A&A 455, 903-921 (2006)
DOI: 10.1051/0004-6361:20065084
E. Flaccomio - G. Micela - S. Sciortino
INAF - Osservatorio Astronomico di Palermo Giuseppe S. Vaiana, Palazzo dei Normanni, 90134 Palermo, Italy
Received 24 February 2006 / Accepted 5 April 2006
Abstract
Aims. This paper's goal is to improve the member census of the NGC 2264 star-forming region and study the origin of X-ray activity in young PMS stars.
Methods. We analyze a deep, 100 ks long, Chandra ACIS observation covering a
field in NGC 2264. The preferential detection in X-rays of low-mass PMS stars gives strong indications of their membership. We study X-ray activity as a function of stellar and circumstellar characteristics by correlating the X-ray luminosities, temperatures, and absorptions with optical and near-infrared data from the literature.
Results. We detect 420 X-ray point sources. Optical and NIR counterparts are found in the literature for 85% of the sources. We argue that more than 90% of these counterparts are NGC 2264 members, thereby significantly increasing the known low-mass cluster population by about 100 objects. Among the sources without counterpart, about 50% are probably associated with members, several of which we expect to be previously unknown protostellar objects. With regard to activity we confirm several previous findings: X-ray luminosity is related to stellar mass, although with a large scatter;
is close to, but almost invariably below, the saturation level, 10-3, especially when considering the quiescent X-ray emission. A comparison between CTTS and WTTS shows several differences: CTTS have, at any given mass, activity levels that are both lower and more scattered than WTTS; emission from CTTS may also be more time variable and is on average slightly harder than for WTTS. However, we find evidence in some CTTS of extremely cool,
keV, plasma which we speculate is heated by accretion shocks.
Conclusions. Activity in low-mass PMS stars, while generally similar to that of saturated MS stars, may be significantly affected by mass accretion in several ways: accretion is probably responsible for very soft X-ray emission directly produced in the accretion shock; it may reduce the average energy output of solar-like coronae, at the same time making them hotter and more dynamic. We briefly speculate on a physical scenario that can explain these observations.
Key words: stars: activity - stars: coronae - stars: pre-main sequence - open clusters and associations: individual: NGC 2264 - X-rays: stars
The collapse of molecular cores and the early evolution of pre-main sequence (PMS) stars+disk systems involve a variety of complex phenomena leading to the formation of main sequence (MS) stars and planetary systems. Most of these phenomena, and their influence on the outcome of the formation process, are not yet fully understood.
The X-ray observations of star-forming regions have proved an invaluable tool for star formation studies. On one hand, because of the much higher luminosity of PMS stars in the X-ray band with respect to older field stars, deep imaging observations are one of the few effective means of selecting unbiased samples of members comprising both classical T-Tauri stars (CTTS) and, most importantly, the otherwise hard to distinguish weak-line T-Tauri stars (WTTS). Selection of a complete member sample is of paramount importance for any star formation study, such as those focused on the initial mass function (Salpeter 1955), the star formation history (e.g. Palla & Stahler 2000), the evolution of circumstellar disks and planetary systems (e.g. Haisch et al. 2005), and binarity (e.g. Lada 2006). On the other hand, the conspicuous X-ray activity of PMS stars is one of the aspects of the PMS stellar evolution that are not yet well understood, both with respect to its physical origin and to its consequences for the stellar/planetary formation process. Indeed, the ionization and heating caused by the penetrating X-ray emission might have a significant impact on the evolution of star/disk systems (Glassgold et al. 2004; Igea & Glassgold 1999), as well as on that of the star forming cloud as a whole (Lorenzani & Palla 2001).
The high X-ray activity levels of PMS stars (e.g. Preibisch et al. 2005) have
often been attributed to a "scaled up'' solar-like corona formed by
active regions. This is the same picture proposed for MS stars, for
which the X-ray activity is related to the stellar rotation (e.g.
Pizzolato et al. 2003), evidence that a stellar dynamo is responsible for the
creation and heating of coronae. For most non-accreting PMS stars
(WTTS), the fractional X-ray luminosity,
,
is
indeed close to the saturation level, 10-3, seen on rapidly
rotating MS stars (Flaccomio et al. 2003b; Preibisch et al. 2005; Pizzolato et al. 2003). This might suggest a
common physical mechanism for the emission of X-rays in WTTS and MS
stars or, at least, for its saturation. However, the analogy with the
Sun and MS stars may not be fully valid, because: i) the relation
between activity and rotation is not observed in the PMS
(Preibisch et al. 2005; Rebull et al. 2006); ii) with respect to the Sun at the maximum
of its activity cycle, saturated WTTS have
times greater and plasma temperatures that are also
significantly higher.
The X-ray emission of CTTS, PMS stars that are still undergoing mass
accretion, is even more puzzling. With their circumstellar disks and
magnetically regulated matter inflows and outflows, CTTS are complex
systems. With respect to their X-ray activity, the bulk of the
observational evidence points toward phenomena similar to those
occurring on WTTS. However, CTTS have significantly lower and
unsaturated values of
and
(Flaccomio et al. 2003b,a; Preibisch et al. 2005; Damiani & Micela 1995). In apparent contradiction to this
latter result, high-resolution X-ray spectra of two observed CTTS, TW Hydrae (Kastner et al. 2002; Stelzer & Schmitt 2004) and BP Tau (Schmitt et al. 2005), have indicated
that soft X-rays may also be produced in accretion shocks at the base
of magnetic funnels. Moreover, magnetic loops connecting the stellar
surface with the inner parts of a circumstellar disk may produce some
of the strongest and longer-lasting flares observed on PMS stars
(Favata et al. 2005). The recent detection of X-ray rotational modulation
(Flaccomio et al. 2005), however, implies that emitting structures are generally
compact, so that these long loops cannot dominate the quiescent X-ray
emission.
NGC 2264 is a 3 Myr-old star-forming region located at
760
pc (Sung et al. 1997) in the Monoceros. Compared to the Orion Nebula Cluster
(ONC) and Taurus, NGC 2264 has intermediate stellar density and total
population, making it an interesting target for investigating the
dependence of star formation on the environment. It is on average older
than the ONC (
Myr), but star formation is still active inside
the molecular cloud in at least two sites where a number of protostars
and prestellar clumps have been detected (Peretto et al. 2006; Young et al. 2006). It is
therefore a useful target for the study of the formation and time
evolution of young stars. Its study is eased by the presence of an
optically thick background cloud, effectively obscuring unrelated
background objects, and by the low and uniform extinction of the
foreground population (Rebull et al. 2002; Walker 1956). Despite being the first
star forming region ever identified as such, the low-mass population of
NGC 2264 is still not well characterized: proper motion studies
(Vasilevskis et al. 1965) have been restricted to high mass objects; several studies
have identified the CTTS population using disk and accretion indicators
(Rebull et al. 2002; Lamm et al. 2004; Park et al. 2000), but have missed WTTS; past X-ray
observations with ROSAT (Flaccomio et al. 2000) have been useful in
identifying the WTTS population, but have not been sensitive enough to
detect low-mass (
)
and embedded stars. We present here
results from the analysis of a deep Chandra observation of the
region. Another similar observation of a region just to the north of
the one considered here has been analyzed by Ramírez et al. (2004a). The X-ray
properties of NGC 2264 members derived in the present paper and by
Ramírez et al. (2004a), augmented with similar data for the Orion Flaking
Fields (Ramírez et al. 2004b) are studied by Rebull et al. (2006) in comparison with
the results of the COUP survey (Getman et al. 2005). Results from the same
Chandra observation analyzed here on the peculiar binary system
KH 15D have been presented by Herbst & Moran (2005). Finally, the properties
of three embedded X-ray sources near Allen's source, observed with XMM-Newton, have been recently presented by Simon & Dahm (2005).
The paper is organized as follows. We begin (Sect. 2) with the presentation of the X-ray data, its reduction, source detection, and photon extraction. In Sect. 3 we then introduce the optical and near infrared data used to complement the X-ray observation. In Sect. 4 we present the temporal and spectral analysis of X-ray sources and derive X-ray luminosities. Sections 5 and 6 then discuss our results with respect to cluster membership and the origin of X-ray activity on PMS stars. We finally summarize and draw our conclusions in Sect. 7.
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Figure 1: Digitized Sky Survey image of NGC 2264. The field of view of the Chandra-ACIS observation discussed in this paper is shown as a white square. The famous Cone Nebula is visible toward the bottom of the image and the O7 star S Mon is close to the upper edge. |
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We obtained a 97 ks long ACIS-I exposure of NGC 2264 on 28 Oct. 2002
(Obs. Id. 2540; GO proposal PI S. Sciortino). The
field of view (FOV) of ACIS is shown in Fig.
1, superimposed on the Digitized Sky Survey image of
the region. It was centered on RA 6
40
58
7, Dec 9$^$34
14
(roll angle: 79
). Figure
2 shows a color rendition of the spatial and
spectral information we obtained. ACIS was operated in FAINT mode with
CCD 0, 1, 2, 3, 6, and 7 turned on. Data obtained with CCD 6 and 7, part
of the ACIS-S array, will not be discussed in the following because of
the very degraded point-spread function (PSF) and effective area
resulting from their large distance from the optical axis.
