., Scottez, V., and Viel, M.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We investigate the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we perform a systematic visual inspection of the $0.7\,\rm{deg}^2$ Euclid ERO data towards the Perseus cluster using both the high-resolution VIS $I_{\scriptscriptstyle\rm E}$ band, and the lower resolution NISP bands. We inspect every extended source brighter than magnitude $23$ in $I_{\scriptscriptstyle\rm E}$ with $41$ expert human classifiers. This amounts to $12\,086$ stamps of $10^{\prime\prime}\,\times\,10^{\prime\prime}$. We find $3$ grade A and $13$ grade B candidates. We assess the validity of these $16$ candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full $14\,000\,{\rm deg}^2$ of the Euclid Wide Survey should contain $100\,000^{+70\,000}_{-30\,000}$ galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of $170\,000$ discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range $0.\!\!^{\prime\prime}68\,<\,\theta_\mathrm{E}\,<1.\!\!^{\prime\prime}24$, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small Einstein radius systems. Whilst it is implausible to visually inspect the full Euclid data set, our results corroborate the promise that Euclid will ultimately deliver a sample of around $10^5$ galaxy-scale lenses., Comment: 21 pages, 20 figures, submitted to A&A

Published
2024

24. Euclid preparation. The Cosmic Dawn Survey (DAWN) of the Euclid Deep and Auxiliary Fields

Author

Euclid Collaboration, McPartland, C. J. R., Zalesky, L., Weaver, J. R., Toft, S., Sanders, D. B., Mobasher, B., Suzuki, N., Szapudi, I., Valdes, I., Murphree, G., Chartab, N., Allen, N., Taamoli, S., Eisenhardt, P. R. M., Arnouts, S., Atek, H., Brinchmann, J., Castellano, M., Chary, R., Ortiz, O. Chávez, Cuby, J. -G., Finkelstein, S. L., Goto, T., Gwyn, S., Harikane, Y., Inoue, A. K., McCracken, H. J., Mohr, J. J., Oesch, P. A., Ouchi, M., Oguri, M., Rhodes, J., Rottgering, H. J. A., Sawicki, M., Scaramella, R., Scarlata, C., Silverman, J. D., Stern, D., Teplitz, H. I., Shuntov, M., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Aussel, H., Baccigalupi, C., Baldi, M., Bardelli, S., Bender, R., Bonino, D., Branchini, E., Brescia, M., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castignani, G., 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., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Fabricius, M., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., George, K., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kunz, M., Kurki-Suonio, H., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sánchez, A. G., Sapone, D., Sartoris, B., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Surace, C., Tallada-Crespi, P., Tavagnacco, D., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zoubian, J., Zucca, E., Biviano, A., Bolzonella, M., Boucaud, A., Bozzo, E., Burigana, C., Di Ferdinando, D., Farinelli, R., Gracia-Carpio, J., Mauri, N., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Bethermin, M., Borgani, S., Borlaff, A. S., Bruton, S., Cabanac, R., Calabro, A., Cañas-Herrera, G., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Coupon, J., Davini, S., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Vigo, J. A. Escartin, Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finelli, F., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Gregorio, A., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Huertas-Company, M., Ilbert, O., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maturi, M., Maurin, L., Metcalf, R. B., Monaco, P., Moretti, C., Morgante, G., Musi, P., Walton, Nicholas A., Odier, J., Patrizii, L., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Schneider, A., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Tao, C., Testera, G., Teyssier, R., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., and Shankar, F.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Euclid will provide deep NIR imaging to $\sim$26.5 AB magnitude over $\sim$59 deg$^2$ in its deep and auxiliary fields. The Cosmic DAWN survey complements the deep Euclid data with matched depth multiwavelength imaging and spectroscopy in the UV--IR to provide consistently processed Euclid selected photometric catalogs, accurate photometric redshifts, and measurements of galaxy properties to a redshift of $z\sim 10$. In this paper, we present an overview of the survey, including the footprints of the survey fields, the existing and planned observations, and the primary science goals for the combined data set., Comment: 16 pages, 10 figures, submitted to A&A; Updated references; Updated author list

Published
2024

25. Distance Learning Implementation in Malaysian and Indonesian Schools: Are City Schools More Superior to Increase Student Independence?

Author

Rini Fatma Kartika, Sa'diyah, Hanifah Mahat, Lula Dwi Amara, Nur Hamid, Rabiatul Adawiyah, and Elvara Norma Aroyandini

Abstract

This study identifies and analyzes the processes and differences in distance learning implementation between city and village schools in Indonesia and Malaysia. Characteristics of students and teachers, distance learning facilities, ability to use technology, and parent's role in the learning process will be analyzed in depth. This research will simultaneously hypothesize the link between these factors and student learning independence. This research is a mixed methods research that combines qualitative research that using observation and interview techniques and quantitative methods that using questionnaire technique. This research was conducted on elementary school students in Indonesia and Malaysia located in rural and urban areas, with a total sample of 240 students ranging from low to high grades. Apart from students, as many as 60 school academics consisting of principals, teachers, and staff were also interviewed to obtain more comprehensive data and data triangulation. Quantitative data were analyzed using a qualitative-quantitative descriptive method where the qualitative statements in the questionnaire were converted into quantitative data, while qualitative data were analyzed using an interactive model mentioned by Miles & Huberman (2014). The study results show that the distance learning implementation between city and village schools experiences a slight difference with the rapid development of information technology. Of the four aspects studied, there are aspects in which village schools are superior to city schools. These four aspects are also investigated for their influence on student learning independence so that further research can be carried out regarding the correlation of these aspects to student learning independence.

Published
2024

26. Ultra-light dark matter with non-canonical kinetics reopening the mass window

Author

Lu, Shiyun, Ilyas, Amara, Ma, Xiao-Han, Wang, Bo, Zhang, Dongdong, and Cai, Yi-Fu

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

Fuzzy dark matter (FDM) with mass around $10^{-22}$ eV is viewed as a promising paradigm in understanding the structure formation of the local universe at small scales. Recent observations, however, begin to challenge FDM in return. We focus on the arguments between the solution to CDM small-scale curiosities and recent observations on matter power spectrum, and find its implication on an earlier formation of small-scale structure. In this article, we propose a scheme of k-ULDM scalar field with a differently-evolving sound speed, thanks to the non-canonical kinetics. With the help of the Dirac-Born-Infeld (DBI) theory, we illustrate to change the behavior of the quantum pressure term countering collapse, therefore change the history of structure growth. We find that it can truly reopen the ULDM mass window closed by the Lyman-$\alpha$ problem. We will discuss such examples in this paper, while more possibilities remain to be explored., Comment: 25 pages, 10 figures

Published
2024

27. A Life-long Learning Intrusion Detection System for 6G-Enabled IoV

Author

korba, Abdelaziz Amara, Sebaa, Souad, Mabrouki, Malik, Ghamri-Doudane, Yacine, and Benatchba, Karima

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

The introduction of 6G technology into the Internet of Vehicles (IoV) promises to revolutionize connectivity with ultra-high data rates and seamless network coverage. However, this technological leap also brings significant challenges, particularly for the dynamic and diverse IoV landscape, which must meet the rigorous reliability and security requirements of 6G networks. Furthermore, integrating 6G will likely increase the IoV's susceptibility to a spectrum of emerging cyber threats. Therefore, it is crucial for security mechanisms to dynamically adapt and learn new attack patterns, keeping pace with the rapid evolution and diversification of these threats - a capability currently lacking in existing systems. This paper presents a novel intrusion detection system leveraging the paradigm of life-long (or continual) learning. Our methodology combines class-incremental learning with federated learning, an approach ideally suited to the distributed nature of the IoV. This strategy effectively harnesses the collective intelligence of Connected and Automated Vehicles (CAVs) and edge computing capabilities to train the detection system. To the best of our knowledge, this study is the first to synergize class-incremental learning with federated learning specifically for cyber attack detection. Through comprehensive experiments on a recent network traffic dataset, our system has exhibited a robust adaptability in learning new cyber attack patterns, while effectively retaining knowledge of previously encountered ones. Additionally, it has proven to maintain high accuracy and a low false positive rate.

