ReadingTimeMachine/rtm-sgt-ocr-v1 · Datasets at Hugging Face

It was becoming fainter in 7 (and /) but brighter in K.

It was becoming fainter in $J$ (and $I$ ) but brighter in $K$.

This can be interpreted as due to a gradual fading of the main object (seen in J and /) and the increasing infrared excess in K.

This can be interpreted as due to a gradual fading of the main object (seen in $J$ and $I$ ) and the increasing infrared excess in $K$.

Therefore when fitting the standard spectra to the DENIS magnitudes obtained on 11 September 1999 (triangles in Fig. 3)).

Therefore when fitting the standard spectra to the DENIS magnitudes obtained on 11 September 1999 (triangles in Fig. \ref{sp03}) ),

expecting that K may be affected by the IR excess. we considered only / and J.

expecting that $K$ may be affected by the IR excess, we considered only $I$ and $J$.

Indeed. as can be seen from Fig. 3..

Indeed, as can be seen from Fig. \ref{sp03},

the K magnitude ts -0.5 magnitude above the fitted spectrum.

the $K$ magnitude is $\sim$ 0.5 magnitude above the fitted spectrum.

Note. however. that if all the three (77K) measurements are taken into account the fits would be quite poor and 7. would lower by - 100KK for class IIl and -200 KK for class I compared to the values given in Table 3..

Note, however, that if all the three $IJK$ ) measurements are taken into account the fits would be quite poor and $T_\mathrm{eff}$ would lower by $\sim$ K for class III and $\sim$ K for class I compared to the values given in Table \ref{evol_t}.

No fit to the 2MASS data on 18 May 1998 (asterisks) is shown in Fig. 3..

No fit to the 2MASS data on 18 May 1998 (asterisks) is shown in Fig. \ref{sp03}.

The spectral range of the data is quite narrow and hence any Τομ between «3000 K and «4500 K could be considered to satisfactorily fit the data.

The spectral range of the data is quite narrow and hence any $T_\mathrm{eff}$ between $\sim$ 3000 K and $\sim$ 4500 K could be considered to satisfactorily fit the data.

Fortunately for this wide Ty range. log 4 of the fits varies by less than 0.20.

Fortunately for this wide $T_\mathrm{eff}$ range, log $\theta$ of the fits varies by less than 0.20.

The mean values of log 8 from these fits are given in Table 3..

The mean values of log $\theta$ from these fits are given in Table \ref{evol_t}.

The last two columns in Table 3. show the stellar radius and luminosity (given in solar units) of V4332 Ser calculated from the angular radius (8) and the effective temperature (Το) assuming a distance of 1.8 kpe.

The last two columns in Table \ref{evol_t} show the stellar radius and luminosity (given in solar units) of V4332 Sgr calculated from the angular radius $\theta$ ) and the effective temperature $T_\mathrm{eff}$ ) assuming a distance of 1.8 kpc.

Figure 4 displays the evolution of principal parameters of V4332 Sgr taken from Table 3..

Figure \ref{evol_f} displays the evolution of principal parameters of V4332 Sgr taken from Table \ref{evol_t}.

The time is in days since 24 February 1994. Le. since the discovery of V4332 Ser in eruption.

The time is in days since 24 February 1994, i.e. since the discovery of V4332 Sgr in eruption.

The effective temperature is in units of 10° K. while the stellar radius and luminosity are in solar units.

The effective temperature is in units of $10^3$ K, while the stellar radius and luminosity are in solar units.

Open symbols show the results from fitting the standard supergiant (I) spectra to the observations.

The same but using the giant (IIL) spectra is presented with the full points.

The same but using the giant (III) spectra is presented with the full points.

Asterisks in panel denote the effective temperatures derived by MWT99 from fitting stellar atmosphere models to their spectra.

Asterisks in panel denote the effective temperatures derived by \cite{martini} from fitting stellar atmosphere models to their spectra.

Let us first discuss the spectral types obtainec from our analysis.

Let us first discuss the spectral types obtained from our analysis.

As can be seen from Table 3 no matter which luminosity class of the standard spectra (I or IL) is used. the resultant spectral types are always very similar,

As can be seen from Table \ref{evol_t} no matter which luminosity class of the standard spectra (I or III) is used, the resultant spectral types are always very similar.