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Figure 2: NGC 2264 as seen in X-rays by ACIS. The true color (RGB) image is constructed from images in three energy bands: [200:1150] eV (red), [1150:1900] (green), and [1900:7000] (blue). Red therefore indicates soft and unabsorbed sources, blue hard and/or absorbed sources. |
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Data reduction, starting from the level 1 event file, was performed in
a standard fashion, using the CIAO 2.3 package and following the
threads provided by the Chandra X-ray Center. Several IDL custom programs were also employed.
First, we corrected the degradation in the spectral response due to the
charge transfer inefficiency (CTI), which occurred in particular during
the first months of the Chandra mission, using the ACIS_PROCESS_EVENTS CIAO task. We then produced a level 2 event
file by retaining only events with
and
.
Finally we corrected the data for the time dependence of the energy
gain using the CORR_TGAIN utility.
The X-ray stellar sources have, on average, a different spectrum with
respect to the ACIS background. The total signal-to-noise ratio (SNR)
of sources can therefore be maximized by filtering out events with
energy outside a suitable spectral band: we first performed a
preliminary source detection as discussed in Sect. 2.2 on
the whole event list. We then defined a radius, R97, for each
source such that 97% of the PSF counts fall within this radius (Sect. 2.3). We extracted source photons for all sources from
circles with
and background photons from a single
background region that excludes photons from all sources within their
respective R97 (a sort of "Swiss cheese'' image with the sources
carved out). We then computed the total SNR of
sources
for a fine grid of minimum and
maximum energy cuts. The highest source SNR was obtained for
and
.
With these cuts, the number of
photons in the source extraction area (including background photons) is
reduced to 96% of the total, while the background is reduced to 28%
of the total. We checked that consistent results are obtained by
maximizing the SNR of faint sources only (<20 net counts), which may
have a different average spectrum and are the ones we are most
interested in for the purpose of detection.
After filtering in energy, the time-integrated background is 0.07
counts per arcsec2, consistent with nominal values. The background
was constant in time except for a small flare with a peak reaching
about twice the quiescent rate. The flare starts 13 ks after the
beginning of the observation and lasts ks. The effect of the
background flare on source light curves is negligible however, even for
faint sources, i.e. those most affected by the background. This is
confirmed by the negative results of Kolmogorov-Smirnov variability
tests (see Sect. 4.1) performed on the background extraction
regions relative to each source. In the study of source lightcurves (Sect. 4.1), we will therefore assume a constant background.
We detected sources using the PWDetect code
(Damiani et al. 1997). The
significance threshold was set to 4.6
.
According to extensive
simulations of source-free fields with the background level of our
observation, this threshold corresponds to an expectancy of 10 spurious
sources in the whole FOV. PWDetect reports 423 sources. Upon careful
inspection we removed three entries relative to sources that were
detected twice, leaving a total of 420 distinct sources. Twenty eight
of these are below the 5.0
significance threshold,
corresponding to the more conservative criteria of one expected
spurious source in the FOV. Background-subtracted source counts in the
0.2-7 keV band are derived by PWDetect directly from the wavelet
transform of the data. Effective exposure times at the source
positions, averaged over the PSF, are also computed by PWDetect from an
exposure map created with standard CIAO tools assuming an input energy
of 2.0 keV
.
Detected sources are listed in Table 1. In the first eight
columns we report source number, sky positions with uncertainty,
distance from the Chandra optical axis, source net counts (in the
0.2-7 keV band), effective exposure time, and the statistical
significance of the detection.
Source and background photon extraction for spectral and timing analysis was performed using CIAO and custom IDL software. We first determined the expected PSF for each source using the CIAO MKPSF tool, assuming a monochromatic source spectrum (E=1.5 keV). We thus determined the expected encircled energy fraction as a function of distance from the source. Photons' extraction circles were defined so as to contain 90% of the PSF, save for 28 sources for which the encircled PSF fractions were reduced to values ranging from 74% to 89% to avoid overlap with neighboring sources. The local background for each source was determined from annuli whose inner radii exclude 97% of the PSF and whose outer radii are twice as large. In order to eliminate contamination of the background regions due to the emission of neighboring sources, we excluded from these annuli all the intersections with the 97% encircled counts circles of other sources. The area of background extraction regions were computed through a mask in which we drilled regions outside the detector boundaries and circles containing 97% of the PSF photons from all sources.
For sources with at least 50 photons, source and background spectra
suited to the XSPEC spectral fitting package were then created using
standard CIAO tools. Corresponding response matrices and effective
areas (RMF and ARF files, respectively) were also produced with CIAO.
For spectral analysis, spectra were energy binned so that each bin
contains a fixed number of photons, depending on net source counts
:
15 photons per bin for
,
10 photons for
,
and 5 photons for
.
Because spectral
analysis (Sect. 4.2) was restricted to energies >0.5 keV, the
first energy bin was forced to begin at that energy.
In this section we discuss the non-X-ray data that we collected from the literature on the known objects in the FOV of our ACIS observation, and in particular on the detected X-ray sources. We first describe the cross-identifications with spatially complete optical, NIR, and X-ray catalogs (Sect. 3.1) and the informations we collected and/or derived from these catalogs (Sect. 3.2). We then briefly discuss the identifications of our X-ray sources with spatially incomplete mid-IR and millimiter catalogs (Sect. 3.3).
We cross-identified our X-ray source list with catalogs from the
following optical/NIR surveys covering the whole area of our ACIS
field: 2Mass (NIR photometry), Walker (1956, optical photometry +
spectroscopy), Rebull et al. (2002, optical/NIR photometry + low resolution
optical spectroscopy), Lamm et al. (2004, optical
photometry + variability), Dahm & Simon (2005, optical photometry +
spectroscopy), Flaccomio et al. (2000, <)413#>X-ray sources#. The seven
cross-identified catalogs are listed in the first column of
Table 2. In the 2nd column, we indicate the number of
objects within the ACIS FOV and, in the third, the number of objects
identified with ACIS sources and of those for which the identification
is unique. Adopted positional tolerances for cross-identifications are
reported in the 4th column, either as a single figure for the whole
catalog or as a range when defined for each individual object (see the
table footnotes). They are based on the uncertainties quoted for each
catalog.
Among the catalogs considered here, the deepest
photometric surveys are
2Mass in the NIR (JHKs) and
Lamm et al. (2004) in the optical (
). A comparison with the
isochrones of Siess et al. (2000, hereafter SDF) in the optical and NIR
color-magnitude diagrams (Figs. 3 and 4)
indicates that at the distance of NGC 2264, 2Mass reaches down to about
0.1
for 10 Myr old stars, i.e. the oldest expected in the
region, while Lamm et al. (2004) reaches slightly deeper for unabsorbed
stars, but is obviously less sensitive to highly absorbed ones. Within
the ACIS FOV, spectral types are given for 7 stars by Walker (1956),
for 87 stars by Rebull et al. (2002), for 150 stars by Lamm et al. (2004), and for
157 stars by Dahm & Simon (2005).
Table 2: Catalogs used for cross-identification
We created a master list of cross-identified objects following a
step-by-step procedure. In the first step we matched ACIS sources with
2Mass objects: first we registered the ACIS coordinates to the 2Mass
ones by iteratively cross-identifying the two catalogs and shifting the
ACIS coordinates by the mean offset of uniquely identified source
pairs. Identification radii were chosen as the quadrature sum of the
position tolerances defined above (Table 2). We then
created a joint catalog of objects containing all matched and unmatched
ACIS and 2Mass objects, assigning them coordinates from 2MASS when
available. In the following step we repeated the above process using
the ACIS+2Mass catalog as reference and matching it with the
Rebull et al. (2002) one. We then repeated the process with the Flaccomio et al. (2000),
X-ray source list, then with the Lamm et al. (2004), Dahm & Simon (2005), and
Walker (1956) catalogs. The coordinate shifts of all catalogs with
respect to the 2Mass system are given in Table 2
(Cols. 5 and 6), along with the median offsets between object
positions and the reference 2Mass catalog. After each step, identified
source pairs and unidentified objects were checked individually and a
small fraction of the identifications ()
were modified. In the
first step, for example, five identifications between four ACIS and
five 2Mass sources were added: in two cases (sources #64 and #404),
2Mass sources were only slightly more distant with respect to the
identification radii. In another case, the X-ray source, #102, was
situated at the edge of the detector, and its position was therefore
more uncertain than the formal error indicated. In the last case the
X-ray source, #237, was detected between two nearby 2Mass objects and
an identification was forced with both.
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Figure 3:
Optical color-magnitude diagram of all the objects in the ACIS
field of view. Larger symbols indicate X-ray sources or likely NGC 2264
members as defined in Sect. 3.2. Filled symbols refer
to ACIS detected
objects. As indicated in the legend, when possible we distinguish
between CTTS and WTTS as defined by the EW of their
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Figure 4: Near-IR color magnitude diagram for the objects in the ACIS FOV. Symbols and tracks as in Fig. 3. |
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Table 3 lists, for the 1888 distinct objects in the ACIS FOV, consolidated coordinates and cross-identifications numbers for each of the seven catalogs. 425 rows refer to objects related to one of the 420 ACIS sources: 351 are identified with a single optical/NIR counterpart, two are identified with 5 and 2 counterparts respectively, and 67 do not have any optical or near-infrared identification. The other 1463 rows in Table 3 refer to non ACIS-detected objects. For these latter we computed upper limits to the ACIS count rate using PWDetect and the same event file used for source detection. Measured count rates, repeated from Table 1, and upper limits are reported in Col. 12 of Table 3.