Published
2024

28. AI-Driven Fast and Early Detection of IoT Botnet Threats: A Comprehensive Network Traffic Analysis Approach

Author

korba, Abdelaziz Amara, Diaf, Aleddine, and Ghamri-Doudane, Yacine

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

In the rapidly evolving landscape of cyber threats targeting the Internet of Things (IoT) ecosystem, and in light of the surge in botnet-driven Distributed Denial of Service (DDoS) and brute force attacks, this study focuses on the early detection of IoT bots. It specifically addresses the detection of stealth bot communication that precedes and orchestrates attacks. This study proposes a comprehensive methodology for analyzing IoT network traffic, including considerations for both unidirectional and bidirectional flow, as well as packet formats. It explores a wide spectrum of network features critical for representing network traffic and characterizing benign IoT traffic patterns effectively. Moreover, it delves into the modeling of traffic using various semi-supervised learning techniques. Through extensive experimentation with the IoT-23 dataset - a comprehensive collection featuring diverse botnet types and traffic scenarios - we have demonstrated the feasibility of detecting botnet traffic corresponding to different operations and types of bots, specifically focusing on stealth command and control (C2) communications. The results obtained have demonstrated the feasibility of identifying C2 communication with a 100% success rate through packet-based methods and 94% via flow based approaches, with a false positive rate of 1.53%.

Published
2024

29. Euclid and KiDS-1000: Quantifying the impact of source-lens clustering on cosmic shear analyses

Author

Linke, L., Unruh, S., Wittje, A., Schrabback, T., Grandis, S., Asgari, M., Dvornik, A., Hildebrandt, H., Hoekstra, H., Joachimi, B., Reischke, R., Busch, J. L. van den, Wright, A. H., Schneider, P., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bardelli, S., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kubik, B., Kuijken, K., 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., Martinet, N., Marulli, F., Massey, R., McCracken, H. J., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sakr, Z., Sapone, D., Sartoris, B., Schirmer, M., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Tallada-Crespí, P., Taylor, A. N., 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., Burigana, C., Pezzotta, A., Porciani, C., Scottez, V., Viel, M., and Brun, A. M. C. Le

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

Cosmic shear is a powerful probe of cosmological models and the transition from current Stage-III surveys like the Kilo-Degree Survey (KiDS) to the increased area and redshift range of Stage IV-surveys such as \Euclid will significantly increase the precision of weak lensing analyses. However, with increasing precision, the accuracy of model assumptions needs to be evaluated. In this study, we quantify the impact of the correlated clustering of weak lensing source galaxies with the surrounding large-scale structure, the so-called source-lens clustering (SLC), which is commonly neglected. We include the impact of realistic scatter in photometric redshift estimates, which impacts the assignment of galaxies to tomographic bins and increases the SLC. For this, we use simulated cosmological datasets with realistically distributed galaxies and measure shear correlation functions for both clustered and uniformly distributed source galaxies. Cosmological analyses are performed for both scenarios to quantify the impact of SLC on parameter inference for a KiDS-like and a \Euclid-like setting. We find for Stage III surveys like KiDS, SLC has a minor impact when accounting for nuisance parameters for intrinsic alignments and shifts of tomographic bins, as these nuisance parameters absorb the effect of SLC, thus changing their original meaning. For KiDS (\Euclid), the inferred intrinsic alignment amplitude $A_\mathrm{IA}$ changes from $0.11_{-0.46}^{+0.44}$ ($-0.009_{-0.080}^{+0.079}$) for data without SLC to $0.28_{-0.44}^{+0.42}$ ($0.022_{-0.082}^{+0.081}$) with SLC. However, fixed nuisance parameters lead to shifts in $S_8$ and $\Omega_\mathrm{m}$. For \Euclid we find that $S_8$ and $\Omega_\mathrm{m}$ are shifted by 0.14 and 0.12 $\sigma$, respectively, when including free nuisance parameters. Consequently, SLC on its own has only a small impact on the inferred parameters., Comment: 17 pages plus appendix, 10 figures, abstract abridged for arXiv

Published
2024

30. Euclid preparation. LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods

Author

Euclid Collaboration, Enia, A., Bolzonella, M., Pozzetti, L., Humphrey, A., Cunha, P. A. C., Hartley, W. G., Dubath, F., Paltani, S., Lopez, X. Lopez, Quai, S., Bardelli, S., Bisigello, L., Cavuoti, S., De Lucia, G., Ginolfi, M., Grazian, A., Siudek, M., Tortora, C., Zamorani, G., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baccigalupi, C., Baldi, M., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Castignani, G., Cimatti, A., Colodro-Conde, C., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dupac, X., Dusini, S., Fabricius, M., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grupp, F., Haugan, S. V. H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., McCracken, H. J., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Neissner, C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Pasian, F., Pedersen, K., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sakr, Z., Sapone, D., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Sefusatti, E., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Steinwagner, J., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Zucca, E., Biviano, A., Boucaud, A., Burigana, C., Calabrese, M., Vigo, J. A. Escartin, Gracia-Carpio, J., Mauri, N., Pezzotta, A., Pöntinen, M., Porciani, C., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Allevato, V., Anselmi, S., Ballardini, M., Bergamini, P., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Contini, T., Cooray, A. R., Cucciati, O., Davini, S., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Giacomini, F., Gianotti, F., Gozaliasl, G., Hall, A., Hemmati, S., Hildebrandt, H., Hjorth, J., Muñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Graet, J. Le, Legrand, L., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mancini, C., 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., Popa, V., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Schneider, A., Schultheis, M., Sereno, M., Simon, P., Mancini, A. Spurio, Stanford, S. A., Tanidis, K., Tao, C., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Verza, G., Zinchenko, I. A., Rodighiero, G., and Talia, M.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

Euclid will collect an enormous amount of data during the mission's lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning algorithms have been presented for computing their photometric redshifts and physical parameters (PPs), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information, to the point where the recovery of some well-established physical relationships between parameters might not be guaranteed. To forecast the reliability of Euclid photo-$z$s and PPs calculations, we produced two mock catalogs simulating Euclid photometry. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF). We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-$z$s, PPs (stellar masses and star formation rates), and the SFMS. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels. For the EWS, we found that the best results are achieved with a mixed labels approach, training the models with wide survey features and labels from the Phosphoros results on deeper photometry, that is, with the best possible set of labels for a given photometry. This imposes a prior, helping the models to better discern cases in degenerate regions of feature space, that is, when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than 3% performance degradation using a COSMOS-like reference sample or removing u band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-$z$, PPs, and the SFMS., Comment: 26 pages, 13 figures. Accepted for publication on A&A

Published
2024

31. Multi-agent Reinforcement Learning-based Network Intrusion Detection System

Author

Tellache, Amine, Mokhtari, Amdjed, Korba, Abdelaziz Amara, and Ghamri-Doudane, Yacine

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. Machine learning has emerged as a popular approach for intrusion detection due to its ability to analyze and detect patterns in large volumes of data. However, current ML-based IDS solutions often struggle to keep pace with the ever-changing nature of attack patterns and the emergence of new attack types. Additionally, these solutions face challenges related to class imbalance, where the number of instances belonging to different classes (normal and intrusions) is significantly imbalanced, which hinders their ability to effectively detect minor classes. In this paper, we propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection. To enhance the capabilities of the proposed model, we have improved the DQN algorithm by implementing the weighted mean square loss function and employing cost-sensitive learning techniques. Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns. Experimental results realized using CIC-IDS-2017 dataset, demonstrate that our approach can effectively handle the class imbalance problem and provide a fine grained classification of attacks with a very low false positive rate. In comparison to the current state-of-the-art works, our solution demonstrates a significant superiority in both detection rate and false positive rate.

