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9. Detection of a Transient Quasi-periodic Oscillation in $\gamma$-Rays from Blazar PKS 2255-282

Author

Sharma, Ajay, Banerjee, Anuvab, Das, Avik Kumar, Mandal, Avijit, and Bose, Debanjan

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We conducted a comprehensive variability analysis of the blazar PKS 2255-282 using Fermi-LAT observations spanning over four years, from MJD 57783.5 to 59358.5. Our analysis revealed a transient quasi-periodic oscillation (QPO) with a period of 93$\pm$2.6 days. We employed a variety of Fourier-based methods, including the Lomb-Scargle Periodogram (LSP) and Weighted Wavelet Z-Transform (WWZ), as well as time domain analysis techniques such as Seasonal and Non-Seasonal Autoregressive Integrated Moving Average (ARIMA) models and the Stochastic modeling with Stochastically Driven Damped Harmonic Oscillator (SHO) models. Consistently, the QPO with a period of 93 days was detected across all methods used. The observed peak in LSP and time-averaged WWZ plots has a significance level of 4.06$\sigma$ and 3.96$\sigma$, respectively. To understand the source of flux modulations in the light curve, we explored various physical models. A plausible scenario involves the precession of the jet with a high Lorentz factor or the movement of a plasma blob along a helical trajectory within the relativistic jet., Comment: 13 pages, 13 Figures, accepted for publication in the Astrophysical Journal (APJ)

