Search

Your search keyword '"Modi, Chirag"' showing total 496 results
Start Over Author "Modi, Chirag"

Refine your results

more »

more »

more »

more »
496 results on '"Modi, Chirag"'

102. Reconstruction of Cosmological Fields in Forward Model Framework

Author

Modi, Chirag

Subjects
Physics, Astrophysics, Cosmology, Forward Models, Large Scale Structures, Machine Learning, Optimization, Reconstruction
Abstract

The large scale structures (LSS) of the Universe contain a vast amount of information about the birth, evolution and composition of our Universe. To mine this information over the next decade, large scale imaging surveys such as the Dark Energy Spectroscopic Survey (DESI), Large Synoptic Survey Telescope (LSST), Euclid and WFIRST will probe the Universe with unprecedented precision, and on the largest scales. This provides an opportunity to shed light on the longstanding mysteries regarding the true nature of dark matter and dark energy, as well as to resolve tensions between different probes of the past decade. However to utilize the full potential these surveys which are no longer statistically limited, it is critical to develop analytic methods that extract the maximum amount of information across all scales. In this regard, the massive data volumes, the non-linearity, and non-Gaussianity of the signal in the regimes probed by these surveys will pose outstanding challenges.The focus of this thesis is to tackle aforementioned challenges with a novel framework for the analysis of the large scale structures by reconstructing cosmological fields using forward models. These forward modeling approaches are the most promising way to jointly model different cosmological observations with requisite accuracy at all scales, while accounting for their individual systematic biases and noise. We approach reconstruction within a Bayesian formalism - by optimizing the likelihood of the observed data and maximizing the corresponding posterior of initial conditions. This requires solving optimization problems in multi-million dimensional space and we develop novel differentiable forward models as well as adiabatic optimization algorithms to assist with this.We begin by using this approach to reconstruct the initial density field from a continuous dark matter field, as well as discrete observables like dark matter halos. We also construct a power spectrum estimator for this reconstructed Gaussian density field, which is the summarystatistic for optimal analysis. With 21-cm intensity mapping, we show how this framework can instead be used for secondary analysis such as de-noising the observed data and recovering lost cosmological information. For this, we also introduce Hidden Valley simulations- a suite of high resolution N-Body simulations and develop models to simulate HI clustering. Having demonstrated the efficacy of this approach, we finally introduce FlowPM - a GPU-accelerated, distributed, and differentiable N-Body solver in TensorFlow that naturallyinterfaces with the most advanced machine learning tools. This will enable the community to efficiently solve large scale cosmological inference problems and develop differentiable forward models for the reconstruction of cosmological fields with the next generation of LSS surveys.

Published
2020

103. International Nosocomial Infection Control Consortium report, data summary of 50 countries for 2010-2015: Device-associated module