Moreover. our spectral classes are also very close to those obtained by MWT99 from the classification of their spectra.

Moreover, our spectral classes are also very close to those obtained by \cite{martini} from the classification of their spectra.

This shows that the general approach adopted in our analysis of the photometric data is consistent and reliable.

The consistency of the effective temperatures derived in the different ways is not as good as that of the spectral types.

As can be seen from Table 3 and Fig. 4

As can be seen from Table \ref{evol_t} and Fig. \ref{evol_f}

initially. when the object was of the K type. both kinds of standard spectra give practically the same values of

initially, when the object was of the K type, both kinds of standard spectra give practically the same values of

by The plasma wave energy density, E.,(r,t), close to the Sun at time t=25 s is displayed in Figure 3 with the corresponding scale of the background plasma inhomogeneity.

by The plasma wave energy density, $E_w(r,t)$, close to the Sun at time $t=25$ s is displayed in Figure \ref{fig:pert_wave_energy_1} with the corresponding scale of the background plasma inhomogeneity.

The unperturbed case has been over plotted for comparison.

Lines have been drawn to indicate the 1010 cm wavelength of sinusoid perturbation to the background plasma.

Lines have been drawn to indicate the $10^{10}$ cm wavelength of sinusoid perturbation to the background plasma.

Periodic oscillation of the background plasma is evident together with the corresponding periodic nature of the plasma wave energy density.

The magnitude of E,(r,t) in the unperturbed case is generally larger than the perturbed case, showing clearly the reduction in wave growth when the background plasma is significantly perturbed.

The magnitude of $E_w(r,t)$ in the unperturbed case is generally larger than the perturbed case, showing clearly the reduction in wave growth when the background plasma is significantly perturbed.

As we get further away from the Sun (5, compared with 2R,) the radial drop of density plays a less dominant role allowing small scale fluctuations to become more important, seen in |L|-!.

As we get further away from the Sun $5R_s$ compared with $2R_s$ ) the radial drop of density plays a less dominant role allowing small scale fluctuations to become more important, seen in $|L|^{-1}$.

With this increased role, the small scale fluctuations increase the suppression of induced plasma wave energy density with respect to the unperturbed case.

Despite fluctuations suppressing plasma waves, the perturbed case displays plasma wave energy density greater than the unperturbed case at peaks in its oscillation.

The instability of the electron beam which induces the plasma waves (Of/Ov> 0) is not fully relaxed to thermal velocities in areas of space where plasma wave production is suppressed.

The instability of the electron beam which induces the plasma waves $\partial f/\partial v > 0$ ) is not fully relaxed to thermal velocities in areas of space where plasma wave production is suppressed.

Another striking feature of Figure 3 is the double peak and trough behaviour of E,(r,t) within one wavelength of background plasma fluctuation.

Another striking feature of Figure \ref{fig:pert_wave_energy_1} is the double peak and trough behaviour of $E_w(r,t)$ within one wavelength of background plasma fluctuation.

The distribution of E,(r,t) in space is substantially different at the latter time of t=100 s, shown in Figure 3..

The distribution of $E_w(r,t)$ in space is substantially different at the latter time of $t=100$ s, shown in Figure \ref{fig:pert_wave_energy_1}.

There is a larger discrepancy in magnitude between the unperturbed and perturbed case.

Moreover, the second peak of E,(r,t) within one wavelength clearly seen at t=25 s is suppressed at the later time of t—100 s. The one remaining pronounced peak does not stay co-spatially with the small scale fluctuation wavelength but shifts backwards with respect to increasing distance from the Sun for this single point in time.

Moreover, the second peak of $E_w(r,t)$ within one wavelength clearly seen at $t=25$ s is suppressed at the later time of $t=100$ s. The one remaining pronounced peak does not stay co-spatially with the small scale fluctuation wavelength but shifts backwards with respect to increasing distance from the Sun for this single point in time.

Density fluctuations at distances &7R, become influential enough over the radial density decrease to generate some positive background density gradients.

Density fluctuations at distances $\approx 7R_s$ become influential enough over the radial density decrease to generate some positive background density gradients.

A positive gradient causes plasma waves to move to higher phase velocities and causes the streaking seen at t=100 s in Figure 2..