Focusing on the identification of ACIS sources with optical/NIR
catalogs, given the relatively large number objects in the field of
view, we can wonder how many of the identifications are due to chance
alignment and not to a true physical association. We can constrain the
number of chance identifications by assuming that positions in the two
lists are fully uncorrelated. Because this is definitively not the case
for our full X-ray source catalog, our estimate can only be considered
a loose upper limit. Furthermore, limiting our X-ray sample to the 28
sources with significance below 5.0,
9 of which are expected to
be spurious and whose positions will indeed be random, we can place an
upper limit on the fraction of spurious sources associated with an
optical object. This value is of interest when studying the X-ray
properties of optically/NIR selected samples. In order to estimate the
number of spurious identifications assuming uncorrelated positions, we
proceeded as follows: for each X-ray source we considered optical
objects within a circular neighborhood of area
,
within which
source density is assumed uniform. We then estimated the fraction of
covered by identification circles. The sum of these fractions
is our upper limit to the number of chance identifications. We repeated
the calculation for radii of the neighborhood circle from 1.0
to 4.0
.
The upper limit to the number of chance identifications
for the full sample of ACIS sources ranges from 13 to 15. For the 28
source with significance
,
we instead estimated no more
than 2.5 chance identifications. If we assume that, out of the 28
sources, only the nine spurious detections have positions that are
indeed uncorrelated with optical objects, we can scale this result and
conclude that
spurious X-ray source will be identified by
chance with an optical object. Conversely, we can most likely locate
spurious sources among the 15 sources whose detection significance is
below 5.0
and that are not identified with optical/NIR
catalogs.
We collected optical and NIR data from the literature for all the stars
in the master catalog assembled in the previous section. In Table
4 we report photometry and spectral types for our X-ray
sources with unique optical identification. A total of 300 X-ray
detected stars were assigned both
and
magnitudes and are
plotted in Fig. 3 as filled symbols; 299 have H and Kmagnitudes and are plotted in Fig. 4 (264 of these also
appear in Fig. 3). In both color-magnitude diagrams
(CMDs), we also show SDF tracks and isochrones for reference,
transformed to colors and magnitudes using the conversion table given
by Kenyon & Hartmann (1995) and shifted along the reddening vectors by the median
extinction of known members (
)
and vertically by the distance
modulus corresponding to the adopted distance. The extinction law was
adopted from Rieke & Lebofsky (1985). For stars with spectral types, we derived
effective temperatures,
,
bolometric corrections, BCI, and
intrinsic colors, (R-I)0, using the relations compiled by
Kenyon & Hartmann (1995) and, for the temperatures of M stars, the intermediate-gravity scale of Luhman (1999).
Using the available
and
photometry, we then derived extinction values (
,
where
E(R-I)=(R-I)0-(R-I)) and bolometric luminosities
(
]). Finally we
estimated masses and ages from the theoretical HR diagram, Fig.
5, through interpolation of the SDF evolutionary tracks. In
summary, out of the 351 X-ray sources with a unique optical/NIR
identification, we estimated
for 165 X-ray sources,
and
for 163, masses and ages for 161.
Other than the sample of X-ray detected stars, that are likely cluster
members, as discussed in Sect. 5, we also consider another
sample of 83 X-ray undetected likely cluster members. These, plotted
with empty symbols in Figs. 3-5, were chosen according to their position in the I vs. R-Idiagram, the strength of the
line (measured either
spectroscopically or photometrically) and optical variability: first,
we defined a cluster locus in the I vs. R-I diagram using the SDF
tracks and the observed concentration of X-ray sources. The cluster
locus was defined as the area either above the 107.1 Myr
isochrone or to the left of the 0.8
evolutionary
track
. We
then considered the following as likely members: (i) stars in the
cluster locus and with strong
emission according to the
narrow-band
photometry of Lamm et al. (2004), using the same
criterion discussed by these authors; (ii) stars in the cluster locus
with moderate (chromospheric)
emission and with variable
optical lightcurves (both periodic and irregular), again as discussed
by Lamm et al. (2004); (iii) stars for which spectroscopic observations of
the
line were available and for which the measured EW was
larger than 10 (the canonical CTTS threshold). In this sample of 83
X-ray undetected likely members, we were able to determine masses and
ages for 16 stars.
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Figure 5: Theoretical HR diagram for the subsample of likely members that could be placed in this diagram through optical photometry and spectral types. Symbols as in Fig. 3. Tracks and isochrones are by SDF. |
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Knowledge of the extinction is fundamental for deriving intrinsic X-ray
luminosities of detected stars or upper limits for undetected ones (Sect. 4.3). For stars with no optical spectral type,
cannot
be determined as indicated above. We therefore estimated extinctions
from the NIR J-H vs. H-K diagram by de-reddening stars that could be
placed in this diagram onto the expected intrinsic locus. This was
taken from Kenyon & Hartmann (1995) for the stellar contribution and supplemented
with the CTTS locus of Meyer et al. (1997). The degeneracy in the reddening
solutions was solved by always taking the first intercept between the
intrinsic locus and the reddening vector. Stars whose position was
within 1.5
of the loci were assigned zero extinction. In
summary, we obtained a total of 278 and 62 extinction values for X-ray
detected stars and undetected likely members, respectively. These
number exceed the number of extinctions derived from optical data by
115 and 46 for the same samples. Although these NIR extinction values
are arguably more uncertain than the ones derived from spectral types,
they are especially valuable for highly extinct objects.
In addition to the wide field surveys described in Sect. 3.1, we also correlated our X-ray sources with three
mid-infrared (MIR) and millimeter (mm) catalogs recently published for
the IRS 2 and IRS 1 regions: Young et al. (2006, hereafter Y06),
Teixeira et al. (2006, T06), and Peretto et al. (2006, P06). These three surveys
target very young and/or embedded objects and are therefore ideal for
checking the nature of X-ray sources with no optical/NIR counterpart.
They are, however, limited in area coverage: T06 lists Class I/0
sources detected with SPITZER in a
arcmin2region close to IRS 2, Y06 lists all SPITZER detected objects in
a dense but even smaller
arcmin2 region slightly
south-east of IRS 2, P06 list the pre/protostellar cores detected at
1.2 mm in both the IRS 1 and IRS 2 regions.
Within the area of Y06, the only MIR work available so far that lists
all the objects detected in the surveyed region, we find MIR
counterparts for 2 out of 4 X-ray sources lacking optical/NIR
counterparts. In total, 4 of the 67 unidentified X-ray sources were
assigned new counterparts: #145 was associated with source 12 of T06,
a Class I/0 source; #228 was associated with the D-MM 15 mm core,
indicated by P06 as probably starless; #244 was associated with
source 43 in Y06 and is, judging from its spectral energy distribution
(SED), an absorbed class II/III PMS star that is relatively bright in
K-band (K=12.15, Y06); finally, #274 was associated with the D-MM 10 mm core,
indicated as starless by P06, with source 1656 in Y06 and source 15 in
T06 (characterized by a steeply rising MIR SED).
Among the X-ray sources with optical/NIR counterparts, 17 in the region
covered by Y06 were identified with SPITZER sources, of which
one, #281, is a likely Class I source (Y06's source 62). Three more
X-ray sources were identified with Class I/0 sources in the list of
T06: #150 and #242 also having NIR counterparts, and #194 (see also
Sect. 6.3) with both NIR and optical (I=19.89) counterpart.
As for the P06 mm-cores, identifications are made uncertain by the
limited spatial resolution of the mm data (cf. P06). The core D-MM 14,
classified as protostellar by P06, is probably associated with ACIS
source #89 (offset: 1.2''), an optically faint (V=20.91) and NIR
bright (K=10.6) star with intense
emission:
.
Quite similarly, C-MM 1, also indicated as a
protostellar core by P06, is offset by 1.3'' from the ACIS source
#361, an optically visible source with strong
(V=18.55,
K=11.85,
)
and it is therefore likely to be
associated with it. Finally, C-MM 5, classified by P06 as a
protostellar core, might be associated with ACIS # 305 (offset:
1.9''). This ACIS source is in turn most likely associated with IRS 1
(offset: 0.8'', Schreyer et al. 2003). Note, however, that P06 suggests
that C-MM 5 may not be associated with IRS 1. A more detailed analysis
of the X-ray properties of young protostars in NGC 2264 will be the
subject of a future paper.
In this section we analyze the X-ray properties of our ACIS sources as indicated by their ACIS lightcurves (Sect. 4.1) and spectra (Sect. 4.2). The results of the spectral analysis are then used in Sect. 4.3 to estimate X-ray luminosities.
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Figure 6:
Fraction of variable stars as a function of count statistics for
stars with
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Source variability was characterized through the Kolmogorov-Smirnov
test. Column 9 of Table 1 reports the resulting
probability that the distribution of photon arrival times is not
compatible with a constant count-rate. Given the sample size (420 sources), a value below 0.1% (obtained for 72 sources) indicates the
light-curve is almost certainly variable, while a <1% value (87 sources) indicates probable variability, although up to
of the variable sources might actually be
constant. If we are not interested in the individual sources but in the
overall fraction of variable sources, we can place a lower limit on
this quantity by computing the minimum number of variable sources as a
function of probability threshold,
:
.
