Datasets:
ReadingTimeMachine
/
rtm-sgt-ocr-v1

Dataset card Data Studio Files Community
2
source
stringlengths
1
2.05k
⌀
target
stringlengths
1
11.7k
but their aree errors preclude a safe conclusion on the age.
but their large errors preclude a safe conclusion on the age.
Moving to the velocity interval -60 « V. « -10 ‘ans1 we find⋅ a sharp decrease in⋅ the main⋅ sequence population⋅ or . ⇁∣ ∪↖↖⇁⊔⊳↸∖⋪↸⊳∏↽∙∪↖↖↽↸∖≼⊳≼⊳↸∖↕⊔⋅∐⊳↕↖↽↴∖↴⋪∐⋅↴∖↴∪⋝↴∖↴↸∖↥⋅↖⇁↸∖≼⋝↖⇁⋀∪∐⋅↕↴∖↴ λε] bv ⋅⇁⋅∙≻(1986).|Such .
Moving to the velocity interval -60 $<$ $V$ $<$ -40 km $\rm s^{-1}$, we find a sharp decrease in the main sequence population for $\simlt$ 3 mag, the total number of objects being comparable to the one relative to the sample -40 $< V <$ -30 km $\rm s^{-1}$.
Fig.
Fig.
; 2shows the etsample having 07/7 ⋅ al. Loin qoiwhich it ⋅⋅can be secu that the verv few stars− with ‘is errors. are found onlv: in the: upper main sequence.
2 shows the sample having $\sigma_ \pi$ $\pi$ $<$ 1, in which it can be seen that the very few stars with large errors are found only in the upper main sequence.
chosen Considering⋅⋅ the sample with⋅ σε π x: 0.15. (CAL not shown) the ageo dnerease⋅ is. confirmed.
Considering the sample with $\sigma_ \pi$ $\pi$ $\leq$ 0.15 (CM not shown) the age increase is confirmed.
. The bulk of. the sunuple with -GO < V < -10 lau s| appears older than ↽⋅ ⊇↓∩⊽≱⋅∏⋅∙
The bulk of the sample with -60 $<$ $V$ $<$ -40 km $\rm s^{-1}$ appears older than 2 $10^9$ yr.
lucreasiug the drift velocity to -80 — V κ -60 kins ! (Fig. 3..
Increasing the drift velocity to -80 $<$ $V$ $<$ -60 km $\rm s^{-1}$ (Fig. \ref{fig5},
left panel). we fud the main sequence populated ouly at magnitudes < Lwiththelowerinainsequencealinostemptug
left panel), we find the main sequence populated only at magnitudes $\simgt$ 4, with the lower main sequence almost empty.
.Theestimatediniuii Vr.
The estimated minimum age is of at least 5 $10^9$ yr.
Because of the substantial differeuce iu total star munber between the latter two CM diagrams. we performed a check on the reality of the change in the age structure between these two velocity saniples. by raudoily reducing the nuuber of main sequence nenmbers du one diagram (-60 -l0). to the number iu the other one (-80 -60) aud comparing the main sequence distributions in A, (samples with a, f/m < .15).
Because of the substantial difference in total star number between the latter two CM diagrams, we performed a check on the reality of the change in the age structure between these two velocity samples, by randomly reducing the number of main sequence members in one diagram (-60 $-$ -40), to the number in the other one (-80 $-$ -60) and comparing the main sequence distributions in $M_v$ (samples with $\sigma_ \pi$ $\pi$ $\leq$ .15).
The resσι+ 19 shown in Fig. Iz
The result is shown in Fig. \ref{fig4}:
the random sample confirms the 2ufriusie differcnee in the turn off location between the two velocity samples.
the random sample confirms the intrinsic difference in the turn off location between the two velocity samples.