A positive gradient causes plasma waves to move to higher phase velocities and causes the streaking seen at $t=100$ s in Figure \ref{fig:pert_movie_1}.

Despite the plasma wave distribution being substantially different, the electron flux remains almost unchanged, agreeing with the numerical results from ?..

Despite the plasma wave distribution being substantially different, the electron flux remains almost unchanged, agreeing with the numerical results from \cite{Kontar2001_a}. .

The group velocity of plasma waves, 3v2.,/v, is small in magnitude, within the range 4x107 cm/s to 4x108 cm/s. At t—25 s (Figure 3)) the removal of the group velocity term has minimal effect.

The group velocity of plasma waves, $3v_{Te}^2/v$, is small in magnitude, within the range $4\times 10^7$ cm/s to $4\times10^8$ cm/s. At $t=25$ s (Figure \ref{fig:pert_wave_energy_1}) ) the removal of the group velocity term has minimal effect.

Waves are moved in space by a small distance dependent upon the magnitude of the group velocity.

The slower energetic electrons at the back of the beam produce waves with higer group velocity and hence the wave energy density is displaced further.

At the later time of t=100 s, E,(r,t) is substantially different when the group velocity term is notpresent,

At the later time of $t=100$ s, $E_w(r,t)$ is substantially different when the group velocity term is notpresent,

c005AL. are able to eject a common envelope and form an sdD. Claims have been mace that the primaries of AA Dor (Rauch2000;Rucinski2009) and IS 2231-2441 (Ostensen2008) are low mass sdDs and the companions therefore substellar.

$\simeq0.08\,M_{\rm \odot}$ are able to eject a common envelope and form an sdB. Claims have been made that the primaries of AA Dor \citep{rauch00,rucinski09} and HS $+$ 2441 \citep{oestensen08} are low mass sdBs and the companions therefore substellar.

However. in the former case (his has been refuted by the measurement of the companions radial velocity (RV) curve (Vuékoviéetal.2003). and in the latter case bv the gravity measurement. 2010).

However, in the former case this has been refuted by the measurement of the companion's radial velocity (RV) curve \citep{vuckovic08} and in the latter case by the gravity measurement \citep{for10}.

. Substellar companions to sdD stars have been found using the light travel time technique (Schuh2010.andreferencestherein)..

Substellar companions to sdB stars have been found using the light travel time technique \citep[][and references therein]{schuh10}.

ILowever. these svstems have wide orbits and are therefore unlikely to have experienced a common envelope phase.

However, these systems have wide orbits and are therefore unlikely to have experienced a common envelope phase.

None of these companions influenced the evolution of its host star. but the presence of such objects in wide orbits may be an indication of current or former close substellar companions to sdDs.

None of these companions influenced the evolution of its host star, but the presence of such objects in wide orbits may be an indication of current or former close substellar companions to sdBs.

llere we report the discovery of the short-period eclipsing IW Vir type binary JO8205-+0008 from the MUCIIFUSS project.

Here we report the discovery of the short-period eclipsing HW Vir type binary J08205+0008 from the MUCHFUSS project.

This svstem is regarded as the first one in which a close substellar companion to an sdD star has been detected unambiguously.

This system is regarded as the first one in which a close substellar companion to an sdB star has been detected unambiguously.

The project Massive Unseen Companions to Hot Faint Underluminous Stars from SDSS (MUCIIFUSS) aims at finding sdBs with compact companions like massive white dwarfs (WDs. Ad>1.0M. ). neutron stars or black holes.

The project Massive Unseen Companions to Hot Faint Underluminous Stars from SDSS (MUCHFUSS) aims at finding sdBs with compact companions like massive white dwarfs (WDs, $M>1.0\,M_{\rm \odot}$ ), neutron stars or black holes.

Details on the survey and target selection procedure are provided in Geieretal.(2010a).. and an analvsis of seven sdB binaries in Geileretal.(2010b).

Details on the survey and target selection procedure are provided in \citet{geier10}, and an analysis of seven sdB binaries in \citet{geier11}.

. The same selection criteria that we applied to find such binaries are also well suited to single out hot subcdwarl stars with constant high radial velocities in the Galactic halo and search for hypervelocity stars.