Author

Desse, J.E., Maurizi, D.M., Montanini, A.M., Chaparro, G.J., Stagnaro, J.P., Romani, A., Bianchi, A.C., Álvarez, G., Palaoro, A., Bernan, M., Cabrera-Montesino, R., Domínguez, C., Rodríguez, C.G., Silva, C.G., Bogdanowicz, E., Riera, F.O., Benchetrit, G., Perez, I., Vimercati, J., Marcos, L.S., Ramasco, L., Caridi, M., Oyola, M.C., Rodríguez, M.C., Spadaro, M.L., Olivieri, M.S., Saul, P., Juarez, P.D., Pérez, R.H., Botta, P., Quintana, D.Q., Ríos, A.M., Chediack, V., Chilon, W., Alsayegh, Ameena Isa, Yaseen, Fatema Hasan, Hani, Lateefa Fardan, Sowar, Saleh Fakher, Magray, Tahira Anwar, Medeiros, E.A., Alves De Oliveira, A., Romario-Mendes, A., Fernandes-Valente, C., Santos, C., Escudeiro, D., Azevedo-Ferreira Lima, D., Azevedo-Pereira, D., Onzi-Siliprandi, E.M., Serpa-Maia, F., Aguiar-Leitao, F., Assuncao-Ponte, G., Dos Anjos-Lima, J., Olszewski, J., Harten Pinto Coelho, K., Alves De Lima, L.F., Mendonca, M., Maciel-Canuto Amaral, M.L., Tenorio, M.T., Gerah, S., Andrade-Oliveira-Reis, M., Moreira, M., Ximenes-Rocha Batista, M., Campos-Uchoa, R.S., Rocha-Vasconcelos Carneiro, R., Amaral De Moraes, R., Do Nascimento, S.C., Moreira-Matos, T., Lima-De Barros Araujo, T.M., De Jesus Pinheiro-Bandeira, T., Machado-Silva, V.L., Santos Monteiro, W.M., Hristozova, E., Kostadinov, E.D., Angelova, K., Velinova, V.A., Dicheva, V.J., Guo, X., Ye, G., Li, R., Song, L., Liu, K., Liu, T., Song, G., Wang, C., Yang, X., Yu, H., Yang, Y., Martínez, A., Vargas-García, A.R., Lagares-Guzmán, A., González, A.P., Linares, C., Ávila-Acosta, C., Santofimio, D., Yepes-Gomez, D., Marin-Tobar, D.A., Mazo-Elorza, D.P., Chapeta-Parada, E.G., Camacho-Moreno, G., Roncancio-Vill, G.E., Valderrama-Marquez, I.A., Ruiz-Gallardo, J.E., Ospina-Martínez, J.O., Osorio, J., Marín-Uribe, J.I., López, J.C., Gualtero, S., Rojas, J.R., Gomez-Nieto, K., Rincon, L.Y.M., Meneses-Ovallos, L., Canas-Giraldo, L.M., Burgos-Florez, L.D., Amaral-Almeida Costa, M., Rodriguez, M., Barahona-Guzmán, N., Mancera-Paez, O., Rios-Arana, P.A., Ortega, R., Romero-Torres, S.L., Pulido-Leon, S.M., Valderrama, S., Moreno-Mejia, V.M., Raigoza-Martinez, W., Villamil-Gomez, W., Pardo-Lopez, Y.A., Argüello-Ruiz, A., Solano-Chinchilla, A., Muñoz-Gutierrez, G.A., Calvo-Hernández, I., Maroto-Vargas, L., Zuniga, M.A., Valverde-Hernandez, M., Chavarria-Ugalde, O., Herrera, B., Díaz, C., Bovera, M.M., Cevallos, C., Pelaez, C., Jara, E., Delgado, V., Coello-Gordon, E.E., Picoita, F., Guerrero-Toapant, F.M., Valencia, F., Santacruz, G., Gonzalez, H., Pazmino, L.N., Garcia, M.F., Arboleda, M., Lascano, M., Alquinga, N., Ramírez, V., Yousef, Reham H.A., Moustafa, Abd El Moniem, Ahmed, A., Elansary, A.M., Ali, Ahmad Mahmoud, Hasanin, Ahmed, Messih, Antoine Abdel, Ramadan, Arwa, El Awady, B.A., Hassan, D.M., Abd El Aziz, Doaa, Hamza, Hala, Agha, Hala Mounir, Ghazi, Islam Abdullorziz, ElKholy, J., Fattah, May Abdel, Elanany, Mervat, Mansour, M., Haleim, M.M.A., Fouda, R., El-Sherif, Rasha Hamed, Bekeit, S., Bayani, V., Elkholy, Y.S., Abdelhamid, Y.M., Salah, Zeinab, Rivera, D.M., Chawla, A., Manked, A.N., Azim, A., Mubarak, A., Thakur, A., Dharan, A.V., Patil, A., Sasidharan, A., Bilolikar, Anil Kumar, Anirban Karmakar, A., Mathew, A.M., Kulkarni, Anuja, Agarwal, Anuradha, Sriram, Anuradha, Dwivedy, A., Dasgupta, Arnab, Bhakta, A., Suganya, Arul Rose, Poojary, A., Mani, Ashwin Kumar, Sakle, Asmita, Abraham, Babu K., Padmini, Baby, Ramachandran, B., Ray, Banambar, Pati, Basanta Kumar, Chaudhury, Bhaskar Narayan, Mishra, Biraj Mohan, Biswas, S., Saibala, M. Bri, Jawadwala, Burhan Q., Rodrigues, C., Modi, Chirag, Patel, Chirag, Khanna, D.K., Devaprasad, Dedeepiya, Divekar, Deepa, Aggarwal, Deepesh G., Divatia, J.V., Zala, Dolatsinh, Pathrose, Edwin, Abubakar, Fazil, Chacko, Felcy, Gehlot, G.S., Khanna, Gautam, Sale, H.K., Roy, Indranil, Shelgaonkar, Jayant, Sorabjee, Jehangir, Eappen, Jincy, Mathew, Justin, Pal, Jyotishka, Varma, Karthikeya, Joshi, Kashmira Limaye, Sandhu, Kavita, Kelkar, R., Ranganathan, Lakshmi, Pushparaj, L., Lavate, Madhav, Latha, Madhavi, Suryawanshi, Madhupriya, Bhattacharyya, M., Kavathekar, Maithili, Agarwal, Manoj Kumar, Patel, Mayur, Shah, Mehul, Sivakumar, M.N., Kharbanda, Mohit, Bej, Mrinmoy, Potdar, Mrunalini, Chakravarthy, M., Karpagam, M., Myatra, S.N., Gita, N., Rao, N.P., Sen, N., Ramakrishnan, N., Jaggi, N., Saini, N., Pawar, N.K., Modi, Nikhil, Pandya, Nirav, Mohanty, Nisith, Thakkar, Pooja, Joshi, Pradnya, Sahoo, Prafulla Kumar, Nair, Pravin Kumar, Kumar, Priyadarshini