Published
2024

17. The Llama 3 Herd of Models

Author

Dubey, Abhimanyu, Jauhri, Abhinav, Pandey, Abhinav, Kadian, Abhishek, Al-Dahle, Ahmad, Letman, Aiesha, Mathur, Akhil, Schelten, Alan, Yang, Amy, Fan, Angela, Goyal, Anirudh, Hartshorn, Anthony, Yang, Aobo, Mitra, Archi, Sravankumar, Archie, Korenev, Artem, Hinsvark, Arthur, Rao, Arun, Zhang, Aston, Rodriguez, Aurelien, Gregerson, Austen, Spataru, Ava, Roziere, Baptiste, Biron, Bethany, Tang, Binh, Chern, Bobbie, Caucheteux, Charlotte, Nayak, Chaya, Bi, Chloe, Marra, Chris, McConnell, Chris, Keller, Christian, Touret, Christophe, Wu, Chunyang, Wong, Corinne, Ferrer, Cristian Canton, Nikolaidis, Cyrus, Allonsius, Damien, Song, Daniel, Pintz, Danielle, Livshits, Danny, Esiobu, David, Choudhary, Dhruv, Mahajan, Dhruv, Garcia-Olano, Diego, Perino, Diego, Hupkes, Dieuwke, Lakomkin, Egor, AlBadawy, Ehab, Lobanova, Elina, Dinan, Emily, Smith, Eric Michael, Radenovic, Filip, Zhang, Frank, Synnaeve, Gabriel, Lee, Gabrielle, Anderson, Georgia Lewis, Nail, Graeme, Mialon, Gregoire, Pang, Guan, Cucurell, Guillem, Nguyen, Hailey, Korevaar, Hannah, Xu, Hu, Touvron, Hugo, Zarov, Iliyan, Ibarra, Imanol Arrieta, Kloumann, Isabel, Misra, Ishan, Evtimov, Ivan, Copet, Jade, Lee, Jaewon, Geffert, Jan, Vranes, Jana, Park, Jason, Mahadeokar, Jay, Shah, Jeet, van der Linde, Jelmer, Billock, Jennifer, Hong, Jenny, Lee, Jenya, Fu, Jeremy, Chi, Jianfeng, Huang, Jianyu, Liu, Jiawen, Wang, Jie, Yu, Jiecao, Bitton, Joanna, Spisak, Joe, Park, Jongsoo, Rocca, Joseph, Johnstun, Joshua, Saxe, Joshua, Jia, Junteng, Alwala, Kalyan Vasuden, Upasani, Kartikeya, Plawiak, Kate, Li, Ke, Heafield, Kenneth, Stone, Kevin, El-Arini, Khalid, Iyer, Krithika, Malik, Kshitiz, Chiu, Kuenley, Bhalla, Kunal, Rantala-Yeary, Lauren, van der Maaten, Laurens, Chen, Lawrence, Tan, Liang, Jenkins, Liz, Martin, Louis, Madaan, Lovish, Malo, Lubo, Blecher, Lukas, Landzaat, Lukas, de Oliveira, Luke, Muzzi, Madeline, Pasupuleti, Mahesh, Singh, Mannat, Paluri, Manohar, Kardas, Marcin, Oldham, Mathew, Rita, Mathieu, Pavlova, Maya, Kambadur, Melanie, Lewis, Mike, Si, Min, Singh, Mitesh Kumar, Hassan, Mona, Goyal, Naman, Torabi, Narjes, Bashlykov, Nikolay, Bogoychev, Nikolay, Chatterji, Niladri, Duchenne, Olivier, Çelebi, Onur, Alrassy, Patrick, Zhang, Pengchuan, Li, Pengwei, Vasic, Petar, Weng, Peter, Bhargava, Prajjwal, Dubal, Pratik, Krishnan, Praveen, Koura, Punit Singh, Xu, Puxin, He, Qing, Dong, Qingxiao, Srinivasan, Ragavan, Ganapathy, Raj, Calderer, Ramon, Cabral, Ricardo Silveira, Stojnic, Robert, Raileanu, Roberta, Girdhar, Rohit, Patel, Rohit, Sauvestre, Romain, Polidoro, Ronnie, Sumbaly, Roshan, Taylor, Ross, Silva, Ruan, Hou, Rui, Wang, Rui, Hosseini, Saghar, Chennabasappa, Sahana, Singh, Sanjay, Bell, Sean, Kim, Seohyun Sonia, Edunov, Sergey, Nie, Shaoliang, Narang, Sharan, Raparthy, Sharath, Shen, Sheng, Wan, Shengye, Bhosale, Shruti, Zhang, Shun, Vandenhende, Simon, Batra, Soumya, Whitman, Spencer, Sootla, Sten, Collot, Stephane, Gururangan, Suchin, Borodinsky, Sydney, Herman, Tamar, Fowler, Tara, Sheasha, Tarek, Georgiou, Thomas, Scialom, Thomas, Speckbacher, Tobias, Mihaylov, Todor, Xiao, Tong, Karn, Ujjwal, Goswami, Vedanuj, Gupta, Vibhor, Ramanathan, Vignesh, Kerkez, Viktor, Gonguet, Vincent, Do, Virginie, Vogeti, Vish, Petrovic, Vladan, Chu, Weiwei, Xiong, Wenhan, Fu, Wenyin, Meers, Whitney, Martinet, Xavier, Wang, Xiaodong, Tan, Xiaoqing Ellen, Xie, Xinfeng, Jia, Xuchao, Wang, Xuewei, Goldschlag, Yaelle, Gaur, Yashesh, Babaei, Yasmine, Wen, Yi, Song, Yiwen, Zhang, Yuchen, Li, Yue, Mao, Yuning, Coudert, Zacharie Delpierre, Yan, Zheng, Chen, Zhengxing, Papakipos, Zoe, Singh, Aaditya, Grattafiori, Aaron, Jain, Abha, Kelsey, Adam, Shajnfeld, Adam, Gangidi, Adithya, Victoria, Adolfo, Goldstand, Ahuva, Menon, Ajay, Sharma, Ajay, Boesenberg, Alex, Vaughan, Alex, Baevski, Alexei, Feinstein, Allie, Kallet, Amanda, Sangani, Amit, Yunus, Anam, Lupu, Andrei, Alvarado, Andres, Caples, Andrew, Gu, Andrew, Ho, Andrew, Poulton, Andrew, Ryan, Andrew, Ramchandani, Ankit, Franco, Annie, Saraf, Aparajita, Chowdhury, Arkabandhu, Gabriel, Ashley, Bharambe, Ashwin, Eisenman, Assaf, Yazdan, Azadeh, James, Beau, Maurer, Ben, Leonhardi, Benjamin, Huang, Bernie, Loyd, Beth, De