2019-05-24T11:13:40Z
Time series for the microlensing event OGLE-2015-BLG-0060
We present the analysis of stellar binary microlensing event OGLE-2015-BLG-0060
based on observations obtained from 13 different telescopes. Intensive coverage
of the anomalous parts of the light curve was achieved by automated follow-up
observations from the robotic telescopes of the Las Cumbres Observatory. We
show that, for the first time, all main features of an anomalous microlensing
event are well covered by follow-up data, allowing us to estimate the physical
parameters of the lens. The strong detection of second-order effects in the
event light curve necessitates the inclusion of longer-baseline survey data in
order to constrain the parallax vector. We find that the event was most likely
caused by a stellar binary-lens with masses M_star1 = 0.87 ± 0.12 Msun and
M_star2 = 0.77 ± 0.11Msun. The distance to the lensing system is 6.41 ± 0.14
kpc and the projected separation between the two components is 13.85 ± 0.16 AU.
This collection contains lightcurves resulting from different data processing
pipelines in a common reduction. See the accompanying paper for details.
Tsapras, Y.
2019MNRAS.487.4603T
Research
Catalog
gravitational-microlensing
galactic-bulge
binary-stars
Optical
(stc2:Coords) {
time: (stc2:TimeCoordinate) {
frame:
(stc2:TimeFrame) {
timescale: UTC
refPosition: BARYCENTER
time0: 2450000.5 }
location: @epoch
}
space:
(stc2:SphericalCoordinate) {
frame: (stc2:SpaceFrame) {
orientation: ICRS
epoch: "J2000.0" }
longitude: @ra
latitude: @dec
}
}
["index", "epoch", "mag", "magerr",
"seeing", "background"]
maskName = self.sourceToken[:-4]+".mask"
if os.path.exists(maskName):
with open(maskName, "r") as f:
masked = set(map(int, f.read().split(",")))
else:
masked = set()
with open(self.sourceToken) as f:
for line in f:
if not line.strip():
continue
row = dict(zip(labels, line.split()))
row["usable"] = int(row["index"]) not in masked
yield row
{
"LCO_COJ_A1": (
"LCO (Dome A), SSO, Australia, old camera",
"SDSS-i'", "opt.I"),
"LCO_COJ_A2": (
"LCO (Dome A), SSO, Australia, new camera",
"SDSS i'", "opt.I"),
"LCO_COJ_B": (
"LCO (Dome B), SSO, Australia",
"SDSS i'", "opt.I"),
"LCO_CPT_A": (
"LCO (Dome A), SAAO, South Africa",
"SDSS i'", "opt.I"),
"LCO_CPT_B": (
"LCO (Dome B), SAAO, South Africa",
"SDSS i'", "opt.I"),
"LCO_CPT_C": (
"LCO (Dome C), SAAO, South Africa",
"SDSS i'", "opt.I"),
"LCO_LSC_A": (
"LCO (Dome A), CTIO, Chile",
"SDSS i'", "opt.I"),
"LCO_LSC_B": (
"LCO (Dome B), CTIO, Chile",
"SDSS i'", "opt.I"),
"LCO_LSC_C": (
"LCO (Dome C), CTIO, Chile",
"SDSS i'", "opt.I"),
"L_DK_Z": (
"Danish Telescope, La Silla, Chile",
"LIred", "opt.I"),
"MOA-I": (
"MOA Telescope, Mount John, New Zealand, using red Filter",
"MOA I", "opt.R"),
"MOA-V": (
"MOA Telescope, Mount John, New Zealand, using blue Filter",
"MOA V", "opt.B"),
"OGLE-I": (
"Warsaw Telescope, Las Campanas, Chile, using I Filter",
"I", "opt.I"),
"OGLE-V": (
"Warsaw Telescope, Las Campanas, Chile, using V Filter",
"V", "opt.V"),
"Salerno-I": (
"Salerno Telescope, Salerno, Italy",
"I", "opt.I"),
"VVV-K": (
"VISTA Telescope, Paranal, Chile",
"K", "ir.K"),
}
{
# this is from lightcurves/scaling_factors.txt
"OGLE-I": (2.308, 0.0004),
"OGLE-V": (1.708, 0.0004),
"L_DK_Z": (4.360, 0.0004),
"MOA-I": (1.979, 0.0004),
"MOA-V": (1.284, 0.0004),
"LCO_LSC_B": (17.011, 0.0004),
"LCO_LSC_C": (11.479, 0.0004),
"LCO_COJ_A2": (1.541, 0.0004),
"LCO_CPT_A": (5.172, 0.0004),
"LCO_COJ_B": (3.393, 0.0004),
"LCO_CPT_C": (3.583, 0.0004),
"LCO_CPT_B": (4.069, 0.0004),
"LCO_LSC_A": (6.795, 0.0004),
"LCO_COJ_A1": (4.178, 0.0004),
"Salerno-I": (3.722, 0.0004),
"VVV-K": (1.526, 0.0004),}
telescopeCode = os.path.basename(self.sourceToken)[:-4]
pars = dict(zip(
("instName", "band", "ucd"), obsdata[telescopeCode]))
pars.update(dict(zip(
["f_i", "sigma_i"], scalingFactors[telescopeCode])))
return pars
magCol = targetTable.tableDef.getColumnByName("mag")
magCol.ucd = "phot.mag;em.%s"%@ucd
magCol.description = ("Unscaled instrumental magnitude in %s"
" (note that microlensing is achromatic)."%@band)
magErrCol = targetTable.tableDef.getColumnByName("mag")
magErrCol.ucd = "stat.error;phot.mag;em.%s"%@ucd
magErrCol.description = (u"Uncertainty in the magnitude"
u" as estimated by the specific pipeline; for this"
u" telescope use f_i=%f and σ_i=%f in the error estimate"
u" in sect. 3.5 of the paper."%(@f_i, @sigma_i))
rd.getById("build_instance")
from gavo import api
destDir = rd.getAbsPath("processed")
if not os.path.exists(destDir):
os.makedirs(destDir)
d = api.makeData(builder, forceSource=self.sourceToken)
destName = os.path.join(
destDir,
os.path.basename(self.sourceToken[:-4]+".vot"))
with open(destName, "w") as f:
api.writeAsVOTable(d, f, version=(1,5))
yield {}
ogle60 web"
processed
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