Senthil, Patil, Priyanka, Mukherjee, Purnima, Mathur, P., Shah, Purvi, Sukanya, R., Arjun, Rajalakshmi, Chawla, Rajesh, Gopalakrishnan, Ram, Venkataraman, Ramesh, Raut, S., Krupanandan, Ravikumar, Tejam, Reshma, Misra, Richa, Debroy, Ritesh, Saranya, S., Narayanan, Sajith, Mishra, Sanghamitra, Saseedharan, Sanjith, Sengupta, Sankar, Patnaik, S.K., Sinha, Saswati, Blessymole, Seelas, Rohra, Seema, Rajagopal, Senthilkumar, Mukherjee, Shanta, Sengupta, Sharmila, John, Sheeba, Bhattacharya, Shefali, Sijo, Bhattacharyya, Sinchan, Singh, S., Sohanlal, T., Vadi, Sonali, Dalal, S.S., Todi, S.K., Kumar, Subodh, Kansal, Sudha, Misra, Sudhjan, Bhattacharyya, Sudipta, Nirkhiwale, Suhas, Purkayastha, Sujit Kar, Mukherjee, Sujoy, Sahu, S., Sharma, Sunil, Kumar, Suresh, Basu, S., Shetty, S., Shah, S., Singhal, T., Francis, Thara, Anand, Thejasvini, Venkateshwar, V., Thomas, Valsa, Kothari, V., Velupandi, Kantroo, Vini, Sitohang, Gortap, Kadarsih, Retno, Sanaei, Anahita, Maghsudi, Behzad, Sabetian, Golnar, Masjedi, Mansoor, Alebouyeh, Masoud, Sherafat, Somayeh Jahani, Mohamed, Yassir Khidir, Al Khamis, Abdulrahim, Alsaadi, Adnan Soliman, Al-Jarie, Ahmed Abdoh, Mutwalli, Aisha Hassan, Rillorta, A.S.K., Thomas, Anumol, Kelany, Ashraf, Manao, Athena, Alamri, Dhaffer Moghram, Santiago, E.B., Cruzpero, E.P., Sawan, Fahad Ahmed, Al Qasmah, Fatima Abdullah, Alabdaly, Haeel, Al-Dossary, Hajer Am, Ahmed, Hala, Roshdi, Hala, Al-Alkami, Halima Yahia, Hanafi, Hana, Ammari, Hassan Eisa, Hani, Hatem Mohd Al, Asiri, Ibrahim A.M. Alzaydani, Mendoza, Jeffrey Asidera, Philipose, Jomol, Selga, Joy Ola, Kehkashan, Ghalilah, Khalid Mohammed, Redito, Leide Shyrine, Josph, L., Al-Alawi, M., Al-Gethamy, Manal Mansour, Madco, Marjory, Manuel, M.G., Girvan, M., Aldalaton, Mervat, De Guzman, M., Alkhamaly, Modhi, Masfar, Mohammad, Karrar, Mohammed Ahmed Ali, Al Azmi, Munayfa Musallam, Quisai, Myrna Lim, Torres, Myrna Misador, Al-Abdullah, N., Tawfic, Nader Ahmed, Elsayed, Nashwa, Abdulkhalik, Nawal Salah, Bugis, Nehad Abdulshakour, Ariola, Neilia Cardinal, Gad, N., Alghosn, Nesreen, Tashkandi, Nidal, Zharani, N. Al, De Vera, P.A., Krishnan, Radhika, Al Shehri, Raed Hassan, Jaha, Rasha Nour Aldeen, Thomas, Reny, Cresencia, Rhea Lynn, Penuliar, R., Lozada, R.V., Al Qahtani, S., Twfik, Sameh, Al Faraj, Sarah Hassan, El-Sherbiny, Sherin, Alih, Siti Jhulmina B., Briones, Solita, Bukhari, Syed Zahid, Alotaibi, Tariq Saad Abdulhadi, Gopal, Uma, Nair, Usha, Abdulatif, Wael Abdulrahman, Hussain, Waleed Mohd, Demotica, Wynndale Mae, Spahija, G., Baftiu, N., Gashi, A., Omar, Abeer A., Mohamed, A.M., Rebello, F.M., Almousa, Haifaa Hassan, Abdo, Naglaa Mahmoud, George, S.M., Khamis, Soad, Thomas, Suja, Ahmad Zaatari, A., Anwar Al Souheil, A., Ayash, H., Zeid, I., Tannous, J., Zahreddine, N.K., Ahmadieh, R., Mahfouz, T., Kardas, T., Tanzi, V., Kanafani, Z., Hammoud, Z., Dagys, A., Grinkeviciute, D., Kevalas, R., Kondratas, T., Petrovska, Milena, Popovska, Katja, Mitrev, Zan, Miteva, Zaneta Bogoevska, Jankovska, Katerina, Guroska, Snezana Tufekcievska, Gan, Chin Seng, Othman, Ainul Auzani, Yusof, Aliza Mohamad, Abidin, Anis Siham Zainal, Aziz, Fadzlina Abdul, Weng, Foong Kit, Zainol, Hasimah, Bakar, Kamal Bashar Abu, Lum, Lucy Chai See, Mansor, Marzida, Zaman, Mazuin Kamarul, Jamaluddin, Mohamad Fadhil Hadi, Hasan, Mohd Shahnaz, Rahman, Raha Abdul, Zaini, Rhendra Hardy Mohamad, Zhazali, Rosliza, Sri Ponnampala, Sasheela Sri La, Chuah, Soo Lin, Shukeri, Wan Fadzlina Wan Mohd, Hassan, Wan Nazaruddin Wan, Yusoff, Wan Nurbayah Wan, Mat, Wan Rahiza Wan, Cureno-Diaz, M.A., Aguirre-Avalos, G., Flores-Alvarado, A., Cerero-Gudino, A., Zamores-Pedroza, A., Cano-Munoz, B., Hernandez-Chena, B.E., Carreon-Martinez, C.C., Coronado-Magana, H., Corona-Jimenez, F., Rodriguez-Noriega, E., Alcala-Martinez, E., Gonzalez-Diaz, E., Guerra-Infante, F.M., Arteaga-Troncoso, G., Martinez-Falcon, G., Leon-Garnica, G., Delgado-Aguirre, H.A., Perez-Gomez, H.R., Sosa-Gonzalez, I.E., Galindo-Olmeda, J.A., Ayala-Gaytan, J.J., Rodriguez-Pacheco, J., Zamorano-Flores, L., Lopez-Pulgarin, J.A., Miranda-Novales, M.G., Ramírez, M., Lopez-Hurtado, M., Lozano, M., Gomez, M.E., Sanchez-Castuera, M.E., Kasten-Monges, M., Gonzalez-Martinez, M., Sanchez-Vargas, M., Culebro-Burguet, M.C., Altuzar-Figueroa, M.A., Mijangos-Mendez, J.C., Ramires, O.G., Espinosa, O.S., De