Optimising image capture for low-light widefield quantitative fluorescence microscopy

Autor: Peterkovic, Zane, Upadhya, Avinash, Perrella, Christopher, Bajraktarevic, Admir, Gonzalez, Ramses Bautista, Lim, Megan, Dunning, Kylie R, Dholakia, Kishan

Low-light optical imaging refers to the use of cameras to capture images with minimal photon flux. This area has broad application to diverse fields, including optical microscopy for biological studies. In such studies, it is important to reduce the

Externí odkaz: http://arxiv.org/abs/2410.19210

A Framework for Automatic Validation and Application of Lossy Data Compression in Ensemble Data Assimilation

Autor: Keller, Kai, Yashiro, Hisashi, Wahib, Mohamed, Gerofi, Balazs, Kestelman, Adrian Cristal, Bautista-Gomez, Leonardo

Ensemble data assimilation techniques form an indispensable part of numerical weather prediction. As the ensemble size grows and model resolution increases, the amount of required storage becomes a major issue. Data compression schemes may come to th

Externí odkaz: http://arxiv.org/abs/2410.03184

Standardised formats and open-source analysis tools for the MAGIC telescopes data

Autor: Abe, S., Abhir, J., Abhishek, A., Acciari, V. A., Aguasca-Cabot, A., Agudo, I., Aniello, T., Ansoldi, S., Antonelli, L. A., Engels, A. Arbet, Arcaro, C., Artero, M., Asano, K., Babić, A., de Almeida, U. Barres, Barrio, J. A., Batković, I., Bautista, A., Baxter, J., González, J. Becerra, Bednarek, W., Bernardini, E., Bernete, J., Berti, A., Besenrieder, J., Bigongiari, C., Biland, A., Blanch, O., Bonnoli, G., Bošnjak, Ž., Bronzini, E., Burelli, I., Busetto, G., Campoy-Ordaz, A., Carosi, A., Carosi, R., Carretero-Castrillo, M., Castro-Tirado, A. J., Cerasole, D., Ceribella, G., Chai, Y., Cifuentes, A., Colombo, E., Contreras, J. L., Cortina, J., Covino, S., D'Amico, G., D'Elia, V., Da Vela, P., Dazzi, F., De Angelis, A., De Lotto, B., de Menezes, R., Delfino, M., Delgado, J., Di Pierro, F., Di Tria, R., Di Venere, L., Prester, D. Dominis, Donini, A., Dorner, D., Doro, M., Elsaesser, D., Escudero, J., Fariña, L., Fattorini, A., Foffano, L., Font, L., Fröse, S., Fukami, S., Fukazawa, Y., López, R. J. García, Garczarczyk, M., Gasparyan, S., Gaug, M., Paiva, J. G. Giesbrecht, Giglietto, N., Giordano, F., Gliwny, P., Gradetzke, T., Grau, R., Green, D., Green, J. G., Günther, P., Hadasch, D., Hahn, A., Hassan, T., Heckmann, L., Llorente, J. Herrera, Hrupec, D., Hütten, M., Imazawa, R., Ishio, K., Martínez, I. Jiménez, Jormanainen, J., Kayanoki, T., Kerszberg, D., Kluge, G. W., Kobayashi, Y., Kouch, P. M., Kubo, H., Kushida, J., Láinez, M., Lamastra, A., Leone, F., Lindfors, E., Lombardi, S., Longo, F., López-Coto, R., López-Moya, M., López-Oramas, A., Loporchio, S., Lorini, A., Lyard, E., Fraga, B. Machado de Oliveira, Majumdar, P., Makariev, M., Maneva, G., Manganaro, M., Mangano, S., Mannheim, K., Mariotti, M., Martínez, M., Martínez-Chicharro, M., Mas-Aguilar, A., Mazin, D., Menchiari, S., Mender, S., Miceli, D., Miener, T., Miranda, J. M., Mirzoyan, R., González, M. Molero, Molina, E., Mondal, H. A., Moralejo, A., Morcuende, D., Nakamori, T., Nanci, C., Neustroev, V., Nickel, L., Rosillo, M. Nievas, Nigro, C., Nikolić, L., Nishijima, K., Ekoume, T. Njoh, Noda, K., Nozaki, S., Ohtani, Y., Okumura, A., Otero-Santos, J., Paiano, S., Paneque, D., Paoletti, R., Paredes, J. M., Peresano, M., Persic, M., Pihet, M., Pirola, G., Podobnik, F., Moroni, P. G. Prada, Prandini, E., Principe, G., Priyadarshi, C., Rhode, W., Ribó, M., Rico, J., Righi, C., Sahakyan, N., Saito, T., Saturni, F. G., Schmidt, K., Schmuckermaier, F., Schubert, J. L., Schweizer, T., Sciaccaluga, A., Silvestri, G., Sitarek, J., Sliusar, V., Sobczynska, D., Spolon, A., Stamerra, A., Strišković, J., Strom, D., Strzys, M., Suda, Y., Suutarinen, S., Tajima, H., Takahashi, M., Takeishi, R., Temnikov, P., Terauchi, K., Terzić, T., Teshima, M., Truzzi, S., Tutone, A., Ubach, S., van Scherpenberg, J., Acosta, M. Vazquez, Ventura, S., Viale, I., Vigorito, C. F., Vitale, V., Vovk, I., Walter, R., Will, M., Wunderlich, C., Yamamoto, T., Jouvin, L., Linhoff, L., Linhoff, M.