The same selection criteria that we applied to find such binaries are also well suited to single out hot subdwarf stars with constant high radial velocities in the Galactic halo and search for hypervelocity stars.

First results of the search for hvpervelocity stars are presented in Tillichetal.(2010).

First results of the search for hypervelocity stars are presented in \citet{tillich10}.

. The MUCTIIFUSS target selection strategy is tailored to single out RV variations on time scales of half an hour or less.

The MUCHFUSS target selection strategy is tailored to single out RV variations on time scales of half an hour or less.

Such variations may indicate the presence of short-period svslenms of relatively low RV amplitude or longer-period binaries with high RY amplitudes.

Such variations may indicate the presence of short-period systems of relatively low RV amplitude or longer-period binaries with high RV amplitudes.

The latter are the prime targets for the core programme of MUCIIFUSS.

The latter are the prime targets for the core programme of MUCHFUSS.

Obviously. the campaign is also bound to find short-period. low RV amplitude systemswith low mass stellar or even substellar companions.

Obviously, the campaign is also bound to find short-period, low RV amplitude systemswith low mass stellar or even substellar companions.

JJO82053.53+000843.4 JJO82053.6+000843. in short JOS205+0008. 14.9 mae) was classified as an sdD star by colour selection ancl visual inspection of SDSS spectra (Abazajianetal. 2009).. whieh are flux. calibrated and cover the wavelength range from 3800Ato 9200A with a resolution of R= 1800.

J082053.53+000843.4 J082053.6+000843, in short J08205+0008, $g=14.9\,{\rm mag}$ ) was classified as an sdB star by colour selection and visual inspection of SDSS spectra \citep{abazajian09}, , which are flux calibrated and cover the wavelength range from $3800\,{\rm \AA}$to $9200\,{\rm \AA}$ with a resolution of $R=1800$ .

The six individual sub-spectra showed

of 111404. and 60062 galaxy spectra had been obtained in jo SCP and NGP regions respectively.

of 111404 and 60062 galaxy spectra had been obtained in the SGP and NGP regions respectively.

From these objects we remove those which did not receive accurate redshifts («3. Colless ct al.

From these objects we remove those which did not receive accurate redshifts $Q<3$, Colless et al.

2001). repeated: observations ancl spectra with particularly low signal-to-noise ratio (< 10).

2001), repeated observations and spectra with particularly low signal-to-noise ratio $\le10$ ).

‘This leaves us with 78994 (SGP) and 44937 (NCDP) galaxies or use in our spectral analysis.

This leaves us with 78994 (SGP) and 44937 (NGP) galaxies for use in our spectral analysis.

Because of interference [rom gakv emission and atmospheric absorption we have made a "urther restriction to our redshift range such that 2x0.15.

Because of interference from sky emission and atmospheric absorption we have made a further restriction to our redshift range such that $z\le0.15$.

This cut. ensures that the Πα line in our spectra is not corrupted.

This cut ensures that the $\alpha$ line in our spectra is not corrupted.

Note that this is only a problem for the small redshift range 0.15.<zQT. however. we have also excluded the galaxies with z20.17 in order to simplify our analvsis.

Note that this is only a problem for the small redshift range $0.15 < z < 0.17$, however, we have also excluded the galaxies with $z>0.17$ in order to simplify our analysis.

Imposing this cut and removing spectra observed in poor quality fields. (Section 4.1) leaves us with 43440 (SGP) and 32140 (NGP) galaxies.

Imposing this cut and removing spectra observed in poor quality fields (Section 4.1) leaves us with 43449 (SGP) and 32140 (NGP) galaxies.

A total of 75589 unique galaxies will therefore be used in our subsequent. analysis and measurement of the LE.

A total of 75589 unique galaxies will therefore be used in our subsequent analysis and measurement of the LF.

The spectral classification presented. here is based. upon a Principal Component Analysis. (PCA) of the galaxy spectra,

The spectral classification presented here is based upon a Principal Component Analysis (PCA) of the galaxy spectra.

PCA is a statistical technique which has been used with considerable success by multiple authors in the past (e.g. Connolly et al.

1995: Folkes. Lahav Macldox. 1996: Glazebrook. Oller Doeclev 1998: Bromley et al.

1995; Folkes, Lahav Maddox 1996; Glazebrook, Offer Deeley 1998; Bromley et al.