Leon-Escobedo, R., Salas-Flores, R., Ruiz-Rendon, R., Petersen-Morfin, S., Aguirre-Diaz, S.A., Esparza-Ahumada, S., Vega-Gonzalez, S., Gaona-Flores, V., Monroy-Colin, V.A., Cruz-Rivera, Z., Bat-Erdene, A., Narankhuu, Batsaikhan, Choijamts, Batsuren, Tuvdennyam, Battsetseg, Batkhuu, Byambadorj, Chuluunchimeg, K.H., Enkhtsetseg, D., Batjargal, G., Bayasgalan, G., Dorj, M., Mendsaikhan, Naranpurev, Baatar, Otgon, Suvderdene, P., Baigalmaa, S., Khajidmaa, T., Begzjav, Tsolmon, Tsuyanga, Ariyasuren, Z., Zeggwagh, A.A., Berechid, K., Abidi, K., Madani, N., Abouqal, R., Koirala, Anjeela, Giri, Rashmi, Sainju, Samana, Acharya, Subhash Prasad, Ahmed, Abeera, Raza, Aun, Parveen, Azra, Sultan, Faisal, Khan, Maria, Paul, Nadeem, Daud, Nargis, Yusuf, Sana, Nizamuddin, Summiya, Garcia-Mayorca, E., Castaño, E.G., Moreno-Castillo, J.L., Ballinas-Aquino, J.M., Lara, L., Vargas, M., Rojas-Bonilla, M.I., Ramos, S.J., Mapp, T., De Iturrado, V., La Hoz Vergara, C.E., Linares-Calderon, C.F., Moreno, D., Ramirez, E., Ramírez Wong, F.M., Montenegro-Orrego, G.M., Sandoval-Castillo, H.R., Pichilingue-Chagray, J., Mueras-Quevedo, J., Aibar-Yaranga, K.F., Castillo-Bravo, L.I., Santivanez-Monge, L.M., Mayorga-Espichan, M.J., Rosario-Tueros, M.L.G., Changano-Rodriguez, M.V., Salazar-Ramirez, N.E., Marquez-Mondalgo, V.A., Tajanlangit, Anna Lyn N., Tamayo, Arnefelina S., Llames, Cristina Mari Jean P., Labro, Ever, Dy, Ana P., Fortin, J.D., Bergosa, L.D., Salvio, L.G., Bermudez, V., Sg-Buenaflor, M.C., Trajano, M.F., Mendoza, M.T., Javellana, O.P., Maglente, R.R., Arreza-Galapia, Y., Navoa-Ng, J.A., Kubler, A., Barteczko-Grajek, B., Dragan, B., Zurawska, M., Mikaszewska-Sokolewicz, M., Zielinska, M., Ramos-Ortiz, G.Y., Florin-Rogobete, A., Vlad, C. Daliborca, Muntean, D., Sandesc, D., Papurica, M., Licker, M., Bedreag, Ovidiu Horea, Popescu, R., Grecu, S.D., Dumitrascu, V., Molkov, A., Galishevskiy, D., Furman, M., Simic, A., Lekic, D., Ristic, G., Eremija, J., Kojovic, J., Nikolic, L., Bjelovic, M., Lesnakova, A., Hlinkova, S., Gamar-Elanbya, M.O., Supa, Namphon, Prasan, Pornpheth, Pimathai, Rungratchanee, Wanitanukool, Suthinee, Somabutr, Suwara, Ben-Jaballah, N., Borgi, A., Bouziri, A., Dilek, A., Oncul, A., Kaya, A., Demiroz, Ali Pekcan, Gunduz, A., Ozgultekin, A., Inan, Asuman, Yalcin, A.N., Ramazanoglu, A., Engin, A., Willke, A., Meco, Basak Ceyda, Aygun, C., Bulut, C., Uzun, C., Becerik, C., Hatipoglu, Cigdem Ataman, Guclu, Cigdem Yildirim, Ozdemir, D., Yildizdas, D., Ugurcan, D., Azak, E., Guclu, E., Yilmaz, Esmeray Mutlu, Sebnem-Erdinc, F., Sirmatel, F., Ulger, F., Sari, Ferhat, Kizilates, Filiz, Usluer, G., Ceylan, G., Ersoz, G., Kaya, G., Ertem, Gunay Tuncer, Senol, G., Agin, Hasan, Cabadak, Hatice, Yilmaz, H., Sungurtekin, Hulya, Zengin, Humeyra, Turgut, H., Ozgunes, I., Devrim, I., Erdem, I., Işcanlı, Inşa Gul Ekiz, Bakir, M. Mehmet, Geyik, M.F., Oral, M., Meric, M., Cengiz, M., Ozcelik, M., Altindis, M., Sunbul, M., Elaldi, N., Kuyucu, N., Unal, N., Oztoprak, N., Yasar, N., Erben, Nurettin, Bayram, Nuri, Dursun, Oguz, Karabay, Oguz, Coskun, O., Horoz, Ozden Ozgur, Turhan, Ozge, Sandal, Ozlem Sarac, Tekin, Recep, Esen, Saban, Erdogan, Selvi Yener, Unal, Serhat, Karacorlu, Sevim, Sen, Suha, Sacar, Suzan, Yarar, Volkan, Oruc, Yeliz, Sahip, Yusuf, Kaya, Zeynep, Philip, Anamma, Elhoufi, Ashraf, Alrahma, Hussain, Sachez, E., Perez, F., Empaire, G.D., Vidal, H., Montes-Bravo, L., Guzman Siritt, M.E., Orozco, N., Navarrete, N., Ruiz, Y., De Anez, Z. Duran-Gil, Van Trang, Dang Thi, Minh, Dao Quang, Co, Dao Xuan, Anh, Dinh Pham Phuong, Thu, Le Thi Anh, Tuyet, Le Thi Diem, Nguyet, Le Thi Thu, Chau, Ngo uy, Binh, Nguyen Gia, Tien, Nguyen Phuc, Anh, Nguyen Quoc, Hang, Phan Thi, Hanh, Tran Thi My, Hang, Tran Thi Thuy, Thu, Truong Anh, Thoa, Vo Thi Hong, Rosenthal, Víctor Daniel, Al-Abdely, Hail M., El-Kholy, Amani Ali, AlKhawaja, Safa A Aziz, Leblebicioglu, Hakan, Mehta, Yatin, Rai, Vineya, Hung, Nguyen Viet, Kanj, Souha Sami, Salama, Mona Foda, Salgado-Yepez, Estuardo, Elahi, Naheed, Morfin Otero, Rayo, Apisarnthanarak, Anucha, De Carvalho, Braulio Matias, Ider, Bat Erdene, Fisher, Dale, Buenaflor, Maria Carmen S.G., Petrov, Michael M., Quesada-Mora, Ana Marcela, Zand, Farid, Gurskis, Vaidotas, Anguseva, Tanja, Ikram, Aamer, Aguilar de Moros, Daisy, Duszynska, Wieslawa, Mejia, Nepomuceno, Horhat, Florin George, Belskiy, Vladislav, Mioljevic, Vesna, Di Silvestre, Gabriela, Furova, Katarina, Ramos-Ortiz, Gloria Y., Gamar Elanbya, May Osman, Satari, Hindra Irawan, Gupta, Umesh, Dendane, Tarek, Raka, Lul, Guanche-Garcell, Humberto, Hu, Bijie, Padgett, Denis, Jayatilleke, Kushlani, Ben Jaballah, Najla, Apostolopoulou, Eleni, Prudencio Leon, Walter Enrique, Sepulveda-Chavez, Alejandra, Telechea, Hector Miguel, Trotter, Andrew, Alvarez-Moreno, Carlos, and Kushner-Davalos, Luis