Instruments for gamma-ray astronomy at Very High Energies ($E>100\,{\rm GeV}$) have traditionally derived their scientific results through proprietary data and software. Data standardisation has become a prominent issue in this field both as a requir

Externí odkaz: http://arxiv.org/abs/2409.18823

ZTF SN Ia DR2: Overview

Autor: Rigault, Mickael, Smith, Mathew, Goobar, Ariel, Maguire, Kate, Dimitriadis, Georgios, Burgaz, Umut, Dhawan, Suhail, Sollerman, Jesper, Regnault, Nicolas, Kowalski, Marek, Amenouche, Melissa, Aubert, Marie, Barjou-Delayre, Chloé, Bautista, Julian, Bloom, Josh S., Carreres, Bastien, Chen, Tracy X., Copin, Yannick, Deckers, Maxime, Fouchez, Dominique, Fremling, Christoffer, Galbany, Lluis, Ginolin, Madeleine, Graham, Matthew, Kasliwal, Mancy M., Kenworthy, W. D'Arcy, Kim, Young-Lo, Kuhn, Dylan, Masci, Frank F., Müller-Bravo, Tomas, Miller, Adam, Johansson, Joel, Nordin, Jakob, Nugent, Peter, Andreoni, Igor, Bellm, Eric, Betoule, Marc, Osman, Mahmoud, Perley, Dan, Popovic, Brodie, Rosnet, Philippe, Rosselli, Damiano, Ruppin, Florian, Senzel, Robert, Rusholme, Ben, Schweyer, Tassilo, Terwel, Jacco H., Townsend, Alice, Tzanidakis, Andy, Wold, Avery, Purdum, Josiah, Qin, Yu-Jing, Racine, Benjamin, Reusch, Simeon, Riddle, Reed, Yan, Lin

We present the first homogeneous release of several thousand Type Ia supernovae (SNe Ia), all having spectroscopic classification, and spectroscopic redshifts for half the sample. This release, named the "DR2", contains 3628 nearby (z < 0.3) SNe Ia d

Externí odkaz: http://arxiv.org/abs/2409.04346

Causal machine learning for sustainable agroecosystems

Autor: Sitokonstantinou, Vasileios, Porras, Emiliano Díaz Salas, Bautista, Jordi Cerdà, Piles, Maria, Athanasiadis, Ioannis, Kerner, Hannah, Martini, Giulia, Sweet, Lily-belle, Tsoumas, Ilias, Zscheischler, Jakob, Camps-Valls, Gustau

In a changing climate, sustainable agriculture is essential for food security and environmental health. However, it is challenging to understand the complex interactions among its biophysical, social, and economic components. Predictive machine learn

Externí odkaz: http://arxiv.org/abs/2408.13155