An Automated Catalog of Long Period Variables using Infrared Lightcurves from Palomar Gattini-IR
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2024
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| author | Suresh, Aswin Karambelkar, Viraj Kasliwal, Mansi M. Ashley, Michael C. B. De, Kishalay Hankins, Matthew J. Moore, Anna M. Soon, Jamie Soria, Roberto Travouillon, Tony Truong, Kayton K. |
| author_facet | Suresh, Aswin Karambelkar, Viraj Kasliwal, Mansi M. Ashley, Michael C. B. De, Kishalay Hankins, Matthew J. Moore, Anna M. Soon, Jamie Soria, Roberto Travouillon, Tony Truong, Kayton K. |
| contents | Stars in the Asymptotic Giant Branch (AGB) phase, dominated by low to intermediate-mass stars in the late stage of evolution, undergo periodic pulsations, with periods of several hundred days, earning them the name Long Period Variables (LPVs). These stars gradually shed their mass through stellar winds and mass ejections, enveloping themselves in dust. Infrared (IR) surveys can probe these dust-enshrouded phases and uncover populations of LPV stars in the Milky Way. In this paper, we present a catalog of 159,696 Long Period Variables using near-IR lightcurves from the Palomar Gattini - IR (PGIR) survey. PGIR has been surveying the entire accessible northern sky ($δ> -28^{\circ}$) in the J-band at a cadence of 2-3 days since September 2018, and has produced J-band lightcurves for more than 60 million sources. We used a gradient-boosted decision tree classifier trained on a comprehensive feature set extracted from PGIR lightcurves to search for LPVs in this dataset. We developed a parallelized and optimized code to extract features at a rate of ~0.1 seconds per lightcurve. Our model can successfully distinguish LPVs from other stars with a true positive rate and weighted g-mean of 0.95. 73,346 (~46%) of the sources in our catalog are new, previously unknown LPVs. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_08000 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Automated Catalog of Long Period Variables using Infrared Lightcurves from Palomar Gattini-IR Suresh, Aswin Karambelkar, Viraj Kasliwal, Mansi M. Ashley, Michael C. B. De, Kishalay Hankins, Matthew J. Moore, Anna M. Soon, Jamie Soria, Roberto Travouillon, Tony Truong, Kayton K. Solar and Stellar Astrophysics Astrophysics of Galaxies Stars in the Asymptotic Giant Branch (AGB) phase, dominated by low to intermediate-mass stars in the late stage of evolution, undergo periodic pulsations, with periods of several hundred days, earning them the name Long Period Variables (LPVs). These stars gradually shed their mass through stellar winds and mass ejections, enveloping themselves in dust. Infrared (IR) surveys can probe these dust-enshrouded phases and uncover populations of LPV stars in the Milky Way. In this paper, we present a catalog of 159,696 Long Period Variables using near-IR lightcurves from the Palomar Gattini - IR (PGIR) survey. PGIR has been surveying the entire accessible northern sky ($δ> -28^{\circ}$) in the J-band at a cadence of 2-3 days since September 2018, and has produced J-band lightcurves for more than 60 million sources. We used a gradient-boosted decision tree classifier trained on a comprehensive feature set extracted from PGIR lightcurves to search for LPVs in this dataset. We developed a parallelized and optimized code to extract features at a rate of ~0.1 seconds per lightcurve. Our model can successfully distinguish LPVs from other stars with a true positive rate and weighted g-mean of 0.95. 73,346 (~46%) of the sources in our catalog are new, previously unknown LPVs. |
| title | An Automated Catalog of Long Period Variables using Infrared Lightcurves from Palomar Gattini-IR |
| topic | Solar and Stellar Astrophysics Astrophysics of Galaxies |
| url | https://arxiv.org/abs/2402.08000 |