Published
2024

32. Global regularity of semi-critical case of anisotropic quasi-geostrophic equations in Sobolev spaces

Author

Amara, Mustapha

Subjects
Mathematics - Analysis of PDEs
Abstract

In this paper, we consider the following anisotropic quasi-geostrophic equations \begin{equation}\tag*{$(AQG)_{\alpha,\beta}$} \partial_t\theta+ u_\theta.\nabla\theta +\mu|\partial_1|^{2\alpha}\theta+\nu |\partial_2|^{2\beta}\theta=0,\quad u_\theta=\mathcal{R}^{\perp}\theta, \end{equation} where $\min\{\alpha,\beta\}=\frac{1}{2}$ et $\max\{\alpha,\beta\}\in \left(\frac{1}{2},1\right)$. This equation is a particular case of the equation introduced by Ye (2019) in \cite{YZ}. In this paper, we prove that for any initial data $\theta^0$ in the Sobolev space $H^{s}(\mathbb{R}^2)$, $s >1$, the equation $(AQG)_{\alpha,\beta}$ has a global solution $\theta$ in $C_b(\mathbb{R}^+,H^s(\mathbb{R}^2)).$

Published
2024

33. Anomaly-based Framework for Detecting Power Overloading Cyberattacks in Smart Grid AMI

Author

Korba, Abdelaziz Amara, Tamani, Nouredine, Ghamri-Doudane, Yacine, and karabadji, Nour El Islem

Subjects
Computer Science - Cryptography and Security, Electrical Engineering and Systems Science - Signal Processing
Abstract

The Advanced Metering Infrastructure (AMI) is one of the key components of the smart grid. It provides interactive services for managing billing and electricity consumption, but it also introduces new vectors for cyberattacks. Although, the devastating and severe impact of power overloading cyberattacks on smart grid AMI, few researches in the literature have addressed them. In the present paper, we propose a two-level anomaly detection framework based on regression decision trees. The introduced detection approach leverages the regularity and predictability of energy consumption to build reference consumption patterns for the whole neighborhood and each household within it. Using a reference consumption pattern enables detecting power overloading cyberattacks regardless of the attacker's strategy as they cause a drastic change in the consumption pattern. The continuous two-level monitoring of energy consumption load allows efficient and early detection of cyberattacks. We carried out an extensive experiment on a real-world publicly available energy consumption dataset of 500 customers in Ireland. We extracted, from the raw data, the relevant attributes for training the energy consumption patterns. The evaluation shows that our approach achieves a high detection rate, a low false alarm rate, and superior performances compared to existing solutions.

Published
2024

34. Federated Learning for Zero-Day Attack Detection in 5G and Beyond V2X Networks

Author

korba, Abdelaziz Amara, Boualouache, Abdelwahab, Brik, Bouziane, Rahal, Rabah, Ghamri-Doudane, Yacine, and Senouci, Sidi Mohammed

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning based solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.

Published
2024

35. Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV

Author

korba, Abdelaziz Amara, Boualouache, Abdelwahab, and Ghamri-Doudane, Yacine

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

The Internet of Vehicles (IoV) is a crucial technology for Intelligent Transportation Systems (ITS) that integrates vehicles with the Internet and other entities. The emergence of 5G and the forthcoming 6G networks presents an enormous potential to transform the IoV by enabling ultra-reliable, low-latency, and high-bandwidth communications. Nevertheless, as connectivity expands, cybersecurity threats have become a significant concern. The issue has been further exacerbated by the rising number of zero-day (0-day) attacks, which can exploit unknown vulnerabilities and bypass existing Intrusion Detection Systems (IDSs). In this paper, we propose Zero-X, an innovative security framework that effectively detects both 0-day and N-day attacks. The framework achieves this by combining deep neural networks with Open-Set Recognition (OSR). Our approach introduces a novel scheme that uses blockchain technology to facilitate trusted and decentralized federated learning (FL) of the ZeroX framework. This scheme also prioritizes privacy preservation, enabling both CAVs and Security Operation Centers (SOCs) to contribute their unique knowledge while protecting the privacy of their sensitive data. To the best of our knowledge, this is the first work to leverage OSR in combination with privacy-preserving FL to identify both 0-day and N-day attacks in the realm of IoV. The in-depth experiments on two recent network traffic datasets show that the proposed framework achieved a high detection rate while minimizing the false positive rate. Comparison with related work showed that the Zero-X framework outperforms existing solutions.

Published
2024

36. AntibotV: A Multilevel Behaviour-based Framework for Botnets Detection in Vehicular Networks

Author

Rahal, Rabah, Korba, Abdelaziz Amara, Ghoualmi-Zine, Nacira, Challal, Yacine, and Ghamri-Doudane, Mohamed Yacine

Subjects
Computer Science - Cryptography and Security, Computer Science - Artificial Intelligence
Abstract

Connected cars offer safety and efficiency for both individuals and fleets of private vehicles and public transportation companies. However, equipping vehicles with information and communication technologies raises privacy and security concerns, which significantly threaten the user's data and life. Using bot malware, a hacker may compromise a vehicle and control it remotely, for instance, he can disable breaks or start the engine remotely. In this paper, besides in-vehicle attacks existing in the literature, we consider new zeroday bot malware attacks specific to the vehicular context, WSMP-Flood, and Geo-WSMP Flood. Then, we propose AntibotV, a multilevel behaviour-based framework for vehicular botnets detection in vehicular networks. The proposed framework combines two main modules for attack detection, the first one monitors the vehicle's activity at the network level, whereas the second one monitors the in-vehicle activity. The two intrusion detection modules have been trained on a historical network and in-vehicle communication using decision tree algorithms. The experimental results showed that the proposed framework outperforms existing solutions, it achieves a detection rate higher than 97% and a false positive rate lower than 0.14%.

Published
2024

37. Euclid preparation. Sensitivity to non-standard particle dark matter model

Author

Euclid Collaboration, Lesgourgues, J., Schwagereit, J., Bucko, J., Parimbelli, G., Giri, S. K., Hervas-Peters, F., Schneider, A., Archidiacono, M., Pace, F., Sakr, Z., Amara, A., Amendola, L., Andreon, S., Auricchio, N., Aussel, H., Baccigalupi, C., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castellano, M., Castignani, G., 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., Di Giorgio, A. M., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Ilić, S., Jahnke, K., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Mainetti, G., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinelli, M., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Neissner, C., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sánchez, A. G., Sapone, D., Sartoris, B., Scaramella, R., Schewtschenko, J. A., 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., Veropalumbo, A., Wang, Y., Weller, J., Zamorani, G., Zucca, E., Biviano, A., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Farinelli, R., Gracia-Carpio, J., Mauri, N., Nucita, A. A., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Ballardini, M., Bertacca, D., Blot, L., Böhringer, H., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Cappi, A., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Cooray, A. R., Davini, S., 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., Hildebrandt, H., Hjorth, J., Munñoz, A. Jimenez, Joudaki, S., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Mannucci, F., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Migliaccio, M., Monaco, P., Moretti, C., Morgante, G., Nadathur, S., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Reimberg, P., Risso, I., Rocci, P. -F., Sahlén, M., Sereno, M., Simon, P., Mancini, A. Spurio, Tao, C., Tessore, N., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., and Verza, G.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

The Euclid mission of the European Space Agency will provide weak gravitational lensing and galaxy clustering surveys that can be used to constrain the standard cosmological model and its extensions, with an opportunity to test the properties of dark matter beyond the minimal cold dark matter paradigm. We present forecasts from the combination of these surveys on the parameters describing four interesting and representative non-minimal dark matter models: a mixture of cold and warm dark matter relics; unstable dark matter decaying either into massless or massive relics; and dark matter experiencing feeble interactions with relativistic relics. We model these scenarios at the level of the non-linear matter power spectrum using emulators trained on dedicated N-body simulations. We use a mock Euclid likelihood to fit mock data and infer error bars on dark matter parameters marginalised over other parameters. We find that the Euclid photometric probe (alone or in combination with CMB data from the Planck satellite) will be sensitive to the effect of each of the four dark matter models considered here. The improvement will be particularly spectacular for decaying and interacting dark matter models. With Euclid, the bounds on some dark matter parameters can improve by up to two orders of magnitude compared to current limits. We discuss the dependence of predicted uncertainties on different assumptions: inclusion of photometric galaxy clustering data, minimum angular scale taken into account, modelling of baryonic feedback effects. We conclude that the Euclid mission will be able to measure quantities related to the dark sector of particle physics with unprecedented sensitivity. This will provide important information for model building in high-energy physics. Any hint of a deviation from the minimal cold dark matter paradigm would have profound implications for cosmology and particle physics., Comment: 31 pages, 21 figures