Published
2016
Full Text
View/download PDF

108. Learning an Effective Evolution Equation for Particle-Mesh Simulations Across Cosmologies

Author

Payot, Nicolas, Lemos, Pablo, Perreault-Levasseur, Laurence, Cuesta-Lazaro, Carolina, Modi, Chirag, Hezaveh, Yashar, Payot, Nicolas, Lemos, Pablo, Perreault-Levasseur, Laurence, Cuesta-Lazaro, Carolina, Modi, Chirag, and Hezaveh, Yashar

Abstract

Particle-mesh simulations trade small-scale accuracy for speed compared to traditional, computationally expensive N-body codes in cosmological simulations. In this work, we show how a data-driven model could be used to learn an effective evolution equation for the particles, by correcting the errors of the particle-mesh potential incurred on small scales during simulations. We find that our learnt correction yields evolution equations that generalize well to new, unseen initial conditions and cosmologies. We further demonstrate that the resulting corrected maps can be used in a simulation-based inference framework to yield an unbiased inference of cosmological parameters. The model, a network implemented in Fourier space, is exclusively trained on the particle positions and velocities., Comment: 7 pages, 4 figures, Machine Learning and the Physical Sciences Workshop, NeurIPS 2023

Published
2023

109. ${\rm S{\scriptsize IM}BIG}$: Galaxy Clustering Analysis with the Wavelet Scattering Transform

Author

Blancard, Bruno Régaldo-Saint, Hahn, ChangHoon, Ho, Shirley, Hou, Jiamin, Lemos, Pablo, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Parker, Liam, Yao, Yuling, Eickenberg, Michael, Blancard, Bruno Régaldo-Saint, Hahn, ChangHoon, Ho, Shirley, Hou, Jiamin, Lemos, Pablo, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Parker, Liam, Yao, Yuling, and Eickenberg, Michael