The maximum
is obtained for
%, for which
and
.
We
conclude that within our observation at least 30% (126/420) of
the lightcurves are statistically inconsistent with constancy. This
fraction is certainly a lower limit to the true number of variable
sources, as our ability to tell a variable source from a constant one
is influenced by photon statistics. This is clearly indicated by the
dependence of the fraction of variable sources on source counts. If for
example, we restrict the above analysis to sources with more than
100(500) counts, a total of 145(33) sources, we find that at least
54%(73%) of these are variable.
We next investigated whether different kinds of stars, classified from
optical data, showed different variability fractions. From the previous
discussion, it is clear that for a meaningful comparison, source
statistics must be taken into account. Figure 6
shows the variability fraction,
,
as a function of
source counts for CTTS and WTTS, as distinguished by their
equivalent width. Variability fractions are computed for sources with
counts in intervals spanning 0.8 dex (because in the figure points are
spaced by 0.1 dex, only one point every eighth is independent). Error
bars on the variability fractions are estimated by assuming binomial
statistics:
,
where
is the number of sources in each count bin. In addition to the expected
increase in the variability fraction with source statistics, we note
that CTTS appear to be more variable with respect to WTTS, at least
when considering stars with more than
200 counts. Figure 7 shows a similar comparison for two mass
segregated sub-samples:
and
.
Lower mass stars appear to be more variable, and again this
difference is noticeable only for stars that are bright enough. We
quantified the differences between variability fractions, by testing,
for each count bin, the null hypothesis that the two samples are drawn
from the same parent population. We chose the difference of the two
observed variability fractions,
,
as statistics. We then
numerically computed the probability that a
equal or larger
than the observed one is obtained by randomly drawing pairs of numbers
from binomial distributions appropriate to the two sample sizes but
characterized by the same probability of observing a variable source,
i.e. the variability fraction. Because this number is not well
constrained, we conservatively took as our confidence level the minimum
that is obtained varying this fraction between 0 and 1. The results of
these tests indicate that the differences in the variability fractions
are statistically not highly significant: in the bin centered at
400 counts, the low mass stars are more variable than higher mass
stars with a 94% confidence, and CTTS are more variable than WTTS with
a 92% confidenceand in the bin centered at
800 counts. We thus
consider these results as tentative. However we note that the
significance of the difference between CTTS and WTTS in NGC 2264 is
strengthened by the fact that a similar result, with a similar
confidence, was also obtained by Flaccomio et al. (2000) using totally
independent ROSAT data.
![]() |
Figure 7:
Fraction of variable stars as a function of count statistics for
stars in two different mass ranges: [0.3-0.7] ![]() ![]() |
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We have analyzed the X-ray spectra of the 199 sources with more than 50
detected photons. Spectral fits were performed with XSPEC 11.3 and with
several shell and TCL scripts to automate the process. For each source
we fit the data in the [0.5-7.0] keV energy interval with several model spectra:
one and two isothermal components ( APEC), subject to
photoelectric absorption from interstellar and circumstellar material
( WABS). Plasma abundances for one-temperature (1T) models were
fixed at 0.3 times the solar abundances (Wilms et al. 2000), while they
were both fixed at that value and treated as a free parameter for
two-temperature (2T) models. The absorbing column densities,
,
were both left as a free parameter and fixed at values corresponding to
the optically/NIR determined extinctions, when available:
(Vuong et al. 2003). A total of three or six models were thus fit
for each source depending on the availability of optical extinction
values. For each model, spectral fits were performed starting from
several initial conditions for the fit parameters as indicated in Table
5. For example for isothermal models with free
,
two values of
and four values of kT were adopted as initial
conditions, for a total of eight distinct fits. For each model the
adopted fit parameter set was chosen from the model fit that minimizes
the
.
This procedure was adopted in order to reduce the risk
that the
minimization algorithm used by XSPEC finds a relative
minimum.
Table 5: Initial conditions for XSPEC models.
Next, we considered which of the available model fits for each source
(three or six, reference model names are given in Table
5) was the most representative of the true source
spectrum, and thus the one to be adopted for the following
considerations. The goal was twofold: to characterize the emitting
plasma, in order to investigate its origin, and to determine accurate
intrinsic band-integrated X-ray luminosities. Crucial for this latter
step is to determine extinction (,
see Sect. 4.3).
Generally speaking, models must have enough components to yield
statistically acceptable fits according to the
or,
equivalently, the null probabilities (n.p.) that the observed spectra
are described by the models. However, models with too many free
parameters with respect to the spectra statistics, while formally
yielding good fits, will not be constrained by the data and will yield
limited physical information. A particularly severe problem with
CCD-quality (ACIS) low-statistic spectra is the degeneracy between
absorption and temperature: an equally good fit can be often obtained
with a cool plasma model with a large emission measure but suffering
high absorption, or with a warmer temperature and a lower extinction.
It is therefore desirable to check the
obtained from the fits
with independent information from optical/NIR data. Figures
8 and 9 show the
cumulative distribution of the n.p. for spectral fits performed with
four different models, respectively for faint and bright sources (
,
). The models are 1T, 2T, and, for sources
with independent
estimates, 1T
and 2T
.
In this kind
of plot the distribution for a perfectly adequate spectral model should
follow the diagonal, that for an oversimplified model should fall below
the diagonal, and that for an over-specified model should lie above.
For faint sources, 1T model appears perfectly adequate. Fixing the
to the optically determined value worsens the agreement somewhat
but still results in fits that are, when considered individually,
acceptable for the most part. A 2T model with free
appears to be
too complicated (and therefore unconstrained), while a 2T model with
fixed
is more acceptable, but still on average too sophisticated
for the quality of these low-statistic spectra. For brighter sources we
notice instead that 1T models are on average not favored, while 2T
models are still not always needed.
![]() |
Figure 8:
Cumulative distribution of null probabilities resulting from X-ray
spectral fits. The curves refer here to X-ray sources with more that
50 counts (the minimum for which we performed spectral fits) and less
than 300 counts. The different lines refer to different physical models
(one and two temperature, with free of fixed ![]() ![]() ![]() |
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![]() |
Figure 9: Same as Fig. 8 for sources with more than 300 counts. |
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For most sources more than one spectral model is statistically
acceptable. We choose a best-guess model as the simplest that still
gives a statistically acceptable fit. The compelling reason to choose
this approach is the mentioned degeneracy between temperature and
absorption. Note for example that, from Fig. 8,
one could be tempted to always choose 1T models with free
for
faint sources, as these models appear to be statistically perfectly
adequate for representing the spectra. However, when examined
individually, many of these 1T spectral fits have rather degenerate fit
solutions, i.e. large and correlated uncertainties on the
and
kT values, also implying large uncertainties on unabsorbed fluxes.
This degeneracy can be broken by using the additional information on
absorption coming from optical/NIR data, when available and compatible
with the X-ray spectra.
After some experimenting, we chose our best-guess model according to
the following empirical scheme: if n.p.(1T
we chose the
1T
model. Otherwise, if n.p.(1T
we chose the 1T model
(with free
). If both of the previous tests for isothermal models
failed, we tried with two temperature models, this time lowering our
n.p. threshold to 5%. First we tried the 2T
model
(n.p.(
)
and then the 2T model (n.p.(2T
). As a
last resort, in case none of the previous models could be adopted, we
chose the 2Tab model (free
,
free abundances). Although the above
scheme was designed so as to favor simple models, the different n.p.
thresholds resulted in four cases (sources #257, #275, #280, and
#300) in adopting 2Tab models that were either statistically worse or
comparable to simpler 1T, 1T
,
or 2Tab
models. We
therefore adopted these latter. After careful examination of individual
fits, the spectral models adopted for four more sources (#97, #104,
#127, and #241) were modified. In these cases the automatic choice
would result in unphysical, unusual, or unconstrained temperatures and
absorptions, whereas our adopted models are both statistically and
physically acceptable
.
The solid gray lines in Figs. 8 and
9 refer to the final choice of best-guess
models. Table 6 lists the end result of our spectral
analysis: we report the adopted model, the source of the adopted ,
the null probability, the plasma temperature(s) and normalization(s),
the observed and absorption corrected fluxes in the [0.5-7] keV band.
In summary, out of the 199 sources with more than 50 counts, we adopted
isothermal models in 147 cases and two component models in the
remaining 52 cases. For 138 sources we adopted a spectral fit in which
the
was fixed, in 101 cases to the value estimated from the
optically determined
and in 37 cases from NIR photometry. In the
remaining 61 cases,
was treated as a free fit parameter. Out of
these 61 cases, independent estimates of extinction were available from
optical and NIR data in 21 and 3 cases respectively, but our algorithm
(or, for sources #97 and #127, our choice) preferred the spectral fit
with free
.
We examine these cases more in detail to assess the
ambiguities in the model fits and the consequences on the derived X-ray
fluxes.
Figure 10 illustrates one such case: source
#375. Here the extinction determined from the 1T (kT=1.3 keV) spectral
fit (90% confidence interval:
cm-2) is lower than
that estimated from the
(
cm-2). Fixing the
to that value yields an unsatisfactory isothermal fit. However, adding
a second cool isothermal component to the spectral model
(kT=0.25 kev), a good fit can be obtained even when fixing the
.