Published
2024

38. Interpolation theorems for conjugations and applications

Author

Amara, Zouheir

Subjects
Mathematics - Functional Analysis, Mathematics - Operator Algebras, 47B15, 47B38, 47A15
Abstract

Let $H$ be a separable complex Hilbert space. A conjugate-linear map $C:H\to H$ is a conjugation if it is an involutive isometry. In this paper, we consider the following interpolation problems: Let $\{x_i\}_{i\in I}$ and $\{y_i\}_{i\in I}$ be two orthogonal sets of vectors in $H$, and let $N$ and $\{N_k\}_{k\in K}$ be normal operators such that the $N_k$'s mutually commute. Then, under which conditions does there exist a conjugation $C$ on $H$ such that (a) $Cx_i=y_i$ and $CN_kC=N_k^*$ for all $i\in I$ and $k\in K$; or (b) $Cx_i=y_i$, for every $i\in I$, and $CNC=-N^*$. Using the spectral projections of normal operators, we provide complete answers to problems (a) and (b). As an application, we characterize hyperinvariant subspaces of normal operators using conjugations. Additionally, we give necessary and sufficient conditions for the existence of solutions to certain equations in $L^{\infty}(\mu)$.

Published
2024

39. Size effect on the structural and magnetic phase transformations of iron nanoparticles

Author

Front, Alexis, Förster, Georg Daniel, Fu, Chu-Chun, Barreteau, Cyrille, and Amara, Hakim

Subjects
Condensed Matter - Materials Science
Abstract

Iron nanoparticles are among the most promising low-dimensional materials in terms of applications. This particularity is attributable to the magnetic properties of these nanoparticles, which exhibit different allotropes as a function of temperature. In this work, we sought to characterise at the atomic scale how their structural and magnetic transformations can be affected by the size. To achieve this objective, we developed a tight-binding model incorporating a magnetic contribution via a Stoner term implemented in a Monte Carlo code to relax the structure and the magnetic state. Using our approach, we show that magnetism is strongly reinforced by the surface, which leads to increase the Curie temperature as the size of the particle decreases contrary to the solid-solid transition temperature. Our work thus provides a deep understanding at the atomic scale of the key factors that determines the structural and magnetic properties of Fe nanoparticles, shedding more light on their unique character which is crucial for further applications.

Published
2024

40. Research Trends for the Interplay between Large Language Models and Knowledge Graphs

Author

Khorashadizadeh, Hanieh, Amara, Fatima Zahra, Ezzabady, Morteza, Ieng, Frédéric, Tiwari, Sanju, Mihindukulasooriya, Nandana, Groppe, Jinghua, Sahri, Soror, Benamara, Farah, and Groppe, Sven

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language
Abstract

This survey investigates the synergistic relationship between Large Language Models (LLMs) and Knowledge Graphs (KGs), which is crucial for advancing AI's capabilities in understanding, reasoning, and language processing. It aims to address gaps in current research by exploring areas such as KG Question Answering, ontology generation, KG validation, and the enhancement of KG accuracy and consistency through LLMs. The paper further examines the roles of LLMs in generating descriptive texts and natural language queries for KGs. Through a structured analysis that includes categorizing LLM-KG interactions, examining methodologies, and investigating collaborative uses and potential biases, this study seeks to provide new insights into the combined potential of LLMs and KGs. It highlights the importance of their interaction for improving AI applications and outlines future research directions.

Published
2024

41. Feasibility of accelerating incompressible computational fluid dynamics simulations with fault-tolerant quantum computers

Author

Penuel, John, Katabarwa, Amara, Johnson, Peter D., Farquhar, Collin, Cao, Yudong, and Garrett, Michael C.

Subjects
Quantum Physics
Abstract

Across industries, traditional design and engineering workflows are being upgraded to simulation-driven processes. Many workflows include computational fluid dynamics (CFD). Simulations of turbulent flow are notorious for high compute costs and reliance on approximate methods that compromise accuracy. Improvements in the speed and accuracy of CFD calculations would potentially reduce design workflow costs by reducing computational costs and eliminating the need for experimental testing. This study explores the feasibility of using fault-tolerant quantum computers to improve the speed and accuracy of CFD simulations in the incompressible or weakly compressible regime. For the example of simulation-driven ship design, we consider simulations for calculating the drag force in steady-state flows, and provide analysis on economic utility and classical hardness. As a waypoint toward assessing the feasibility of our chosen quantum approach, we estimate the quantum resources required for the simpler case of drag force on a sphere. We estimate the product of (logical qubits)$\times$($T$ gates) to range from $10^{22}$ to $10^{28}$. These high initial estimates suggest that future quantum computers are unlikely to provide utility for incompressible CFD applications unless significant algorithmic advancements or alternative quantum approaches are developed. Encouraged by applications in quantum chemistry that have realized orders-of-magnitude improvements as they matured, we identify the most promising next steps for quantum resource reduction as we work to scale up our estimates from spheres to utility-scale problems with more complex geometry., Comment: Version 2: Block encoding the Carleman $A$-matrix section edited for clarity

Published
2024

42. Knowledge-grounded Adaptation Strategy for Vision-language Models: Building Unique Case-set for Screening Mammograms for Residents Training

Author

Khan, Aisha Urooj, Garrett, John, Bradshaw, Tyler, Salkowski, Lonie, Jeong, Jiwoong Jason, Tariq, Amara, and Banerjee, Imon

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

A visual-language model (VLM) pre-trained on natural images and text pairs poses a significant barrier when applied to medical contexts due to domain shift. Yet, adapting or fine-tuning these VLMs for medical use presents considerable hurdles, including domain misalignment, limited access to extensive datasets, and high-class imbalances. Hence, there is a pressing need for strategies to effectively adapt these VLMs to the medical domain, as such adaptations would prove immensely valuable in healthcare applications. In this study, we propose a framework designed to adeptly tailor VLMs to the medical domain, employing selective sampling and hard-negative mining techniques for enhanced performance in retrieval tasks. We validate the efficacy of our proposed approach by implementing it across two distinct VLMs: the in-domain VLM (MedCLIP) and out-of-domain VLMs (ALBEF). We assess the performance of these models both in their original off-the-shelf state and after undergoing our proposed training strategies, using two extensive datasets containing mammograms and their corresponding reports. Our evaluation spans zero-shot, few-shot, and supervised scenarios. Through our approach, we observe a notable enhancement in Recall@K performance for the image-text retrieval task.

Published
2024

43. Euclid preparation. Observational expectations for redshift z<7 active galactic nuclei in the Euclid Wide and Deep surveys

