论文标题
通过灰尘的暗镜头:VVV中的视差微透明事件
Dark lenses through the dust: parallax microlensing events in the VVV
论文作者
论文摘要
我们使用来自Via-lactea(VVV)调查中Vista变量的近红外光度法和天文学来分析包含年度微透析视差信息的微透明事件。这些事件位于银河系凸起的高度灭绝和低纬度区域,通常是光学微透镜调查的禁区。我们适合先前在VVV中发现的$ 1959 $活动目录,并提取$ 21 $微验证视差候选人。使用嵌套采样进行拟合,以自动表征年度微透析视差信号的多模式和退化后验分布。我们使用源源运动和圆盘和凸起偏转器的银河系模型来计算镜头质量距离的概率密度。通过比较从主序列镜头到基线幅度和混合参数的预期通量,我们确定了4个候选物,这些候选物具有概率$> 50 $%的镜头。最强大的候选人对应于附近的($ \ of0.78 $ kpc),中等质量($ 1.46^{+1.13} _ { - 0.71} \ m _ {\ odot} $)的深色残余物为镜头。在下一个最强的镜头位于HeliePentric距离$ \ 55.3 $ kpc。这是一个黑暗的残余物,质量为$ 1.63^{+1.15} _ { - 0.70} \ m _ {\ odot} $。这两个候选人都很可能是中子星,尽管可能是高质量的白色矮人。最后两个事件也可能是由黑暗残余物引起的,尽管由于数据限制,我们无法排除其他可能性。
We use near-infrared photometry and astrometry from the VISTA Variables in the Via Lactea (VVV) survey to analyse microlensing events containing annual microlensing parallax information. These events are located in highly extincted and low-latitude regions of the Galactic bulge typically off-limits to optical microlensing surveys. We fit a catalog of $1959$ events previously found in the VVV and extract $21$ microlensing parallax candidates. The fitting is done using nested sampling to automatically characterise the multi-modal and degenerate posterior distributions of the annual microlensing parallax signal. We compute the probability density in lens mass-distance using the source proper motion and a Galactic model of disc and bulge deflectors. By comparing the expected flux from a main sequence lens to the baseline magnitude and blending parameter, we identify 4 candidates which have probability $> 50$% that the lens is dark. The strongest candidate corresponds to a nearby ($\approx0.78$ kpc), medium-mass ($1.46^{+1.13}_{-0.71} \ M_{\odot}$) dark remnant as lens. In the next strongest, the lens is located at heliocentric distance $\approx5.3$ kpc. It is a dark remnant with a mass of $1.63^{+1.15}_{-0.70} \ M_{\odot}$. Both of those candidates are most likely neutron stars, though possibly high-mass white dwarfs. The last two events may also be caused by dark remnants, though we are unable to rule out other possibilities because of limitations in the data.