This is quite a typical example of the degeneracy in the fitting of
sources with low/moderate statistics. We can estimate the uncertainty
in the X-ray unabsorbed fluxes derived from the spectral fits due to
this degeneracy, as the difference between the fluxes derived from the
two acceptable fits: for source #375, for example, this is
0.14 dex. More generally, acceptable fits with
fixed to the
optical/NIR values could be obtained in 22 of the 24 cases in which our
procedure preferred the spectral fit with free
.
In five
cases
, among which
the one described above, in order to reconcile the observed spectra
with the optical extinction, an additional cool thermal component
(
kT=0.22-0.34) would be required to compensate for the higher
.
If
for these five sources the true
were the optically derived ones,
we are underestimating the unabsorbed fluxes by 0.14-0.37 dex (mean
0.2dex) by choosing the 1T fit with free (lower)
.
For the other
17 sources, fixing the
would not require an additional component:
reasonable fits (
%) were obtained even with the same models
(1T in 16 cases, 2Tab for source #187). In 10 of these 17 cases the
adopted fits with free
lead to smaller absorptions and slightly
hotter (0.2-0.3 keV) temperatures (the cool temperature for #187),
while the opposite happens in the 7 other cases. Had we fixed the
to the optical values in these 17 cases, we would have obtained
unabsorbed fluxes on average
0.03 dex larger and always within
0.20 dex (in either direction) of the adopted ones.
![]() |
Figure 10:
Upper panel: spectrum of source #375 with overlaid three different
spectral models, 1T with both free and fixed ![]() ![]() ![]() ![]() |
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From this discussion of the 22 cases with most ambiguous extinctions
(
of the sources for which we have optical/NIR
), we
conclude that (i) typical uncertainties in the unabsorbed flux are less
than 0.2 dex and that (ii) individual X-ray fluxes corrected using an
from spectral fits can be as wrong as
0.4 dex, if the cool
temperature is altogether missed by the spectral fit with a consequent
underestimation of the
.
Two more sources have incompatible X-ray and optical extinctions
according to our choice of best model. Source #234 did not enter
into the previous discussion because it has the least acceptable fits
of the whole sample, at most
% for a 2T model with free
(note, however, that with an adequate spectral model, we would expect
three sources out of 199 to have lower n.p.). Adopting the optical
(
)
would result in an unabsorbed flux that is
only 0.07dex larger with respect to the one obtained leaving
as a
free fit parameter (
). Source
#71 is a more interesting case. Figure 11
shows its spectrum, with spectral fits obtained with free and fixed
.
The X-ray-derived extinction (
)
is about 17 times larger than estimated from the optical reddening
(
)
. The discrepancy is highly significant
and independent of the considered spectral model. We note that the
detected emission from this source is dominated by a powerful flare,
with a peak count rate
100 times what it was before and after the
flare. A possible scenario to explain the high absorption might involve
a solar-like coronal mass ejection associated with the flare, providing
the additional absorbing material. This hypothesis has been formulated
by Favata & Schmitt (1999) to explain a more modest increase in
during a
powerful flare observed on Algol. Further discussion of the physical
interpretation of spectral fit results is deferred to Sect. 6.
![]() |
Figure 11:
Some as Fig. 10 for source #71. In
this case we plot the 1T model with free ![]() ![]() ![]() |
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![]() |
Figure 12:
Count-rate to unabsorbed flux conversion factor as a function
of ![]() ![]() ![]() ![]() ![]() |
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Extinction-corrected X-ray luminosities in the [0.5-7.0] keV band were
estimated for all sources for which an indication of extinction was
available. For the 199 sources with more than 50 counts and for which
spectral fits were performed, we computed
from the
(unabsorbed fluxes) column in Table 6, adopting a
distance of 760 pc, adequate for NGC 2264 members. For the fainter
sources, for which spectral fits were not performed, we derived a
count-rate to unabsorbed flux conversion factors using the results of
the spectral fits for the brighter stars. Figure 12 shows the
run of the flux/rate ratio vs.
for this sample. Different symbols
indicate different origins of the absorption values (see previous
section). We then performed polynomial fits to the data points for the
whole sample, for the 56 sources with more than 300 counts, and for the
59 sources with less 100 counts. Results are shown in Fig.
12. Because the sources for which we want to determine fluxes
have <50 counts, we adopt the latter fit as our relation between
and the conversion factor:
In this section we define a sample of 491 likely NGC 2264 members
within the FOV of our ACIS observation. The X-ray sources account for
408 objects while the remaining 83 are selected on the basis of the
strength of
line and of optical variability as described in
Sect. 3.2. The likely members are indicated in Table 3 by an asterisk following the identification number. We
first consider the possibility of extragalactic contamination and then
discuss the nature of X-ray sources with and without optical/NIR
counterparts separately.
Extragalactic contamination is expected only among X-ray sources that
lack an optical and/or NIR counterpart. We reach this conclusion by
considering the 489 ACIS sources of a probably non stellar origin
detected in the Chandra Deep Field North (CDFN;
Barger et al. 2003; Alexander et al. 2003). After defining random positions in our FOV for each
CDFN source, we compared their observed count rates with upper limits
computed from our ACIS data at those positions (see Sect. 3.1). We then selected CDFN sources that would have been
detected with our exposure: a total of
objects
. Next, we compared the positions of these simulated CDFN AGNs
in the optical (I vs. R-I) and NIR (H vs. H-K) color-magnitude
diagrams
with those of the X-ray sources in the NGC 2264 exposure that
could be placed in the same diagrams. These plots show that AGNs are
considerably fainter on average than our identified X-ray sources that
can be placed in either of these diagrams, and only two or three of the
AGNs that we could have detected occupy positions that overlap with the
loci where our X-ray sources are found. Moreover, in this comparison we
have totally neglected the effect of extinction. Because of the dark
cloud in our line of sight, the number of AGNs we are sensitive to is
certainly much reduced with respect to the above estimate, and their
optical and NIR luminosities should also be considerably reduced. We
therefore conclude that the contamination of AGNs to the sample of
X-ray-selected probable members with optical/NIR identification is
negligible.
In Fig. 3 we show the optical CMD for the 300 X-ray
sources that can be placed in such a diagram and for the 83 other
undetected problable members discussed in Sect. 3.2. Note
that the X-ray sources are found for the most part in the locus
expected for NGC 2264 members (i.e. the previously defined cluster
locus). However some X-ray sources lie below that locus, their position
being compatible with the one expected for MS foreground stars. Very
few, if any, of the X-ray sources in this diagram may be background MS
stars, which are expected to lie below the 1 Gyr isochrone. In order to
reduce contamination in the sample of likely members to a minimum, we
excluded X-ray sources that lie outside the cluster locus, i.e. below the
107.1 Myr isochrone and to the right of the 0.8 evolutionary track, but we made an exception for two sources: (i) #305
(I=17.28, R-I=0.98), associated with the IRAS source IRS-1, classified
as a Class I object by Margulis et al. (1989) but more recently suggested as a
deeply embedded B star in a more evolved evolutionary stage
(Schreyer et al. 2003)
; (ii) #309 (I=16.45, R-I=0.76), a
known member, because indeed the peculiar and well-studied accreting
binary system KH 15D (e.g. Dahm & Simon 2005; Herbst & Moran 2005; Hamilton et al. 2005). The 8 sources
that lie to the right of the 0.1
track are considered likely
members, either of very low (sub)stellar mass or else very absorbed.
Since we lack
values from optical spectra, we can check these two
possibilities using our X-ray data and the JHK photometry. Out of these
8 sources, 4 (#125, #192, #251, #273) are highly absorbed as
indicated by their X-ray spectra (or hardness ratios) and/or their
positions in the J-H vs. H-K diagram. Another three sources (#215,
#316, #358) however appear to have negligible absorption and are good
candidates for detected brown dwarfs.
Only 10 X-ray sources (out of 300) are excluded as members because they
are incompatible with our cluster locus in the optical CMD and might be
associated with field stars. Among them only
one (source #258, an X-ray faint,
,
and soft G5
star) could be placed in the HR diagram of Fig. 5, where it
lies on the 100Myr isochrone. Although we exclude these 10 sources
from our sample of likely members, some of them might actually be
members.
Fifty-one more X-ray sources were identified with optical or NIR
objects but could not be placed in the optical CMD (14 have Imagnitudes but no R, 37 are only detected in 2MASS). Extending the
previous result, and because we can exclude an extragalactic nature for
these sources, we consider these stars as additional candidate members.
Only four of them had previous indication of membership from their
or optical variability.
![]() |
Figure 13: Spatial distributions of sources around the ACIS FOV (the area within the dotted square). Light gray points: cataloged optical/NIR objects not detected in X-rays. Black symbols: X-ray detected objects. Squares with dots: X-ray sources that might be non-members because of their position in the optical CMD (Sect. 5). Empty circles with size indicating source statistics (see legend): unidentified X-ray sources. |
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Sixty-seven ACIS sources are not identified with any object listed in
the full-field optical/NIR catalogs we have considered. None of
them was detected by Flaccomio et al. (2000) with the ROSAT HRI
. They have significantly fewer detected counts
than identified sources. Fifteen of them have detection significance
,
and the 10 expected spurious detections (cf. Sect. 2.2) are most likely found in this group. Nine have more
than 50 counts and were subject to spectral analysis.
