A Comparison of Ultraluminous X-ray Sources in NGC 1399 and the Antennae Galaxies (NGC 4038/4039)

The temporal and spectral properties of ultraluminous X-ray sources (ULXs, L>2x10^39 ergs/s) and bright X-ray sources (L>3x10^38 ergs/s) are examined and compared in two extremely different host environments: the old elliptical galaxy NGC 1399 and the young, starforming Antennae galaxies (NGC 4038/4039). ULXs in NGC 1399 show little variability on either long or short time scales. Only 1 of 8 ULXs and 10 of 63 bright sources in NGC 1399 are variable at a confidence level of 90%. On long timescales, the NGC 1399 sources are steadier than most Galactic black hole X-ray binaries, but similar to GRS 1915+105. The outburst duration of the NGC 1399 sources is about 20 yrs, again, similar to that of GRS 1915+105. The bright X-ray sources in NGC 1399 may be black hole X-ray binaries with giant star companions similar to GRS 1915+105. The brightest ULX (PSX-1) in NGC 1399 is coincident with a globular cluster, shows a hard spectrum with a photon index around 1.5, and has a nearly constant luminosity around 5x10^39 erg/s. It may be an intermediate-mass black hole (IMBH) in a hard spectral state. In contrast to NGC 1399, the ULXs in the Antennae are all variable and a large fraction of the bright sources (9 of 15) are also variable. The variability and luminosity of ULXs in the Antennae suggest they are black hole high mass X-ray binaries accreting via Roche-lobe overflow. A flare with a duration of about 5 ks is found from Antennae X-42. The most luminous ULX, X-16, with a very hard spectrum (Gamma=1.0~1.3) and a luminosity which varies by a factor of 10, could be an IMBH candidate.Comment: 10 pages, 9 figures, accepted for publication in Ap

AutoEncoder by Forest

Auto-encoding is an important task which is typically realized by deep neural networks (DNNs) such as convolutional neural networks (CNN). In this paper, we propose EncoderForest (abbrv. eForest), the first tree ensemble based auto-encoder. We present a procedure for enabling forests to do backward reconstruction by utilizing the equivalent classes defined by decision paths of the trees, and demonstrate its usage in both supervised and unsupervised setting. Experiments show that, compared with DNN autoencoders, eForest is able to obtain lower reconstruction error with fast training speed, while the model itself is reusable and damage-tolerable