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Low-Mass Companions to Nine Stars

Niedzielski A., Jaros R., Srivastava D., Adamów M., Wolszczan A., Villaver E., Maciejewski G., Deka-Szymankiewicz B.

Acta Astronomica

2025

Vol. 75, No. 2

63--95

We present an independent spectroscopic and radial velocity analysis for nine stars from the Pennsylvania-Toruń Planet Search. For BD+24 4697, we present an updated true companion's mass (0.16±0.02 M⊙) as well as evidence of stellar activity. For BD+54 1640 and BD+65 1241 we present true masses of companions, m=0.15±0.04 M⊙) and m=0.091± 0.005 M⊙), respectively. For BD+63 974 and BD+69 935 we find low mass companions with m sin i=0.046±0.001 M⊙) and m sin i=0.090±0.005 M⊙). For BD+52 1281, BD+54 1382, TYC 2704-2680-1, and TYC 3525-02043-1 we present evidence of low-mass companions with m sin i of 0.115±0.006 M⊙), 0.083±0.007 M⊙), 0.279±0.009 M⊙), and 0.064±0.006 M⊙), respectively. Consequently, BD+54 1382, BD+63 974, BD+65 1241, BD+69 935 and TYC 3525-02043-1 appear to be brown dwarf host candidates.

Vision Transformer for Transient Noise Classification

Srivastava D., Niedzielski A.

Acta Astronomica

2024

Vol. 74, No. 3

231--238

Transient noise (glitches) in LIGO data hinders the detection of gravitational waves (GW). The Gravity Spy project has categorized these noise events into various classes. With the O3 run, there is the inclusion of two additional noise classes and, thus, a need to train new models for effective classification. We aim to classify glitches in LIGO data into 22 existing classes from the first run plus 2 additional noise classes from O3a using the Vision Transformer (ViT) model. We train a pre-trained Vision Transformer (ViT-B/32) model on a combined dataset consisting of the Gravity Spy dataset with the additional two classes from the LIGO O3a run. We achieve a classification efficiency of 92.26%, demonstrating the potential of Vision Transformer to improve the accuracy of gravitational wave detection by effectively distinguishing transient noise.