![]() |
Figure 14: Lightcurves for four "flaring'' sources (#152, #244, #327, and #376) with no optical/IR counterpart. The background subtracted count rate is plotted vs. the time since the beginning of the observation. Source number, net counts, and the result of the KS variability test are given in the upper part of each panel. |
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Depending on the optical depth of the background cloud, a number of
AGNs are expected to be detected in our FOV and will be found among the
non-identified sources (Sect. 5.1). It is therefore
reasonable to ask whether the characteristics of our sources without
counterparts are compatible with an AGN nature. The X-ray spectra of
AGNs should be rather hard and well fit by power-law models with
indexes between 0.9 and
1.9
. Their
lightcurves should be constant or slowly varying. They should be
distributed uniformly in space or anti-correlated with the cloud
optical depth. With respect to this latter point, Fig.
13 shows, with different symbols, the spatial
distribution of several classes of objects: X-ray sources without
counterparts in three source count ranges, cross-identified X-ray
sources, all the other X-ray undetected objects in our master catalog.
This last group contains for the most part background field stars whose
density is a good indicator of the optical depth of the molecular
cloud. Note how the X-ray sources with counterparts, i.e. likely
members, lie preferentially in front of or close to the cloud and have
a highly structured distribution (cf., Lamm et al. 2004), with at
least two concentrations in the south and toward the field center,
corresponding to two well-known embedded sub-clusters roughly centered
on IRS 1 (Allen's source, e.g. Schreyer et al. 2003) and IRS 2 (e.g.
Williams & Garland 2002).
![]() |
Figure 15:
Large black symbols: kT vs. ![]() ![]() |
Open with DEXTER |
We first discuss the nine unidentified sources with more than 50
counts: two of them (#244, #327), located in the IRS 1 and IRS 2
regions, show distinct long-lasting flares (Fig. 14) and
are therefore most likely PMS stars or YSOs. As discussed in Sect. 3.3, source #244 is actually identified with a SPITZER source and, although missing in the 2MASS catalog, it is
rather bright in K. The spectra of the other seven are compatible with
both isothermal models and power-law models. Assuming isothermal
models, they have higher extinctions and temperatures with respect to
the 190 sources with counterparts and >50 counts (cf. Fig. 15): the median
is
,
vs.
and the median kT is 13.7 keV vs. 1.33 keV. Neither
the
nor the kT are incompatible with those of other highly
extinct X-ray sources with counterparts (cf. Fig. 15),
which we have argued are most likely not AGNs. Due to the high
extinction, the
of these sources (if at the distance of NGC 2264)
is also high: median
vs.
for identified
sources. If instead we assume that the correct models are power laws, the
best-fit indexes range between
0.252.0-0.23 and
4.16.52.5 (median 1.6). The two flaring sources have power
indexes with 90% confidence above the range expected for AGNs, and the
same is true also for source # 162 (
),
which also lies in the IRS 2 region and is therefore likely to be a
star (the isothermal fit yield a rather common kT=1.27 keV). Two more
(# 228, # 274) of the remaining six objects, although with rather
hard and absorbed spectra, are clustered around IRS 2 and are therefore
also likely to be YSOs. We thus conclude that 50% (5 out of 9) or more
of the unidentified sources with more than 50 counts are likely to be
stars.
![]() |
Figure 16:
HR1=(M-S)/(M+S) vs.
HR2=(H-M)/(H+M), where H, M, and Sare the photon fluxes in the [500:1700] eV, [1700:2800] eV, and
[2800:7000] eV bands. Sources for which 1![]() ![]() ![]() |
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Turning to the remaining 58 sources with less than 50 counts, two show
flares (#152 and #376, Fig. 14) and are likely to be
stars. The others, although maybe a little less spatially concentrated
than sources with counterparts, seem to follow a similar distribution
in the sky. We conclude that, rather qualitatively, a sizable fraction
(on the order of )
of them are associated with NGC 2264. This
conclusion is corroborated by the distribution of unidentified sources
in the HR1 vs. HR2 hardness ratio diagram, Fig.
16. We show for reference a grid of expected loci for
absorbed isothermal spectra and the region where power-law sources with
indexes between 0.9 and 1.9 should lie. Both grids are computed using
PIMMS. We note that both HR1 (most sensitive to
)
and HR2(most sensitive to kT) are on average significantly different for
sources with and without counterparts, indicating that non-identified
sources are on average characterized by hotter and more absorbed
emission. However, the region occupied by about half of the
unidentified sources is also occupied by a number of absorbed sources
with counterparts, i.e. probable members, while the rest appear to be
significantly hotter and possibly compatible with the expected AGN
locus.
In conclusion, our 67 sources lacking optical/NIR counterparts are good
candidates for new embedded members. Given their absorption, these are
rather luminous X-ray sources. They are thus unlikely to be low-mass
stars that have escaped optical/NIR detection because intrinsically
fainter than the detection limit. Given the small dependency of
on mass (Flaccomio et al. 2003a; Preibisch et al. 2005), low-mass stars are indeed
usually faint in X-rays. Non-identified sources might be embedded
protostars (class I and class 0 objects), medium/high-mass very
obscured PMS members of NGC 2264 or extragalactic objects shining
through the background molecular cloud. They certainly deserve to be
followed up with more sensitive IR observations.
Our data indicate that the source of X-ray emission in NGC 2264
low-mass members is hot (0.3-10 keV) thermal plasma. The X-ray emission
is highly variable in time, the most prominent phenomena being
impulsive flares due to magnetic reconnection events. These
observations fit well with a solar-like picture of coronal emission and
are quite usual for PMS stars. They are, for example, in qualitative
agreement with those recently reported for the 1 Myr old stars in
the ONC by the COUP collaboration (Preibisch et al. 2005). The spectral and
temporal characteristics of PMS stars are, broadly speaking, also
similar to those of active MS stars, e.g. in the young Pleiades
cluster.
The most striking differences with respect to MS stars are the X-ray
luminosities and the plasma temperatures, both of which are usually
found to be higher. As for ,
we note that the fractional X-ray
luminosities (
)
of PMS stars are comparable or smaller
than those of saturated MS stars. Therefore, the higher
can be
explained by the almost saturated emission of PMS stars and by their
larger bolometric luminosities. The higher temperatures might instead
indicate a difference in the heating mechanism and/or, a larger
contribution to the average flux of flares with hard spectra. At least
three questions remain open. First and foremost, the nature of the
ultimate mechanism that sustains PMS coronae. While for partially
convective MS stars this is identified with the
dynamo
thanks to the observed relation between activity and stellar rotation,
no such evidence is available for PMS stars (Flaccomio et al. 2003a; Preibisch et al. 2005; Rebull et al. 2006).
Second, the extent and geometry of coronae and, in particular, the
possibility of interactions of plasma-filled magnetic structures and
circumstellar disks (Jardine et al. 2006; Favata et al. 2005). Third, the role of accretion
and outflows: soft X-ray emission has been inferred to originate both
at the interface between the accretion flow and the photosphere and
within the stellar jets (Bally et al. 2003; Kastner et al. 2002). In this section we now use
our data to statistically investigate the dependence of activity, in
terms of emission level, variability, and spectra, on the stellar and
circumstellar characteristics. First, however, we discuss the results
of our X-ray spectral analysis in more detail, with respect to plasma
temperatures and absorption values.
Figure 15 shows the plasma temperature as a function of
absorption for all the sources with spectral fits and for which we
adopted isothermal models. Figure 17 similarly shows the
two best-fit plasma temperatures of sources for which we adopted 2T
models. First of all we observe that 2T models were required only for
,
probably because higher column densities absorb the
cool component to the extent that it becomes unobservable. Moreover, in
the
range covered by both models, 2T models were statistically
favored in sources with higher statistics while low-statistic sources
were in most cases successfully fit with a single plasma component with
temperature roughly intermediate between those of the two components in
2T models. For 1T models we also note a certain correlation between kTand
(Fig. 15). While a positive slope of the lower
envelope of the datapoints is easily explained as a selection effect,
this is not the case for the upper envelope. The paucity of sources
with low
and high kT indicates that the X-ray emitting plasma
of highly extinct sources is intrinsically hotter than that of
optically revealed PMS stars. These hot, highly extinct sources are
good candidates for embedded Class 0/I protostars, which have already
been suggested to have harder X-ray spectra (Imanishi et al. 2001).
Figure 18 shows the run of kT1 and kT2 with
stellar mass for 2T models. As for the two previously discussed plots,
we also show for reference the temperatures obtained by the COUP
collaboration (Preibisch et al. 2005) for 1 Myr old stars in the
ONC
. With respect to
the ONC, temperatures in NGC 2264 appear to be lower on average. This
could indicate that at
3 Myr the hot flaring component of the
X-ray emission has become less important. It could also result from a
lower fraction of CTTSs given that CTTS tend to have slightly higher
plasma temperatures than WTTS (see Sect. 6.1).