Author

Euclid Collaboration, Selwood, M., Fotopoulou, S., Bremer, M. N., Bisigello, L., Landt, H., Bañados, E., Zamorani, G., Shankar, F., Stern, D., Lusso, E., Spinoglio, L., Allevato, V., Ricci, F., Feltre, A., Mannucci, F., Salvato, M., Bowler, R. A. A., Mignoli, M., Vergani, D., La Franca, F., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Giocoli, C., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoekstra, H., Holliman, M. S., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Laureijs, R., Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Niemi, S. -M., Nightingale, J. W., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Rix, Hans-Walter, Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Scaramella, R., Schirmer, M., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Surace, C., Tallada-Crespí, P., Tavagnacco, D., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zucca, E., Biviano, A., Bolzonella, M., Bozzo, E., Burigana, C., Colodro-Conde, C., De Lucia, G., Di Ferdinando, D., Vigo, J. A. Escartin, Farinelli, R., George, K., Gracia-Carpio, J., Martinelli, M., Mauri, N., Neissner, C., Sakr, Z., Scottez, V., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Baccigalupi, C., Ballardini, M., Bethermin, M., Blanchard, A., Blot, L., Borgani, S., Bruton, S., Cabanac, R., Calabro, A., Canas-Herrera, G., Cappi, A., Carvalho, C. S., Castignani, G., Castro, T., Chambers, K. C., Contarini, S., Contini, T., Cooray, A. R., Cucciati, O., Davini, S., De Caro, B., Desprez, G., Díaz-Sánchez, A., Di Domizio, S., Dole, H., Escoffier, S., Ferrari, A. G., Ferrero, I., Finelli, F., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gautard, V., Gaztanaga, E., Giacomini, F., Gozaliasl, G., Hall, A., Hildebrandt, H., Hjorth, J., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Legrand, L., Libet, G., Loureiro, A., Macias-Perez, J., Maggio, G., Magliocchetti, M., Maoli, R., Martins, C. J. A. P., Matthew, S., Maurin, L., Metcalf, R. B., Monaco, P., Moretti, C., Morgante, G., Nadathur, S., Nicastro, L., Walton, Nicholas A., Patrizii, L., Pezzotta, A., Pöntinen, M., Popa, V., Porciani, C., Potter, D., Risso, I., Rocci, P. -F., Sahlén, M., Sánchez, A. G., Schneider, A., Sefusatti, E., Sereno, M., Simon, P., Mancini, A. Spurio, Steinwagner, J., Testera, G., Teyssier, R., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Verza, G., Weaver, J. R., and Zinchenko, I. A.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We forecast the expected population of active galactic nuclei (AGN) observable in the Euclid Wide Survey (EWS) and Euclid Deep Survey (EDS). Starting from an X-ray luminosity function (XLF) we generate volume-limited samples of the AGN expected in the survey footprints. Each AGN is assigned an SED appropriate for its X-ray luminosity and redshift, with perturbations sampled from empirical distributions. The photometric detectability of each AGN is assessed via mock observation of the assigned SED. We estimate 40 million AGN will be detectable in at least one band in the EWS and 0.24 million in the EDS, corresponding to surface densities of 2.8$\times$10$^{3}$ deg$^{-2}$ and 4.7$\times$10$^{3}$ deg$^{-2}$. Employing colour selection criteria on our simulated data we select a sample of 4.8$\times$10$^{6}$ (331 deg$^{-2}$) AGN in the EWS and 1.7$\times$10$^{4}$ (346 deg$^{-2}$) in the EDS, amounting to 10% and 8% of the AGN detectable in the EWS and EDS. Including ancillary Rubin/LSST bands improves the completeness and purity of AGN selection. These data roughly double the total number of selected AGN to comprise 21% and 15% of the detectable AGN in the EWS and EDS. The total expected sample of colour-selected AGN contains 6.0$\times$10$^{6}$ (74%) unobscured AGN and 2.1$\times$10$^{6}$ (26%) obscured AGN, covering $0.02 \leq z \lesssim 5.2$ and $43 \leq \log_{10} (L_{bol} / erg s^{-1}) \leq 47$. With this simple colour selection, expected surface densities are already comparable to the yield of modern X-ray and mid-infrared surveys of similar area. The relative uncertainty on our expectation for detectable AGN is 6.7% for the EWS and 12.5% for the EDS, driven by the uncertainty of the XLF., Comment: 36 pages, 21 figures, submitted to A&A

Published
2024

44. Euclid: ERO -- NISP-only sources and the search for luminous $z=6-8$ galaxies

Author

Weaver, J. R., Taamoli, S., McPartland, C. J. R., Zalesky, L., Allen, N., Toft, S., Sanders, D. B., Atek, H., Bowler, R. A. A., Stern, D., Conselice, C. J., Mobasher, B., Szapudi, I., Eisenhardt, P. R. M., Murphree, G., Valdes, I., Ito, K., Belladitta, S., Oesch, P. A., Serjeant, S., Mortlock, D. J., Hatch, N. A., Kluge, M., Milvang-Jensen, B., Rodighiero, G., Bañados, E., Diego, J. M., Gavazzi, R., Congedo, G., Shuntov, M., Dole, H., Rocci, P. -F., Saifollahi, T., Miluzio, M., Ezziati, M., Hughes, A. C. N., Cuillandre, J. -C., Laureijs, R., Paltani, S., Schirmer, M., Stone, C., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoar, J., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Keihänen, E., Kermiche, S., Kiessling, A., Kitching, T., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Masters, D. C., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Moscardini, L., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., 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., Sapone, D., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zucca, E., Burigana, C., Castignani, G., Sakr, Z., Scottez, V., Viel, M., Simon, P., Martín-Fleitas, J., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

This paper presents a search for high redshift galaxies from the Euclid Early Release Observations program "Magnifying Lens." The 1.5 deg$^2$ area covered by the twin Abell lensing cluster fields is comparable in size to the few other deep near-infrared surveys such as COSMOS, and so provides an opportunity to significantly increase known samples of rare UV-bright galaxies at $z\approx6-8$ ($M_{\rm UV}\lesssim-22$). Beyond their still uncertain role in reionisation, these UV-bright galaxies are ideal laboratories from which to study galaxy formation and constrain the bright-end of the UV luminosity function. Of the 501994 sources detected from a combined $Y_{\rm E}$, $J_{\rm E}$, and $H_{\rm E}$ NISP detection image, 168 do not have any appreciable VIS/$I_{\rm E}$ flux. These objects span a range in spectral colours, separated into two classes: 139 extremely red sources; and 29 Lyman-break galaxy candidates. Best-fit redshifts and spectral templates suggest the former is composed of both $z\gtrsim5$ dusty star-forming galaxies and $z\approx1-3$ quiescent systems. The latter is composed of more homogeneous Lyman break galaxies at $z\approx6-8$. In both cases, contamination by L- and T-type dwarfs cannot be ruled out with Euclid images alone. Additional contamination from instrumental persistence is investigated using a novel time series analysis. This work lays the foundation for future searches within the Euclid Deep Fields, where thousands more $z\gtrsim6$ Lyman break systems and extremely red sources will be identified., Comment: 22 pages, 13 figures, paper submitted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

Published
2024

45. Euclid: Early Release Observations -- A preview of the Euclid era through a galaxy cluster magnifying lens

Author

Atek, H., Gavazzi, R., Weaver, J. R., Diego, J. M., Schrabback, T., Hatch, N. A., Aghanim, N., Dole, H., Hartley, W. G., Taamoli, S., Congedo, G., Jimenez-Teja, Y., Cuillandre, J. -C., Bañados, E., Belladitta, S., Bowler, R. A. A., Franco, M., Jauzac, M., Mahler, G., Richard, J., Rocci, P. -F., Serjeant, S., Toft, S., Abriola, D., Bergamini, P., Biviano, A., Dimauro, P., Ezziati, M., Golden-Marx, J. B., Grillo, C., Hughes, A. C. N., Kang, Y., Kneib, J. -P., Lombardi, M., Mamon, G. A., McPartland, C. J. R., Meneghetti, M., Miyatake, H., Montes, M., Mortlock, D. J., Oesch, P. A., Okabe, N., Rosati, P., Taylor, A. N., Tarsitano, F., Weller, J., Kluge, M., Laureijs, R., Paltani, S., Saifollahi, T., Schirmer, M., Stone, C., Mora, A., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Basset, A., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Cardone, V. F., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Galeotta, S., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Hailey, M., Haugan, S. V. H., Hoar, J., Hoekstra, H., Holliman, M. S., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Keihänen, E., Kermiche, S., Kiessling, A., Kitching, T., Kohley, R., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Mei, S., Mellier, Y., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Paech, K., Pasian, F., Peacock, J. A., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Schneider, P., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Tallada-Crespí, P., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Zucca, E., Baccigalupi, C., Burigana, C., Castignani, G., Sakr, Z., Scottez, V., Viel, M., Simon, P., Stern, D., Martín-Fleitas, J., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present the first analysis of the Euclid Early Release Observations (ERO) program that targets fields around two lensing clusters, Abell 2390 and Abell 2764. We use VIS and NISP imaging to produce photometric catalogs for a total of $\sim 500\,000$ objects. The imaging data reach a $5\,\sigma$ typical depth in the range 25.1-25.4 AB in the NISP bands, and 27.1-27.3 AB in the VIS band. Using the Lyman-break method in combination with photometric redshifts, we identify $30$ Lyman-break galaxy (LBG) candidates at $z>6$ and 139 extremely red sources (ERSs), most likely at lower redshift. The deeper VIS imaging compared to NISP means we can routinely identify high-redshift Lyman breaks of the order of $3$ magnitudes, which reduces contamination by brown dwarf stars and low-redshift galaxies. Spectroscopic follow-up campaigns of such bright sources will help constrain both the bright end of the ultraviolet galaxy luminosity function and the quasar luminosity function at $z>6$, and constrain the physical nature of these objects. Additionally, we have performed a combined strong lensing and weak lensing analysis of A2390, and demonstrate how Euclid will contribute to better constraining the virial mass of galaxy clusters. From these data, we also identify optical and near-infrared counterparts of known $z>0.6$ clusters, which exhibit strong lensing features, establishing the ability of Euclid to characterize high-redshift clusters. Finally, we provide a glimpse of Euclid's ability to map the intracluster light out to larger radii than current facilities, enabling a better understanding of the cluster assembly history and mapping of the dark matter distribution. This initial dataset illustrates the diverse spectrum of legacy science that will be enabled by the Euclid survey., Comment: Paper submitted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations. 17 pages, 12 figures