1998: E99) to deal with large multi-cimensional data sets.

1998; F99) to deal with large multi-dimensional data sets.

A detailed mathematical formulation of the PCA adopted here in is given in FOO.

A detailed mathematical formulation of the PCA adopted here in is given in F99.

Note that the most significant dillerence between this formulation and that used hy οrer authors (e.g. Connolly 1995) is that our spectra have been mean-subtracted (Fig. 1))

Note that the most significant difference between this formulation and that used by other authors (e.g. Connolly 1995) is that our spectra have been mean-subtracted (Fig. \ref{aver}) )

before constructing the covariance matrix.

This makes no substantial dillerence to the analysis since using other methods simply vields the mean spectrum as the first component.

This makes no substantial difference to the analysis since using other methods simply yields the mean spectrum as the first component.

We note that throughout the remainder of this paper we will denote the cigenvectors (herein eigenspectra) as PCA.PC» otc.

We note that throughout the remainder of this paper we will denote the eigenvectors (herein eigenspectra) as $\bmath{PC_1}$ $\bmath{PC_2}$ etc.

and the projections onto these axes by per. peo etc.

and the projections onto these axes by $pc_1$, $pc_2$ etc.

PCA is a useful technique in that it allows us to. easily visualise a multi-dimensional population in terms of just a handful of significant components.

PCA is a useful technique in that it allows us to easily visualise a multi-dimensional population in terms of just a handful of significant components.

It does this by identifying the components of the data (in this case the galaxy spectra) which are the most discriminatory between each galaxy.

The significance of each. component is measured. in terms of its contribution to the variance over the sample and is determined in the PCA.

The significance of each component is measured in terms of its contribution to the variance over the sample and is determined in the PCA.

This allows us to identify just the most significant components for future use.

Lt is clear from such a formalism that any clustering in a space defined by the PCA is indicative of distinct sub-populations within the sample In terms of reduced. climensionality we find. after applving the PCA to our galaxy spectra. that rather than using the original 738 spectral channels to describe. each

It is clear from such a formalism that any clustering in a space defined by the PCA is indicative of distinct sub-populations within the sample In terms of reduced dimensionality we find, after applying the PCA to our galaxy spectra, that rather than using the original 738 spectral channels to describe each

Putting these results together. a clear and direct way to constrain AGN feedback preseuts itself

Putting these results together, a clear and direct way to constrain AGN feedback presents itself.

This can be sununarized as follows: The results of carrving out this procedure over (2.2 deg) "nof sky calculated frou: our simulations. are shown in Figure 11.

This can be summarized as follows: The results of carrying out this procedure over (2.2 $^2$ of sky calculated from our simulations are shown in Figure \ref{fig:Eplot}.

Tere we have grouped all bulges iu logavithimic bius of stellar mass with width 0.25. again takine a fixed ratio of LOO between μοι and Mp. To calculate the uncertainty in each bin we have accounted both for Poisson noise and intrinsic scatter. talking iubErica)=[Εμμbiu|8onceEyhorual)|VN. where τα] Is the average thermal cucrey du a eiven bin. OyeήEnea) Is the RAIS scatter between sources du a eiven bin. where we computed Nu conservatively as the number of sources iu a eiven bin relative to a signal realization ofthe map (1/1 of the [2.2 deg)? region iu the AGN feedback zun and 1/16 of this region iu the comparison run).

Here we have grouped all bulges in logarithmic bins of stellar mass with width $0.25$, again taking a fixed ratio of 400 between $M_{\rm bulge}$ and $M_{\rm BH}.$ To calculate the uncertainty in each bin we have accounted both for Poisson noise and intrinsic scatter, taking $\sigma_{\rm bin}(E_{\rm thermal}) = [\bar E_{\rm thermal,bin} + \sigma_{\rm source}(E_{\rm thermal})]/\sqrt{N_{\rm bin}},$ where $\bar E_{\rm thermal,bin}$ is the average thermal energy in a given bin, $\sigma_{\rm source}(E_{\rm thermal})$ is the RMS scatter between sources in a given bin, where we computed $N_{\rm bin}$ conservatively as the number of sources in a given bin relative to a signal realization of the map (1/4 of the [2.2 $^2$ region in the AGN feedback run and 1/16 of this region in the comparison run).