The -NO <<V« -60 km s1 saluple is very scautilv populated for imag: thesurceyisnomoresuf Ficienttodesceribethelowermeainseq
The -80 $< V <$ -60 km $\rm s^{-1}$ sample is very scantily populated for $>$ 5 mag: the survey is no more sufficient to describe the lower main sequence.
We need to take into account the complete IBpparcos catalogue. which reaches fainter imaenitudes than the survey.
We need to take into account the complete Hipparcos catalogue, which reaches fainter magnitudes than the survey.
Objects at these hieh maenitudes have a heterogencous origin.
Objects at these high magnitudes have a heterogeneous origin.
The targets with iL LShaccheenseleetcdinainty fromcataloguestlike Gliese (1969. 1971) loss): fortheirhighgalacticlatitude, andonthebasiso fs
The targets with $>$ 4 $-$ 5 have been selected mainly from catalogues like Gliese (1969, 1974), LHS (Luyten 1976) and NLTT (Luyten 1979); from the Michigan Spectral Survey (Houk Cowley 1975, Houk 1978, Houk 1982, Houk Smith-Moore 1988); for their high galactic latitude, and on the basis of spectral peculiarities and subdwarf characteristics (Perryman et al.
null
1989).
poetic «n éDonug.
So the choice was based largely on high proper motion and peculiarities like weak metal lines, but also very nearby stars (Gliese catalogue 1969, 1974 and Gliese and Jahreiss extension 1979), stars at an estimated distance smaller than 40 pc, and red dwarf stars have been included for $>$ 5 mag.
Vow are the properties of the lower main sequence population affected by the obscrvationa selection?
How are the properties of the lower main sequence population affected by the observational selection?
Proper motion catalogues favour objects with lieh proper motions.
Proper motion catalogues favour objects with high proper motions.
Notwithstanding this preference. the Ilipparcos catalogue is such that. even for |V| < 10 kms. 7 (sec Fig. 5).
Notwithstanding this preference, the Hipparcos catalogue is such that, even for $\vert V \vert$ $<$ 10 km $\rm s^{-1}$ (see Fig. \ref{fig6}) ),
the main sequence locus is well populated up to aabout LO imag by stars of solar composition.
the main sequence locus is well populated up to about 10 mag by stars of solar composition.
If nonal stars are present in a nunber sufficient to describe the lower main sequence for low |W]. their absence - or . ) ↻↥⋅↸∖↴∖↴↸∖∐↸⊳↸∖≓⋜↧↕⋜∐⋅∶↴∙⊾↸∖↥⋅∏↴⋯⊔⋈∖↸⊳∪∐↴∖↴↕≼∐∖↥⋅↸∖≼↧⋯↸∖⋜∐∐∐∶↴
If normal stars are present in a number sufficient to describe the lower main sequence for low $\vert V \vert$, their absence - or presence - at larger $\vert V \vert$ can be considered meaningful.
∙⊾↕∏↕∙ A possible bias is given by the fact that a sample ⊳↕∪↴∖↴↸∖∐∪↕⊔↘↽∐∐∖⋯⋜↧⊓↸⊳⋜↧↕↴⋝⋜↧↴∖↴↕↴∖↴↖↖⇁∪∏∐.: : ↸∖∐≺⊔∪∐∐↴∖↴↴∖↴∐∐∖↑⋜↧⊔⋯∪↥⋅ stars without halo kinematical characteristics. such as the | ⋅talligity. beettrici ⊥⋅↜⋠≻ Lby Nori a
A possible bias is given by the fact that a sample chosen on kinematical basis would tend to miss metal poor stars without halo kinematical characteristics, such as the low metallicity, low eccentricity stars observed by Norris et al. (
nd Norris
1985) and by Norris (1986).
au occurrence l0; Vi 30hms at ‘least partiallyBEN avoided ⋅by the presence ..of targets <. ou the basis of spectral peculiarities. among which large ue wealk-lned objects| (supposedlyn ισα. weak).