![]() |
Figure 17:
kT1 and kT2 as a function of ![]() |
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![]() |
Figure 18: kT1 and kT2 as a function of stellar mass for sources that are both placed in the theoretical HR diagram and for which we adopted a 2T spectral model. Symbols and legend as in Fig. 17. |
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We also note that the temperatures of the two isothermal components
show a larger scatter in the NGC 2264 stars than in the COUP data. This
could be due to: i) larger uncertainties in the NGC 2264 results
because of the shorter exposure time and longer distance with respect
to the ONC; ii) the shorter exposure time resulting in a stronger
influence of spectral time variability on the time-averaged spectra;
iii) a real evolutionary effect (e.g. most ONC stars are CTTS, while in
NGC 2264 we observe a more varied mixture of CTTS and WTTS); iv) the
existence of an additional thermal component that is only sometimes
present and/or revealed by the spectral fitting process. In this
respect Figs. 17 and 18 indicate, for
sources fit with 2T models, an interesting feature: an apparent
separation of the temperatures into two branches, more evident for
the cool isothermal component. Taking kT1=0.5 keV as the dividing
line between the two branches, we have 23 and 29 sources in the cool and hot branches, respectively. The hot branch has median
kT1 and kT2 of 0.8 and 3.6 keV, respectively, roughly coincident
with the temperatures of most COUP sources. The cool branch has median
kT1 and kT2 of 0.3 and 1.3 keV, respectively. Since this latter
median kT2 is similar to the temperatures found for sources that
were successfully fit by 1T models, it is reasonable to hypothesize
that the EM distributions of these sources have three peaks: one
corresponding to the cool 0.3 keV component that is not present
and/or visible for sources in the hot branch, and two peaks at hotter
temperatures (i.e.
0.8 and
3.6 keV) that are, however,
well-represented by an isothermal component with intermediate
temperature. Given the limited statistics of our sources, three
component spectral models are not needed, although physically
reasonable, and would therefore remain unconstrained by the data.
As for the physical origin of the two branches, we note that, with
respect to the hot branch, stars in the cool branch have lower counts
(median counts 270 vs. 630), are significantly less variable according
to the KS test (variability fraction 30% vs. 66%, at the 1%
confidence level), and are slightly more likely to be CTTS (CTTS
fraction: 33% vs. 23%). We note that the difference in variability
fractions is unlikely to be explained by statistics alone (cf. Figs. 6 and 7). If the
keV component is due to flaring, it is then possible that the
keV component becomes detectable preferentially when
flaring is absent, so that the hot component is suppressed. This soft
emission might be physically assimilated to the X-ray emission from the
solar corona, which would indeed show temperatures similar to 0.3 keV if
analyzed with ASCA SIS (cf. Table 1 in Orlando et al. 2001), an
instrument with a response similar to ACIS. The emission measures we
derive for the
0.3 keV component of our NGC 2264 sources
(
cm-3), although much larger
than those estimated for the Sun, could still be explained by much
larger filling factors (
1) and/or larger scale heights of the
densest structures (i.e. active region cores).
Comparing the plasma temperatures of CTTS and WTTS that are well fit by
a single temperature model (37 CTTS, 58 WTTS), we learn that the former
are statistically hotter, with a median kT of 1.5 keV vs. 1.3 keV (K-S
test probability that the two distributions are compatible: 0.2%). A
similar comparison for the two plasma temperatures of stars that
required a 2T spectral model (12 CTTS and 31 WTTS) is inconclusive,
possibly because of the lower number of stars. However, we have noted
above that CTTS appear to be more common among stars with low values of
kT1 (Fig. 17). In particular, the three stars with
the lowest kT1 are all CTTS. Figure 19 shows the ACIS
spectra for these three sources, #17, #111, and #183. The two
thermal components of the best-fit models are shown separately, and the
temperatures and absorptions are reported from Table 6.
The most striking case appears to be source #183, which has the
highest kT1 (0.14 keV) and shows a clear double-peaked spectrum. The
emission measure (EM) of the cool plasma is estimated to be
.
The other two sources have lower kT and
higher EMs (#17: kT1=0.09 keV,
;
#111: kT1=0.12 keV,
). We also note
that among the three sources #183 has the largest
line
equivalent width: 27.9 vs. 10.9 and 18.2 for #17 and #111.
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Figure 19:
ACIS spectra of three CTTS with a very low kT1. At the top
of each panel, we report the source
number, fit model, goodness of fit (null probability), kT1, kT2,
and ![]() ![]() ![]() |
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As noted in the previous section, low kT1 values are usually associated with low kT2. For the three CTTS just discussed, for example, the kT2 values, 0.87-0.95 keV, are among the lowest observed and are very similar to the cool component for the majority of PMS stars. This suggests that the ultra-cold component is present and/or observable with our spectra only when kT=2-4 kev plasma is absent.
A similar trend can be observed for 1T fits (Fig. 15):
among the six lowest kTs (kT < 0.68 keV), the EW(
)
is known
for three stars, and it indicates accretion (i.e. a CTTS) in all cases.
CTTS thus appear to possess both warmer and cooler plasma than WTTS. If
this result is confirmed with more statistical significance by further
observations, it could imply that the accretion process results, on one
hand, in more frequent/energetic flares and, on the other, in a very
cool X-ray plasma produced in the accretion shock (cf. the cases of TW
Hydrae and BP Tau: Schmitt et al. 2005; Kastner et al. 2002; Stelzer & Schmitt 2004).
We now investigate the X-ray activity levels (
and
)
as a function of bolometric luminosity, stellar mass, and accretion
properties for the subsample of stars for which stellar masses were
derived by placement in the theoretical HR diagram (Fig. 5)
and interpolation of the SDF tracks (160 X-ray detected stars,
excluding one possible non-member and two stars outside the tracks).
For this investigation we also include upper limits for 16 X-ray
undetected likely members. Note that a more exhaustive account of the
relation between activity and stellar properties, using the X-ray data
presented here in conjunction with those of Ramírez et al. (2004a) for another
field in NGC 2264 and those of Ramírez et al. (2004b) for the "Orion Flanking
Fields'', can be found in Rebull et al. (2006). In that paper the samples for
each cluster included many more stars than we have here, but with more
uncertain stellar parameters on average. Here we take a different
approach, focusing only on the NGC 2264 members in our FOV that are
well characterized optically.
Our -mass scatter plot is shown in Fig. 20. We
observe the commonly found mass-
correlation (e.g.
Rebull et al. 2006), although with a large spread. The position of upper limits
indicates that our sensitivity limit is
ergs/s,
which appears to correspond to a completeness limit in mass at about
0.3-0.4
.
Moreover we note that, at each stellar mass for which
our sample is reasonably complete, CTTS are on average fainter and more
scattered with respect to WTTS, confirming the results obtained for ONC
stars (Flaccomio et al. 2003a; Preibisch et al. 2005). A similar plot is shown for
in Fig. 21.
appears to be generally
high, roughly between 10-4 and the saturation level
10-3.0. Twenty-two sources actually have measured
above the saturation limit. However, a large fraction of these, 73%,
are variable, a significantly higher variability fraction than among
sources below the saturation threshold (16%). Moreover, most of the
sources with the highest values of
show large flares,
which if excluded would bring them close to or below the saturation
level. Flaccomio et al. (2003a) found evidence for ONC stars of a decrease in
at the very lowest masses. Our sample of stars with mass
estimates is not complete enough at those masses to study this effect
in detail. However, consistent with these results, we do note a
decrease in the upper envelope of the
vs. mass relation
for
.
Considering CTTS and WTTS separately,
the difference in activity is less striking in this plot compared to
the
-mass one. However, Fig. 22 shows,
separately for the two PMS classes, the distributions of
,
both for the whole mass range and for the subsample with
.
As noted above this latter sample should be almost
complete, according to the
-mass relation. All the distributions
take into account upper limits via the Kaplan-Meier estimator. For both
samples, and in particular for the mass-restricted one, we observe that
CTTS are less active than WTTS. Median
values differ
by 0.32 and 0.41 dex for the two subsamples. The statistical
significance of the difference is confirmed by the five two-sample
tests in the ASURV package (Feigelson & Nelson 1985), giving probabilities that the
distributions of CTTS and WTTS are taken from the
same parent distribution of <0.02% for the whole stellar sample and
of <0.3% for the mass-restricted one. We note that this latter
result differs from that of Preibisch et al. (2005), who find a statistically
significant difference in the activity levels of CTTS and WTTS in the
ONC only for stars in the 0.2-0.5
mass range.
![]() |
Figure 20:
log ![]() |
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![]() |
Figure 21:
log
![]() |
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![]() |
Figure 22:
Distribution of log
![]() ![]() |
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Finally we used our data to repeat the correlation analysis between
and
performed by Preibisch et al. (2005) for ONC stars classified
as CTTS and WTTS. Using the estimation maximization (EM) algorithm in
the ASURV package, we found very nearly linear correlations between
and
for the two classes of stars:
for WTTS (
dispersion: 0.4 dex) and
for CTTS (
dispersion: 0.5 dex). As in the ONC
case, accreting stars in NGC 2264 thus appear to be fainter on average
than non accreting ones with the same
(in this case by a
factor of 2) and to have slightly more scattered
values. However,
we note that the power-law slope derived by Preibisch et al. (2005) for
accreting stars in the ONC (
)
is significantly shallower
than the one derived here and that the
dispersion of points
with respect to the best-fit relations appear to be larger in the ONC:
0.5 vs. 0.4 dex for WTTS and 0.7 vs. 0.5 for CTTS. The differences
might be interpreted as an evolutionary effect, given that the ONC is
younger than NGC 2264 (
1 vs.