Published
2024

46. Euclid: Early Release Observations -- Dwarf galaxies in the Perseus galaxy cluster

Author

Marleau, F. R., Cuillandre, J. -C., Cantiello, M., Carollo, D., Duc, P. -A., Habas, R., Hunt, L. K., Jablonka, P., Mirabile, M., Mondelin, M., Poulain, M., Saifollahi, T., Sánchez-Janssen, R., Sola, E., Urbano, M., Zöller, R., Bolzonella, M., Lançon, A., Laureijs, R., Marchal, O., Schirmer, M., Stone, C., Boselli, A., Ferré-Mateu, A., Hatch, N. A., Kluge, M., Montes, M., Sorce, J. G., Tortora, C., Venhola, A., Golden-Marx, J. B., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Battaglia, P., Bender, R., Bodendorf, C., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Fotopoulou, S., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Hailey, M., Haugan, S. V. H., Hoar, J., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hu, D., Hudelot, P., Jahnke, K., Jhabvala, M., Keihänen, E., Kermiche, S., Kiessling, A., Kitching, T., Kohley, R., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Rebolo, R., Refregier, A., Renzi, A., Rhodes, J., Riccio, G., Rix, H. -W., Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Scaramella, R., Schneider, P., Secroun, A., Seidel, G., Seiffert, M., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Tsyganov, A., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Williams, O. R., Zamorani, G., Zucca, E., Baccigalupi, C., Biviano, A., Burigana, C., De Lucia, G., George, K., Scottez, V., Viel, M., Simon, P., Mora, A., Martín-Fleitas, J., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We make use of the unprecedented depth, spatial resolution, and field of view of the Euclid Early Release Observations of the Perseus galaxy cluster to detect and characterise the dwarf galaxy population in this massive system. The Euclid high resolution VIS and combined VIS+NIR colour images were visually inspected and dwarf galaxy candidates were identified. Their morphologies, the presence of nuclei, and their globular cluster (GC) richness were visually assessed, complementing an automatic detection of the GC candidates. Structural and photometric parameters, including Euclid filter colours, were extracted from 2-dimensional fitting. Based on this analysis, a total of 1100 dwarf candidates were found across the image, with 638 appearing to be new identifications. The majority (96%) are classified as dwarf ellipticals, 53% are nucleated, 26% are GC-rich, and 6% show disturbed morphologies. A relatively high fraction of galaxies, 8%, are categorised as ultra-diffuse galaxies. The majority of the dwarfs follow the expected scaling relations. Globally, the GC specific frequency, S_N, of the Perseus dwarfs is intermediate between those measured in the Virgo and Coma clusters. While the dwarfs with the largest GC counts are found throughout the Euclid field of view, those located around the east-west strip, where most of the brightest cluster members are found, exhibit larger S_N values, on average. The spatial distribution of the dwarfs, GCs, and intracluster light show a main iso-density/isophotal centre displaced to the west of the bright galaxy light distribution. The ERO imaging of the Perseus cluster demonstrates the unique capability of Euclid to concurrently detect and characterise large samples of dwarfs, their nuclei, and their GC systems, allowing us to construct a detailed picture of the formation and evolution of galaxies over a wide range of mass scales and environments., Comment: 44 pages, 24 figures, 5 tables, paper submitted to A&A as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

Published
2024

47. Euclid: Early Release Observations -- Overview of the Perseus cluster and analysis of its luminosity and stellar mass functions

Author

Cuillandre, J. -C., Bolzonella, M., Boselli, A., Marleau, F. R., Mondelin, M., Sorce, J. G., Stone, C., Buitrago, F., Cantiello, Michele, George, K., Hatch, N. A., Quilley, L., Mannucci, F., Saifollahi, T., Sánchez-Janssen, R., Tarsitano, F., Tortora, C., Xu, X., Bouy, H., Gwyn, S., Kluge, M., Lançon, A., Laureijs, R., Schirmer, M., Abdurro'uf, Awad, P., Baes, M., Bournaud, F., Carollo, D., Codis, S., Conselice, C. J., De Lapparent, V., Duc, P. -A., Ferré-Mateu, A., Gillard, W., Golden-Marx, J. B., Jablonka, P., Habas, R., Hunt, L. K., Mei, S., Miville-Deschênes, M. -A., Montes, M., Nersesian, A., Peletier, R. F., Poulain, M., Scaramella, R., Scialpi, M., Sola, E., Stephan, J., Ulivi, L., Urbano, M., Zöller, R., Aghanim, N., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castander, F. J., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Frailis, M., Franceschi, E., Galeotta, S., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoar, J., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kitching, T., Kohley, R., Kubik, B., Kuijken, K., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Melchior, M., Mellier, Y., Meneghetti, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Moscardini, L., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Raison, F., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Saglia, R., Sapone, D., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Wang, Y., Weller, J., Zucca, E., Biviano, A., Burigana, C., Castignani, G., De Lucia, G., Scottez, V., Mora, A., Simon, P., Martín-Fleitas, J., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

The Euclid ERO programme targeted the Perseus cluster of galaxies, gathering deep data in the central region of the cluster over 0.7 square degree, corresponding to approximately 0.25 r_200. The data set reaches a point-source depth of IE=28.0 (YE, JE, HE = 25.3) AB magnitudes at 5 sigma with a 0.16" and 0.48" FWHM, and a surface brightness limit of 30.1 (29.2) mag per square arcsec. The exceptional depth and spatial resolution of this wide-field multi-band data enable the simultaneous detection and characterisation of both bright and low surface brightness galaxies, along with their globular cluster systems, from the optical to the NIR. This study advances beyond previous analyses of the cluster and enables a range of scientific investigations summarised here. We derive the luminosity and stellar mass functions (LF and SMF) of the Perseus cluster in the Euclid IE band, thanks to supplementary u,g,r,i,z and Halpha data from the CFHT. We adopt a catalogue of 1100 dwarf galaxies, detailed in the corresponding ERO paper. We identify all other sources in the Euclid images and obtain accurate photometric measurements using AutoProf or AstroPhot for 138 bright cluster galaxies, and SourceExtractor for half a million compact sources. Cluster membership for the bright sample is determined by calculating photometric redshifts with Phosphoros. Our LF and SMF are the deepest recorded for the Perseus cluster, highlighting the groundbreaking capabilities of the Euclid telescope. Both the LF and SMF fit a Schechter plus Gaussian model. The LF features a dip at M(IE)=-19 and a faint-end slope of alpha_S = -1.2 to -1.3. The SMF displays a low-mass-end slope of alpha_S = -1.2 to -1.35. These observed slopes are flatter than those predicted for dark matter halos in cosmological simulations, offering significant insights for models of galaxy formation and evolution., Comment: Submitted to A&A, 44 pages, 35 figures, Part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

Published
2024

48. Euclid: Early Release Observations -- Globular clusters in the Fornax galaxy cluster, from dwarf galaxies to the intracluster field