Such an occurrence is at least partially avoided by the presence of targets chosen on the basis of spectral peculiarities, among which are weak-lined objects (supposedly metal weak).
These spectroscopically selected objects cau exhibit iu priuciple any space velocity, aud could populate the subdwart | ↴∖↴↸∖≺⋯∖∐↸⊳↸∖⋜↧↑⋜⋯↖↽↖⇁↸∖↕∪↸
These spectroscopically selected objects can exhibit in principle any space velocity, and could populate the subdwarf sequence at any velocity bin.
⊳↕↑↖↽↴⋝↕∐∙ ↖↖⊽↸∖↴∖↴∐∪↖↖↽≺∏∶↴
We show (Fig. \ref{fig5}) )
⋁∙∶≩⊔↑↕∐⋅↸∖↸∖≼⊲⋀∖↕≼∐⋜↧∶↴∙⊾↥⋅⋜∐⊔↴∖↴↥⋅↸∖↕⋜↧↑↕↖⇁↸∖↑≺⊢≺∖∖∩ Voxc-60lns i0ue based on objects from the survey with o./m <1. and two from the complete catalogue. both with C.ím < l aud x 0.15.
three CM diagrams relative to -80 $< V <$ -60 km $\rm s^{-1}$: one based on objects from the survey with $\sigma_\pi$ $\pi$ $<$ 1, and two from the complete catalogue, both with $\sigma_\pi$ $\pi$ $<$ 1 and $\leq$ 0.15.
They give equivalent information ou the mimi aee of the velocity biu population.but. as mentioned before. the survey sample las very few stars
They give equivalent information on the minimum age of the velocity bin population,but, as mentioned before, the survey sample has very few stars
and so we instead looked for overdensities of sources in the WISE images.
and so we instead looked for overdensities of sources in the WISE images.
Specifically, we counted the total number of sources in the public-release WISE catalogues for band W1 within a 3 arcmin radius aperture centered on our (3.4Bolocamwm) SZ centroid.
Specifically, we counted the total number of sources in the public-release WISE catalogues for band W1 (3.4 $\mu$ m) within a 3 arcmin radius aperture centered on our Bolocam SZ centroid.
This aperture size is motivated by the fact that it corresponds to ~1 Mpc at z=0.4, the median redshift of the six clusters originally published as unconfirmed in the eSZ (Storyetal.2011;Hurley-Walkeretal. 2011).
This aperture size is motivated by the fact that it corresponds to $\simeq 1$ Mpc at $z=0.4$, the median redshift of the six clusters originally published as unconfirmed in the eSZ \citep{story11, hurley-walker11}.
. These source counts were then compared to counts within 1000 randomly-located and identically-sized apertures in the region within ~2 deg of each eSZ candidate.
These source counts were then compared to counts within 1000 randomly-located and identically-sized apertures in the region within $\simeq 2$ deg of each eSZ candidate.
We limit these blank-sky apertures to the region immediately surrounding the eSZ candidates because there are variations in WISE coverage and in the density of nearby sources, which causes the blank-sky source density to vary over the WISE survey.
We limit these blank-sky apertures to the region immediately surrounding the eSZ candidates because there are variations in WISE coverage and in the density of nearby sources, which causes the blank-sky source density to vary over the WISE survey.
We find 140 sources within 3 arcmin of PLCKESZ G115.71 compared to a local-blank-sky-average of 108 sources, and 9/1000 nearby blank-sky regions have more than 140 sources.
We find 140 sources within 3 arcmin of PLCKESZ G115.71 compared to a local-blank-sky-average of 108 sources, and 9/1000 nearby blank-sky regions have more than 140 sources.
We find 67 sources within 3 arcmin of PLCKESZ G189.84 compared to a local-blank-sky-average of 57 sources, and 168/1000 nearby blank-sky regions have more than 67 sources (see Figures 3 and 4)).