Paola, Beto, Paranjape, Bhargavi, Liu, Bing, Wu, Bo, Ni, Boyu, Hancock, Braden, Wasti, Bram, Spence, Brandon, Stojkovic, Brani, Gamido, Brian, Montalvo, Britt, Parker, Carl, Burton, Carly, Mejia, Catalina, Wang, Changhan, Kim, Changkyu, Zhou, Chao, Hu, Chester, Chu, Ching-Hsiang, Cai, Chris, Tindal, Chris, Feichtenhofer, Christoph, Civin, Damon, Beaty, Dana, Kreymer, Daniel, Li, Daniel, Wyatt, Danny, Adkins, David, Xu, David, Testuggine, Davide, David, Delia, Parikh, Devi, Liskovich, Diana, Foss, Didem, Wang, Dingkang, Le, Duc, Holland, Dustin, Dowling, Edward, Jamil, Eissa, Montgomery, Elaine, Presani, Eleonora, Hahn, Emily, Wood, Emily, Brinkman, Erik, Arcaute, Esteban, Dunbar, Evan, Smothers, Evan, Sun, Fei, Kreuk, Felix, Tian, Feng, Ozgenel, Firat, Caggioni, Francesco, Guzmán, Francisco, Kanayet, Frank, Seide, Frank, Florez, Gabriela Medina, Schwarz, Gabriella, Badeer, Gada, Swee, Georgia, Halpern, Gil, Thattai, Govind, Herman, Grant, Sizov, Grigory, Guangyi, Zhang, Lakshminarayanan, Guna, Shojanazeri, Hamid, Zou, Han, Wang, Hannah, Zha, Hanwen, Habeeb, Haroun, Rudolph, Harrison, Suk, Helen, Aspegren, Henry, Goldman, Hunter, Damlaj, Ibrahim, Molybog, Igor, Tufanov, Igor, Veliche, Irina-Elena, Gat, Itai, Weissman, Jake, Geboski, James, Kohli, James, Asher, Japhet, Gaya, Jean-Baptiste, Marcus, Jeff, Tang, Jeff, Chan, Jennifer, Zhen, Jenny, Reizenstein, Jeremy, Teboul, Jeremy, Zhong, Jessica, Jin, Jian, Yang, Jingyi, Cummings, Joe, Carvill, Jon, Shepard, Jon, McPhie, Jonathan, Torres, Jonathan, Ginsburg, Josh, Wang, Junjie, Wu, Kai, U, Kam Hou, Saxena, Karan, Prasad, Karthik, Khandelwal, Kartikay, Zand, Katayoun, Matosich, Kathy, Veeraraghavan, Kaushik, Michelena, Kelly, Li, Keqian, Huang, Kun, Chawla, Kunal, Lakhotia, Kushal, Huang, Kyle, Chen, Lailin, Garg, Lakshya, A, Lavender, Silva, Leandro, Bell, Lee, Zhang, Lei, Guo, Liangpeng, Yu, Licheng, Moshkovich, Liron, Wehrstedt, Luca, Khabsa, Madian, Avalani, Manav, Bhatt, Manish, Tsimpoukelli, Maria, Mankus, Martynas, Hasson, Matan, Lennie, Matthew, Reso, Matthias, Groshev, Maxim, Naumov, Maxim, Lathi, Maya, Keneally, Meghan, Seltzer, Michael L., Valko, Michal, Restrepo, Michelle, Patel, Mihir, Vyatskov, Mik, Samvelyan, Mikayel, Clark, Mike, Macey, Mike, Wang, Mike, Hermoso, Miquel Jubert, Metanat, Mo, Rastegari, Mohammad, Bansal, Munish, Santhanam, Nandhini, Parks, Natascha, White, Natasha, Bawa, Navyata, Singhal, Nayan, Egebo, Nick, Usunier, Nicolas, Laptev, Nikolay Pavlovich, Dong, Ning, Zhang, Ning, Cheng, Norman, Chernoguz, Oleg, Hart, Olivia, Salpekar, Omkar, Kalinli, Ozlem, Kent, Parkin, Parekh, Parth, Saab, Paul, Balaji, Pavan, Rittner, Pedro, Bontrager, Philip, Roux, Pierre, Dollar, Piotr, Zvyagina, Polina, Ratanchandani, Prashant, Yuvraj, Pritish, Liang, Qian, Alao, Rachad, Rodriguez, Rachel, Ayub, Rafi, Murthy, Raghotham, Nayani, Raghu, Mitra, Rahul, Li, Raymond, Hogan, Rebekkah, Battey, Robin, Wang, Rocky, Maheswari, Rohan, Howes, Russ, Rinott, Ruty, Bondu, Sai Jayesh, Datta, Samyak, Chugh, Sara, Hunt, Sara, Dhillon, Sargun, Sidorov, Sasha, Pan, Satadru, Verma, Saurabh, Yamamoto, Seiji, Ramaswamy, Sharadh, Lindsay, Shaun, Feng, Sheng, Lin, Shenghao, Zha, Shengxin Cindy, Shankar, Shiva, Zhang, Shuqiang, Wang, Sinong, Agarwal, Sneha, Sajuyigbe, Soji, Chintala, Soumith, Max, Stephanie, Chen, Stephen, Kehoe, Steve, Satterfield, Steve, Govindaprasad, Sudarshan, Gupta, Sumit, Cho, Sungmin, Virk, Sunny, Subramanian, Suraj, Choudhury, Sy, Goldman, Sydney, Remez, Tal, Glaser, Tamar, Best, Tamara, Kohler, Thilo, Robinson, Thomas, Li, Tianhe, Zhang, Tianjun, Matthews, Tim, Chou, Timothy, Shaked, Tzook, Vontimitta, Varun, Ajayi, Victoria, Montanez, Victoria, Mohan, Vijai, Kumar, Vinay Satish, Mangla, Vishal, Albiero, Vítor, Ionescu, Vlad, Poenaru, Vlad, Mihailescu, Vlad Tiberiu, Ivanov, Vladimir, Li, Wei, Wang, Wenchen, Jiang, Wenwen, Bouaziz, Wes, Constable, Will, Tang, Xiaocheng, Wang, Xiaofang, Wu, Xiaojian, Wang, Xiaolan, Xia, Xide, Wu, Xilun, Gao, Xinbo, Chen, Yanjun, Hu, Ye, Jia, Ye, Qi, Ye, Li, Yenda, Zhang, Yilin, Zhang, Ying, Adi, Yossi, Nam, Youngjin, Yu, Wang, Hao, Yuchen, Qian, Yundi, He, Yuzi, Rait, Zach, DeVito, Zachary, Rosnbrick, Zef, Wen, Zhaoduo, Yang, Zhenyu, and Zhao, Zhiwei