Abstract

The non-Gaussisan spatial distribution of galaxies traces the large-scale structure of the Universe and therefore constitutes a prime observable to constrain cosmological parameters. We conduct Bayesian inference of the $\Lambda$CDM parameters $\Omega_m$, $\Omega_b$, $h$, $n_s$, and $\sigma_8$ from the BOSS CMASS galaxy sample by combining the wavelet scattering transform (WST) with a simulation-based inference approach enabled by the ${\rm S{\scriptsize IM}BIG}$ forward model. We design a set of reduced WST statistics that leverage symmetries of redshift-space data. Posterior distributions are estimated with a conditional normalizing flow trained on 20,000 simulated ${\rm S{\scriptsize IM}BIG}$ galaxy catalogs with survey realism. We assess the accuracy of the posterior estimates using simulation-based calibration and quantify generalization and robustness to the change of forward model using a suite of 2,000 test simulations. When probing scales down to $k_{\rm max}=0.5~h/\text{Mpc}$, we are able to derive accurate posterior estimates that are robust to the change of forward model for all parameters, except $\sigma_8$. We mitigate the robustness issues with $\sigma_8$ by removing the WST coefficients that probe scales smaller than $k \sim 0.3~h/\text{Mpc}$. Applied to the BOSS CMASS sample, our WST analysis yields seemingly improved constraints obtained from a standard PT-based power spectrum analysis with $k_{\rm max}=0.25~h/\text{Mpc}$ for all parameters except $h$. However, we still raise concerns on these results. The observational predictions significantly vary across different normalizing flow architectures, which we interpret as a form of model misspecification. This highlights a key challenge for forward modeling approaches when using summary statistics that are sensitive to detailed model-specific or observational imprints on galaxy clustering., Comment: 11+5 pages, 8+2 figures

Published
2023

114. A forward modeling approach to analyzing galaxy clustering with SIMBIG.

Author

ChangHoon Hahn, Eickenberg, Michael, Ho, Shirley, Jiamin Hou, Lemos, Pablo, Massara, Elena, Modi, Chirag, Dizgah, Azadeh Moradinezhad, Blancard, Bruno Régaldo-Saint, and Abidi, Muntazir M.

Subjects
GALAXY clusters, N-body simulations (Astronomy), POWER spectra, CLUSTER analysis (Statistics)
Abstract

We present cosmological constraints from a simulation-based inference (SBI) analysis of galaxy clustering from the SimBIG forward modeling framework. SimBIG leverages the predictive power of high-fidelity simulations and provides an inference framework that can extract cosmological information on small nonlinear scales. In this work, we apply SimBIG to the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample and analyze the power spectrum, Pl(k), to kmax = 0.5 h/Mpc. We construct 20,000 simulated galaxy samples using our forward model, which is based on 2,000 high-resolution QUIJOTE N-body simulations and includes detailed survey realism for a more complete treatment of observational systematics. We then conduct SBI by training normalizing flows using the simulated samples and infer the posterior distribution of ACDM cosmological parameters: Ωm, Ωb, h, ns, σ8. We derive significant constraints on Ωm and σ8, which are consistent with previous works. Our constraint on C8 is 27% more precise than standard Pl analyses because we exploit additional cosmological information on nonlinear scales beyond the limit of current analytic models, k > 0.25 h/Mpc. This improvement is equivalent to the statistical gain expected from a standard Pl analysis of galaxy sample ~60% larger than CMASS. While we focus on Pl in this work for validation and comparison to the literature, SimBIG provides a framework for analyzing galaxy clustering using any summary statistic. We expect further improvements on cosmological constraints from subsequent SimBIG analyses of summary statistics beyond Pl. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

115. A real-time hybrid battery state of charge and state of health estimation technique in renewable energy integrated microgrid applications.

Author

Gopahanal Manjunath, Madhu, Chintamani, Vyjayanthi, and Modi, Chirag

Subjects
RENEWABLE energy sources, MICROGRIDS, ELECTRIC batteries, ELECTRIC vehicle batteries, OPEN-circuit voltage, LEAD-acid batteries, TEMPERATURE effect
Abstract

This paper presents a novel real-time hybrid battery state of charge (SoC) and state of health (SoH) estimation technique with less computational effort for optimal operation in renewable energy integrated microgrid applications. The proposed SoC estimation technique utilizes battery terminal voltage and current information along with stress factors like battery charge–discharge rates and temperature effects to accurately estimate the SoC. In addition, it considers the open-circuit voltage (OCV) and SoC relation to dynamically recalibrate the SoC during idle conditions. The proposed SoH estimation technique uses a modified coulomb counting method and variation of battery capacity at different charge–discharge rates to precisely estimate the SoH of the battery. Simulation studies are carried out by considering the aging factor, temperature effect, and charge–discharge rates to analyze the performance of the proposed techniques under various dynamic conditions. A LabVIEW-based application is developed, and experimental verification in terms of estimation accuracy, real-time monitoring is carried out to verify the efficacy of the proposed technique. A comparative analysis with state-of-the-art estimation techniques is presented for validating the effectiveness and usefulness in real-time applications. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

123. "Seroprevalence of COVID-19 among health care personnel at a tertiary care teaching hospital".