We find 67 sources within 3 arcmin of PLCKESZ G189.84 compared to a local-blank-sky-average of 57 sources, and 168/1000 nearby blank-sky regions have more than 67 sources (see Figures \ref{fig:three} and \ref{fig:wise}) ).
In addition to the two eSZ candidates described in this manuscript, we have also observed 12 other eSZ clusters as part of separate Bolocam programs (Abell 209, Abell 697, Abell 963, Abell 2204, Abell 2219, Abell 2261, Abell $1063, MACS J0417.5, MACS J0717.5, MACS J1149.5, MACS J1206.2, and MACS J2211.7).
In addition to the two eSZ candidates described in this manuscript, we have also observed 12 other eSZ clusters as part of separate Bolocam programs (Abell 209, Abell 697, Abell 963, Abell 2204, Abell 2219, Abell 2261, Abell S1063, MACS J0417.5, MACS J0717.5, MACS J1149.5, MACS J1206.2, and MACS J2211.7).
Results for Abell 697 have already been published in Sayersetal. (2011),, and results for the other clusters will appear in future publications, including a detailed comparison of the Bolocam and Planck measurements.
Results for Abell 697 have already been published in \citet{sayers11}, and results for the other clusters will appear in future publications, including a detailed comparison of the Bolocam and Planck measurements.
The Planck eSZ candidates were selected based on the mm/submm-wave SZ signal strength measured by Planck, and the full eSZ catalogue spans a relatively narrow range of SZ signal Since Bolocam is sensitive to the mm-wave SZ signal from these clusters, we therefore expect a correspondingly narrow spread in the SZ signals measured by Bolocam.
The Planck eSZ candidates were selected based on the mm/submm-wave SZ signal strength measured by Planck, and the full eSZ catalogue spans a relatively narrow range of SZ signal Since Bolocam is sensitive to the mm-wave SZ signal from these clusters, we therefore expect a correspondingly narrow spread in the SZ signals measured by Bolocam.
However, we note that the vastly different angular scales probed by Planck (0>5 arcmin) and Bolocam (1€0<15 arcmin) may weaken the correlation between SZ measurements from Planck and Bolocam.
However, we note that the vastly different angular scales probed by Planck $\theta \ge 5$ arcmin) and Bolocam $1 \le \theta \le 15$ arcmin) may weaken the correlation between SZ measurements from Planck and Bolocam.
Since the Bolocam observations of these previously known eSZ clusters span a range of noise RMSs, from 16—49 p-arcmin,, with peak ¢=8.3—21.9, we scale the Bolocam values of ¢ to the S/Nsexpected value of ¢at a noise RMS of 30 (630) in order to
Since the Bolocam observations of these previously known eSZ clusters span a range of noise RMSs, from $16-49$ , with peak S/Ns $\zeta = 8.3-21.9$, we scale the Bolocam values of $\zeta$ to the expected value of $\zeta$at a noise RMS of 30 $\zeta_{30}$ ) in order to
in the disk. the protostellar gravitv sets the orbital velocity. shear. and strength of the stratification.
in the disk, the protostellar gravity sets the orbital velocity, shear, and strength of the stratification.
Because the Brunt-Vaiisalla Irequency for the GRS is so much faster than the other (mescales. (he vertical and horizontal dvnanmies can be decoupled: the GRS is with two-dimensional fluid dynamics.
Because the Brunt-Väiisällä frequency for the GRS is so much faster than the other timescales, the vertical and horizontal dynamics can be decoupled; the GRS is well-modeled with two-dimensional fluid dynamics.
No such clear separation of timescales occurs for a protoplanetary disk. and the horizontal aud vertical dynamics cannot be decouplecd.
No such clear separation of timescales occurs for a protoplanetary disk, and the horizontal and vertical dynamics cannot be decoupled.
In general. when the Rossby and Froude numbers are small rotation and stratification dominate over shear and nonlinear advection). then the horizontal ancl vertical motions can usually be decoupled and two-dimensional analyses can safely be applied.