Author

Saifollahi, T., Voggel, K., Lançon, A., Cantiello, Michele, Raj, M. A., Cuillandre, J. -C., Larsen, S. S., Marleau, F. R., Venhola, A., Schirmer, M., Carollo, D., Duc, P. -A., Ferguson, A. M. N., Hunt, L. K., Kümmel, M., Laureijs, R., Marchal, O., Nucita, A. A., Peletier, R. F., Poulain, M., Rejkuba, M., Sánchez-Janssen, R., Urbano, M., Abdurro'uf, Altieri, B., Baes, M., Bolzonella, M., Conselice, C. J., Cote, P., Dimauro, P., Gonzalez, A. H., Habas, R., Hudelot, P., Kluge, M., Lonare, P., Massari, D., Romelli, E., Scaramella, R., Sola, E., Stone, C., Tortora, C., van Mierlo, S. E., Knapen, J. H., Martín-Fleitas, J., Mora, A., Román, J., Aghanim, N., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Basset, A., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conversi, L., Copin, Y., Courbin, F., Courtois, H. M., Cropper, M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Fabricius, M., Farina, M., Farrens, S., Ferriol, S., Fosalba, P., Frailis, M., Franceschi, E., Fumana, M., Galeotta, S., Garilli, B., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Granett, B. R., Grazian, A., Grupp, F., Guzzo, L., Haugan, S. V. H., Hoar, J., Hoekstra, H., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Jahnke, K., Jhabvala, M., Keihänen, E., Kermiche, S., Kiessling, A., Kitching, T., Kohley, R., Kubik, B., Kuijken, K., Kunz, M., Kurki-Suonio, H., Lahav, O., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Melchior, M., Mellier, Y., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Racca, G. D., Raison, F., Rebolo, R., Refregier, A., Renzi, A., Rhodes, J., Riccio, G., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Tallada-Crespí, P., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tsyganov, A., Tutusaus, I., Valentijn, E. A., Valenziano, L., Vassallo, T., Kleijn, G. Verdoes, Veropalumbo, A., Wang, Y., Weller, J., Williams, O. R., Zamorani, G., Zucca, E., Biviano, A., Burigana, C., Scottez, V., Simon, P., Balogh, M., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

We present an analysis of Euclid observations of a 0.5 deg$^2$ field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase. With these data, we investigate the potential of Euclid for identifying GCs at 20 Mpc, and validate the search methods using artificial GCs and known GCs within the field from the literature. Our analysis of artificial GCs injected into the data shows that Euclid's data in $I_{\rm E}$ band is 80% complete at about $I_{\rm E} \sim 26.0$ mag ($M_{V\rm } \sim -5.0$ mag), and resolves GCs as small as $r_{\rm h} = 2.5$ pc. In the $I_{\rm E}$ band, we detect more than 95% of the known GCs from previous spectroscopic surveys and GC candidates of the ACS Fornax Cluster Survey, of which more than 80% are resolved. We identify more than 5000 new GC candidates within the field of view down to $I_{\rm E}$ mag, about 1.5 mag fainter than the typical GC luminosity function turn-over magnitude, and investigate their spatial distribution within the intracluster field. We then focus on the GC candidates around dwarf galaxies and investigate their numbers, stacked luminosity distribution and stacked radial distribution. While the overall GC properties are consistent with those in the literature, an interesting over-representation of relatively bright candidates is found within a small number of relatively GC-rich dwarf galaxies. Our work confirms the capabilities of Euclid data in detecting GCs and separating them from foreground and background contaminants at a distance of 20 Mpc, particularly for low-GC count systems such as dwarf galaxies., Comment: Paper submitted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

Published
2024

49. Euclid: Early Release Observations -- Unveiling the morphology of two Milky Way globular clusters out to their periphery

Author

Massari, D., Dalessandro, E., Erkal, D., Balbinot, E., Bovy, J., McDonald, I., Ferguson, A. M. N., Larsen, S. S., Lançon, A., Annibali, F., Goldman, B., Kuzma, P. B., Voggel, K., Saifollahi, T., Cuillandre, J. -C., Schirmer, M., Kluge, M., Altieri, B., Amara, A., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Basset, A., Bender, R., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Candini, G. P., Capobianco, V., Carbone, C., Carlberg, R. G., Carretero, J., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Congedo, G., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Degaudenzi, H., Dinis, J., Dubath, F., Dupac, X., Dusini, S., Fabricius, M., Farina, M., Farrens, S., Ferriol, S., Frailis, M., Franceschi, E., Garilli, B., Gillis, B., Giocoli, C., Grazian, A., Guzzo, L., Hoar, J., Hoekstra, H., Holliman, M. S., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Keihänen, E., Kermiche, S., Kiessling, A., Kitching, T., Kohley, R., 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., Martinet, N., Marulli, F., Massey, R., Maurogordato, S., Medinaceli, E., Mei, S., Mellier, Y., Meneghetti, M., Meylan, G., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Paltani, S., Pasian, F., Pedersen, K., Percival, W. J., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Pozzetti, L., Racca, G. D., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Rix, Hans-Walter, Romelli, E., Roncarelli, M., Rossetti, E., Saglia, R., Sapone, D., Sartoris, B., Schneider, P., Schrabback, T., Secroun, A., Seidel, G., Seiffert, M., Serrano, S., Sirignano, C., Sirri, G., Skottfelt, J., Stanco, L., Tallada-Crespí, P., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tutusaus, I., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zoubian, J., Zucca, E., Bolzonella, M., Burigana, C., Morris, P. W., Scottez, V., Simon, P., Martín-Fleitas, J., and Scott, D.

Subjects
Astrophysics - Astrophysics of Galaxies
Abstract

As part of the Euclid Early Release Observations (ERO) programme, we analyse deep, wide-field imaging from the VIS and NISP instruments of two Milky Way globular clusters (GCs), namely NGC 6254 (M10) and NGC 6397, to look for observational evidence of their dynamical interaction with the Milky Way. We search for such an interaction in the form of structural and morphological features in the clusters' outermost regions, which are suggestive of the development of tidal tails on scales larger than those sampled by the ERO programme. Our multi-band photometric analysis results in deep and well-behaved colour-magnitude diagrams that, in turn, enable an accurate membership selection. The surface brightness profiles built from these samples of member stars are the deepest ever obtained for these two Milky Way GCs, reaching down to $\sim30.0$ mag~arcsec$^{-2}$, which is about $1.5$ mag arcsec$^{-2}$ below the current limit. The investigation of the two-dimensional density map of NGC 6254 reveals an elongated morphology of the cluster peripheries in the direction and with the amplitude predicted by $N$-body simulations of the cluster's dynamical evolution, at high statistical significance. We interpret this as strong evidence for the first detection of tidally induced morphological distortion around this cluster. The density map of NGC 6397 reveals a slightly elliptical morphology, in agreement with previous studies, which requires further investigation on larger scales to be properly interpreted. This ERO project thus demonstrates the power of Euclid in studying the outer regions of GCs at an unprecedented level of detail, thanks to the combination of large field of view, high spatial resolution, and depth enabled by the telescope. Our results highlight the future Euclid survey as the ideal data set to investigate GC tidal tails and stellar streams., Comment: 15 pages, 18 figures. Paper accepted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