Author

Joshi, Pinal, Patel, Rupal, and Modi, Chirag

Subjects
MEDICAL personnel, TERTIARY care, COVID-19, SEROPREVALENCE, HOSPITAL care
Abstract

Introduction: COVID-19 seroprevalence studies for Health Care Personnel (HCP) can provide relevant information on the proportion of people who have experienced a recent or past infection and provide information about populations that may be immune and potentially protected. Aim of the study was to determine the seroprevalence of COVID-19 and associated factors among HCP and to correlate COVID-19 antibody results with respect to time duration since laboratory confirmed positive COVID-19 test and vaccination. Methodology: A prospective, cross-sectional study in HCP working at Shree Krishna Hospital and Pramukhswami Medical College, Karamsad was conducted at Microbiology section of Central Diagnostic Laboratory (CDL). Following approval from Institutional Ethics Committee, the study was conducted from March 2021- June 2022. The serological test to measure COVID-19 IgG antibody was done by Enzyme Linked Fluorescent Assay (ELFA, VIDAS, Biomerieux) in 118 eligible HCP. Antibody response was correlated with age, gender, occupation, work area, symptoms, test requests and test results. Correlation of COVID-19 antibody results with respect to time duration since laboratory confirmed positive COVID-19 test and after vaccination was done. All the data was entered and analysed in Microsoft Excel 2010. Results: Seroprevalence of COVID-19 was found to be 97.45% (115/118) in HCP. Seroprevalence was 100 % in all the age groups except 18-30 and 41-50 years; 100% in all the professional categories except consultant doctors and resident doctors; 100% in HCP working in non-COVID-19 unit and 100% in participants not tested for COVID-19 test. 13 (37.14%) HCP were seropositive beyond 11 months duration after positive COVID-19 test. In 49 (44.54%) HCP, SARS-CoV-2 IgG antibody were found beyond 7 months duration after COVID-19 vaccine. SARS-CoV-2 IgG antibody were positive in eight (6.96%) non-vaccinated and 107 (93.04%) vaccinated participants. Conclusions: COVID-19 infection and/or COVID-19 vaccination might have contributed to excellent seroprevalence in health care personnel (HCP) of our hospital. Persistence of SARS-CoV-2 IgG antibody for longer duration was observed in HCP with past infection as well as past vaccination. Natural COVID-19 infection might have contributed to seropositivity in a few nonvaccinated, asymptomatic & RTPCR/RAT negative/not done HCP. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

124. HISTOPATHOLOGICAL EVALUATION OF BRAIN AND RETINA OF ADULT ZEBRAFISH EXPOSED TO SILVER NITRATE.

Author

Patel, Pavan M., Modi, Chirag M., Ramchandani, Divya M., Patel, Urvesh D., Patel, Harshad B., Patel, Harsh R., Paida, Bhulesh V., and Bhadaniya, Amit R.

Subjects
SILVER nitrate, BRACHYDANIO, RETINA, NERVOUS system, ZEBRA danio
Abstract

The purpose of the study was to assess the histological alterations in the adult zebrafish (Danio rerio L.) brain and retina after long-term exposure to silver nitrate (AgNO3) at concentrations of 6.25, 12.5, 25, and 50 µg/L for 28 days. Although zebrafish subjected to varying concentrations of AgNO3 did not die, they did demonstrate a progression in the anatomical abnormalities in their brains. Following exposure to silver nitrate at various concentrations for 28 days, mild to moderate pathological changes have been seen in the corpus cerebella, hypothalamus, lobuscaudalis cerebella, medulla, and dorsal telencephalic area of the opticum tectum in the brain, but there have been no discernible changes in the retina. It was concluded that silver nitrate has the potential to harm the nervous system. [ABSTRACT FROM AUTHOR]

Published
2023
Full Text
View/download PDF

128. The DESI N-body Simulation Project-II. Suppressing sample variance with fast simulations

Author

National Natural Science Foundation of China, National Science Foundation (US), National Key Research and Development Program (China), Flatiron Institute, Simons Foundation, Department of Energy (US), Agenzia Spaziale Italiana, Gordon and Betty Moore Foundation, Ministerio de Economía y Competitividad (España), Ding, Zhejie, Chuang, Chia-Hsun, Yu, Yu, Garrison, Lehman H., Bayer, Adrian E., Feng, Yu, Modi, Chirag, Eisenstein, Daniel J., White, Martin, Variu, Andrei, Zhao, Cheng, Zhang, Hanyu, Meneses Rizo, Jennifer, Brooks, David, Dawson, Kyle, Doel, Peter, Gaztañaga, Enrique, Kehoe, Robert, Krolewski, Alex, Landriau, Martin, Palanque-Delabrouille, Nathalie, Poppett, Claire L., National Natural Science Foundation of China, National Science Foundation (US), National Key Research and Development Program (China), Flatiron Institute, Simons Foundation, Department of Energy (US), Agenzia Spaziale Italiana, Gordon and Betty Moore Foundation, Ministerio de Economía y Competitividad (España), Ding, Zhejie, Chuang, Chia-Hsun, Yu, Yu, Garrison, Lehman H., Bayer, Adrian E., Feng, Yu, Modi, Chirag, Eisenstein, Daniel J., White, Martin, Variu, Andrei, Zhao, Cheng, Zhang, Hanyu, Meneses Rizo, Jennifer, Brooks, David, Dawson, Kyle, Doel, Peter, Gaztañaga, Enrique, Kehoe, Robert, Krolewski, Alex, Landriau, Martin, Palanque-Delabrouille, Nathalie, and Poppett, Claire L.

