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| Format: | Preprint |
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2025
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| Online-Zugang: | https://arxiv.org/abs/2511.22597 |
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| author | Hazboun, Jeffrey S. Simon, Joseph Baier, Jeremy Larsen, Bjorn Oliver, Daniel J. Baker, Paul T. Bécsy, Bence Chen, Siyuan Hernandez, Alberto Diaz Ellis, Justin A. Holgado, A. Miguel Islo, Kristina Johnson, Aaron Kaiser, Andrew R. Laal, Nima McEwen, Alexander Pol, Nihan S. Key, Joey Shapiro Kim, Min Young Samson, Matthew Shapiro-Albert, Brent J. Sun, Jerry P. Taylor, Stephen R. Witt, Caitlin A. Volpe, Jeremy Ye, Christine Blumer, Harsha Brook, Paul R. Chatterjee, Shami Cordes, James M. Crawford, Fronefield Cromartie, H. Thankful DeCesar, Megan E. Demorest, Paul B. Dolch, Timothy Ferdman, Robert D. Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Garver-Daniels, Nathan Gentile, Peter A. Good, Deborah C. Jennings, Ross J. Jones, Megan L. Kaplan, David L. Lam, Michael T. Lazio, T. Joseph W. Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Madison, Dustin R. McLaughlin, Maura A. Mingarelli, Chiara M. F. Ng, Cherry Nice, David J. Pennucci, Timothy T. Ransom, Scott M. Ray, Paul S. Siemens, Xavier Spiewak, Renée Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Swiggum, Joseph K. Turner, Jacob E. Vallisneri, Michele Vigeland, Sarah J. |
| author_facet | Hazboun, Jeffrey S. Simon, Joseph Baier, Jeremy Larsen, Bjorn Oliver, Daniel J. Baker, Paul T. Bécsy, Bence Chen, Siyuan Hernandez, Alberto Diaz Ellis, Justin A. Holgado, A. Miguel Islo, Kristina Johnson, Aaron Kaiser, Andrew R. Laal, Nima McEwen, Alexander Pol, Nihan S. Key, Joey Shapiro Kim, Min Young Samson, Matthew Shapiro-Albert, Brent J. Sun, Jerry P. Taylor, Stephen R. Witt, Caitlin A. Volpe, Jeremy Ye, Christine Blumer, Harsha Brook, Paul R. Chatterjee, Shami Cordes, James M. Crawford, Fronefield Cromartie, H. Thankful DeCesar, Megan E. Demorest, Paul B. Dolch, Timothy Ferdman, Robert D. Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Garver-Daniels, Nathan Gentile, Peter A. Good, Deborah C. Jennings, Ross J. Jones, Megan L. Kaplan, David L. Lam, Michael T. Lazio, T. Joseph W. Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Madison, Dustin R. McLaughlin, Maura A. Mingarelli, Chiara M. F. Ng, Cherry Nice, David J. Pennucci, Timothy T. Ransom, Scott M. Ray, Paul S. Siemens, Xavier Spiewak, Renée Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Swiggum, Joseph K. Turner, Jacob E. Vallisneri, Michele Vigeland, Sarah J. |
| contents | Pulsar timing arrays (PTAs) have recently entered the detection era, quickly moving beyond the goal of simply improving sensitivity at the lowest frequencies for the sake of observing the stochastic gravitational wave background (GWB), and focusing on its accurate spectral characterization. While all PTA collaborations around the world use Fourier-domain Gaussian processes to model the GWB and intrinsic long time-correlated (red) noise, techniques to model the time-correlated radio frequency-dependent (chromatic) processes have varied from collaboration to collaboration. Here we test a new class of models for PTA data, Gaussian processes based on time-domain kernels that model the statistics of the chromatic processes starting from the covariance matrix. As we will show, these models can be effectively equivalent to Fourier-domain models in mitigating chromatic noise. This work presents a method for Bayesian model selection across the various choices of kernel as well as deterministic chromatic models for non-stationary chromatic events and the solar wind. As PTAs turn towards high frequency (>1/yr) sensitivity, the size of the basis used to model these processes will need to increase, and these time-domain models present some computational efficiencies compared to Fourier-domain models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22597 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The NANOGrav 12.5-year Data Set: Chromatic Noise Characterization & Mitigation with Time-Domain Kernels Hazboun, Jeffrey S. Simon, Joseph Baier, Jeremy Larsen, Bjorn Oliver, Daniel J. Baker, Paul T. Bécsy, Bence Chen, Siyuan Hernandez, Alberto Diaz Ellis, Justin A. Holgado, A. Miguel Islo, Kristina Johnson, Aaron Kaiser, Andrew R. Laal, Nima McEwen, Alexander Pol, Nihan S. Key, Joey Shapiro Kim, Min Young Samson, Matthew Shapiro-Albert, Brent J. Sun, Jerry P. Taylor, Stephen R. Witt, Caitlin A. Volpe, Jeremy Ye, Christine Blumer, Harsha Brook, Paul R. Chatterjee, Shami Cordes, James M. Crawford, Fronefield Cromartie, H. Thankful DeCesar, Megan E. Demorest, Paul B. Dolch, Timothy Ferdman, Robert D. Ferrara, Elizabeth C. Fiore, William Fonseca, Emmanuel Garver-Daniels, Nathan Gentile, Peter A. Good, Deborah C. Jennings, Ross J. Jones, Megan L. Kaplan, David L. Lam, Michael T. Lazio, T. Joseph W. Lorimer, Duncan R. Luo, Jing Lynch, Ryan S. Madison, Dustin R. McLaughlin, Maura A. Mingarelli, Chiara M. F. Ng, Cherry Nice, David J. Pennucci, Timothy T. Ransom, Scott M. Ray, Paul S. Siemens, Xavier Spiewak, Renée Stairs, Ingrid H. Stinebring, Daniel R. Stovall, Kevin Swiggum, Joseph K. Turner, Jacob E. Vallisneri, Michele Vigeland, Sarah J. Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology Pulsar timing arrays (PTAs) have recently entered the detection era, quickly moving beyond the goal of simply improving sensitivity at the lowest frequencies for the sake of observing the stochastic gravitational wave background (GWB), and focusing on its accurate spectral characterization. While all PTA collaborations around the world use Fourier-domain Gaussian processes to model the GWB and intrinsic long time-correlated (red) noise, techniques to model the time-correlated radio frequency-dependent (chromatic) processes have varied from collaboration to collaboration. Here we test a new class of models for PTA data, Gaussian processes based on time-domain kernels that model the statistics of the chromatic processes starting from the covariance matrix. As we will show, these models can be effectively equivalent to Fourier-domain models in mitigating chromatic noise. This work presents a method for Bayesian model selection across the various choices of kernel as well as deterministic chromatic models for non-stationary chromatic events and the solar wind. As PTAs turn towards high frequency (>1/yr) sensitivity, the size of the basis used to model these processes will need to increase, and these time-domain models present some computational efficiencies compared to Fourier-domain models. |
| title | The NANOGrav 12.5-year Data Set: Chromatic Noise Characterization & Mitigation with Time-Domain Kernels |
| topic | Instrumentation and Methods for Astrophysics High Energy Astrophysical Phenomena General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/2511.22597 |