In general, when the Rossby and Froude numbers are small rotation and stratification dominate over shear and nonlinear advection), then the horizontal and vertical motions can usually be decoupled and two-dimensional analyses can safely be applied.
In many of the common 2D models “shallow-water”. quasigeostrophic. or barotropic). vorticitv (or a potential vorlicity) is an aclvectively conserved (quantity (because baroclinicitv and vortex lilling are neglected).
In many of the common 2D models “shallow-water”, quasigeostrophic, or 2D-barotropic), vorticity (or a potential vorticity) is an advectively conserved quantity (because baroclinicity and vortex tilting are neglected).
In 2D. only the z-component of vorticity is nonzero. and vortex lines can only be oriented. vertically.
In 2D, only the $z$ -component of vorticity is nonzero, and vortex lines can only be oriented vertically.
The rotation axes of long-lived coherent vortices as well as transient turbulent eddies are constrained (o be aligned parallel (or anti-parallel) to the vertical axis.
The rotation axes of long-lived coherent vortices as well as transient turbulent eddies are constrained to be aligned parallel (or anti-parallel) to the vertical axis.
Furthermore. shear will stretch out. and destroy. (ose vortices (hat do not have (he same sense of rotation as the shear.
Furthermore, shear will stretch out and destroy those vortices that do not have the same sense of rotation as the shear.
Thus. a 2D shear flow will be populated with vortices that are all perfectly. aligned vertically and with the same sense of rotation.
Thus, a 2D shear flow will be populated with vortices that are all perfectly aligned vertically and with the same sense of rotation.
It has been demonstrated in both laboratory experiments ancl numerical simulations (hat in such restricted. flows. small regions of vorticity merge to form larger vortices this phenomenon is called an "inverse cascade” of enerey [rom small (ο large scales (Ixraichnan1967:Lesieur1997:Paret&Tabeling1998:Daroudetal.2003) Out of an apparently chaotic flow filled with small-scale. transient edcdies. large-scale coherent features Coal) emerge.
It has been demonstrated in both laboratory experiments and numerical simulations that in such restricted flows, small regions of vorticity merge to form larger vortices — this phenomenon is called an “inverse cascade” of energy from small to large scales \citep{kraichnan67,lesieur97,paret98,baroud03} Out of an apparently chaotic flow filled with small-scale, transient eddies, large-scale coherent features can emerge.
In 3D. the dvnamics of turbulence are very different.
In 3D, the dynamics of turbulence are very different.
Vortex lines can be oriented in any direction. and (hey can twist and bend.
Vortex lines can be oriented in any direction, and they can twist and bend.
Vortices and eclelies tilt ancl stvetch their neighbors. disrupting them and eventually destrovxing them as (μον give up their energy to smaller eddies.
Vortices and eddies tilt and stretch their neighbors, disrupting them and eventually destroying them as they give up their energy to smaller eddies.
In. fact. this is the foundation lor the Richardson ancl [Ixolmogorov. model of 3D. isotropic turbulence: turbulent. kinetic energy is (rauslered (via nonlinear interactions) from large to small to smaller eddies on down "forward cascade) until it is destroved. by viscous dissipation.
In fact, this is the foundation for the Richardson and Kolmogorov model of 3D, isotropic turbulence: turbulent kinetic energy is transfered (via nonlinear interactions) from large to small to smaller eddies on down “forward cascade”) until it is destroyed by viscous dissipation.
An open question (and a very active area of basic research in fIuid dynamics) is what happens when rotation aud stratification are important. but not overwhelmingly dominant when the Rossby aud/or Froude numbers are of order unit)?
An open question (and a very active area of basic research in fluid dynamics) is what happens when rotation and stratification are important, but not overwhelmingly dominant when the Rossby and/or Froude numbers are of order unity)?