Abstract

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches ∼20h−3Gpc3⁠. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. ABACUSSUMMIT is a suite of high-resolution dark-matter-only simulations designed for this purpose, with 200h−3Gpc3 (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-N-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. k<0.3hMpc−1 for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in ABACUSSUMMIT suite to extend the effective volume ∼20 times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.

Published
2022

139. Deviations handling and corrective actions and preventive actions: Case studies for parenteral dosage form

Author

Shah Manan, Modi Chirag, Kothari Charmy, and Shah Kavina

Subjects
Quality management system, Computer science, business.industry, Operating procedures, media_common.quotation_subject, Pharmaceutical Science, Operations management, Quality (business), business, Parenteral Dosage Form, Pharmaceutical industry, media_common
Abstract

In the pharmaceutical industry, each and every activity is done according to standard operating procedures and protocols. Standard operating procedures are the pre-established stepwise instructions...

Published
2019
Full Text
View/download PDF

141. A Statewide Multi-Institutional Study of Asymptomatic Pretreatment Testing of Radiation Therapy Patients for SARS-CoV-2 in a High-Incidence Region of the United States

Author

Modi, Chirag, primary, Dragun, Anthony E., additional, Henson, Clarissa F., additional, Jain, Sheena, additional, Ahlawat, Stuti, additional, Eastwick, Gary, additional, Kubicek, Gregory J., additional, Mezera, Megan, additional, Mulvihill, David J., additional, Perri, Jennifer, additional, Juneja, Badal, additional, Khullar, Karishma, additional, Ennis, Ronald D., additional, and Haffty, Bruce G., additional

Published
2021
Full Text
View/download PDF

143. DESI N-body Simulation Project – II. Suppressing sample variance with fast simulations.

Author

Ding, Zhejie, Chuang, Chia-Hsun, Yu, Yu, Garrison, Lehman H, Bayer, Adrian E, Feng, Yu, Modi, Chirag, Eisenstein, Daniel J, White, Martin, Variu, Andrei, Zhao, Cheng, Zhang, Hanyu, Meneses Rizo, Jennifer, Brooks, David, Dawson, Kyle, Doel, Peter, Gaztanaga, Enrique, Kehoe, Robert, Krolewski, Alex, and Landriau, Martin

Subjects
N-body simulations (Astronomy), DARK energy, STATISTICAL accuracy, LARGE scale structure (Astronomy), PHYSICAL cosmology, GALACTIC halos
Abstract

Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches |$\sim 20\, h^{-3}\, \mathrm{Gpc}^{3}$|⁠. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. AbacusSummit is a suite of high-resolution dark-matter-only simulations designed for this purpose, with |$200\, h^{-3}\, \mathrm{Gpc}^{3}$| (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi- N -body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. |$k \lt 0.3\, h\, \mathrm{Mpc}^{-1}$| for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in AbacusSummit suite to extend the effective volume ∼20 times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data. [ABSTRACT FROM AUTHOR]

Published
2022
Full Text
View/download PDF

145. A survey on various handoff methods in mobile ad hoc network environment

Author

Elçi, Atilla, Sa, Pankaj Kumar, Modi, Chirag N., Olague, Gustavo, Sahoo, Manmath N., Bakshi, Sambit, Thomas, Libin, Sandeep, J., Goswami, Bhargavi, Paulose, Joy, Elçi, Atilla, Sa, Pankaj Kumar, Modi, Chirag N., Olague, Gustavo, Sahoo, Manmath N., Bakshi, Sambit, Thomas, Libin, Sandeep, J., Goswami, Bhargavi, and Paulose, Joy

Abstract

Communication has never been the same since the advent of cellular phones and numerous applications with different functionalities seem to crop up on a daily basis. Various applications seem to crop up on a daily basis. Ad hoc networks were developed with the intent of creating networks made up of interconnected nodes, on-the-go. Ad hoc networks have numerous applications, the most popular being vehicular ad hoc networks (VANETs). In VANETs, moving vehicles are considered to be the mobile nodes and mobile vehicular nodes move at high speeds. Mobility of the nodes makes it difficult to maintain stable communication links between the nodes and the access points. A process known as handoff is used to bridge this gap and is considered to be one of the solutions for unstable communication links over larger distances. Handoff can usually be seen when the nodes are mobile and start to move away from the access points. This paper discusses and compares various handoff methods that were proposed by various researchers with an intent to increase positive attributes while negating the rest of the components that do not support in increasing the efficiency of the handoff process.

Published
2020

146. Pharmacokinetics of Marbofloxacin Following Single and Repeated Dose Intravenous Administration in Broiler Chickens

Author

H. Bhadarka Dixita, B. Patel Harshad, D. Patel Urvesh, and M. Modi Chirag

Subjects
040301 veterinary sciences, business.industry, 0402 animal and dairy science, Broiler, 04 agricultural and veterinary sciences, Pharmacology, 040201 dairy & animal science, 0403 veterinary science, Pharmacokinetics, Marbofloxacin, medicine, business, Administration (government), medicine.drug
Published
2018
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results