Published
2024

50. Euclid. V. The Flagship galaxy mock catalogue: a comprehensive simulation for the Euclid mission

Author

Euclid Collaboration, Castander, F. J., Fosalba, P., Stadel, J., Potter, D., Carretero, J., Tallada-Crespí, P., Pozzetti, L., Bolzonella, M., Mamon, G. A., Blot, L., Hoffmann, K., Huertas-Company, M., Monaco, P., Gonzalez, E. J., De Lucia, G., Scarlata, C., Breton, M. -A., Linke, L., Viglione, C., Li, S. -S., Zhai, Z., Baghkhani, Z., Pardede, K., Neissner, C., Teyssier, R., Crocce, M., Tutusaus, I., Miller, L., Congedo, G., Biviano, A., Hirschmann, M., Pezzotta, A., Aussel, H., Hoekstra, H., Kitching, T., Percival, W. J., Guzzo, L., Mellier, Y., Oesch, P. A., Bowler, R. A. A., Bruton, S., Allevato, V., Gonzalez-Perez, V., Manera, M., Avila, S., Kovács, A., Aghanim, N., Altieri, B., Amara, A., Amendola, L., Andreon, S., Auricchio, N., Baldi, M., Balestra, A., Bardelli, S., Bender, R., Bodendorf, C., Bonino, D., Branchini, E., Brescia, M., Brinchmann, J., Camera, S., Capobianco, V., Carbone, C., Casas, S., Castellano, M., Cavuoti, S., Cimatti, A., Conselice, C. J., Conversi, L., Copin, Y., Corcione, L., Courbin, F., Courtois, H. M., Da Silva, A., Degaudenzi, H., Di Giorgio, A. M., Dinis, J., Douspis, M., Dubath, F., Duncan, C. A. J., Dupac, X., Dusini, S., Ealet, A., Farina, M., Farrens, S., Ferriol, S., Fotopoulou, S., Fourmanoit, N., Frailis, M., Franceschi, E., Franzetti, P., Galeotta, S., Gillard, W., Gillis, B., Giocoli, C., Gómez-Alvarez, P., Granett, B. R., Grazian, A., Grupp, F., Haugan, S. V. H., Holliman, M. S., Holmes, W., Hook, I., Hormuth, F., Hornstrup, A., Hudelot, P., Jahnke, K., Jhabvala, M., Joachimi, B., Keihänen, E., Kermiche, S., Kiessling, A., Kilbinger, M., Kohley, R., Kubik, B., Kümmel, M., Kunz, M., Kurki-Suonio, H., Lahav, O., Laureijs, R., Mignant, D. Le, Ligori, S., Lilje, P. B., Lindholm, V., Lloro, I., Maino, D., Maiorano, E., Mansutti, O., Marggraf, O., Markovic, K., Martinet, N., Marulli, F., Massey, R., Masters, D. C., Maurogordato, S., McCracken, H. J., Medinaceli, E., Mei, S., Melchior, M., Meneghetti, M., Merlin, E., Meylan, G., Mohr, J. J., Moresco, M., Moscardini, L., Munari, E., Nakajima, R., Nichol, R. C., Niemi, S. -M., Padilla, C., Paech, K., Paltani, S., Pasian, F., Peacock, J. A., Pedersen, K., Pettorino, V., Pires, S., Polenta, G., Poncet, M., Popa, L. A., Raison, F., Rebolo, R., Renzi, A., Rhodes, J., Riccio, G., Romelli, E., Roncarelli, M., Rosset, C., Rossetti, E., Saglia, R., Sapone, D., Schirmer, M., Schneider, P., Schrabback, T., Scodeggio, M., Secroun, A., Seidel, G., Serrano, S., Sirignano, C., Sirri, G., Stanco, L., Starck, J. -L., Taylor, A. N., Teplitz, H. I., Tereno, I., Toledo-Moreo, R., Torradeflot, F., Tsyganov, A., Valenziano, L., Vassallo, T., Veropalumbo, A., Wang, Y., Weller, J., Zacchei, A., Zamorani, G., Zerbi, F. M., Zoubian, J., Zucca, E., Baccigalupi, C., Bernardeau, F., Boucaud, A., Bozzo, E., Burigana, C., Calabrese, M., Casenove, P., Castignani, G., Colodro-Conde, C., Di Ferdinando, D., Vigo, J. A. Escartin, Fabbian, G., Finelli, F., Gracia-Carpio, J., Ilić, S., Liebing, P., Marcin, S., Martinelli, M., Matthew, S., Mauri, N., Pöntinen, M., Porciani, C., Sakr, Z., Scottez, V., Sefusatti, E., Steinwagner, J., Tenti, M., Viel, M., Wiesmann, M., Akrami, Y., Anselmi, S., Archidiacono, M., Atrio-Barandela, F., Aubourg, E., Balaguera-Antolinez, A., Ballardini, M., Bertacca, D., Bethermin, M., Blanchard, A., Böhringer, H., Borgani, S., Bouvard, T., Cabanac, R., Calabro, A., Quevedo, B. Camacho, Canas-Herrera, G., Cappi, A., Caro, F., Carvalho, C. S., Castro, T., Chambers, K. C., Contarini, S., Contini, T., Cooray, A. R., Costanzi, M., Cucciati, O., Davini, S., De Caro, B., de la Torre, S., Desprez, G., Díaz-Sánchez, A., Diaz, J. J., Di Domizio, S., Dole, H., Escoffier, S., Ezziati, M., Ferrari, A. G., Ferreira, P. G., Ferrero, I., Finoguenov, A., Fontana, A., Fornari, F., Gabarra, L., Ganga, K., García-Bellido, J., Gasparetto, T., Gaztanaga, E., Giacomini, F., Gianotti, F., Gonzalez, A. H., Gozaliasl, G., Hall, A., Hartley, W. G., Hildebrandt, H., Hjorth, J., Holland, A. D., Ilbert, O., Joudaki, S., Jullo, E., Kajava, J. J. E., Kansal, V., Karagiannis, D., Kirkpatrick, C. C., Graet, J. Le, Legrand, L., Lesgourgues, J., Liaudat, T. I., Loureiro, A., Macias-Perez, J., Magliocchetti, M., Mancini, C., Mannucci, F., Maoli, R., Martins, C. J. A. P., Maurin, L., Metcalf, R. B., Migliaccio, M., Miluzio, M., Mora, A., Moretti, C., Morgante, G., Nadathur, S., Nicastro, L., Walton, Nicholas A., Oguri, M., Patrizii, L., Popa, V., Pourtsidou, A., Reimberg, P., Risso, I., Rocci, P. -F., Rollins, R. P., Rusholme, B., Sahlén, M., Sánchez, A. G., Schaye, J., Schewtschenko, J. A., Schneider, A., Schultheis, M., Sereno, M., Shankar, F., Shulevski, A., Silvestri, A., Simon, P., Mancini, A. Spurio, Stanford, S. A., Tanidis, K., Tao, C., Tessore, N., Testera, G., Tewes, M., Toft, S., Tosi, S., Troja, A., Tucci, M., Valieri, C., Valiviita, J., Vergani, D., Vernizzi, F., Verza, G., Vielzeuf, P., Weaver, J. R., Zalesky, L., Dimauro, P., Duc, P. -A., Fang, Y., Ferguson, A. M. N., Gutierrez, C. M., Kova{č}ić, I., Kruk, S., Brun, A. M. C. Le, Montoro, A., Murray, C., Pagano, L., Paoletti, D., Sarpa, E., Viitanen, A., Martín-Fleitas, J., and Yung, L. Y. A.

Subjects
Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

We present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of dark matter and dark energy on the largest scales of the Universe. It probes structure formation over more than 10 billion years primarily from the combination of weak gravitational lensing and galaxy clustering data. The breath of Euclid's data will also foster a wide variety of scientific analyses. The Flagship simulation was developed to provide a realistic approximation to the galaxies that will be observed by Euclid and used in its scientific analyses. We ran a state-of-the-art N-body simulation with four trillion particles, producing a lightcone on the fly. From the dark matter particles, we produced a catalogue of 16 billion haloes in one octant of the sky in the lightcone up to redshift z=3. We then populated these haloes with mock galaxies using a halo occupation distribution and abundance matching approach, calibrating the free parameters of the galaxy mock against observed correlations and other basic galaxy properties. Modelled galaxy properties include luminosity and flux in several bands, redshifts, positions and velocities, spectral energy distributions, shapes and sizes, stellar masses, star formation rates, metallicities, emission line fluxes, and lensing properties. We selected a final sample of 3.4 billion galaxies with a magnitude cut of H_E<26, where we are complete. We have performed a comprehensive set of validation tests to check the similarity to observational data and theoretical models. In particular, our catalogue is able to closely reproduce the main characteristics of the weak lensing and galaxy clustering samples to be used in the mission's main cosmological analysis. (abridged), Comment: Paper submitted as part of the A&A special issue `Euclid on Sky', which contains Euclid key reference papers and first results from the Euclid Early Release Observations

Published
2024

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