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition
Abstract

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Published
2024

33. Detection of gamma-ray quasi-periodic oscillations in non-blazar AGN PKS 0521-36

Author

Sharma, Ajay, Prince, Raj, and Bose, Debanjan

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

Quasi-periodic oscillations (QPOs) have been detected in many Fermi-detected bright blazars. In this letter, we report multiple QPOs detected in a non-blazar AGN PKS 0521-36 searched over the entire 15 years of Fermi-LAT data. QPOs are detected at 268 days, at 295 days, and at 806 days timescales with more than 3$\sigma$ significance. The QPO detected at 806 days happens to be the third harmonic of QPO at 268 days. The time scales are consistent in both Lomb-Scargle and Wavelet analysis. Furthermore, the Gaussian Process modeling of the light curve is performed with stochastically driven damped harmonic oscillator (SHO) and damped random walk (DRW) modeling to uncover the presence of QPOs. The constructed power spectral density (PSD) exhibits two QPOs, with observed timescales of approximately 283 days and 886 days. This is the first non-blazar AGN where the long-term QPO is detected. Earlier studies show this source has a weak beamed jet. The exact cause for these QPOs remains unclear. We also assembled the $\gamma$-ray QPO detected in various blazar and tested the QPO time scale dependent on the black hole mass. No significant correlation is found., Comment: 10 pages, 7 figures

Published
2023

34. Probing the disc-jet coupling in S4 0954+65, PKS 0903-57, & 4C +01.02 with $\gamma$-rays

Author

Sharma, Ajay, Kamaram, Sushanth Reddy, Prince, Raj, Khatoon, Rukaiya, and Bose, Debanjan