3 Myr) and that accretion disks
are expected to have evolved significantly in the older cluster
(Flaccomio et al. 2003c). We note, however, that the comparison between the
effects of accretion on X-ray activity in the two regions is made
uncertain by the different accretion indicators used in the two cases,
the
and the Ca II equivalent widths for NGC 2264 and
the ONC, respectively.
The peculiar binary system KH 15D has been the subject of many investigations. Herbst & Moran (2005), in particular, analyzed the same X-ray data discussed here to conclude that the system is a very weak source of X-ray emission for its mass and age. They tentatively attribute the low X-ray emission to the high eccentricity of the binary system and/or to the close periastron approach of the two stars, which may either disrupt the stellar magnetosphere and/or adversely affect the stellar dynamo.
The X-ray luminosity we derive for KH 15D (our source #309), based on
21.6 detected photons, is
,
lower by 0.07 dex than the
value reported by Herbst & Moran (2005). This difference is small and within
the uncertainties but, given that the two values are derived from the
same X-ray data, we investigated the matter further. This discrepancy
can be fully explained by the different estimated number of source
photons, 21.6 vs. 22.5, the assumed value of interstellar absorption,
vs.
cm-2, and the assumed source
spectrum
. The
0.07 dex difference in
,
however, is not particularly relevant for
the physical conclusions regarding the low
of the system. KH 15D
is not shown in our
-mass scatter plot (Fig. 20)
because the system is located in the HR diagram below the grid of the
evolutionary tracks (Fig. 5) and we did not derive a mass.
By extrapolating the tracks one would estimate a mass of
0.6-0.7
,
consistent with the value used by Herbst & Moran (2005),
0.6
.
Thus placing KH 15D in the
-mass diagram we notice
that it would fall below the bulk of the other NGC 2264 members but in
an area that is populated by other CTTSs. The value of
we derive from our data, -3.45, is moreover perfectly in
line with most of the other NGC 2264 members (cf. Fig.
21). We therefore tend to believe that, rather than
being affected by the peculiar binary orbits, the low X-ray emission of
KH 15D is due to the same mechanism that suppresses activity in CTTSs.
Simon & Dahm (2005, hereafter SD05) studied three embedded X-ray sources close to IRS 1 in detail (see also Sect. 3.3) using XMM-Newton EPIC data taken in March 2002, i.e. 7 months before our ACIS exposure. All three sources are retrieved in our data and we now compare the results with respect to spectral characteristics, variability, and average X-ray luminosities.
EPIC source #26 (our source #194) was the stablest of the three
sources. The lightcurves were constant in both observations; the
absorption was identical,
cm-2; and the kT was
also the same within uncertainties
:
keV (EPIC) vs.
kT=2.44.81.5 keV (ACIS). The X-ray
luminosity, however, when corrected for the different energy band used
by SD05 (1-10 keV vs. our 0.5-7 keV) and for the different assumed
distance (d=800 pc, vs. our 760 pc), seems to have dropped by a factor
2 between the two observations.
The EPIC source #10 (our source #141) showed a dramatic flare toward
the end of SD05's exposure with the peak count rate reaching 100 times brighter than the quiescent emission before the flare. Our
lightcurve is instead compatible with constant emission. DS05 have
analyzed the spectrum during the flare, while we report results for the
average spectrum, which is, however, built from only
65 photons.
There is no evidence of variation in the absorption: DS05 find
cm-2 vs. our
cm-2. The temperatures are, however, very different owing to the
bright flare in the EPIC data:
keV vs.
kT=2.06.11.0 keV. The X-ray luminosities are also very
different, with the SD05 value (
)
about 2 dex larger than
the value obtained from our analysis and corrected for the different
bands and distances. It is, therefore, likely that we observed the
source in a state similar to the pre-flare state in the SD05 data.
Finally, the lightcurve of EPIC source #1 (our source #296) showed a
remarkable rise in count rate during the XMM exposure, by a factor
7 in about 8 ks, and then began what appears a slow decay for
the remaining 30 ks of the observation. An isothermal spectral fit
gave a very high absorption,
cm-2,
temperature,
keV, and total luminosity
erg s-1 (in the 1-10 keV band). From our ACIS data we
derive a two order-of-magnitude lower luminosity
,
a
somewhat colder plasma,
kT=4.89.63.0 keV and a 5-fold lower
absorption
cm-2. The lightcurve
indicates a roughly linear decay of the count rate during the 100 ks
of our observation from
6 cts ks-1 to
2 cts ks-1. We note that our value of
(90% confidence interval
corresponding to
)
agrees roughly with the absorption SD05
derived for the source from NIR spectroscopy (
)
and,
contrary to the value observed during the XMM-Newton exposure,
does not imply a higher gas-to-dust ratio than in the interstellar
medium. We speculate that the high absorption seen by SD05 was due to a
solar-like CME associated with the flare as we have also speculated for
our source #71 in Sect. 4.2.
We observed NGC 2264 with Chandra-ACIS for 97 ks, detecting a
total of 420 X-ray point sources. We identified 85% of the X-ray
sources with known optical and NIR objects, while 67 sources remain
with no counterparts in the considered optical/NIR catalogs. More than
90% of the 353 X-ray sources with counterparts are expected to be
members of NGC 2264. Using
and optical variability data from
the literature we selected a further sample of 83 X-ray undetected
likely members in the FOV of our ACIS observation, bringing the census
of likely members with optical/NIR counterparts to more than 400 stars.
Taking the small estimated contamination from field stars into account,
we have thus increased the known member census of the region by about
100 objects, mostly very low-mass stars, and including some candidate
brown dwarfs. A further group of 10 X-ray sources, excluded from the
member sample because their position in the optical CMD is discrepant
with the cluster locus, is also likely to include members. Moreover,
among the 67 sources with no optical/NIR counterparts, we argue that
about half are previously unrecognized embedded members and good
candidates for X-ray detected Class 0/I sources. The other half are
instead likely associated with extragalactic objects. The coming SPITZER data will be very useful for clarifying the nature of each
source and will allow a systematic study of X-ray activity in the
protostellar phase.
We determined X-ray unabsorbed fluxes and luminosities for 326 sources
for which absorption could be estimated, either from the X-ray spectra,
from optical spectral types and photometry (), or from NIR
photometry. With the aim of shedding light on PMS X-ray activity, we
then performed a detailed study of X-ray lightcurves and spectra and
studied the relation between the properties of X-ray emitting plasma
and stellar/circumstellar characteristics. We confirm several previous
findings: X-ray luminosity is related to bolometric luminosity and to
stellar mass;
is on average high and fairly independent
of mass, other than for a possible drop at
2
and a shallow
decrease for
.
The mass-
relation
appears to be better defined for WTTS than for CTTS, and CTTS have on
average lower activity levels at any given bolometric luminosity and
mass. We found tentative evidence that CTTS are more time variable with
respect to WTTS, which might be related to the time-variable nature of
the accretion process if it plays a role in the X-ray emission. With
respect to spectral characteristics, the plasma on CTTS is on average
slightly hotter than on WTTS, a finding possibly related to the higher
variability of CTTS. However, we also observe that the sources with the
coolest plasma are preferentially CTTS. Three CTTSs in particular
appear to have plasma at
keV, i.e. comparable to the
temperatures expected for plasma heated by accretion shocks, as
observed on TW Hydrae (
keV, Kastner et al. 2002; Stelzer & Schmitt 2004). The
estimated emission measures of this cool plasma are between 4 and 17 times larger than on TW Hydrae (
cm-3),
maybe as a result of the expected higher accretion rates of NGC 2264
stars. These results, taken as a whole, reinforce the mounting evidence
that activity in low-mass PMS stars, while generally similar to that of
saturated MS stars, is significantly affected by mass accretion. This
influence has at least two aspects: accretion is, on one hand, a
positive source of very soft X-ray emission produced in the accretion
shock. On the other hand, it reduces the average energy output of
coronae and makes the emission more time variable. Preibisch et al. (2005)
discuss several possible explanations for the suppression of activity.
They favor the idea that accretion modifies the magnetic field geometry
and results in the "mass-loading'' of field lines, thus hampering the
heating of plasma to X-ray temperatures. It is at the same time
conceivable that the resulting magnetic field structure will be less
stable because of the the temporal variability of the mass accretion
rate, as well as the rotational shear at the inner edge of the
circumstellar disk. To tackle the so-far elusive activity-accretion
relation, a better characterization of the circumstellar/accretion
properties, e.g. measures of massaccretion rates, is essential.
Particularly useful in this respect would be contemporary observations
in the X-ray band and in accretion/outflow sensitive optical/NIR lines.
Acknowledgements
The authors wish to thank the anonymous referee for comments that helped to improve this work and acknowledge financial support from the Ministero dell'Istruzione dell'Universitá e della Ricerca, PRIN-INAF and contract ASI-INAF I/023/05/0.
Table 1: Catalog of X-ray ACIS detections.
Table 3: Master catalogs of objects in the ACIS FOV.
Table 4: Optical/NIR properties of X-ray sources.
Table 6: Spectral properties of ACIS sources with more than 50 counts.