At both high and low redshifts the ACN feedback un shows a clear excess; which scales linearly with ALiape. as expected from (111.

At both high and low redshifts the AGN feedback run shows a clear excess, which scales linearly with $M_{\rm bulge}$ as expected from \ref{eq:ekin}) ).

For comparison. we use this equation to plot the euergv added to the ICM. as a function of bulge mass.

For comparison, we use this equation to plot the energy added to the IGM as a function of bulge mass.

Since kinetic energy is converted iuto thermal energy as the outflows accrete material. and ax radiative losses are siall for these objects Oli Benson 2003: Scannapiceco Ol 2001). we expect that most of this energy should be observable as Eua. Iu fact. this figure shows that at both low and high redshift. the thermal SZ excess closely traces this energy input as a function of bulge mass.

Since kinetic energy is converted into thermal energy as the outflows accrete material, and as radiative losses are small for these objects Oh Benson 2003; Scannapieco Oh 2004), we expect that most of this energy should be observable as $E_{\rm thermal}.$ In fact, this figure shows that at both low and high redshift, the thermal SZ excess closely traces this energy input as a function of bulge mass.

Thus ACN feedback is not only detectable by the SZ effect. but the level of this feedback as a function of mass can be obtained cirectly from SZ micasurcmicuts.

Thus AGN feedback is not only detectable by the SZ effect, but the level of this feedback as a function of mass can be obtained directly from SZ measurements.

Accepted for publication in the Astrophysical Journal

input tanecutial velocities (Cransce ταν) Normalized by the expected error (Ανω) for a set of GO experiments onu our idealized data sets.

input tangential velocities $v_{\rm tan, rec}-v_{\rm tan}$ ) normalized by the expected error $\Delta v_{\rm tan}$ ) for a set of 60 experiments on our idealized data sets.

Note that the method for bootstrapping estimates of errors from observed data sets adopted by vanderMarel&Corhathakurta(2008) was found to agree well with this cstimate.

Note that the method for bootstrapping estimates of errors from observed data sets adopted by \citet{2008VanDerMarel} was found to agree well with this estimate.

Using our results so far we can assess which real galaxy clusters iav be interesting targets for a study of PR ic. those for which our estimated uncertainty (Ac) is less thau the expected signal ( which is comparable to σ for galaxy clusters).

Using our results so far we can assess which real galaxy clusters may be interesting targets for a study of PR — i.e. those for which our estimated uncertainty $\Delta v_{\rm tan}$ ) is less than the expected signal $\sim \sigma_{\rm pec}$ — which is comparable to $\sigma$ for galaxy clusters).

yxFigure 2. shows contours of f=Acfo for objects of various 0, aud IN. with current values for some nearby galaxy clusters overlaid.

Figure \ref{fig:nrho} shows contours of $f=\Delta v_{\rm tan}/\sigma$ for objects of various $\theta_{\rm max}$ and $N$, with current values for some nearby galaxy clusters overlaid.

It is clear that the πο” of measurements for these objects is just become interesting for our purpose those that have f£<1 (ie. within lightest-eray area ofthe plot) should have errors due to random motions of order σ which is simular in amplitude to what we are trviug to measure.

It is clear that the number of measurements for these objects is just becoming interesting for our purpose — those that have $f \le1$ (i.e. within lightest-gray area ofthe plot) should have errors due to random motions of order $\sigma$ which is similar in amplitude to what we are trying to measure.

Nevertheless.a straightforward application of our erid-based search to the N=379.0,44;=3.1 Virgo galaxies selected by Rines&Celler(2008). from the Sloan Digital Sky Survey spectroscopic data base. and augmented to N=520.0444—4.67 with the Bineechctal.(1987) siuuple. found a tangential velocity of several thousand liun/s with even larger error bars.

Nevertheless,a straightforward application of our grid-based search to the $N=379, \theta_{\rm max}=3.4^{\circ}$ Virgo galaxies selected by \citet{rines08} from the Sloan Digital Sky Survey spectroscopic data base, and augmented to $N=520, \theta_{\rm max}=7.7^\circ$ with the \citet{binggeli87} sample, found a tangential velocity of several thousand km/s with even larger error bars.