Low much rotation aud stratification are necessary for a real. 3D flow to exhibit 2D characteristics. such as inverse cascades?
How much rotation and stratification are necessary for a real, 3D flow to exhibit 2D characteristics, such as inverse cascades?
Braccoetal.(1998) and Goclon&Livio(1999.2000) showed that small vortices merged io form larger vortices in 2D simulations. but these results have vet to be confirmed with 3D simulations.
\citet{bracco98} and \citet{godon99b,godon00} showed that small vortices merged to form larger vortices in 2D simulations, but these results have yet to be confirmed with 3D simulations.
corresponding to a detection significance>150.
corresponding to a detection significance$> 15\sigma$.
The first steep increase occurs between 6:00 and 15:00 UTC on September 18.
The first steep increase occurs between 6:00 and 18:00 UTC on September 18.
In 12 hours, the flare reaches a maximum flux of (8.6+1.1)x1079phcm"?s71.
In 12 hours, the flare reaches a maximum flux of $(8.6 \pm 1.1)\times 10^{-6}~\mathrm{ph~cm^{-2}~s^{-1}}$.
The second flux increase occurs about two days after, between 12:00 and 24:00 on September 20.
The second flux increase occurs about two days after, between 12:00 and 24:00 on September 20.
The peak flux is (9.4+1.0)x1079phcm?s-!.
The peak flux is $(9.4 \pm 1.0)\times 10^{-6}~\mathrm{ph~cm^{-2}~s^{-1}}$.
The last flux increase was detected between 18:00 on September 21 and 18:00 on September 22, reaching a value of (7.5+1.1)x1079phcm?s-!.
The last flux increase was detected between 18:00 on September 21 and 18:00 on September 22, reaching a value of $(7.5 \pm 1.1)\times 10^{-6}~\mathrm{ph~cm^{-2}~s^{-1}}$.
In all cases, the flare decay time was about 1 day.
In all cases, the flare decay time was about 1 day.
On the basis of the lightcurve reported in Fig. 2,,
On the basis of the lightcurve reported in Fig. \ref{fig:lc},
we divide the data into three different sets as reported in Table 1..
we divide the data into three different sets as reported in Table \ref{tab:intervals}.
In Fig. 3,,
In Fig. \ref{fig:pulse_fermi},
we compare the pulse profile measured before and during the flares.
we compare the pulse profile measured before and during the flares.
We can estimate the contribution of the nebula, by assuming that the pulsar emission is negligible in the phase ranges ó<0.25 and ó>0.8.
We can estimate the contribution of the nebula, by assuming that the pulsar emission is negligible in the phase ranges $\phi<0.25$ and $\phi>0.8$.
Before the flares, the average rate of the nebular emission is (2.44+0.29)x10-5phcm~?s~ ο whereas during the flare it is (8.12+0.55)x10-§phcm?s7}bin;
Before the flares, the average rate of the nebular emission is $(2.44 \pm 0.29) \times 10^{-8}~\mathrm{ph~cm^{-2}~s^{-1}~bin^{-1}_{phase}}$ , whereas during the flare it is $(8.12 \pm 0.55) \times 10^{-8}~\mathrm{ph~cm^{-2}~s^{-1}~bin^{-1}_{phase}}$.
After subtracting the contribution from the nebula, we hase:compute the average rate of the pulsed emission: before the flare it is (9.32+0.66)x1075phcm?s!binsse: whilst during the flares it is (8.524-0.90)x1075phcm?s!binsse
After subtracting the contribution from the nebula, we compute the average rate of the pulsed emission: before the flare it is $(9.32 \pm 0.66) \times 10^{-8}~\mathrm{ph~cm^{-2}~s^{-1}~bin^{-1}_{phase}}$, whilst during the flares it is $(8.52 \pm 0.90) \times 10^{-8}~\mathrm{ph~cm^{-2}~s^{-1}~bin^{-1}_{phase}}$.