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present a comprehensive variability study on three blazars, S4 0954+65, PKS 0903-57, and 4C +01.02 covering a mass range of log(M/M$_{\odot}$) = 8--9, by using $\sim$15 years-long $\gamma$-ray light curves from \textit{Fermi}-LAT. The variability level is characterized by the fractional variability amplitude which is higher for $\gamma$-rays compared to optical/UV and X-rays emissions. A power spectral density (PSD) study and damped random walk (DRW) modeling are done to probe the characteristic timescale. The PSD is fitted with a single power-law (PL) and bending power-law models and the corresponding success fraction was estimated. In the case of PKS 0903-57, We observed a break in the $\gamma$-ray PSD at 256 days which is comparable to the viscous timescale in the accretion disc suggesting a possible disk-jet coupling. The non-thermal damping timescale from the DRW modeling is compared with the thermal damping timescale for AGNs including our three sources. Our sources lie on the best-fit of the $\mathrm{\tau^{rest}_{damping}} - M_{BH}$ plot derived for AGN suggesting a possible accretion disc-jet connection. If the jet's variability is linked to the disc's variability, we expect a log-normal flux distribution, often connected to the accretion disc's multiplicative processes. Our study observed a double log-normal flux distribution, possibly linked to long and short-term variability from the accretion disk and the jet. In summary, PSD and DRW modeling results for these three sources combined with blazars and AGNs studied in literature favor a disc-jet coupling scenario. However, more such studies are needed to refine this understanding., Comment: 15 pages, 19 figures, accepted for publication in MNRAS

Published
2023
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41. Quasi-periodic oscillation detected in $\gamma$-rays in blazar PKS 0346-27

Author

Prince, Raj, Banerjee, Anuvab, Sharma, Ajay, das, Avik Kumar, Gupta, Alok C., and Bose, Debanjan

Subjects
Astrophysics - High Energy Astrophysical Phenomena
Abstract

We present a variability study of the blazar PKS 0346-27 from December 2018 to January 2022 in its archival $\gamma$-ray observation by Fermi-LAT. We use the Lomb-Scargle periodogram and the weighted wavelet transform methods in order to detect the presence of periodicity/quasi-periodicity and localize this feature in time and frequency space. The significance of the periodicity feature has been estimated using the Monte-Carlo simulation approach. We have also determined the global significance of the periodicity to test the robustness of our claim. To explore the most probable scenario, we modeled the light curve with both a straight jet and a curved jet model. We detect a periodicity feature of $\sim$ 100 days duration for the entire period of observation with a statistical significance of $3\sigma$, which amounts to a 99.7\% confidence level. The global significance of this feature is found to be 96.96\%. Based on the Akaike Information Criteria, the most probable explanation is that the observed emission is enhanced due to the helical motion of a blob within a curved jet. The origin of this QPO is very likely a region of enhanced emission moving helically inside a curved jet. This work presents strong evidence for jet curvature in the source and an independent (albeit a little serendipitous) procedure to estimate the curvature in blazar jets., Comment: 7 pages, 4 figures, Accepted for publication in A&A, in Press

Published
2023
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45. MMS hotspots: a cross-sectional comparison of U.S. counties with and without Mohs micrographic surgery.

Author

Sharma, Ajay, Peterman, Nicholas, Juhasz, Margit, and Shive, Melissa

Subjects
Epidemiology, Geospatial analysis, Health disparities, Medicare, Melanoma, Mohs, Mohs micrographic surgery, Nonmelanoma, Skin cancer, Spatial analysis, Aged, Humans, United States, Mohs Surgery, Cross-Sectional Studies, Medicare, Skin Neoplasms, Surgeons, Retrospective Studies
Abstract

Healthcare access greatly impacts skin cancer diagnosis and mortality rates. Recognition of current disparities in Mohs micrographic surgery (MMS) access can assist future policy and clinical decisions to correct them. For the years 2014-2018, the CPT codes for MMS (17,311 and 17,313) were counted on a per county level across the United States per the Medicare Centers for Medicare & Medicaid Services (CMS) Medicare Prescriber Database. Any county with 0 MMS CPT codes recorded were classified as without MMS cases. MMS hotspots were identified as counties that possessed a high average number of MMS cases compared to the national average, while also being surrounded by counties that possessed a low average number of MMS cases compared to the national average. Three thousand eighty-four counties in the United States were analyzed; 785 (25%) counties were designated as with MMS cases and 2301 (75%) without MMS cases. There were no significant differences in age, ethnicity distribution, or cost per enrollee between the two designations. 74% of counties with MMS cases were considered urban, while only 25% of those without cases were urban (p

Published
2023

46. Alopecia areata-like presentations with mogamulizumab therapy

Author

Kincaid, Colin M, Sharma, Ajay N, Lee, Bonnie A, Pinter-Brown, Lauren C, Smith, Janellen, Linden, Kenneth, and Mesinkovska, Natasha A

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Sézary syndrome, alopecia, alopecia areata, cutaneous T-cell lymphoma, drug rash, hair loss, mogamulizumab, mycosis fungoides, Clinical sciences
Published
2023

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