During the flares, we conclude that the flux from the nebula increased by a factor 3.33+0.46.
During the flares, we conclude that the flux from the nebula increased by a factor $3.33 \pm 0.46$.
In contrast, the pulsar flux remained constant within the errors.
In contrast, the pulsar flux remained constant within the errors.
This result confirms the preliminary claim of ?..
This result confirms the preliminary claim of \cite{2010ATel.2879....1H}.
To study the high energy spectrum of theCrab,, we exploit the maximum likelihood method for the dataset listed in Table 1..
To study the high energy spectrum of the, we exploit the maximum likelihood method for the dataset listed in Table \ref{tab:intervals}.
Following ?,, we model the spectral emission from the using two power-law components for the nebula, which represent respectively the synchrotron and IC emissions, and a power-law with an exponential cutoff to describe the pulsar contribution.
Following \citet{2010ApJ...708.1254A}, we model the spectral emission from the using two power-law components for the nebula, which represent respectively the synchrotron and IC emissions, and a power-law with an exponential cutoff to describe the pulsar contribution.
As the flare is not pulsed, we fix the parameter related to the pulsar emission to these of ?,, and assume that the IC contribution of the nebula has not changed during the flare.
As the flare is not pulsed, we fix the parameter related to the pulsar emission to these of \citet{2010ApJ...708.1254A}, and assume that the IC contribution of the nebula has not changed during the flare.
The only free parameters are the synchrotron contribution and the normalization of the Galactic emission.
The only free parameters are the synchrotron contribution and the normalization of the Galactic emission.
The synchrotron emission is found to increase from a flux of (5.6+1.3)x1077phcm?s! to (32.42.7)x1077phcm?s! in the 0.1-300 GeV band.
The synchrotron emission is found to increase from a flux of $(5.6 \pm 1.3)\times 10^{-7}\,\mathrm{ph~cm^{-2}~s^{-1}}$ to $(32.4 \pm 2.7)\times 10^{-7}\,\mathrm{ph~cm^{-2}~s^{-1}}$ in the 0.1-300 GeV band.
To discriminate among the various models capable of reproducing the quasi-exponential turnover of the synchrotron emission of the nebula that peaks below the LAT energy window, we studied the Fermi data complemented by archival CGRO/COMPTEL data (0.75- MeV).
To discriminate among the various models capable of reproducing the quasi-exponential turnover of the synchrotron emission of the nebula that peaks below the LAT energy window, we studied the Fermi data complemented by archival CGRO/COMPTEL data (0.75-30 MeV).
A single power-law cannot reproduce the pre-flare data (x?/d.o.f.~ 44/15).
A single power-law cannot reproduce the pre-flare data $\chi^2 / d.o.f. \sim 44 / 15$ ).
A power-law with a high energy exponential cutoff can instead reproduce the data (x2/d.o.f.~ 4/14).
A power-law with a high energy exponential cutoff can instead reproduce the data $\chi^2 / d.o.f. \sim 4 / 14$ ).
To model the nebular synchrotron spectrum, we used the following function The best-fit solution yields [=--2.20+0.08 and No=(4.3+1.9)-10:19 phcm"?s!ΜονἹ.
To model the nebular synchrotron spectrum, we used the following function The best-fit solution yields $\Gamma = -2.20 \pm 0.08$ and $N_0 = (4.3 \pm 1.9) \cdot 10^{-10}$ $\mathrm{ph~cm^{-2}~s^{-1}~MeV^{-1}}$.
The difference between the quiescent and flaring spectra can be understood by considering two different extreme cases of either a constant power-law normalisation or a constant cutoff energy.
The difference between the quiescent and flaring spectra can be understood by considering two different extreme cases of either a constant power-law normalisation or a constant cutoff energy.
In the former case, an increase in the energy cutoff of a factor of nearly 5 (from 77+15 MeV to 367+45 MeV) is needed (as illustrated in Fig. 4)).
In the former case, an increase in the energy cutoff of a factor of nearly 5 (from $77 \pm 15$ MeV to $367 \pm 45$ MeV) is needed (as illustrated in Fig. \ref{fig:sed}) ).
This increase is averaged over the whole flaring period, thus represents a lower limit, since in each single flare the cutoff energy might have been higher.
This increase is averaged over the whole flaring period, thus represents a lower limit, since in each single flare the cutoff energy might have been higher.
In the latter case, the spectral variability can be explained by raising the continuum normalization by a factor of ~5.
In the latter case, the spectral variability can be explained by raising the continuum normalization by a factor of $\sim5$.
We note that the non-detection of any significant hard X-ray variability during the flare does not allow us to differentiate between the two possibilities as several electron populations are probably present in the nebula.
We note that the non-detection of any significant hard X-ray variability during the flare does not allow us to differentiate between the two possibilities as several electron populations are probably present in the nebula.
As reported by ?,, the COMPTEL data are characterized by a flattening of the spectrum that can be ascribed to the synchrotron emission of a separate electron population confined in compact regions such as wisps or knots.
As reported by \cite{1998nspt.conf..439A}, , the COMPTEL data are characterized by a flattening of the spectrum that can be ascribed to the synchrotron emission of a separate electron population confined in compact regions such as wisps or knots.
The
The
(Wart1951:Leightonetal.1962) Ww| 30 ~1 Sinon&Leighton(1961) Steinetal.(2007). Cazonotal.Schou(2003)... ποσο&I&osovichev.(2007) Rastetal.(2001) Rieutord&Rincon(2010)..
\citep{1954MNRAS.114...17H, 1962ApJ...135..474L} $20$ $30$ $\sim 1$ \citet{1964ApJ...140.1120S} \citet{2007AIPC..948..111S} \citet{2003Natur.421...43G},\citet{2003ApJ...596L.259S}, \citet{2007ApJ...665L..75G} \citet{2004ApJ...608.1156R} \citet{2004ApJ...608.1167L} \citet{2010LRSP....7....2R}.
Dikpatietal.(2010) (DeRosaetal.2002)... Toomre2002:\Geschetal.2006).. Tsiuetaetal.2008)) (I&osu
\citet{2010GeoRL..3714107D} \citep{2002ApJ...581.1356D}. \citep[e.g.,][]{2002ApJ...570..865B, 2006ApJ...641..618M}, \citealt{2008SoPh..249..167T})
gictal.2007)
\citep{2007SoPh..243....3K}
The results are summarized in Figure 5.. which show the empirical coverage of confidence intervals (left column) and errors (right column) obtained fom the marked point bootstrap and with Poisson errors. for each of the four Thomas models.
The results are summarized in Figure \ref{fig:r20simulation}, which show the empirical coverage of confidence intervals (left column) and errors (right column) obtained from the marked point bootstrap and with Poisson errors, for each of the four Thomas models.
The plots qualitatively show the same relative performance between Poisson errors and bootstrap as found in the earlier simulation study.
The plots qualitatively show the same relative performance between Poisson errors and bootstrap as found in the earlier simulation study.
When the range of clustering is large. (he empirical coverage of confidence intervals based on Poisson errors and the normal approximation is verv low (second and fourth plots on the left column of Figure 5)).
When the range of clustering is large, the empirical coverage of confidence intervals based on Poisson errors and the normal approximation is very low (second and fourth plots on the left column of Figure \ref{fig:r20simulation}) ).
The coverage of the bootstrap intervals are affected too. but by much less.
The coverage of the bootstrap intervals are affected too, but by much less.
When the correlation is large. the Poisson errors substantially under-estimate the true errors. while the marked bootstrap errors were more realistic.
When the correlation is large, the Poisson errors substantially under-estimate the true errors, while the marked bootstrap errors were more realistic.